GPM (Global Precipitation Measurement) Mission
GPM is a cooperative international US/Japanese Earth science mission with the prime agencies of NASA and JAXA, respectively. GPM is a follow-on and expanded mission to TRMM (Tropical Rainfall Measuring Mission), launched Nov. 27, 1997 and still in operation in late 2012 (in Oct. 2005, NASA decided to continue the TRMM mission until at least 2009 and possibly until 2012). TRMM has been demonstrating the benefits of rain measurement from space. The overall objectives of the GPM mission are to observe global precipitation more frequently and more accurately than TRMM. GPM will build on the work of TRMM and extend the science from understanding tropical rainfall to using that understanding to improve climate, weather, and hydrological forecasts on a global basis. 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18)
The top-level science objectives are:
• Climate change: Improve ongoing efforts to predict climate by providing near-global measurement of precipitation, its distribution, and physical processes. Providing this information is a key indicator of the global water cycle and its response to climate change
• Weather prediction: Improve the accuracy of weather and precipitation forecasts through more accurate measurement of rain rates and latent heating. These are key inputs needed by computer models to produce better weather predications
• Flood/fresh water resource prediction (water cycle): Provide more frequent and complete sampling of the Earth's precipitation. This will provide better prediction of flood hazards and management of life-sustaining activities dependent upon fresh water.
The goal is to: a) understand the horizontal and vertical structure of rainfall and its microphysical elements, and b) to achieve global coverage on rain rates with substantially improved sampling of the diurnal cycle.
The delivery of data products in near real-time (3-hour latency max). The bias error should be 1/2 that of TRMM (accuracy threshold) with a 25% precision threshold. The project calls for a precipitation measure of the 4-D structure of rates and drop size distribution at 5 km horizontal and 250 m vertical resolution.
The GPM definition team at NASA has identified four major elements that are necessary for the development of an effective and viable program - in support of the objectives:
1) A Core spacecraft that makes accurate rainfall measurements, collects information on cloud dynamics and rainfall processes, and serves a calibration reference for other instruments used within the GPM Program for taking rainfall measurements
2) A multi-satellite constellation, with each satellite equipped with a passive microwave radiometer that measures rainfall and other forms of precipitation over broad measurement swaths
3) A Ground Validation (GV) program that provides ground truth verification and measurement validation at various locations on the earth that are representative of precipitation events associated with different climates and geo-locations (e.g., tropical, oceanic)
4) A Precipitation Processing System (PPS) that collects and processes the measurement data obtained by the Core and the constellation of spacecraft, and that disseminates precipitation data products to the user community.
The baseline GPM configuration consists of a NASA core satellite to measure the precipitation structure and to provide a calibration standard for the constellation spacecraft, and a constellation of several microsatellites, consisting of a NASA spacecraft and those provided internationally. The objective is to obtain frequent precipitation measurements on a global basis (sufficient to resolve the diurnal cycle). In addition, the mission architecture makes use of ground calibration/validation sites with a broad array of precipitation-measuring instruments. A Global Precipitation Data Center will produce and distribute precipitation maps, application products, and climate quality global products. The success of GPM is very much dependent on international partnerships.
The US GPM project was funded by NASA in early 2007 which includes the Core Observatory with a planned launch in 2014.
• GPM Core spacecraft, provided by NASA and launched by JAXA (orbit of 65º inclination and 400 km altitude) 22)
• GCOM-W1 (Global Change Observation Mission-Water/Wind) of JAXA with a launch on May 17, 2012. Data from the AMSR-2 (Advanced Microwave Scanning Radiometer-2) on GCOM-W1.
• DMSP (Defense Meteorological Satellite Program) of NOAA. The SSMIS (Special Sensor Microwave Imager Sounder) instrument series (F-16 since 2003, F-17 since 2006, F-18 since Oct. 2009, (and F-19 to -20 yet to be launched) will be a key constellation members in the GPM era. 23) 24)
• Megha Tropiques, a joint French (CNES) and Indian (ISRO) low inclination (20º), tropical rainfall measurement satellite (launch Oct. 12, 2011). Data from the MADRAS (Multi-Frequency Microwave Scanning Radiometer). 25)
• MetOp-A of EUMETSAT (launch Oct. 19, 2006). The MetOp-B was launched on Sept. 17, 2012. Both spacecraft operate in a co-planar sun-synchronous orbit, phased 174º.
• NOAA-19 (NOAA-N') with a launch on Feb. 6, 2009 and operational on June 2, 2009. Data from the MHS (Microwave Humidity Sounder). 26)
• Suomi-NPP (NPOESS Preparatory Project) of NASA with a launch on Oct. 28, 2011.
• JPSSS (Joint Polar Satellite System) satellites of NOAA/NASA, each equipped with CMIS (Conical-scanning Microwave Imager/Sounder). A launch of the JPSS-1 spacecraft is planned for 2017. 27)
Figure 1: Overview of the GPM mission architecture (image credit: NASA)
Figure 2: GPM mission architecture (image credit: NASA) 28)
Figure 3: Estimated launch schedules and life spans of GPM constellation satellites, with blue denoting the primary mission phase and yellow the extended mission phase. GPM Core Observatory operations beyond the primary mission phase are subject to science and satellite performance evaluation after launch (image credit: GPM Team, Ref. 18)
The GPM Core Spacecraft
On July 30, 2009, JAXA and NASA signed a MOU (Memorandum of Understanding) on development and operation activities for the GPM mission. The agreement was signed at the KSC (Kennedy Space Center) in Cape Canaveral, Florida. 29) 30)
The joint mission agreements call for:
• NASA to provide the core (and a companion spacecraft), the companion spacecraft launch, the US ground system, and the Precipitation Data Center. Regarding instrumentation, NASA will contribute a conical-scanning, polarization-sensitive, multi-frequency radiometer, GMI, for both the core and a companion satellite. NASA will also contribute to and participate in algorithm development and data validation activities.
• JAXA to provide the DPR instrument for the core spacecraft, the launch of the core spacecraft, and a data stream from the JAXA GCOM-B1 spacecraft to NASA.
The NASA GPM core spacecraft is of TRMM heritage. The Core spacecraft bus is being developed in-house at NASA/GSFC. The mission CDR (Critical Design Review) took place Dec. 14-17 2009.
The spacecraft bus features an aluminum and composite structure, the bus is modular and has fully redundant avionics consistent with its Class B reliability designation. A steerable high-gain antenna on a dual-hinged boom provides nearly continuous data downlink of science data from the GMI and DPR instruments via the TDRSS (Tracking and Data Relay Satellite System) in MA (Multiple Access) mode at ~230 kbit/s and SA (Single Access) mode at ~2300 kbit/s.
The GPM spacecraft has a mass of about 3850 kg and a power of ~1.95 kW. The design life is 3 years with a goal of at least 5 years of operation. The spacecraft bus uses 12 hydrazine thrusters (4 forward, 8 aft) for the regular orbit maintenance required for its 407 km altitude 65º inclination orbit. The full capacity propulsion tank is providing a fuel margin beyond the 5 year consumable requirement. Also, the increased battery capacity to 200 Ah ensures a mission life beyond the 5 year consumable requirement. 31)
Figure 4: Artist's rendition of the deployed GPM spacecraft in orbit (image credit: NASA)
Figure 5: Overview of the GPM Core spacecraft, bottom view shows instrument locations (image credit: NASA)
ADCS (Attitude Determination and Control Subsystem): A suite of state-of-the-art sensors are used to determine the 3D attitude in space to ensure proper attitude control and Earth-pointing for instrument data acquisition. GPM incorporates star trackers and an IRU (Inertial Reference Unit) as main attitude data sources. Two wide-angle star trackers are used to acquire imagery of the sky that is analyzed by a software algorithm that compares the acquired star pattern with a catalog to precisely determine the spacecraft's orientation in space. The star trackers are connected to the 1553 data bus to relay precise attitude data to the vehicle's control system. 32)
The IRU used on the GPM spacecraft is referred to as SIRU ( Scalable IRU) reference system provided by Northrop Grumman. The system uses gyroscopes to precisely measure changes in rotational attitude on all three axes to provide accurate attitude and rate data to the spacecraft control system. The IRU is internally redundant.
Figure 6: Photo of the SIRU reference system (image credit: Northrop Grumman)
Two MSS (Medium Sun Sensors) are also part of GPM's guidance suite. These sensors have a smaller field of view than the coarse sun sensors and provide higher accuracy in their measurements. The two units have a FOV (Field of View) of ~17.5º and measure the position of the sun with an accuracy of 2º. The MSS data is used in initial attitude hold mode and during spacecraft safe modes.
Two magnetometer units are installed on GPM to determine the spacecraft attitude relative to Earth's magnetic field. Three magnetic torque rods with redundant coils are used to create angular momentum by running a current through coils in the presence of Earth's magnetic field. The torquers are regulated by computers that control the current that is passing through the coils in order to control the force generated on each axis. The magnetic torquers are used during momentum dumps.
A GPS receiver unit aboard the GPM spacecraft determines the spacecraft position, altitude and velocity for navigation, antenna pointing and science data processing.
Attitude control is primarily provided by a RWA (Reaction Wheel Assembly). The wheels are spun by electric motors at variable speed that is changed when making attitude maneuvers. Each RWA has a mass of 10 kg, the wheels spin as fast as 6,000 rpm. The thrusters are used for periodic angular momentum desaturation - slowing down the reaction wheels and countering the resulting force with the thrusters so that the wheels can then be accelerated during standard attitude operations.
C&DHS (Command and Data Handling Subsystem): The C&DHS is in charge of command reception and execution, payload system operations, housekeeping operations and spacecraft control. The C&DHS uses key-components developed for the LRO (Lunar Reconnaissance Orbiter) mission, based on the VxWorks operating system for realtime operations. GPM uses a Spacewire, 1553 data bus and an analog RS-422 system for data transfer within the spacecraft. Housekeeping data and science data is stored in a solid-state recorder before downlink or is processed and downlinked in realtime.
GPM is equipped with a PowerPC RAD750 microprocessor that features a single-card computer manufactured by BAE Systems of Manassas, VA. The processor can endure radiation doses that are a million times more extreme than what is considered fatal to humans. The RAD750 CPU itself can tolerate 200-1,000 krad. Also, the RAD750 will not suffer more than one event requiring interventions from Earth over a 15-year period.
The RAD750 card is designed to accommodate all those single event effects and survive them. The ultimate goal is one upset is allowed in 15 years. An upset means an intervention from Earth — one 'blue screen of death' in 15 years.
RAD-750 was released in 2001 and made its first launch in 2005 aboard the Deep Impact spacecraft. The CPU has 10.4 million transistors. The RAD750 processors operate at up to 200 MHz, processing at 400 MIPS. The CPU has an L1 cache memory of 2 x 32 kB (instruction + data) - to improve performance, multiple 1MB L2 cache modules can be implemented depending on mission requirements.
Figure 7: Photo of the PowerPC RAD750 microprocessor card (image credit: BAE Systems)
EPS (Electrical Power Subsystem): GPM is equipped with two solar arrays with four panels on each array. The arrays are attached to booms that interface with the second panel in order to individually tilt the arrays to track the sun and optimize power generation. Each panel features 800 to 1,200 individual Gallium-Arsenide solar cells (total solar cell area is 26.5 m2). CSS (Coarse Sun Sensors) are installed on the arrays to provide guidance for array tilting to achieve optimal illumination. Generally, CSS are imagers with a field of view of 85 º to determine the sun direction with an error of 10º.
Power from the arrays is passed to a suite of power controllers facilitated in a single Power System Electronics Box to distribute electrical power to the different subsystems and payloads that use a regulated, redundant power bus at 28 Volts with an operational range of 23 to 35 V. A dedicated controller is used to regulate the state of charge of a single 200 Ah Li-ion battery. Overall, GPM generates 1.95 kW at EOL (End of Life).
Figure 8: Photo of a deployed GPM solar array at NASA/GSFC (image credit: NASA)
TCS (Thermal Control Subsystem): GPM uses a combination of active and passive thermal control. Passive thermal control is accomplished by the use of thermal covers, coatings and multilayer insulation that prevents sunlight from excessively heating the satellite and heat from dissipating into space in darkness. The outer layer of the multilayer insulation is used to minimize heat loss and is also designed to protect the spacecraft against corrosion by atomic oxygen and electrical charging. The outer MLI layer is made of Germanium Black Kapton. GPM uses blanket tents and form-fitting blankets to protect most of its non-radiating surfaces.
Active thermal control uses heat rejection systems as well as heaters and temperature sensors to keep the satellite at an operating temperature. Because all electronics of the satellite generate heat, GPM has to be outfitted with a heat rejection system. Most electronic components of the spacecraft reject heat through their baseplates that are mounted on structural surfaces using a thermal interface material to improve the heat transfer. The heat is then transported via constant conductance heat pipes - the Avionics Module uses one U-shaped and one S-shaped heat pipe while the Power System Electronics Box has two L-shaped heat pipes. The battery assembly has four dedicated heat pipes.
Heat is rejected via radiators installed on the +Y side of the spacecraft that never faces the sun during nominal mission operations. These high-emittance radiators include an avionics radiator, a pocketed battery radiator, a Lower Bus Structure Radiator, dedicated radiators for the two solar array drive assemblies and separate radiators for the RF communications system.
RCS (Reaction Control Subsystem): GPM uses a chemical propulsion system for attitude control, reaction wheels momentum dumps and to maintain its Low Earth Orbit. A total of 12 thrusters are installed on the spacecraft. Eight of those 12 thrusters are installed on the aft section of the satellite while the remaining four thrusters are installed on the forward-facing section. Four thrusters have 90º nozzles, the other eight have straight nozzles. All thrusters are used for attitude control and momentum dumps while orbital maneuvers only use the four forward thrusters that are facing the same direction.
The RCS utilizes high-purity hydrazine fuel that is stored in a single Composite Overwrap Pressure Vessel tank. It uses an outer shell made of graphite composite, a tank skirt consisting of graphite composite with metallic inserts and an Aluminum 6061 alloy liner. The tank is filled with 545 kg of hydrazine at launch pressurized to 27.6 bar. The tank has been designed to operate at pressures of up to 34.5 bar and a burst pressure of 55.2 bar. The minimum flight pressure is 6.8 bar. The tank has been built to maintain a temperature of 2 to 50 ºC with a ten-year minimum storage life of the hydrazine inside without performance degradation. Tank pressurization is accomplished using 6.2 kg of high-pressure nitrogen.
The thrusters generate thrust by the catalytic decomposition of hydrazine propellant using heated platinum/palladium catalyst beds. Operated in blowdown mode, the thrusters provide a maximum thrust of 44.5 N at a feed pressure of 27.6 bar and 13.3 N at 6.8 bar feed pressure. Operation of the thrusters is accomplished in pulse mode for attitude control and steady-state mode for orbital maneuvers. The minimum pulse duration of the thrusters is <50 ms supplying a repeatable impulse bit. Engine thrust is calculated to within 5% for any given feed pressure.
For attitude control during ΔV burns, the thrusters are operated at duty cycles of 33, 67 or 83% followed by steady-state burns of several seconds while momentum unloading requires duty cycles of 17, 33 or 67% for periods of several seconds separated by non-firing periods of up to several minutes. Steady-state burn time of the Orbit Correction Engines is 35 s at the maximum feed pressure at the start of the mission and 70 s at the end of the mission when the minimum feed pressure has been reached.
Overall, the thrusters consume 0.06 kg/s of hydrazine during a 4-thruster burn without attitude control and 0.12 kg/s for a maneuver with attitude control. The expected propellant consumption at the start of the mission is 1.4 kg/maneuver that increases to 1.9 kg at the end of the mission. These drag makeup maneuvers will be performed every 12.4 days (on average).
Table 2: Overview of the GPM Core spacecraft parameters
RF communications subsystem: The GPM Core Observatory uses an S-band communications system for data exchange with the ground. The GPM HGA (High Gain Antenna) system features a deployable boom that facilitates the high gain antenna dish of the spacecraft which is installed on a two-axis gimbal mechanism to orient the antenna for communications with NASA's TDRSS (Tracking and Data Relay Satellite System).
Realtime payload and housekeeping data is downlinked via the TDRSS MA (Multi-Access) service that allows a TDRS satellite to relay data from several lower data rate users. Stored science data is downlinked via the SA (Single-Access) service of TDRSS that uses dedicated antennas on the TDRS satellites to achieve high downlink data rates. The data rate is ~230 kbit/s in MA mode and ~2.3 Mbit/s in SA mode.
TDRSS data is downlinked to White Sands, New Mexico from where the GPM data is transferred to the MOC (Mission Operations Center) that then distributes the acquired instrument data for processing and publication. For realtime downlink, instrument data will be available within 15 minutes to achieve a near realtime coverage. Typical data latencies from measurement to user availability are expected to be in the order of 15 minutes. A primary application for the short latency GMI data is for integration into a NRT (Near-Real-Time) global rainfall map created from measurements by all the GPM constellation radiometric sensors and with overall rain map data latency less than 3 hours.
The command uplink is also accomplished via the HGA system in nominal mission modes. GPM is equipped with omnidirectional S-band antennas that are used to communicate with ground stations. These antennas are used for telemetry downlink and command uplink and in case of spacecraft safe modes.
Project development status:
• In March, 2012, the DPR (Dual-frequency Precipitation Radar) of JAXA was delivered to NASA/GSFC. Following installation of the DPR on the GPM Core Spacecraft, NASA will perform the spacecraft system testing at GSFC. 35)
• In October 2012, the GPM spacecraft went through its first complete CPT (Comprehensive Performance Test), beginning on Oct. 4, 2012 at NASA/GSFC. The testing ran twenty-four hours, seven days a week and lasted ten days as the entire spacecraft was put through its paces. 38)
Figure 9: Photo of the DPR instrument integrated onto the GPM Core Observatory (image credit: NASA) 39)
• The GPM core spacecraft completed the first comprehensive performance test in October 2012 and thermal vacuum test in January 2013.
• The GPM Core satellite successfully completed vibration testing in July 2013, at NASA/GSFC, Greenbelt, MD. The tests ensure that the spacecraft can withstand the vibrations caused by the JAXA H-IIA rocket during satellite’s launch early in 2014. Sitting on a specialized mobile platform, the GPM spacecraft was abruptly moved back and forth in each of its three spatial orientations. 42)
Figure 10: GPM attached to the shaker table for horizontal vibration testing (image credit: NASA)
• On Nov. 23, 2013, a USAF C-5 transport aircraft carrying the GPM (Global Precipitation Measurement) Core Observatory landed at Kitakyushu Airport in Japan. From Kitakyushu Airport, the spacecraft was loaded onto a barge heading to JAXA's Tanegashima Space Center on Tanegashima Island in southern Japan, where it will be prepared for launch in 2014 on an H-IIA rocket. 43)
Figure 11: Photo of the C-5 transport aircraft, carrying the GPM Observatory, landing at Kitakyushu Airport (image credit: JAXA, NASA)
• On Dec. 26, 2013, NASA and JAXA announced the launch date for GPM. They selected Feb. 27, 2014 as the launch date and launch window for a Japanese H-IIA rocket carrying the Global Precipitation Measurement (GPM) Core Observatory satellite from JAXA's Tanegashima Space Center. 44)
Figure 12: The GPM Core Observatory in the clean room at Tanegashima Space Center, Japan (image credit: JAXA, NASA)
Launch: The GPM Core Observatory was launched on February 27, 2014 (at 18:37:00 UTC) on the H-IIA No 23 launch vehicle of JAXA from the Tanegashima Space Center, Japan. JAXA sponsored the launch on the H-IIA vehicle from the Tanegashima Space Center, Japan with MHI (Mitsubishi Heavy Industries, Ltd.) as the service provider. 45) 46) 47) 48) 49) 50) 51)
MHI is Japan's largest aerospace and defence contractor, MHI manufactures the H-II family of rockets for JAXA and has been responsible for conducting H-IIA launches since 2007 – taking over launches of the more powerful H-IIB as well in 2013.
The secondary Japanese payloads manifested by JAXA on the GPM Core mission were: 52)
• ShindaiSat (Shinshu University Satellite), a microsatellite (35 kg) to demonstrate LED light as an optical communications link.
• The STARS-2 (Space Tethered Autonomous Robotic Satellite-2) nanosatellite technology mission of Kagawa University, Takamatsu, Kagawa, Japan
• TeikyoSat-3, a bioscience microsatellite (~20 kg) of Teikyo University
• ITF-1 (Imagine The Future-1), a 1U CubeSat of the University of Tsukuba, Tsukuba, Japan.
• OPUSat (Osaka Prefecture University Satellite), a 1U CubeSat
• INVADER (INteractiVe satellite for Art and Design Experimental Research) of Tama Art University, a 1U CubeSat
• KSat-2 (Kagoshima University Satellite-2), a CubeSat mission with a mass of ~ 1.5 kg. 53)
Orbit of the core satellite: Non-sun-synchronous circular orbit, altitude = 407 km, inclination = 65º. The orbit of the core spacecraft cuts across the orbits of the constellation spacecraft, sample the latitudes where nearly all precipitation occurs, and sample different times of day.
Figure 13: The graphic compares the area covered by three TRMM orbits (yellow) versus three orbits of the GPM Core Observatory (blue), image credit: NASA) 54)
Orbit of constellation satellites: Sun-synchronous (polar) circular orbit, altitude 635 km. The constellation is actually a collection of spacecraft, most with missions independent of GPM (like the DMSP S/C series). Many of the spacecraft are sun-synchronous, but their altitudes and orbital periods are different. The DMSP spacecraft orbit at 833 km, while GCOM-W will orbit at 802 km. The different orbital periods cause the ground tracks to move with respect to each other, oscillating between overlapping coverage and missed coverage.
Figure 14: Schematic view of the observation geometries with the GPM CORE instruments (image credit: NASA)
• August 25, 2021: Tropical Storm Henri did not reach the northeast U.S. coast at hurricane force, but the slow-moving storm still left a soggy mark on the region as it became a rare tropical cyclone to make landfall in New England. Months of rain fell in a few hours across New Jersey, New York, and several other states from August 21-23, landing on soils that were already soaked by an excessively wet summer. 55)
- Tropical Storm Henri made landfall near Westerly, Rhode Island, on August 22, 2021, with sustained winds of 60 miles (95 km) per hour and gusts to 70 mph (110 km/h). In anticipation of the storm, the cities of Providence, Rhode Island, and New Bedford, Massachusetts, raised storm surge barriers in their ports for the first time since Hurricane Sandy in 2012. But the storm surge never became as severe as feared.
- Rainfall was a different story. Widespread accumulations of 4 to 9 inches (10 to 23 centimeters) were recorded in New York, New Jersey, Pennsylvania, and Connecticut. Much of that rainfall fell to the western side of the storm, whereas the eastern and northern sides saw more moderate rainfall.
- A broad satellite estimate of rain distribution is captured in the map above (right), which shows data from August 19–23, 2021. The data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) satellite mission. Local rainfall amounts can be significantly higher when measured from the ground.
- The other map of Figure 15 (left) describes how wet the soil was before Henri even arrived. Using data from the Crop Condition and Soil Moisture Analytics (Crop-CASMA) product, the map shows soil moisture anomalies on August 21, 2021, or how the water content in the top meter (3 feet) of soil compared to normal conditions for the time of year. Crop-CASMA integrates measurements from NASA’s Soil Moisture Active Passive (SMAP) satellite and vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA’s Terra and Aqua satellites.
- Henri meandered across the Atlantic toward New England for several days, and also moved slowly after landfall. Some meteorologists pointed to a blocking ridge of high pressure over the North Atlantic that kept the storm from moving east and northeast as it usually would. While still well offshore, Henri became connected to other weather systems along the East Coast that pulled in moisture from the outer edges of the storm to provoke downpours.
- The soaking started half a day before Henri made landfall. Gauges in New York City’s Central Park recorded 1.94 inches of rainfall between 10 and 11 p.m. on August 21, and a total of 8.19 inches by the time the storm had passed on August 23. Another 9.95 inches fell in Brooklyn. In New Jersey, the town of Cranbury was soaked with 8.91 inches, while Oakland caught 9.22 inches. Ardmore, Pennsylvania, recorded 4.25 inches, while New London, Connecticut—just west of the landfall—received 3.71 inches.
- “Henri’s slow westward-then-eastward loop across southern New England over a period of more than 24 hours while still classified as a tropical cyclone is extremely unusual if not unprecedented,” wrote meteorologist Bob Henson in a blog post. “Nearly all tropical depressions, tropical storms, or hurricanes in the region move more rapidly north or northeast, hauled toward the Atlantic by strong upper-level winds more typical at that latitude than the exceptionally weak steering currents present on Monday.”
- The soaking rain from Henri fell on a region that was already enduring one of its top three wettest summers on record. According to the National Weather Service, more than 23 inches of rain have fallen on New York City since June 1. A typical June through August period brings 12 inches.
Figure 15: Intense rain fell on Northeast U.S. soils that were already saturated (image credit: NASA Earth Observatory images by Joshua Stevens, using soil moisture data from Crop Condition and Soil Moisture Analytics (Crop-CASMA) and IMERG data from the Global Precipitation Mission (GPM) at NASA/GSFC. Story by Michael Carlowicz)
• May 19, 2021: NASA combined data from multiple satellites in the GPM Constellation to estimate precipitation rates and totals from Tropical Cyclone Tauktae in May 2021. The below animation shows precipitation rates (blue/yellow shading) and accumulations (green shading) at half-hourly intervals from May 12-19, 2021, derived from NASA's IMERG algorithm. Underneath the precipitation data, cloud cover is shown in shades of white/gray based on geosynchronous satellite infrared observations. On top of the precipitation data, the cyclone's approximate track is displayed based on estimates from the Joint Typhoon Warning Center (JTWC). 56)
Figure 16: In mid-May, the tropical cyclone Tauktae approached the west coast in India (image credit: NASA)
Figure 17: The tropical Indian Ocean was covered by large areas of cloudiness and precipitation consistent with an active Madden-Julian Oscillation (MJO). An active phase of the MJO occurs several times each year, marked by 20-90-day alternating wet/dry conditions. The MJO event was accompanied by a westerly wind burst just north of the equator, which contributed to the formation of a tropical cyclone in the Arabian Sea (video credit: NASA, Jason West)
- The cyclone, named 'Tauktae' by the Indian Meteorological Department, tracked northward over very warm water, intensifying to its peak as a Category 4-equivalent cyclone on May 17 as it passed Mumbai. It made landfall over the Indian state of Gujarat later on May 17 as a Category 3-equivalent cyclone before weakening as it continued inland. Over the approximately 7-day period of the animation, the greatest amount of precipitation fell near Tauktae's track over the Arabian Sea. Accumulations over land were smaller, but nearly the entire western coastline of India was affected by Tauktae's rainfall, with IMERG estimating some of the highest precipitation accumulations of over 250 mm (10 inches) in the area around Mumbai, consistent with 7-day measurements by the Indian Meteorological Department.
• March 13, 2021: Torrential downpours and destructive flash floods swamped parts of Hawaii in March 2021. A strong low-pressure system fed by abundant moisture from the tropics fueled slow-moving storms that dropped inches of rain per hour in certain areas. 57)
- Hard hit areas included the northeastern side of Kauai, the windward slopes of the Ko‘olau Range on the island of O’ahu, the windward slopes of Haleakalā volcano on the island of Maui, and the southeast side of the Big Island of Hawai’i, according to National Weather Service meteorologists.
- Dozens of homes have been damaged or destroyed and many roads have been closed due to floods and landslides. Some areas faced widespread power outages. According to news reports, flooding was particularly severe in Haleiwa, a community in Honolulu. In Maui, thousands of people were forced to evacuate after water filled the Kaupakalua dam and reservoir, prompting fears that the dam could fail. On March 9, the governor declared a state of emergency.
- While the most intense rains had subsided by March 11, forecasters are continuing to monitor unsettled weather and the possibility of more flash floods in the coming days.
Figure 18: Flash floods, landslides, and power outages plagued the islands after torrential rains fell. This map shows the rainfall accumulation across the region from March 5 to 12, 2021. The data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) mission. The darkest oranges and reds indicate places where GPM detected rainfall totals exceeding 4 inches (10 cm) during this period. Due to averaging of the satellite data, local rainfall amounts may be significantly higher when measured from the ground. The National Weather Service reported rainfall totals in several towns that topped 10 inches (25 cm) over a 72-hour period [image credit: NASA Earth Observatory image by Joshua Stevens, using IMERG data from the Global Precipitation Mission (GPM) at NASA/GSFC, and topographic data from the Shuttle Radar Topography Mission (SRTM). Story by Adam Voiland]
• September 10, 2020: Lake Victoria is the largest lake in Africa and an economic and food security lifeline for roughly 30 million people living near its shores in Uganda, Kenya, and Tanzania. But it also takes lives. Cyclical, daily weather patterns around the lake create violent nighttime thunderstorms that kill roughly 3,000 to 5,000 fishermen per year. 58)
- “This is definitely one of the stormiest places on Earth,” said Wim Thiery, a climate scientist at the Vrije Universiteit Brussel who has studied Lake Victoria for several years. “Almost every night, you see these intense thunderstorms and sometimes even water tornadoes developing over the lake because it’s a really favorable environment for storms.”
Figure 19: This animation shows rainfall patterns around Lake Victoria across a typical 24-hour period during the wet season (March, April, and May). It represents the precipitation rate as calculated every half hour and averaged across 18 years (2000-2018). The data come from the newest Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm, which was released in late 2019 and provides one of the longest continuous records of high-resolution precipitation data. The IMERG product combines data from the Tropical Rainfall Measuring Mission (TRMM) satellite, which operated from 1997 to 2015, and Global Precipitation Measurement (GPM) satellite. which has been collecting data since 2014. Both missions were joint efforts between the Japanese Aerospace Exploration Agency (JAXA) and NASA (image credit: NASA Earth Observatory, images and video by Joshua Stevens, using Global Precipitation Measurement (GPM) Mission data courtesy of Jackson Tan/USRA/NASA/GSFC. Story by Kasha Patel)
- “Having nearly a 20-year record helps us focus on average long-term patterns, as opposed to year to year variability,” said Jackson Tan, a researcher with USRA at NASA’s Goddard Space Flight Center who recently published a paper on the latest IMERG product. “A longer record is more representative of a region’s climatology.” 59)
- The diurnal weather pattern near Lake Victoria is generally characterized by afternoon storms over surrounding land and intense nighttime storms over the lake. The storms are due to a combination of the lake's location, size, and nearby topography. Similar day-night storm patterns also appear over the lakes surrounding Lake Victoria, but are less pronounced due to their smaller size.
- “We don't have too many large lakes located across the tropical belt like Lake Victoria to observe this weather behavior,” said Kris Bedka, a climate scientist at NASA’s Langley Research Center. “Lake Victoria is special because these storms are so repetitive and a daily occurrence during some times of the year.”
- In general, thunderstorms here form through the interaction of cold and warm air masses. Daytime breezes flow from the relatively cool surface of Lake Victoria toward the hotter, sun-baked land. The warm air over the land rises higher into the atmosphere; as it cools, water vapor condenses into small water droplets and clouds. The cooled air sinks lower in the atmosphere to compensate for updrafts. If this cycle of rising and sinking air—or process of convection— is intense, it can create a thunderstorm. The thunderstorm continues to develop as long as it has warm air fueling it from below.
Figure 20: In the animation, precipitation peaks over land at around 4:30 pm EAT (16:30 hr) local time, when thunderstorms are typically most active due to afternoon surface heating from the Sun. Rainfall is particularly apparent to the northeast and west of Lake Victoria, where mountain ranges also help drive the upward motion of warm air masses (image credit: NASA Earth Observatory)
- The process reverses at night. When the Sun goes down, the land cools but Lake Victoria stays warm (water retains heat longer than land). The temperature difference between the lake and the air over the land causes breezes to develop and blow toward the lake. When the wind-driven air masses converge over the lake, they rise in the convection process that triggers thunderstorms.
- “The lake is a repository of moisture, and it’s really hot and humid. But you need the uplift of warm air to trigger a thunderstorm,” said Bedka. “The converging air flows set things in motion.”
- “We are actually able to predict a lot of these extreme nighttime thunderstorms by looking at the afternoon conditions on land,” said Thiery, who co-authored a study with Bedka that demonstrated the ability to predict the nighttime storms. “We discovered that the intensity of storms occurring during the afternoon can tell us something about what we can expect at night over the lake.”
- According to Thiery and Bedka, stronger afternoon storms lead to stronger nighttime storms. As Thiery explained, afternoon thunderstorms bring more rain and moisture over land. When the breezes blow from the land toward the lake at night, they carry that extra moisture, which provides more fuel for thunderstorms. The afternoon thunderstorms also cool the land surface, which creates an even larger temperature difference between the land and lake, leading to stronger land breezes at night.
- “Having this precipitation data sampled every half hour, we're able to see storms grow and propagate across the lake,” said Bedka. “You would never know or understand this complete process unless you had this frequent sampling across such a long data record.”
• July 23, 2020: Rainfall that accompanies Asia’s summer monsoon plays an important role in the region’s economy by refilling aquifers, generating hydroelectric energy, and providing water for crops. In some years, however, the amount of rainfall breaks records in places. That has been the case in 2020. By mid-July, severe flooding affected millions of people across South and East Asia. 60)
Figure 21: This map shows the rainfall accumulation across the region from June 1 (the start of the summer monsoon season) to July 20, 2020. The data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) mission. The darkest reds indicate places where GPM detected rainfall totals exceeding 100 cm (40 inches) during this period. Due to averaging of the satellite data, local rainfall amounts may be significantly higher when measured from the ground (NASA Earth Observatory, image by Joshua Stevens, using IMERG data from the Global Precipitation Mission (GPM) at NASA/GSFC. Story by Kathryn Hansen)
- Note the particularly high rainfall totals across India. According to the World Meteorological Organization, many parts of central, north, and northeast India had received 15 centimeters (6 inches) more rainfall than usual by the middle of July. The northeast Indian state of Assam, for example, had received a total of 89 cm (35 inches) of rain between June 1 and July 22, about 20 percent more than normal. The rainfall spurred deadly flooding that cut off villages and displaced thousands of people. There were also reports of animals—including rare one-horned rhinos—that drowned when water levels rose in Assam’s Kaziranga National Park.
- Since early June, unusually strong, stationary weather systems have produced frequent storms and heavy rainfall across south-central and eastern China. Dozens of rivers and lakes swelled to record high levels. Poyang Lake, for example, reached a record high of 22.6 meters on July 13, surpassing the annual average maximum of 19.2 meters. Across the wider region, flooding and landslides threatened villages and affected millions of people.
- Monsoon rains also doused Japan. Just one week into July, parts of western Japan had received three times the amount of rainfall that is typical for the entire month. The deluge triggered floods and landslides that, according to news reports, caused the country’s highest rain-related death toll in more than three decades.
- The monsoon is a familiar pattern in middle latitudes, occurring alongside seasonal shifts in atmospheric circulation patterns. This year, the low-pressure systems of Asia’s summer monsoon have been especially strong and stationary, allowing them to pick up even more moisture from the Indian and Pacific oceans and deliver it to land. Scientists continue to investigate changes in the Asian monsoon system that happen from year-to-year, as well as over millions of years.
• June 6, 2020: The 2020 Atlantic hurricane season is off to a busy start. By the first week of June, Tropical Storm Arthur had already brushed North Carolina, Tropical Storm Bertha had drenched South Carolina, and the third named storm of the year— Cristobal—was dropping torrential rain on the Yucatán Peninsula. 61)
- The storm first developed in the Pacific in late May as Tropical Storm Amanda, spinning off the southern end of a seasonal low-pressure pattern called the Central American Gyre. After making landfall in Guatemala and causing deadly floods in El Salvador, Amanda weakened and became less organized as it crossed Central America. It then reorganized and began to intensify as it reached the Atlantic Ocean and encountered the north end of the gyre. While lingering over the Yucatán Peninsula for several days, the storm dropped tremendous amounts of rain on parts of Mexico, Belize, and Guatemala.
- According to the U.S. National Hurricane Center, the storm dropped as much as 65 cm (25 inches) of rain on parts of Mexico; some locations in Guatemala and El Salvador saw 90 cm (35 inches). Deadly flooding swamped hundreds of homes in El Salvador, prompting that country’s president to declare a state of emergency.
- National Hurricane Center forecast models show the storm moving northward over the Gulf of Mexico toward Louisiana and other Gulf Coast states from June 6-8.
Figure 22: The map shows rainfall accumulation in Central America from May 27 to June 5, 2020. Rainfall totals were particularly intense in the Mexican states of Yucatán, Campeche, and Quintana Roo. These data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) mission. Local rainfall amounts can be significantly higher when measured from the ground (image credit: NASA Earth Observatory, image by Joshua Stevens, using IMERG data from the Global Precipitation Mission (GPM) at NASA/GSFC. Story by Adam Voiland)
• January 25, 2020: For much of the 2019-2020 austral summer, plumes of bushfire smoke have billowed from southeastern Australia in such large amounts that the ground was barely visible in satellite images. In mid-January, some of those plumes were finally quelled by a few days of much-needed rainfall. 62)
- According to news reports, the largest accumulations in New South Wales occurred north of Sydney, where rainfall averaged between 20 and 30 cm. In Victoria, areas near Melbourne received a month’s worth of rain in a single day. The weather system was spotty, however, and some areas along the southeast coast saw less than a centimeter of precipitation.
- The rain could not extinguish every fire, but it helped reduce the numbers. According to the New South Wales Rural Fire Service, 64 fires (16 uncontained) were burning across the state on January 21. That’s down from 88 fires (39 uncontained) on January 15.
- When the rain ended, the totals were not enough to bring any area out of drought. That is a concern, as the Bureau of Meteorology noted on January 21 that high temperatures and gusty winds have once again elevated the fire danger across New South Wales, Victoria, and South Australia.
Figure 23: Bushfire counts dipped in mid-January when much-needed rainfall poured down on New South Wales and Victoria. The map shows rainfall accumulation from January 15-21, 2020, in New South Wales and neighboring states. These data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) mission. Local rainfall amounts can be significantly higher when measured from the ground (image credit: NASA Earth Observatory, image by Joshua Stevens, using IMERG data from the GPM mission at NASA/GSFC. Story by Kathryn Hansen)
• October 16, 2019: NASA’s Precipitation Measurement Missions (PMM) have collected rain and snowfall from space for nearly 20 years, and for the first time in 2019, scientists can access PMM’s entire record as one data set. 63)
Figure 24: The ten satellites in the Global Precipitation Measurement Constellation provide unprecedented information about the rain and snow across the entire Earth (image credit: NASA’s Scientific Visualization Studio)
- PMM includes two missions – the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth from 1997 to 2015, and its successor, the joint NASA-JAXA Global Precipitation Measurement mission (GPM), which has been collecting data since 2014. This year, however, the GPM project upgraded its data algorithms to calibrate and incorporate TRMM data into its release, giving researchers, modelers and meteorologists access to the entire 19-year record.
- By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.
Figure 25: By being able to compare and contrast past and present data, researchers can make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security (video credit: NASA's Goddard Space Flight Center / Ryan Fitzgibbons)
Watching Precipitation to Improve the World
- GPM provides a four-dimensional view of rain, snow, sleet and storms from space: It not only records the size of droplets or pellets, but how heavy the precipitation is and how it changes over time. This perspective is used not only for global science, like studying Earth’s water and energy cycles and spotting extreme weather around the world, but it is also useful for studying single events, like hurricanes or droughts.
- GPM’s signature algorithm is the IMERG ( Integrated Multi-satellitE Retrievals for GPM). IMERG calibrates and combines data from its main satellite, the GPM Core Observatory, and the GPM Constellation, a group of international satellites that contribute data to GPM while also performing their own missions. While the full IMERG product takes time to process and prepare, it also generates a near-real-time summary of global precipitation every half-hour, which is useful for time-sensitive applications like weather forecasting and disaster recovery.
- Researchers, emergency responders, health professionals and resource managers use IMERG data to see how precipitation shaped events in the past, to help them prepare for similar events in the future. By creating a reliable, multiple-decade baseline of rain and snow, IMERG shows how precipitation may deviate from normal, informing models that predict crop yields, disease outbreaks and landslides.
- IMERG data also supports applications like water resource management, said Andrea Portier, GPM’s applications coordinator. For example, in the Navajo nation, located in the southwestern United States, precipitation data are critical for water resource managers supervising scarce water for farming, drinking and caring for animals. GPM rainfall measurements and maps help them know what areas are at risk of drought.
Figure 26: On the Navajo nation, located in the southwestern United States, precipitation data is critical for water resource managers supervising scarce water for farming, drinking and caring for animals. GPM rainfall measurements and maps help them know what areas are at risk of drought and in need of additional care (photo credit: NASA / Amber McCullum)
Eyeing the Past to Predict the Future
- Studying IMERG data from a longer perspective gives scientists a different view: What regions received the most or least rainfall, where did the biggest storms strike, how does precipitation change across the seasons?
- “For the last five years, with GPM, we’ve had a multi-satellite precision data set that covers practically the whole world,” said George Huffman, IMERG’s lead scientist and GPM’s deputy project scientist. “But five years is a short time. We needed to have something longer ... extending the multi-satellite record over the entire two missions gives us a chance to get long-term statistics and analyze past conditions.”
- One important application for past precipitation data is in weather and climate modeling, the foundation for studying short-term weather and long-term climate regionally and globally. Scientists use sophisticated computer programs to analyze large quantities of observed data on air temperature, atmospheric pressure, wind, precipitation, soil moisture and many other variables. These computer programs then generate forecasts for short-term weather or long-term climate.
- “We need the past to model the future. The past gives us the baseline we need to understand future events,” said Dalia Kirschbaum, GPM’s deputy project scientist for applications. “For example, in the case of extreme weather, like hurricanes, we can better understand what ‘extreme’ means if we have a baseline for comparison. This update is a milestone by supporting more accurate precipitation estimates that can be used as ‘ground truth’ in working toward more accurate future predictions.”
- Another set of processes the team hopes to understand more completely are changes in precipitation from day to night and across seasons.
- “One of the important things we’re looking for is understanding how the Earth system works,” Huffman said. “GPM gives us information about what the environment is doing and enables us to look at how rainfall may interact with other Earth system variables, such as soil moisture, air quality and vegetation.”
- By looking back to see where rain and snow fell in the past 19 years, scientists can help people around the world prepare for the future, from localized short-term drizzles to large-scale, decadal patterns.
- Data from both GPM and TRMM are free and available to the public. The PMM website lists the access points for various datasets and provides tutorials and webinars on how to download and use them. The tutorials range from basic data access and use to specific applications, such as flood management, agriculture, and disease monitoring and response. IMERG will continue providing data for the life of the GPM mission, expected to last to the mid-2030’s or beyond.
• February 27, 2019: Five years ago, on 27 February 2014, the Global Precipitation Measurement (GPM) Core Observatory, a joint satellite project by NASA and JAXA (Japan Aerospace Exploration Agency), lifted off aboard a Japanese H-IIA rocket. Since then, the cutting-edge instruments on GPM have provided advanced measurements about the rain and snow particles within clouds, Earth’s precipitation patterns, extreme weather and myriad ways precipitation around the world affects society. Among the uses of GPM data are helping to forecast disease outbreaks in developing countries, producing global crop reports and identifying endangered Amazon river basins. 64)
Figure 27: On Feb. 27, 2019, we celebrate five years in orbit for the NASA/JAXA GPM (Global Precipitation Measurement) mission (video credit: NASA Goddard/Ryan Fitzgibbons)
- Unlike many NASA missions, which are research satellites with delayed data delivery, GPM was engineered to get data to scientists, operational and application users as soon as possible for societal benefits. It would help answer questions such as: Where is that hurricane? Will there be a flood? Should I water my crops?
- GPM obtains data quickly using the Tracking and Data Relay Satellite (TDRS) 12-member satellite constellation, which serves as an information pipeline between Earth-orbiting satellites and NASA ground stations. On average, GPM can take 1 to 3 hours to get data into users’ hands, but in emergencies, the average delivery time can be pushed to between 15 and 90 minutes.
- The mission’s main satellite, the Core Observatory, has two instruments: the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI).
- JAXA manages the DPR, which uses two radar frequencies to measure precipitation in clouds, recording data about snow and rain particle sizes, shapes and rates. Using two radar bands, the DPR detects precipitation ranging from light to heavy, and yields a three-dimensional picture of where and how many raindrops, snowflakes or ice pellets of different sizes are distributed throughout a storm cloud.
- The GMI, managed by NASA, uses 13 channels to measure microwave energy emitted within GMI’s field of view, including precipitation in the atmosphere. Like the DPR, the GMI can measure a range of precipitation types and severity. The low-frequency channels measure moderate-to-heavy precipitation, while the higher frequencies measure moderate-to-light precipitation.
- The combination of the DPR and GMI gives scientists and meteorologists new insights into precipitation processes at both micro (particles within the clouds) and macro (regional to global) levels, making precipitation estimates and forecasts more accurate.
- GPM’s main data source is the Core Observatory, but the mission receives data from the GPM Constellation, which consist of satellites with microwave sensors from the United States, Japan, India and Europe. Most of these satellites have unique objectives and oversight agencies, but by sharing their microwave data with GPM, they expand the mission’s global coverage and consistency.
- The satellites’ data are combined with ground data to create the final product, the Integrated Multi-satellite Retrievals for GPM (IMERG), which is used for predicting weather, building climate models, managing water resources and forecasting extreme weather. While the full IMERG data product takes time to clean up and prepare, a near-real-time visualization of current global precipitation is available every 30 minutes at regional scales (10 km by 10 km).
- GPM’s ground validation system provides a yardstick against which to measure the quality of its satellite-based data. Rather than relying on satellite data alone to measure precipitation and develop forecasts, the GPM team compares space-based data with information collected by ground-based radar from the National Oceanic and Atmospheric Administration (NOAA), traditional rain gauges, and disdrometers, or drop size measuring tools. When the ground and space data disagree, the team investigates the differences and makes updates to the algorithm to make future data collection more accurate.
- With GPM’s accurate estimates of where, when, and how precipitation falls around the world, scientists gain knowledge of the inner workings of rain clouds that improves weather and climate forecasts.
- In 2017, data visualizers and scientists worked together to create one of the first 3D models of a hurricane that mapped not only precipitation amounts, but also particle sizes and types. GPM data also plays a key role in building disaster prediction models, like the Landslide Hazard Assessment model for Situational Awareness (LHASA), which warns about imminent landslides based on heavy rainfall data. GPM helps inform everyday decisions — Do I need to evacuate? — and long-term planning — How are precipitation patterns changing in a warming climate?
- GPM has advanced scientists’ understanding of Earth’s water and energy cycles in its first five years, and it is just getting started. The mission is expected to last into the mid-2030s. If this forecast is correct, GPM will continue raining down valuable data for many years to come.
• December 21, 2018: Tropical Cyclone Kenanga in the southern Indian Ocean is now on a weakening trend and NASA’s GPM core satellite provided a look at the rainfall and cloud heights within the storm. 65)
- On December 20, 2018, NASA’s GPM core observatory satellite passed over the Tropical Cyclone Kenanga and captured the storm beginning to weaken as predicted. The GPM satellite had an excellent view of the Kenanga when the tropical cyclone’s maximum sustained winds were at 90 knots (166 km/hr). GPM’s pass showed the eye of the storm, visible the day before, had since filled in.
- GPM’s instruments including the Microwave Imager (GMI) and the Dual-Frequency Precipitation Radar (DPR) revealed that the powerful storms south of Kenanga’s center are still producing very heavy rainfall at the rate of 214 mm/hr in that area. The rainfall in the northern half of the storm had decreased significantly. This GPM 3-D view of Kenanga (Figure 28) is looking southwest and was derived by the DPR Ku-band of the radar on the satellite. It showed extremely powerful storms south of Kenanga’s deteriorating eye were returning very strong reflectivity values which help to map the severity of the storm and the rainfall totals. The storm tops of the eyewall which had remained intact on the western side of the cyclone were reaching heights of 12.7 km.
- On Dec. 21 at 10 a.m. EST (1500 UTC) Tropical Cyclone Kenanga was located approximately 672 nautical miles southeast of Diego Garcia and has tracked westward. Maximum sustained winds dropped to 60 knots, so it is now a tropical storm.
- The Joint Typhoon Warning Center predicts that Kenanga will continue to weaken rapidly as the dry air within the storm gets colder and heavier causing downdrafts. It is the dry air higher aloft that contributes to stronger convective wind gusts and therefore stronger storms. Kenanga is forecast to dissipate in the next 72 hours as it tracks within the northeast periphery of Tropical Cyclone Cilida.
Figure 28: GPM observed powerful storms south of Kenanga’s center on Dec. 20, still producing very heavy rainfall at the rate of 214 mm/hr in that area. The rainfall in the northern half of the storm had decreased significantly. The storm tops of the eyewall which had remained intact on the western side of the cyclone were reaching heights of 12.7 km (image credit: NASA/JAXA, Hal Pierce)
• December 4, 2018: Heavy precipitation recently fell in areas of California that were recently devastated by deadly wildfires such as the Camp Fire and the Woolsey fire. This flooding rainfall has resulted in evacuations in burn scarred areas such as Butte County where the deadly Camp Fire hit this month. NASA used data from satellites and other sources to calculate the amount of rainfall that has occurred recently. 66)
Figure 29: NASA IMERG rainfall estimates show the heaviest rainfall concentration in north central California, northwest of Sacramento(between the Mendocino and Plumas National forests) where rainfall totaled between 160 mm and 240 mm. Lesser rainfall fell, as much as 160 mm from the Sierra National Forest and areas just north of Fresno, north to the Plumas National Forest. That heavy rainfall fell in the National Forests between Plumas and Sierra, and included: Eldorado, Stanislaus, and Yosemite National Forests (image credit: NASA/JAXA, Hal Pierce)
- At NASA/GSFC in Greenbelt, Maryland, NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) data were used to show rainfall total estimates over the western United States. Various satellites in the GPM (Global Precipitation Measurement) mission constellation provide data that are calibrated with measurements from the GPM Core Observatory and rain gauge networks around the world. GPM is a joint mission between NASA and JAXA (Japan Aerospace Exploration Agency). IMERG data are generated in near-realtime at half hourly intervals by NASA's Precipitation Processing System.
- The analysis at NASA Goddard showed total rainfall accumulation estimates using IMERG data produced during the seven day period from 23-30 November 2018.
- NASA IMERG rainfall estimates show the heaviest rainfall concentration in north central California, in an area northwest of Sacramento, between the Mendocino and Plumas National forests, where rainfall totaled between 160 mm and 240 mm.
- Flash floods, debris flows and mudslides were predicted in areas where deadly wildfires stripped away vegetation. On a positive note, these Pacific storms are expected to dampen wildfires and replenish the Sierra Nevada snowpack. This snowpack is an important source of water for California's streams and rivers.
• August 22, 2018: Abnormally heavy monsoon rains drenched Southeast Asia, leading to the worst flooding in the state of Kerala since 1924. The event, which started with rains on August 8, 2018, displaced over 300,000 people, led to hundreds of deaths, damaged over 50,000 houses throughout the region, and severely affected 13 of the 14 districts in Kerala. While it brought the region's most intense flooding this summer, the rain was one of many high precipitation events in Kerala this monsoon season. 67)
- Kerala's August rain played a part in the nearly once-in-a-century flooding, although the flooding was worsened when water was released from several full dams. Instead of gradually releasing water during drier times, authorities were forced to open 80 dams in the region, including the Idukki Dam, which is one of the largest arch dams in Asia. Thirty-five of those dams were opened for the first time.
- “The dam releases came way too late, and it coincided with the heavy rain that was occurring,” said Sujay Kumar, research scientist at NASA's Goddard Space Flight Center.
- Intense rainfall events have hit other areas of Southeast Asia as well. Eastern Myanmar experienced torrential downpours in mid-July and August, causing fatalities and displacing 150,000 people in one month. The floods were the worst in 30 years. The Bago and Sittaung rivers swelled to their highest levels in more than five decades, with the Sittaung river 7 feet above danger levels in areas.
- These rainfall data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals (IMERG), a product of the Global Precipitation Measurement (GPM) mission. The GPM satellite is the core of a rainfall observatory that includes measurements from NASA, the Japan Aerospace Exploration Agency, and five other national and international partners. Local rainfall amounts can be significantly higher when measured from the ground.
Figure 30: The image shows satellite-based rainfall accumulation from July 19 to August 18, 2018. Rainfall peaked in Kerala on July 20 and again reached abnormally high levels between August 8 and 16. Since the beginning of June, the region received 42 percent more rainfall than normal for this time period. In the first 20 days of August, the region experienced 164 percent more rain than normal (image credit: NASA Earth Observatory, images by Joshua Stevens, using IMERG data from the GPM (Global Precipitation Mission) at NASA/GSFC, story by Kasha Patel)
• July 10, 2018: After being soaked in just a few days with double the amount of rain that falls in a normal July, parts of Japan are facing their worst flooding disaster in 35 years. Storms and flooding caused deadly landslides and numerous fatalities, while leading millions of people to evacuate their homes and businesses. Prime Minister Shinzo Abe has called for 73,000 nationwide rescue workers to provide emergency assistance as forecasts predict additional landslides and rain this week. 68)
- These rainfall data are remotely-sensed estimates that come from the IMERG (Integrated Multi-Satellite Retrievals), a product of the GPM (Global Precipitation Measurement) mission. The GPM satellite is the core of a rainfall observatory that includes measurements from NASA, the Japan Aerospace Exploration Agency, and five other national and international partners. Local rainfall amounts can be significantly higher when measured from the ground.
Figure 31: This map shows rainfall accumulation from 3 a.m. (Japan Standard Time) on July 2 to 3 a.m. on July 9, 2018. Thirteen prefectures on Japan’s mainland received deadly amounts of rain. Hiroshima and Okayama, in the southern part of Honshu Island, were among the worst flooded areas (image credit: NASA Earth Observatory image by Joshua Stevens, using IMERG data from the GPM mission at NASA/GSFC, text by Kasha Patel)
• May 31, 2018: Subtropical Storm Alberto brought soaking rainfall to the southeastern U.S. up through the Tennessee and Ohio Valleys. Using a variety of resources to gather data, including the Global Precipitation Measurement mission or GPM core satellite, NASA estimated the rainfall Alberto created over its path. — By May 31, Alberto became a post-tropical cyclone as it moved to exit northeastern lower Michigan. 69)
Figure 32: IMERG rainfall estimates were compiled for the 1-week period from May 23 at 4:30 a.m. EDT (08:30 UTC) to May 30 at 4 a.m. EDT (08:00 UTC) showed upwards of 500 mm of rain (~20 inches) over the northwestern Caribbean. Rainfall amounts of at least 5 to 15 inches (shown in dark red, purple and pink) cover most of western Cuba (image credit: NASA/JAXA, Hal Pierce)
- Alberto's History: Alberto formed out of a broad area of low pressure at the surface that was located over and around the Yucatan Peninsula. Because the area of low pressure was under the influence of a nearby upper-level trough, Alberto was designated as a subtropical storm by the National Hurricane Center (NHC) on the morning of Friday May 25, which is rather unusual as most subtropical storms form at higher latitudes. The storm initially formed just east of the Yucatan Peninsula.
- A large subtropical ridge over the southwestern Atlantic steered Alberto on a northward track, and the storm brushed the far western tip of Cuba on Saturday May 26 before the center re-formed as it moved northward into the southeast Gulf of Mexico as a still minimum subtropical storm with maximum sustained winds of around 40 mph. As it moved north further into the Gulf, Alberto initially struggled to organize and intensify. The storm remained under the influence of an upper-level trough (elongated area of low pressure) with most of the active thunderstorms located well to the east of the center.
- Finally, as it was passing through the central Gulf around midday on May 27, Alberto showed signs of strengthening with thunderstorm activity becoming closer to the center and beginning to wrap around the western side of the storm. However, despite some intensification, NHC reported that dry air was wrapping around the storm and inhibiting the thunderstorms and hence Alberto's ability to strengthen. As a result, Alberto made landfall the next day on May 28 on the northern Gulf Coast still as a subtropical storm with maximum sustained winds of 45 mph.
- The center made landfall near Laguna Beach in the Florida panhandle at around 4 p.m. CDT and proceeded to track north-northwest through the center of Alabama where it weakened into a depression before moving into central Tennessee.
- Estimating Alberto's Rainfall Track: At NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland, the IMERG (Integrated Multi-satellitE Retrievals for GPM) is used to make estimates of precipitation from a combination of passive microwave sensors, including the GMI microwave sensor onboard the GPM satellite, and geostationary IR (infrared) data. GPM is a joint satellite mission between NASA and the Japan Aerospace Exploration Agency.
• May 4, 2018: Heavy seasonal rainfall has recently caused flooding in Kenya and NASA analyzed and estimated the total rainfall using data from a suite of satellites and gauges. 70)
Figure 33: From April 27 to early May 4, 2018, NASA’s IMERG product calculated rainfall over eastern Africa. Rainfall totals in some areas near the Indian Ocean coast were estimated by IMERG to be greater than 430 mm. Over western Kenya and eastern Uganda rainfall was estimated by IMERG to frequently exceed 200 mm (image credit: NASA/JAXA, Hal Pierce)
- The heavy rainfall has resulted in the displacement of over 244,000 people. This deluge follows the severe drought that afflicted East Africa in 2017. The estimated death toll from flooding and mudslides has recently been increased to about 100 people.
- At NASA's Goddard Space Flight Center in Greenbelt, Maryland, NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) created a merged precipitation product from the GPM ( Global Precipitation Measurement) mission constellation of satellites. Rainfall accumulation estimates were calculated and summarized for the period from April 27 to early May 4, 2018.
- During this period heavy seasonal precipitation fell over Kenya. Rainfall totals in some areas near the Indian Ocean coast were estimated by IMERG to be greater than 430 mm. Over western Kenya and eastern Uganda rainfall was estimated by IMERG to frequently exceed 200 mm . IMERG data are produced using data from the satellites in the GPM Constellation, and are calibrated with measurements from the GPM Core Observatory satellite as well as rain gauge networks around the world.
• Mai 2, 2018: The north shore of the Hawaiian island Kauai may have set a national record for the most rainfall ever in a 24-hour period, according to preliminary data. Rain gauges in Waipa, about a mile west of Hanalei on the north shore, recorded 49 inches (124 cm) on April 14 and 15—more precipitation than gauges measured during Hurricane Harvey over Texas in September 2017. 71)
- Intense rainfall cut a path through Hanalei, which received 28 inches (713 mm) of rainfall. The U.S. Army and National Guard airlifted more than 220 people from the Haena and Wainiha areas after Kuhio Highway, the only road that leads to them, was blocked by landslides. Ocean water around Kauai was an unusual shade of orange for a week after the storm because red–orange clay from the mountaintops was swept into the water by the rain. Floodwaters carried off bison, some which were rescued from the ocean.
- The torrential rain began on April 12, when an elongated area of low pressure disrupted the normal flow of the northeast trade winds above Oahu. This caused heavy rainfall as the system strengthened and moved slowly over Kauai. The rain event lasted through April 19.
- The GPM satellite is the core of a rainfall observatory that includes measurements from NASA, the Japan Aerospace Exploration Agency, and five other national and international partners. Local amounts can be significantly higher when measured from the ground.
- Disaster recovery crews are working to clear thousands of pounds of mud and debris from Kuhio Highway. Officials are planning to reopen one lane on May 7 for emergency vehicles, but officials have not stated when the public roads will reopen.
Figure 34: This map shows rainfall accumulation over the Kauai area from April 12 to April 19. These rainfall data are remotely–sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement mission (image credit: NASA Earth Observatory, image by Joshua Stevens, using IMERG data from the Global Precipitation Mission (GPM) at NASA/GSFC, story by Kasha Patel)
• March 22, 2018: For the first time, scientists can look at landslide threats anywhere around the world in near real-time, thanks to satellite data and a new model developed by NASA. The model, developed at NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland, estimates potential landslide activity triggered by rainfall. Rainfall is the most widespread trigger of landslides around the world. If conditions beneath Earth's surface are already unstable, heavy rains act as the last straw that causes mud, rocks or debris — or all combined — to move rapidly down mountains and hillsides. 72)
- The model is designed to increase our understanding of where and when landslide hazards are present and improve estimates of long-term patterns. A global analysis of landslides over the past 15 years using the new open source LHASA (Landslide Hazard Assessment for Situational Awareness) model was published in a study released online on March 22 in the journal Earth's Future. 73)
- "Landslides can cause widespread destruction and fatalities, but we really don’t have a complete sense of where and when landslides may be happening to inform disaster response and mitigation," said Dalia Kirschbaum, a landslide expert at Goddard and co-author of the study. "This model helps pinpoint the time, location and severity of potential landslide hazards in near real-time all over the globe. Nothing has been done like this before."
- The model estimates potential landslide activity by first identifying areas with heavy, persistent and recent precipitation. Rainfall estimates are provided by a multi-satellite product developed by NASA using the NASA and JAXA's GPM ( Global Precipitation Measurement) mission, which provides precipitation estimates around the world every 30 minutes. The model considers when GPM data exceeds a critical rainfall threshold looking back at the last seven days.
- In places where precipitation is unusually high, the model then uses a susceptibility map to determine if the area is prone to landslides. This global susceptibility map is developed using five features that play an important role in landslide activity: if roads have been built nearby, if trees have been removed or burned, if a major tectonic fault is nearby, if the local bedrock is weak and if the hillsides are steep.
- If the susceptibility map shows the area with heavy rainfall is vulnerable, the model produces a "nowcast" identifying the area as having a high or moderate likelihood of landslide activity. The model produces new nowcasts every 30 minutes.
Figure 35: This animation shows the potential landslide activity by month averaged over the last 15 years as evaluated by NASA's LHASA (Landslide Hazard Assessment model for Situational Awareness) model. Here, you can see landslide trends across the world (image credit: NASA/GSFC / Scientific Visualization Studio)
- The study shows long-term trends when the model's output was compared to landslide databases dating back to 2007. The team’s analysis showed a global "landslide season" with a peak in the number of landslides in July and August, most likely associated with the Asian monsoon and tropical cyclone seasons in the Atlantic and Pacific oceans.
- "The model has been able to help us understand immediate potential landslide hazards in a matter of minutes," said Thomas Stanley, landslide expert with USRA (Universities Space Research Association) at Goddard and co-author of the study. "It also can be used to retroactively look at how potential landslide activity varies on the global scale seasonally, annually or even on decadal scales in a way that hasn't been possible before."
• March 1, 2018: The GPM (Global Precipitation Measurement) mission core satellite provided forecasters with a look at the rainfall rates in storms drenching Arkansas and Tennessee. 74)
- NOAA's National Weather Service has issued flood advisories and flood warnings for large areas of Arkansas and Tennessee on March 1. Large parts of the Ohio Valley and Mississippi valley have received flooding rainfall during the past week. Arkansas has seen more rain than any other state.
Figure 36: On February 28, 2018 at 11:15 p.m. CST, GPM data was used to create 3D views that showed intense storms extending from Oklahoma into southwestern Arkansas. Storm tops in the area were shown by GPM to reach heights above 9 km (image credit: NASA / JAXA, Hal Pierce)
- Life threatening flood conditions have resulted from over 10 inches (254 mm) of rain falling in extensive areas of central Arkansas.
- The GPM core observatory satellite had an excellent view of the storms that were producing flooding rainfall in Arkansas when it flew above the state on Wednesday February 28, 2018 at 11:15 p.m. CST (March 1 at 0515 UTC). A rainfall analysis was derived from data received by the satellite with GMI (GPM Microwave Imager) and DPR (Dual Frequency Precipitation Radar) instruments. GPM's radar passed directly above storms that were dropping heavy rain over southwestern Arkansas. GPM's radar (DPR) indicated that some of these storms were dropping rain at greater than 5.1 inches (30.7 mm) per hour. GPM is a joint mission between NASA and the Japan Aerospace Exploration Agency, JAXA.
• January 11, 2018: Winter rains falling on recently burned ground triggered deadly mudslides in Santa Barbara County, California on January 9. NASA calculated the amount of rain fall between January 8 and 10, 2018 and calculated the potential for landslides. 75)
Figure 37: NASA's IMERG (Integrated Multi-Satellite Retrievals for GPM) analysis of Jan 8 through 10, 2018 revealed that the heaviest rainfall occurred over the Sacramento Valley where over 8 inches (203 mm) were indicated. A rainfall total of 5 inches (127 mm) was reported in Ventura County (image credit: NASA/JAXA/Hal Pierce)
- At NASA's Goddard Space Flight Center in Greenbelt, Maryland, a landslide potential map was generated by the global LHASA (Landslide Hazard Assessment for Situational Awareness) model, a model that combines precipitation data from the GPM (Global Precipitation Measurement) mission satellite with a global landslide susceptibility map. LHASA gives a broad overview of landslide hazard in nearly real time, but site-specific information should be obtained prior to emergency operations or building projects.
- At least 17 residents of southern California have been killed by the deadly mudslides. A storm moving in from the Pacific Ocean dropped heavy rain over soil that was laid bare by last month's wild fires. Heavy rainfall loosened surface sediments in Santa Barbara County early Tuesday, Jan. 9 caused deadly mudslides. According to the California Department of Transportation parts of US 101, a major highway connecting northern and southern California has been closed because of mud and debris.
- NASA's GPM satellite provides information on precipitation from its orbit in space. GPM is a joint mission between NASA and JAXA (Japan Aerospace Exploration Agency). GPM also utilizes a constellation of other satellites to provide a global analysis of precipitation that are used in rainfall calculations.
- At NASA/GSFC, a rainfall analysis was constructed using NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) data. Precipitation data acquired from satellites in the GPM Constellation during the period from January 8 to 10, 2018 were used in creating a rainfall accumulation map. The analysis showed the heavy rainfall that occurred over California during the past three days.
Figure 38: The landslide potential map was generated by the global LHASA (Landslide Hazard Assessment for Situational Awareness) model. LHASA gives a broad overview of landslide hazard in nearly realtime (image credit: NASA)
• November 22, 2017: As intense rain storms moved into Jeddah, Saudi Arabia on Nov. 21, NASA’s GPM (Global Precipitation Measurement) Mission core satellite analyzed the severe storms. Heavy downpours caused schools and universities to close and the General Authority of Meteorology and Environment Protection predicted that heavy rain will continue for a couple days. 76)
- NASA's GPM Core Observatory satellite measures precipitation from space with the first spaceborne Ku/Ka-band DPR (Dual-frequency Precipitation Radar) and a multi-channel GMI (GPM Microwave Imager). The satellite passed over western Saudi Arabia on Nov. 21, 2017 at 01:23 UTC . GMI and DPR collected data that revealed heavy rain rates within powerful storms that were headed toward Jeddah, Saudi Arabia.
- GMI indicated that an intense thunderstorm located over Saudi Arabia north of Jeddah was dropping rain at a rate of over 115.5 mm/ hour. GPM's DPR swath measured precipitation in a storm located over the Red Sea that was producing rain at a rate of over 90 mm/hour.
Figure 39: On Nov. 21 at 01:23 UTC, GPM indicated an intense thunderstorm located over Saudi Arabia north of Jeddah was dropping rain at a rate of over 115.5 mm/hr. GPM's DPR swath (shown in lighter shades) measured precipitation in a storm located over the Red Sea that was producing rain at a rate of over 90 mm/hr (image credit: NASA/JAXA, Hal Pierce)
Figure 40: This 3-D image of rainfall in storms over western Saudi Arabia on Nov. 21 was created using GPM DPR's Ku-band instrument. GPM found that a few storm tops over the Red Sea were reaching heights above 10 km (image credit: NASA/JAXA, Hal Pierce)
• September 23, 2017: When Hurricane Maria swept across Puerto Rico on September 20, 2017, meteorologists expected it to deliver a tremendous amount of rain in a short period of time. Satellite data confirm that that is exactly what happened. 77)
- The map of Figure 41 shows spaceborne measurements of rainfall in the Caribbean near Puerto Rico. It depicts measurements from the evening of September 18 to the evening of September 20. The brightest areas reflect the highest rainfall amounts—as much as 500 mm in places.
- Rain bands trailing the storm continued to deliver heavy rain to the island on September 21-22. The National Hurricane Center reported catastrophic flooding, particularly in mountainous areas, and noted that mudslides should be expected. Mountainous terrain channels floodwater into streams and rivers. Gages in these rivers show that many, such as the Rio Grande de Manati, have reached record-high levels.
- After Hurricane Maria left Puerto Rico, it moved on to the Dominican Republic and battered parts of the island with heavy winds and rain. According to news reports, most parts of this island saw at least 150–300 mm, with some areas seeing as much as 500 mm.
- These measurements are a product of the GPM (Global Precipitation Measurement) mission, which is a partnership between NASA, JAXA (Japan Aerospace Exploration Agency), and five national and international partners. The rainfall totals are regional, remotely-sensed estimates. Each pixel shows 0.1º of the globe (about 11 km at the equator), and the data are averaged across each pixel. Individual ground-based measurements within a pixel can be significantly higher or lower than the average.
- Data for the map comes from the IMERG (Integrated Multi-Satellite Retrievals for GPM), a product of the U.S. GPM science team. IMERG compiles precipitation estimates from passive microwave and infrared sensors on several satellites, as well as monthly surface precipitation gauge data, to provide precipitation estimates between 60º North and South latitude.
Figure 41: IMERG map of Hurricane Maria's rainfall in the Caribbean near Puerto Rico, acquired with GPM on 19-21 Sept. 2017 (image credit: NASA Earth Observatory, image by Joshua Stevens, using IMERG data from GPM at NASA/GSFC, caption by Kathryn Hansen)
• August 30, 2017: NASA's GPM mission team has produced rainfall accumulation graphics and unique views of the structure of Harvey during various phases of development and landfall. GPM products help to identify the center of circulation and intense eyewall convection, and these images are provided to NOAA's (National Oceanic and Atmospheric Administration’s) National Hurricane Center via NASA’s SPoRT (Short-term Prediction Research and Transition) Center and the NRL (Naval Research Laboratory). Based at NASA/MSFC (Marshall Space Flight Center) in Huntsville, Alabama, SPoRT is a project to transition unique observations and research capabilities to the operational weather community to improve short-term forecasts on a regional scale. 78)
Figure 42: NASA's GPM Core Observatory captured these images of Hurricane Harvey at 6:45 a.m. CDT (1145 UTC) and 4:25 p.m. CDT (2125 UTC) on August 27, nearly two days after the storm made landfall near Victoria, Texas. The image shows rain rates derived from GPM's GMI microwave imager (outer swath) and dual-frequency precipitation radar or DPR (inner swath) overlaid on enhanced infrared data from NOAA’s GOES East satellite (image credit: NASA Scientific Visualization Studio)
• June 16, 2017: At least 156 people in Bangladesh were killed during the past week by landslides and floods caused by heavy rainfall. NASA calculated the amount of rain that has fallen using data from satellites. 79)
Figure 43: From June 12 to 14, 2017 heaviest rainfall accumulation estimates (purple) by IMERG were located over southeastern Bangladesh. IMERG estimates indicated that landslide inducing rainfall totals there were greater than 510 mm (image credit: NASA/JAXA, Hal Pierce)
- Monsoon rainfall has been especially heavy over this area that includes southeastern Bangladesh, northeastern India and western Burma (Myanmar). This disaster follows quickly on the heels of deadly cyclone Mora which hit the same area a couple weeks ago.
- This rainfall analysis was made at NASA's Goddard Space Flight Center in Greenbelt, Maryland using NASA's near-real time IMERG (Integrated Multi-satellitE Retrievals for GPM) data. GPM is the Global Precipitation Measurement mission satellite and constellation of satellites that are managed by both NASA and the Japan Aerospace Exploration Agency or JAXA.
- Those IMERG data were assembled during the period from June 12 to 14, 2017. The heaviest rainfall accumulation estimates by IMERG were located over southeastern Bangladesh. IMERG estimates indicated that landslide inducing rainfall totals there were greater than 510 mm.
- Monsoon rainfall is expected to continue to affect the area. IMERG rainfall totals have been adjusted to reflect observed values in other similar extreme rainfall events.
• May 17, 2017: Heavy rainfall has recently caused widespread flooding and landslides in Jamaica. Occasional showers from trade winds are normal in Jamaica but recent rainfall from slow moving troughs have been unusually heavy. A 1009 mb low pressure center located in the western Caribbean was disrupting the normal trade winds over Jamaica today. 80)
- This rainfall analysis was constructed using data from NASA's IMERG (Integrated Multi-satelliE Retrievals for GPM). Data collected in near-real time were utilized in IMERG's rainfall accumulation estimates for the Caribbean. This analysis covers the period from May 13 through early May 17, 2017. Rainfall totals greater than239 mm were shown by IMERG data extending from south of Jamaica to southwestern Haiti. Some rainfall totals of greater than 12 inches (304.8 mm) were estimated in the waters between Jamaica and southwestern Haiti. IMERG rainfall totals in this analysis have been adjusted to reflect observed values in other similar extreme rainfall events.
- IMERG is a unified U.S. algorithm that provides a multi-satellite precipitation product. IMERG is run twice in near-real time with the “Early” multi-satellite product being created at about 4 hours after observation time and a “Late” multi-satellite product is provided at about 12 hours after observation time.
Figure 44: IMERG analyis of heavy rainfall in the Carribean in the period May 13-17, 2017 (image credit: NASA/GSFC, Hal Pierce)
• March 23, 2017: The GPM core observatory measured the heavy rainfall that caused extensive flooding and loss of life in Peru. Extreme flooding and frequent landslides that occurred in March have forced many from their homes. An El Niño-like condition with warm ocean waters developed near Peru's coast. This extremely warm water off Peru's western coast has been blamed for promoting the development of these storms. Equatorial SSTs (Sea Surface Temperatures) are about average elsewhere in the central and east central Pacific. 81)
- When the GPM core observatory satellite flew above Peru on March 20, 2017 at 0826 UTC, GPM identified locations of storms that were dropping heavy rainfall over northwestern Peru. Data collected by the GMI (GPM Microwave Imager) and DPR (Dual-Frequency Precipitation Radar) instruments during this pass revealed that very heavy precipitation was falling in that area. GPM's radar (DPR Ku-band) data indicated that some storms were dropping rain at the extreme rate of greater than 137 mm /hour. These extreme rainfall rates were found in the line of storms extending southwestward from Peru's coast.
- The DPR Ku-band was also used to examine the 3D structure of precipitation within the storms near and over northwestern Peru. GPM's examination showed that several storms located in the Pacific had cloud tops that were reaching altitudes above 13 km . GPM is a joint mission between NASA and the Japanese space agency JAXA.
- IMERG (Integrated Multi-satellitE Retrievals for GPM) data were used to show rainfall in areas that were not covered by the GPM core observatory satellite swath. Those estimates are the result of unifying precipitation measurements from a constellation of research and operational satellites. Those rainfall estimates were generated by NASA's Precipitation Processing System every half hour.
- That data was made into an animation at NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland and showed real-time IMERG rainfall estimates based on data collected during the period from March 14 to 21, 2017. The animation of seven days of data showed scattered storms developing over Peru and Brazil and moving over Peru. The animation showed rainfall rates between 25 mm and 50 mm per hour in many storms.
- On March 18, Peru's National Meteorological and Hydrological Service noted, that from March 19 to 25, "rains will intensify on the north coast and the entire western slope of the Sierra. On the north coast (La Libertad, Lambayeque, Piura and Tumbes) heavy rains accompanied by [lightning] will intensify between March 19 and 23. In the interior of Piura and Lambayeque, the rainfall is expected to exceed 150 mm/day; while in the coastal zone of Piura, Lambayeque, Tumbes and the interior of La Libertad, it could exceed 50 mm/day."
- The devastating downpour killed 67 persons in Peru and thousands more were forced to evacuate by the intense rains which damaged 115,000 homes and destroyed more than 100 bridges in Peru’s worst floods in recent memory. The disaster – which came after a period of severe drought – has been blamed on abnormally high temperatures in the Pacific Ocean, and fuelled criticism that the country is ill-prepared for the growing challenges of climate change. 82)
Figure 45: When the GPM core observatory satellite flew above Peru on March 20, 2017 at 0826 UTC, GPM identified locations of storms that were dropping heavy rainfall over northwestern Peru (image credit: NASA/JAXA, Hal Pierce)
• March 3, 2017: Rainfall from spring-like downpours in the U.S. from February 25 to March 1 were analyzed at NASA using data from the Global Precipitation Measurement mission or GPM satellite (Figure 46). 83) — As of Feb. 27, 2017, GPM is 3 years on orbit.
- Record breaking warm temperatures this winter have caused plants to bloom early in the eastern United States. Unfortunately this has also resulted in the formation of spring-like severe thunderstorms and deadly tornadoes. Multiple tornado sightings were made in three of the last seven days. On Saturday February 25, 2017 destructive tornadoes were reported in Maryland, Pennsylvania and Massachusetts.
- On February 28, twisters were reported in the states of Arkansas, Iowa, Illinois and Michigan. Three people were killed in Illinois and four others were injured in Arkansas with this tornado outbreak. Severe weather on March 1, 2017 also included reports of tornado sightings in Ohio, Tennessee, West Virginia, Kentucky and Georgia.
- IMERG (Integrated Multi-satellitE Retrievals for GPM) data were used to show the rainfall that occurred during the past week. The analysis was done at NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland. Intense downpours from storms over this period resulted in flash floods in several states.
- The GPM core observatory satellite had a good view of severe weather as it moved into the Appalachian Mountains on March 1, 2017 at 15:25 UTC (10:25 a.m. EST). The GPM satellite measures rain and snow using the GMI (GPM Microwave Imager) and DPR (Dual-Frequency Precipitation Radar) instruments. GPM's DPR measured rain falling at a rate of over 159 mm per hour as powerful storms moved through Tennessee and Kentucky. Storm top heights of over 9.8 km were found by GPM's radar as it sliced through those intense thunderstorms.
- Due to above average temperatures, frozen precipitation, other than hail, was unusual over the eastern United States in the GPM analysis. GPM's radar data (DPR) showed that the average height of the freezing level was above 3 km. In Alabama, the freezing level was much higher up. It was shown by GPM's data to be higher than 4 km.
Figure 46: This GPM rainfall image, combined with infrared cloud data from NOAA's GOES-West satellite, shows the line of storms that stretched from Pennsylvania to Alabama on March 1, 2017. Red areas indicate rainfall up to 50 mm per hour (image credit: NASA/JAXA, Hal Pierce)
• January 13, 2017: Widespread flooding has recently caused the deaths of dozens of people in southern Thailand. Frequent and persistent downpours have resulted in record rainfall totals and NASA calculated rainfall over the region from January 5 to January 12, 2017. The GPM (Global Precipitation Measurement) mission core satellite is part of a constellation of satellites that can measure rainfall from space. GPM is a joint mission between NASA and JAXA and the data is input into NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) data product. 84)
- Rainfall has greatly increased over Thailand during this La Nina year 2016. Very low rainfall totals occurred over Thailand during last year's El Nino event. At NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland, a rainfall anomaly analysis was made by comparing the former TRMM (Tropical Rainfall Measuring Mission) calibrated rainfall climatology to "near real-time" Multi-satellite Precipitation Analysis data collected over a thirty day period.
- The TMPA (TRMM-based, near-real time Multi-satellite Precipitation Analysis) has been used to monitor rainfall over the global Tropics for many years. By subtracting the long-term average rainfall or climatology, rainfall anomalies can be constructed to show deviations from the normal pattern.
- The TRMM mission was in operation from 1997 to April 2015. It was designed to measure rainfall over the global Tropics using both passive and active sensors, including the first and at the time only precipitation radar in space. With its combination of passive microwave and active radar sensors, TRMM was used to calibrate rainfall estimates from other satellites to expand its coverage. The TRMM satellite produced over 17 years of precipitation measurements that were a valuable contribution to global rainfall climatology.
Figure 47: NASA calculated rainfall over southern Thailand from Jan. 5 to 12, 2017. Extreme rainfall totals of over 700 mm were found over the Gulf of Thailand. Highest totals over land were greater than 500 mm on the eastern coast of the Malay Peninsula in the Bang Saphan District (image credit: NASA/JAXA, Hal Pierce)
Figure 48: In this rainfall analysis, the panel on the left shows rainfall departure from normal during the 2016 El Nino event. The panel on the right shows the extreme increase in rainfall over southern Thailand during the current La Nina event (image credits: NASA/JAXA, Hal Pierce, Ref. 84)
• July 26, 2016: Most of the Hawaiian Islands were spared serious damage from tropical storm Darby. The location of Darby's track through the Hawaiian Islands resulted in the islands of Hawaii and Oahu being the most affected. Flash flooding was common on Oahu due to a reported 177.8 mm of rain drenching the island. Interstate H-1 was flooded in some locations. Lightning damage was reported in Kaneohe on the windward side of Oahu. 85)
Figure 49: Estimates of rainfall accompanying tropical storm Darby were produced using NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) data. These IMERG rainfall accumulation totals were calculated for the period from July 19-26, 2016.Darby had weakened from a category one hurricane to a tropical storm before moving into the Central Pacific. The IMERG estimates indicate that Darby dropped extremely heavy rainfall at times. The greatest rainfall total estimates during this period were located north of Oahu where 480 mm fell (image credit: SSAI/NASA/GSFC, Hal Pierce)
• March 31, 2016: Not all raindrops are created equal. The size of falling raindrops depends on several factors, including where the cloud producing the drops is located on the globe and where the drops originate in the cloud. For the first time, scientists have three-dimensional snapshots of raindrops and snowflakes around the world from space, thanks to the joint NASA and JAXA GPM (Global Precipitation Measurement) mission. With the new global data on raindrop and snowflake sizes this mission provides, scientists can improve rainfall estimates from satellite data and in numerical weather forecast models, helping us better understand and prepare for extreme weather events. 86)
- "The drop size distribution is one of many factors that determines how big a storm will grow, how long it will last and how much rain it will ultimately produce,” said Joe Munchak, research meteorologist at NASA/GSFC (Goddard Space Flight Center) in Greenbelt, Maryland. “We’ve never been able to see how water droplet sizes vary globally until now."
- Storm clouds contain a wide variety of drop sizes that ultimately fall as rain or snow. In general, in the cores of clouds the drops tend to be bigger because they collide with each other and aggregate as they fall towards the Earth's surface, while smaller droplets occur at the edges and higher altitudes. Drops tend to be small when they miss colliding into others or break apart. Scientists refer to the number of drops and snowflakes of different sizes at various locations within a cloud as the "particle size distribution."
- In order to accurately know how much precipitation is falling in a storm, scientists need to understand the ratio of large drops to smaller or medium sized drops. Previously, researchers had to make assumptions of the ratio because earlier studies were conducted in isolated locations and global data were limited, said Munchak. “Without knowing the relationship or the ratio of those large drops to the smaller or medium sized drops, we can have a big error in how much rain we know fell and that can have some big implications for knowing long term accumulations which can help with flash flood predictions,” said Munchak.
- With GPM’s three-dimensional snapshots of drop size distribution, scientists can also gain insight into the structure of a storm and how it will behave. Drop size distribution influences storm growth by changing the rate of evaporation of rain as it falls through dry air, said Munchak. Smaller drops, for instance, will tend to evaporate faster and subsequently cool the air more. This leads to stronger flow of downward moving air that can cause damaging winds when they reach the ground. However, these same downdrafts can interfere with the upward flowing air that fuels the storm and cause the storm to weaken or dissipate.
Figure 50: This is a conceptual image showing how the size and distribution of raindrops varies within a storm. Blues and greens represent small raindrops that are 0.5-3 mm in size. Yellows, oranges, and reds represent larger raindrops that are 4-6 mm in size. A storm with a higher ratio of yellows, oranges, and reds will contain more water than a storm with a higher ratio of blues and greens (image credit: NASA/GSFC)
• March 3, 2016: Heavy rainfall recently caused flooding, landslides and power outages in some areas of Peru. NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM) measured that rainfall by using a merged precipitation product from a constellation of satellites. 87)
- GPM is the Global Precipitation Measurement mission, which is a satellite co-managed by NASA and JAXA (Japan Aerospace Exploration Agency) and is used in NASA's IMERG data. GPM provides next-generation observations of rain and snow worldwide every three hours.
- Extremely heavy rainfall was reported in northern Peru on February 26 and February 27, 2016. Thousands were made homeless and at least two people were reportedly killed from the severe weather. The strong El Niño was partially blamed for the abnormally high rainfall in that area.
- NASA's IMERG data collected from February 23-29, 2016 were used to estimate rainfall totals over this area of South America. The highest rainfall total estimates for this period were over 700 mm. These extreme rainfall total estimates were shown east of the Andes in southeastern Peru and Bolivia.
- The satellites used in IMERG include the DMSP series from the U.S. Department of Defense, GCOM-W from JAXA, Megha-Tropiques from CNES (Centre National D’etudies Spatiales) and ISRO (Indian Space Research Organization), the NOAA series satellites, Suomi-NPP from NOAA-NASA, and MetOps from EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). All of the instruments (radiometers) onboard the constellation partners are intercalibrated with information from the GPM Core Observatory’s GMI (GPM Microwave Imager) and DPR (Dual-frequency Precipitation Radar).
- On March 3, Peru's National Meteorological and Hydrological Service said that rain was forecast to continue along the North Coast. The service said that in 10 hours, the Lancones (Piura) station recorded a total of 110 mm, while in the city of Tumbes recorded 60 mm of rain.
Figure 51: NASA's IMERG data collected from February 23-29, 2016 were used to estimate rainfall totals over this area of South America. The highest rainfall total estimates for this period were over 700 mm (27.6 inches). These extreme rainfall total estimates were shown east of the Andes in southeastern Peru and Bolivia (image credit: NASA, JAXA, SSAI, Hal Pierce)
• Dec. 10, 2015: NASA's GPM mission and a cadre of other satellites have been gathering data on the extreme rainfall in the Pacific Northwest. The continued "training" of rainfall into the area has caused flooding in the Portland, Oregon area with at least one death reported. Western Washington is also on flood alert due to the deluge. 88)
- "Riding a pumped up jet stream, a convoy of wet storms have pummeled and drenched the Pacific Northwest for the past week," said Bill Patzert, climatologist at NASA/JPL (Jet Propulsion Laboratory) in Pasadena, California. "Following a couple of years of regional drought, all this rain and mountain snow has whiplashed many Washington and Oregon communities from extremely dry conditions into flooding and even landslides. Once again, the old adage, 'Great droughts end in great floods' comes to mind.'" Patzert said that this hose of heavy moisture is originating in the far Western Pacific Ocean. Sweeping out of the tropics, meteorologists refer to these relatively narrow, moisture laden rain and snow producers as atmospheric rivers. For the U.S. West coast states, these storms supply up to 50% of their water supply. "They can be 'fast and furious' and damaging, but play a large role in sustaining our water supplies in the normally dry West," said Patzert.
- Rainfall that occurred from December 2 to 9, 2015 was measured with data from NASA's IMERG (Integrated Multi-satellitE Retrievals for GPM). IMERG found that many areas from northern California through the state of Washington had rainfall totals greater than 160 mm . Even more extreme rainfall was measured by IMERG over the open waters of the Pacific Ocean where rainfall totals during the past week were found to be over 310 mm.
- IMERG creates a merged precipitation product from the GPM constellation of satellites. These satellites include DMSPs from the U.S. Department of Defense, GCOM-W from JAXA, Megha-Tropiques from CNES and ISRO (Indian Space Research Organization), the NOAA series Suomi-NPP from NOAA-NASA, and MetOps from EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). All of the instruments (radiometers) onboard the constellation partners are intercalibrated with information from the GPM Core Observatory’s GPM GMI (Microwave Imager) and DPR (Dual-frequency Precipitation Radar).
- On Dec. 9, there were a lot of watches and warnings in the region. Flood watches, warnings and flood advisories were in effect for portions of the Pacific Northwest as well as parts of northern California and northern Idaho. Winter storm watches and warnings were in effect for the Sierra Nevada Range in California and parts of the intermountain west. High wind watches, warnings and wind advisories were in effect for portions of the northwest U.S., especially in the higher terrain.
Figure 52: NASA's IMERG measured rainfall from Dec. 2 to 9 and found that many areas from northern California through the state of Washington had rainfall totals greater than 160 mm. Over open waters of the Pacific Ocean some rainfall totals reached over 310 mm (image credit: NASA/JAXA/SSAI, Hal Pierce)
• On December 1–2, 2015, the Indian city of Chennai received more rainfall in 24 hours than it had seen on any day since 1901. The deluge followed a month of persistent monsoon rains that were already well above normal for the Indian state of Tamil Nadu. At least 250 people have died, several hundred have been critically injured, and thousands have been affected or displaced by the flooding that has ensued. 89)
- The rainfall data of Figure 53 come from the IMERG (Integrated Multi-Satellite Retrievals for GPM), a product of the Global Precipitation Measurement mission. The brightest shades on the maps represent rainfall totals approaching 400 mm during the 48-hour period. These regional, remotely-sensed estimates may differ from the totals measured by ground-based weather stations. According to Hal Pierce, a scientist on the GPM team at NASA/GSFC (Goddard Space Flight Center), the highest rainfall totals exceeded 500 mm in an area just off the southeastern coast.
- Meteorologists in India and abroad attributed the rains to a super-charged northeast monsoon. In the winter, prevailing winds blow from northeast to southwest across the country, which tends to have a drying effect in most places, particularly inland. But those northeasterly winds also blow over the warm waters of the Bay of Bengal, where they evaporate a great deal of moisture from the sea and dump it over southern and eastern India. Coastal eastern India receives 50-60% of its yearly rainfall during this winter monsoon.
- In 2015, this pattern was amplified by record-warm seas and by the long-distance effects of El Niño. The city of Chennai recorded 1218.6 mm of rain in November 2015, according to Weather Underground blogger Bob Henson. India’s meteorological department noted that rainfall was 50 to 90 percent above normal in the eastern states. Then 345 mm more fell on Chennai in the December 1–2 storm, which was fueled by a low-pressure system offshore.
• Nov. 4, 2015: The image of Figure 54 shows satellite-based estimates of rainfall from a particularly large storm that passed over the area on October 18. The rainfall data are from GPM/IMERG (Global Precipitation Measurement/Integrated Multi-Satellite Retrievals), a product of the Global Precipitation Measurement mission. Green-white colors represent the largest rainfall totals, which in some areas reached upward of 70 mm during the 24-hour period displayed in the animation. These regional, remotely-sensed estimates may differ from the totals measured by ground-based weather stations. Some areas of Death Valley National Park in California were hit particularly hard. 90)
Figure 54: In October 2015, a series of storms passed across the U.S. Southwest and brought a deluge of rain to the area‘s desert valleys, acquired on Oct. 18-19, 2015 with GPM/IMERG (image credit: NASA Earth Observatory, USGS)
• Oct. 5, 2015: The GPM satellite of NASA/JAXA measured record rainfall that fell over the Carolinas from September 26 to October 5 from a plume of moisture from Hurricane Joaquin when it was located over the Bahamas and moved to Bermuda. The IMERG (Integrated Multi-Satellite Retrievals for GPM) showed highest rainfall totals near 1,000 mm (39.3 inches) in a small area of South Carolina and rainfall between 700 and 900 mm (27.5 and 37.4 inches) over a large area of South Carolina. 91)
Figure 55: The GPM mission recorded the '1000 Year' rainfall event heaping death and devastation across wide areas of South Carolina from the combined actions of a freak Nor’easter and Hurricane Joaquin (image credit: SSAI/NASA/JAXA, Hal Pierce)
• April 2, 2015: The GMI (GPM Microwave Imager), built by BATC (Ball Aerospace & Technologies Corp.) under contract for NASA, has performed flawlessly in its first year on orbit as the most accurately calibrated radiometer in the twelve-satellite GPM constellation. GMI provides a standard of calibration that will substantially improve the accuracy of the precipitation data measured by other radiometers in the constellation. 92)
- As an essential part of an international satellite mission, GMI is capturing next-generation observations of rain and snow worldwide every three hours. The GPM Core Observatory is delivering unprecedented 3-D views of hurricanes and snowstorms and contributes to monitoring and forecasting weather events such as droughts, floods and landslides.
• Feb. 27, 2015: The GPM Core Observatory of NASA and JAXA, launched from Tanegashima Space Center, Japan, on February 27th, 2014, is celebrating its first year on orbit. 93)
- Last month, NASA released the agency’s most comprehensive global rain and snowfall product to date from the GPM mission made with data from a network of 12 international satellites and the Core Observatory. The Core Observatory acts as a tuner to bring together measurements of other satellites, providing a nearly global picture of rain and snow called the IMERG (Integrated Multi-satellite Retrievals for GPM).
Figure 56: The GPM mission produced its first global rainfall & snowfall map from April to September 2014 (image credit: NASA/GSFC, the video can be seen in Ref. 93)
• The Precipitation Processing System (PPS) has begun producing updated GPM radiometer products as of Dec. 4, 2014 due to an error discovered in the calculation of the Sun Angle in the PPS Geolocation Toolkit. This is considered a minor update with the Product Version being incremented in letter only. 94)
• November 2014: The GPM mission is providing precipitation rates almost everywhere in the world. 95)
- During the evening and afternoon of August 6, 2014 (Figure 57), the TRMM and GPM satellites saw a swarm of thunderstorms over a portion of the Sahara Desert where rain is particularly rare. Each satellite revealed a different structure to the cluster, perhaps because of the different time of day or the different part of the cluster that they observed.
• As of September 4, 2014, the data of the NASA/JAXA GPM observatory are being made freely available through NASA's Precipitation Processing System at Goddard Space Flight Center in Greenbelt, Maryland. Scientists and modelers (registered users) can use the new GPM data for weather forecasts, estimating snowpack accumulation for freshwater resources, flood and landslide prediction, or tracking hurricanes. 96)
- The most accurate and comprehensive collection of rain, snowfall and other types of precipitation data ever assembled now is available to the public. This new resource for climate studies, weather forecasting, and other applications is based on observations by the GPM Core Observatory, a joint mission of NASA and JAXA, with contributions from a constellation of international partner satellites. 97)
• July 8, 2014: The GPM spacecraft flew over Hurricane Arthur five times between July 1 and July 5, 2014. Arthur is the first tropical cyclone of the 2014 Atlantic hurricane season. The five GPM passes over Arthur are the first time a precipitation-measuring satellite has been able to follow a hurricane through its full life cycle with high-resolution measurements of rain and ice. In the July 3 image, Arthur was just off the coast of South Carolina. GPM data showed that the hurricane was asymmetrical, with spiral arms, called rain bands, on the eastern side of the storm but not on the western side. 98)
- The GPM Core Observatory’s observations of storms like Arthur will also help scientists decipher some of the thorniest questions about hurricanes, such as how and why they intensify. Hurricane intensity is one of the most difficult aspects to predict and is an area of active research that GPM's observations will contribute to.
Figure 58: A 3-D view of Hurricane Arthur in July 2014, taken from instruments aboard the NASA/JAXA GPM observatory (image credit: NASA)
• April 11, 2014: The Global Precipitation Measurement mission's Core Observatory is performing normally. Calibration of the DPR (Dual-frequency Precipitation Radar) and the GMI (GPM Microwave Imager) continued. 99)
• March 25, 2014: NASA and JAXA have released the first images captured by the GPM sensor complement. The images show precipitation falling inside a March 10 cyclone over the northwest Pacific Ocean, approximately 1,600 km east of Japan. The data were collected by the GPM Core Observatory's two instruments: JAXA's DPR (Dual-frequency Precipitation Radar), which imaged a three-dimensional cross-section of the storm; and NASA's GMI (GPM Microwave Imager), which observed precipitation across a broad swath. 100) 101)
Legend to Figure 59: The colors show the rain rate: red areas indicate heavy rainfall, while yellow and blue indicate less intense rainfall. The upper left blue areas indicate falling snow. In addition to seeing all types of rain, GMI's technological advancements allow the instrument to identify rain structures as small as about 5 to 15 km across. This higher resolution is a significant improvement over the capability of an earlier instrument flown on the TRMM (Tropical Rainfall Measurement Mission), launched in 1997.
Legend to Figure 60: The vertical cross-section about 7 km high shows rain rates: red areas indicate heavy rainfall while yellow and blue indicate less intense rainfall.
These first GPM Core Observatory images were captured during the first few weeks after launch, when mission controllers at the NASA Goddard Mission Operations Center put the spacecraft and its science instruments through their paces to ensure they were healthy and functioning as expected. The engineering team calibrates the sensors, and Goddard's team at the Precipitation Processing System verifies the accuracy of the data. — GPM science data are expected to be available starting in Sept. 2014 when all items have been calibrated and tested.
• On March 17, 2014, the team executed GPM's first scheduled yaw turn to change the orientation of the spacecraft by 180º. Yaw is the left/right orientation in the horizontal plane of the spacecraft's motion. The spacecraft is now "flying backwards." Yaw maneuvers will be performed approximately every 40 days for spacecraft thermal control, as the angle between the spacecraft's orbit and the sun changes. This keeps the side of the spacecraft that is designed to remain cold from overheating. Yaw maneuvers are performed primarily using the spacecraft's reaction wheels (Ref. 102).
- On March 19, 2014, the team performed a 50 s ΔV maneuver, an increase in speed to boost the altitude of its orbit, using its thrusters. GPM has twelve thrusters: four forward and eight aft. The March 19 maneuver was the first ΔV performed using the forward thrusters, since the spacecraft is now in in the opposite orientation after the yaw turn.
• March 8, 2014: The DPR instrument was activated, and the teams in the mission operations center and launch support room at NASA/GSFC in Greenbelt, MD, began the instrument's checkout period. The DPR data is being sent through the Precipitation Processing System at Goddard to JAXA's MOS (Mission Operations System) in Tsukuba, Japan (Ref. 102).
• March 6, 2014: GPM is performing normally. The initial checkout of the GMI instrument and the spacecraft showed both are performing as expected, and the GMI instrument continues to collect science data on rain and snowfall (Ref. 102).
• March 5, 2014: The GPM Core Observatory is performing normally. The GMI continues in science mode, and GMI data is being sent to the PPS (Precipitation Processing System) at NASA/GSFC in Greenbelt, MD. Using the initial data, the instrument team has verified that GMI is working well on-orbit. The GPM spacecraft will have a 60 day on-orbit check out period (commissioning phase) to ensure the healthy operation of the spacecraft and instruments. Precipitation data will be released from the PPS no later than 6 months post-launch, after the science teams verify their accuracy. 102)
• March 4, 2014: GMI's electronics have been turned on and all seven launch restraints released, deploying the instrument. GMI (GPM Microwave Imager) began spinning today collecting the first science data of the mission. The GMI will complete several additional check-out procedures during the commissioning process. 103)
• March 1, 2014: Following activation and warm up of the GMI electronic systems, the team at NASA’s Goddard Space Flight Center in Greenbelt, Md., deployed the main reflector of the U.S. science instrument for the GPM Core Observatory (Ref. 102).
- A significant step was also achieved today in the activation of the science instrument provided by JAXA with the turning on of the controller for the DPR (Dual-Frequency Precipitation Radar).
- GPM flight controllers at NASA/GSFC began using the satellite’s High Gain Antenna system for high-rate data rate transmissions through NASA’s orbiting fleet of TDRS (Tracking Data Relay Satellites).
• Feb. 28, 2014: The GPM Core Observatory is performing normally. The GPS system has been switched on. This tells the satellite the time and its location with respect to the Earth's surface. The team is readying the spacecraft to use its High Gain Antenna for high data-rate communication through the Tracking and Data Relay Satellite System.
• After the release of the GPM Core Satellite, the second stage performed attitude maneuvers and slightly changed its orbit for the deployment of the seven secondary payloads that include small spacecraft and CubeSats dedicated to scientific missions, technical demonstrations and outreach projects (Ref. 104).
Table 3: Launch sequence of GPM mission and secondary payloads
• The GPM spacecraft separated from the rocket ~16 minutes after launch, at an altitude of 398 km. Following spacecraft separation, GPM initiated a pre-programmed sequence to establish a stable three-axis orientation in attitude safe mode and acquire communications with ground stations. GPM's signal was received - confirming that the spacecraft was alive and well after its ride into orbit. The solar arrays deployed 10 minutes after spacecraft separation, to power the spacecraft. 104)
Sensor complement of the core mission: (DPR, GMI)
The JAXA-supplied DPR, composed of Ka and Ku band radar subsystems, will provide:
• Increased sensitivity (~12 dBZ) for light rain and snow detection relative to TRMM
• Better precipitation measurement accuracy with differential attenuation correction, and
• Detailed precipitation microphysical information of DSD (Drop Size Distribution), mean mass diameter, particle number density) and identification of liquid, ice, and mixed-phase regions.
The multi-frequency (10-183 GHz) GMI conical scan microwave radiometer will provide:
• Higher spatial resolution (IFOV: 6-26 km) than its TRMM Microwave Imager (TMI) predecessor
• Improved light rain & snow detection
• Improved signals of solid precipitation over land (especially over snow- covered surfaces), and
• Four-point calibration to serve as a radiometric reference for constellation radiometers.
The resulting combined radar-radiometer rainfall retrievals utilizing data from the two instruments will together provide greater constraints on possible solutions to improve retrieval accuracy. An observation-based a-priori cloud database will be used for constellation radiometer retrievals.
DPR (Dual-frequency Precipitation Radar):
The DPR instrument, of PR heritage flown on TRMM, is being designed and developed in a collaborative effort between JAXA (Japan Aerospace Exploration Agency) and NICT (National Institute of Information and Communications Technology), Tokyo. (the industrial partner is NEC Toshiba Ltd., Tokyo). The objective is to extend the instrument capability of TRMM in such a way to fully address the key science questions from microphysical to climate time scales. DPR will provide the accurate amount of precipitation including snowfall over both ocean and land. The DPR data will also be used to calibrate the MWRs (Microwave Radiometers) in the GPM constellation. The DPR package will provide a global database of precipitation characteristics, such as storm heights, freezing levels, DSDs (Drop Size Distributions), the mean structure of precipitation profiles, and so on. 109) 110) 111) 112) 113) 114) 115) 116) 117) 118) 119) 120) 121) 122) 123)
Figure 61: Goals and objectives of JAXA's GPM/DPR project (image credit: JAXA) 124)
Figure 62: The GPM Core spacecraft with the DPR on-orbit configuration (image credit: JAXA)
The DPR instrument is comprised of two, essentially independent radars. One radar operates in the Ku-band at 13.6 GHz, it is referred to as PR-U, also known as KuPR (Ku-band Precipitation Radar). The other radar operates in the Ka-band at 35.55 GHz, it is referred to as PR-A (also known as KaPR (Ka-band Precipitation Radar). By measuring the reflectivities of rain at two widely different radar frequencies, it is possible to infer information regarding rain rate, cloud type and its three-dimensional structure, and drop-size distribution. Both radars have almost the same design as the PR instrument on TRMM. The specific objectives of DPR are to:
• To provide the three-dimensional precipitation structure including snowfall over both ocean and land
• To improve the sensitivity and accuracy of precipitation measurement
• To calibrate the estimated precipitation amount by MWRs (Microwave Radiometers) and MWSs (Microwave Sounders) on the constellation satellites.
Each radar has 128 slot array antennas, transmitters (Solid State Power Amplifier: SSPA), receivers (Low Noise Amplifier: LNA), Phase Shifters (PHS), and so on. The FCIF (Frequency Converter Intermediate Frequency) and the SCDP (System Control Data Processing) of both instruments, KuPR and KaPR, have almost the same designs. To make the structures lighter, one SCDP installed on KuPR is used to control both KuPR and KaPR. The other SCDP, which is installed on KaPR, is just for redundancy. There are two major differences from the TRMM/PR: 125)
- One major difference is that the T/R module groups one SSPA, LNA, and PHS together, and one T/R unit consists of 8 T/R modules. In each radar, there are 16 T/R units.
- Another one is the design change of Divider/Combiner (DIV/COMB), Circulator (CIR) and Hybrid (HYB) to eliminate a single failure point in the RF line.
The analysis results using subsystems and components design and parameters reviewed in the critical design of the DPR have achieved the required technical performance of frequency, range resolution, spatial resolution, swath width, minimum detectable rainfall rate, beam matching accuracy, observable range, dynamic range, received power accuracy, and so on.
Figure 63 illustrates the dual-frequency measurement concept of precipitation in the various detectable dynamic ranges. Predominantly, the KaPR will detect snow and light rain, while the KuPR will detect the heavy rain regime. Both instruments have a common effective dynamic range to provide the DSD (Drop Size Distribution) information and more accurate rainfall estimates, implemented by the dual-frequency algorithm. The dual-frequency algorithm employs the difference in rain attenuation from the matched beam data observed by KuPR and KaPR.
The data obtained from DPR will contribute to a global database of precipitation characteristics, to derive such such parameters as precipitation heights, freezing levels, DSDs, the mean structure of precipitation profiles and so on. This database must also serve to improve the MWR and MWS algorithms.
Figure 63: DPR concept of dual-frequency measurement of precipitation (image credit: JAXA, NICT) 126)
Legend to Figure 63: Left: Vertical precipitation structure; Right: Relations between radar reflectivity and height for KuPR and KaPR.
Figure 64: The DPR antenna scanning concept (image credit: JAXA)
Each radar uses a phased array, slotted wave guide antenna. Both radars of the DPR can be electronically steered up to ±17º to either side of the spacecraft nadir, providing a 245 km measurement swath. The KaPR also has a selectable high sensitivity mode which provides an interlacing scan with a swath width of 120 km; this high sensitivity mode will aid in the measurement of light rain and snow. The two phased array antennas will be aligned so that identically sized coincident measurement footprints of 4.5 km diameter can be taken.
The pulse repetition frequency (PRF) of both KuPR and KaPR will vary according to the satellite altitude variation as a function of latitude. This variable PRF technique improves the signal to noise ratio because of the larger sampling numbers it offers. The KuPR has a swath width of about 245 km (comprised of 49 footprints each 5 km in width), which is the same as TRMM PR, while the KaPR observes a swath width of about 120 km. In the overlapping scan area, measurements will be performed synchronously to match the two beams of KuPR and KaPR. While the KuPR observes the outer swath area, the KaPR can measure snow and light rain in the interlacing scan area in a high-sensitivity mode with a double pulse width. Another reason for the narrow swath width of KaPR is that the sidelobe clutter contamination in larger scan angles will hinder measuring shallow snow clouds.
Table 4: Comparison of GPM instrument parameters with TRMM PR 127)
The two radars are designed to provide temporally matching footprints with the same spatial size and scan pattern. Both radar antennas are carefully aligned to ensure co-alignment of the beams.
Figure 65: Illustration of the EM (Engineering Model) of the KaPR instrument (image credit: JAXA, NICT, Ref. 109)
Figure 66: BBM (Breadboard Model) of KuPR (left) and KaPR (right), 1 T/R unit respectively (image credit: NICT)
DPR status 2010 (Ref. 109) JAXA Dual-frequency Precipitation Radar (DPR) in Phase-C development
- JAXA DPR CDR (Critical Design Review) completed in August, 2009
- DPR engineering model tests for design verification completed
- NASA/JAXA DPR Interface Preliminary Design Review completed in October 2009 and GMI-DPR interference test completed in Dec. 2009 in Japan.
- Currently (2010) manufacturing and testing all of the DPR PFM (Proto-Flight Model) components
- Delivery of DPR simulator to NASA GSFC in the fall of 2010.
- The DPR protoflight test is underway, will be completed in October 2011. 128)
DPR operation modes:
DPR has 7 operation modes (Ref. 126):
1) Observation Mode - is the normal operation mode where the KuPR and the KaPR perform normal rain echo measurements with the ±17º scanning for the KuPR and with the ±8.5º scanning for the KaPR. System noise, surface return, and mirror image data are also collected. In the contingency case that the signal between the KuPR and the KaPR is lost, the SCDP (System Control Data Processing) in the KuPR and the SCDP in the KaPR are operated simultaneously so that both radars perform the observation independently. But beam matching is impossible in this case.
2) Internal Calibration Mode - is used to calibrate FCIF (Frequency Converter Intermediate Frequency) and SCDP. During this mode, RF radiation does not occur.
3) External Calibration Mode - is used for the end to end calibration of the DPR using the ARC (Active Radar Calibrator) on the ground.
4) Analysis Mode - provides the LNAs (Low Noise Amplifiers) and the SSPAs (Solid State Power Amplifiers) status data. During this mode, science observations do not occur.
5) Health-Check Mode - is for checking the ROMs (Random Access Memory) and the ROMs (Read Only Memory) used in the SCDP. During this mode, science observations and RF transmissions do not occur.
6) Standby Mode - is used for re-loading the phase code and the VPRF (Variable Pulse Repetition Frequency) data, changing the timing offset between the KuPR and the KaPR, and re-writing the onboard software in the SCDP. During this mode, science observations and RF transmissions will not occur. This mode is also used when the GPM observatory is in the sun point mode due to the minor spacecraft failure. In this case, only the SCDP continues to be powered on and all other components are powered off.
7) Safety Mode - is the mode that the DPR is off except for the survival heater. This mode is used when the GPM observatory is in the period from launch to early stages of the on-orbit checkout period and when the GPM observatory is in the sun point mode due to the power load-shedding fault.
DPR onboard calibration and validation:
There are two types of calibrations: external and internal calibration. 129)
• External calibration in the initial checkout period: The agreement of the observation volume of the two radars (KuPR and KaPR) must be confirmed by external calibration; that is, the radar beam direction corresponding form the comparison of the antenna patterns. It is necessary to make the assumption of where in the footprint the RC (Radar Calibrator) existed. To proofread the strength of the transmitting and receiving signal, it is assumed by the method of the repetition of less beam direction (from five directions by about ten directions) scanning two or more times. But in the observation, usually 49 direction beams are scanned in one scanning sweep (0.7 seconds).
• External calibration for transmitting on orbit: The RxRC (Receive Radar Calibrator) measures the transmitting power form the DPR, and assumes in which position the RxRC is set up in the footprint when the DPR transmit beam scan forming is changed. The scan beam form change is available.
The TxRC (Transmit Radar Calibrator) transmits to the DPR continuous wave (CW) by f1 and f2. The DPR receives the CW of TxRC; hence, the DPR reception characteristics can be checked.
Figure 67: Illustration of the external and internal calibration scheme (image credit: NICT)
A prototype RC antenna was developed which combines the two frequencies of the Ku- and the Ka-band.
Figure 68: Photo of DPR KuPR (left) and KaPR (right) at GSFC (image credit: NASA, Ref. 28)
GMI (GPM Microwave Imager):
The NASA GMI instrument, of SSM/I, TMI, and SSMIS heritage, is a conical-scanning, polarization-sensitive, multi-frequency passive radiometer for rainfall measurement. GMI will be used to make calibrated, radiometric measurements from space at multiple microwave frequencies and polarizations. In addition, radiometric measurements from GMI and radar measurements from the DPR will be used together to develop a retrieval transfer standard for the purpose of calibrating precipitation retrieval algorithms. This calibration standard will establish a reference against which other retrieval algorithms using only microwave radiometers (and without the benefit of the DPR) on other satellites in the GPM constellation will be compared. 130)
In March 2005, NASA awarded a contract to BATC (Ball Aerospace and Technologies Corporation) to design and built GMI. A successful preliminary design review took place in Nov. 2006. In June 2009, the instrument has completed the Critical Design Review phase of the program. The delivery of the two flight units is planned for 2012 and 2013, respectively.
The conical scan geometry of GMI is shown in Figure 70. The off-nadir-angle defining the cone swept out by the GMI is set at 48.5º which represents an Earth incidence angle of 52.8º (identical to that of TMI on TRMM). The offset parabolic reflector rotates about the vertical axis of the instrument with a rate of 32 rpm; during each revolution the Earth-viewing scan sector is about 140º centered along the S/C velocity vector. The remaining 260º of each scan (revolution) is used for instrument calibration and housekeeping functions. The (140º) GMI swath represents an arc of 885 km on Earth's surface. 131) 132) 133) 134) 135)
The SMA (Spin Mechanism Assembly) is a precision electro-mechanical bearing and power transfer drive assembly mechanism that supports and spins the GMI instrument at a constant rate of 32 rpm continuously for the 3 year plus mission life. The SMA design has to meet a challenging set of requirements and is based on the BATC space mechanisms heritage and lessons learned changes made to the WindSat BAPTA mechanism that is currently (fall 2011) operating on-orbit and has recently surpassed 8 years of successful Flight operation. 136)
The SMA provides a spin accuracy of 0.1% using a pair of angular contact bearings, separated axially on a shaft driven by a 3-phase direct current torque motor with a 2-speed resolver for communication and position feedback. The high-precision electro-optical bearing hosts a power and data transfer drive. The instrument has its own momentum compensation. The control hardware and software that control instrument spinning and momentum compensation reside within the instrument controller assembly. This assembly consists of the controller itself and a momentum wheel for momentum compensation, installed under the structure supporting the GMI sensor.
Figure 69: Schematic view of the SMA (image credit: NASA, BATC)
The instrument features 13 microwave channels (similar to those of TMI) in the frequency range of 10-190 GHz as outlined in Table 5. The noise equivalent delta temperature (NEDT) values are valid for the corresponding integration times where the integration times represent scan movement through one antenna beam width. The GMI beam efficiencies for all channels will exceed 90%, where beam efficiency is defined as the percentage of energy collected from an isotropic scene within the solid angle defined by 2.5 times the channel half-power beam widths and approximating the antenna main lobe between first nulls.
Figure 71: Illustration of the GMI instrument (image credit: NASA, BATC)
The GMI instrument design employs a total power type radiometer with through-the-feed hot and cold calibration. featuring an offset parabolic antenna with an aperture size of 1.2 m. The antenna subsystem includes four feedhorns serving the nine channels. Each frequency is allocated an independent feedhorn with the exception of a shared feedhorn for the 18.7 GHz and 23.8 GHz channels. The antenna subsystem and receiver electronics rotate at 32 rpm. A stationary thermal shroud, with an opening to cold space, surrounds the rotating instrument subsystems. GMI features its own momentum compensation. The control circuitry and logic governing instrument spinning and momentum compensation is contained within the instrument controller assembly. The instrument controller assembly and momentum wheel, providing momentum compensation, are mounted beneath the shelf supporting the GMI sensor.
The 1.22 m diameter aperture of GMI provides excellent spatial resolution (IFOVs) for channels 1 through 5, the channels for which the entire aperture is utilized in beam formation. These GMI channels offer fine spatial resolution when compared to other conical-scanning radiometers (they are between 50-60% better than those of TMI on TRMM).
Figure 72: View of the 183 GHz mixer design (image credit: Millitech Inc.)
The GMI uses a set of frequencies that have been optimized over the past two decades to retrieve heavy, moderate, and light precipitation using the polarization difference at each channel as an indicator of the optical thickness and water content. The GMI has the following channel selections (Ref. 105):
• 10 GHz channel for measuring the heaviest precipitation encountered in the tropics
• 9 and 37 GHz channels for measuring moderate to light precipitation over ocean
• 21 GHz channel for correction of the absorption by water vapor in other channels
• 89 GHz channel for detection of the presence of large cloud ice particles, which is used for delineating convective from stratiform precipitation over ocean and for measuring heavy precipitation over land
• 166 GHz channel for measuring light precipitation in frontal structures outside the tropics
• Two 183 GHz water-vapor sounding channels for detecting scattering signals from small ice particles and shielding the surface in regions of high water vapor to estimate light rain and snowfall rates over snowcovered land.
Figure 73: Channel footprint scheme of GMI in successive along-sans (image credit: NASA)
The choice of GMI sampling times is governed by the desire to achieve “Nyquist spatial sampling” in the along-scan direction of the swath. In addition, samples from individual channels must be co-registered on the Earth surface. The sample times are slightly larger than the integration times due to latencies inherent to the digital sampling electronics. To satisfy the Nyquist criterion, all channels are being sampled at a minimum of two times as the GMI scans through a single IFOV. To guarantee co-registration, the sample times for each channel are being made integral multiples of each other.
Instrument calibration: The primary calibration of the GMI instrument is provided through a hot load and cold sky reflector. The hot load design minimizes thermal gradients and provides thermal stability. The hot load has a shroud that limits the exposure of the hot load to solar radiation. In addition 14 thermistors are provided within the hot load to allow spatial and temporal variations to be tracked accurately. The size of the cold sky reflector has been maximized within the mechanical constraints to provide a high beam efficiency to cold space. Calibration is accomplished using a cold sky target and a precisely controlled hot load. The cold sky target is a reflector targeted at space to provide the coldest possible target for calibration purposes. The sky reflector has been sized to provide a high beam efficiency to cold space.
In addition to the hot load and cold sky reflector, GMI has internal noise diodes that provide additional information for tracking the calibration. The noise diodes will be used to track the stability of the non-linearity of the receivers over the life of the instrument. The noise diodes will also be used to verify the short-term stability of the hot and cold sky calibration points and could be used to provide short-term replacement of these loads.
Figure 75: Illustration of the cold load reflector and the hot load device on the GMI platform (image credit: NASA, BATC)
The GMI radiometer will serve as a `transfer standard' in two contexts:
1) as a radiometric transfer standard for the other radiometers of the GPM constellation, and
2) as a constellation. Both transfer standards represent areas of scientific research. 137)
In the context of item 1, the GMI radiometric calibration will serve as a reference for other radiometers. In this method, the brightness temperature calibration of constellation member radiometers will be adjusted to achieve a common basis with that of the GMI. This technique will reduce precipitation retrieval differences between sensors due to biases from inter-sensor calibration.
The context of item 2 refers to a precipitation transfer standard. Specifically, this concerns the measurement synergy created by the GMI and the DPR instruments aboard the Core spacecraft. The mutual overlap of actively sensed, vertically-profiled, radar data at two frequencies in combination with the multi-channel passive data of GMI is a unique capability of the Core observatory.
The GPM project has developed the concept of products that contain inter-calibrated brightness temperatures (Tb) that are mission consistent across constellation radiometers. These products will be the main brightness temperature products distributed both in production and near-realtime.
A process for inter-calibration has been developed that is based on pair-wise comparison of brightness temperatures from constellation radiometers. This process is an outgrowth of the early prototype work using TMI as the surrogate for GMI and further honed by the extensive comparisons carried out by the x-cal (cross-calibration) team. In addition the process led to the discovery of time-dependent biases in TMI brightness temperatures and also led to the development of consistent TMI products. This helps to vindicate that the process developed can lead to consistent, well-calibrated Tb for GPM radiometers thereby leading to greatly improved retrievals. 138)
GMI RDA (Reflector Deployment Assembly):
The GMI RDA is an articulating structure that accurately positions and supports the main reflector of the GMI (Global Microwave Imager) throughout the 3 year mission life. For GMI to fit within the launch vehicle fairing, the main reflector must be stowed toward the front of the spacecraft bus (Figure 76). 139)
Launch restraints secure the main reflector and RDA to the GMI main structure. After launch, the restraints deploy and the RDA must maneuver the reflector from its stowed location and position it into a precise orientation above the instrument for operation. The on-orbit deployed reflector must match the ground alignment orientation to within 0.5 mm in position and 40 arcsec in order to maintain tight off nadir angle pointing requirements. The RDA must maintain stable reflector orientation throughout the 3 year GMI life.
Figure 77 shows the architecture of the GMI instrument. The ISS (Instrument Support Structure) deck consists of a composite panel that contains the interface to the spacecraft, supports the instrument computer and provides the structure to support the stowed main reflector. The IBA (Instrument Bay Assembly) consists of a hexagonal composite structure with a circular top deck that supports the RF subsystem and the RDA (Reflector Deployment Assembly). The calibration targets are supported on a calibration support structure that is despun and connected back through the stator assembly of the SMA (Spin Motor Assembly) which provides the rotational motion and allows for power and signal transfer between the rotating and stationary elements. 140)
The primary connection between the two composite structures is the SMA and the IBS Launch Restraints (IBS LR). The 3 IBS LR’s act as a load bypass mechanism for the bearings contained within the SMA and provide a direct load path for the supported mass into the spacecraft interface. Actuation of the IBS LR’s allows the SMA to spin the rotating elements. The Main Reflector Launch Restraints (MR LR) provide the stowed interface for the Main Reflector providing a load path through the composite structure to the spacecraft interface. The Main Reflector is positioned to its deployed orientation by the RDA after the launch restraints are released. The deployed orientation can be seen in Figure 74.
The calibration targets are connected to the stator elements of the SMA through a bellows that couples the slip ring on the SMA to the Despin Assembly which supports the calibration assembly and holds the calibration assembly stationary. This calibration support structure contains the Calibration Launch Restraint (CAL LR) which provides an alternate load path to support the mass of the calibration targets without over loading the Despin Assembly.
The GMI RDA is a kinematically determinate structure consisting of an aft bipod structure and forward and aft side strut assemblies that attach to four locations on the instrument upper deck and three locations on the outer perimeter of the main reflector. The RDA is constructed from composite tubes bonded to titanium fittings that are attached to a variety of joints designed to allow the structure to fold into a stowed configuration. When stowed, the reflector is in a defined location where it is restrained for launch, and the RDA strut tubes are positioned into limited available areas within the stowed envelope.
Deployment force is provided by a torsion spring attached to the aft bipod assembly with speed controlled by a fluid damper. Deployment reliability is enhanced by eliminating the possibility of binding of the strut joints. This is accomplished by using combination of spherical and revolute hinges configured such that the structure is effectively under constrained. To provide control during deployment, a novel auxiliary synchronization linkage directs the motion of the reflector during the majority of the operation until the spring loaded side strut elbow joints lock out completing the deployment and forming a geometrically determinate structure. Figure 78 shows the primary elements of the assembly.
The location of the stowed reflector is defined by available volume on the spacecraft and launch vehicle thus driving the geometry of the stowed RDA. The RDA must reliably deploy the 12 kg reflector from this location to a repeatable position, and maintain its orientation when exposed to the on-orbit environment throughout the mission life. Table 7 lists the primary performance requirements of the RDA.
The RDA strut tubes are cylindrical graphite epoxy tubers with a titanium fitting bonded to each end. They were manufactured with the aid of precisely aligned bond tooling. Once bonded and cured, they were thermal cycled, proof tested and then assembly began by bolting the appropriate hinge fitting to each end of the strut tube.
Deployment testing: The primary performance tests imposed on the RDA included deployment repeatability, deployment duration, deployed stiffness, torque margin, deployment over operating temperature, off-nominal deployment and kinematic model validation.
The GMI RDA development and validation program has successfully demonstrated the capability to precisely deploy a payload over a large range of motion in a controlled and reliable manner. The RDA avoids the complexity and reliability concerns associated with a metrology/feedback closed-loop motorized deployment scheme in favor of a passively powered, kinematically determinate approach. The RDA manages this using a lightweight strut design that is inherently flexible until fully deployed where it becomes a rigid structure. This configuration can be tailored for a variety of payload sizes and deployment requirements. The performance demonstrated by the RDA is applicable to the requirements for most RF antennas, as well as a wide range of optical payloads. These other applications would benefit by leveraging the increased understanding and capabilities gained by the RDA program (Ref. 139).
Science Discipline Areas of GPM
1) Climate Diagnostics: refining & extending precipitation climatologies including snow climatologies; detecting statistically significant global & regional precipitation trends
2) GWEC (Global Water & Energy Cycle) / Hydrological Predictability: global water & energy cycle analysis & modeling; water transports; water budget closure; hydrometeorological modeling; fresh water resources prediction
3) Climate Change / Climate Predictability: climate-water-radiation states; climate-change analysis & prediction; GWEC response to climate change & feedback
4) Data Assimilation / Weather & Storms Predictability: rainfall data assimilation; global-regional scale NWP techniques
5) MBL (Marine Boundary Layer) Processes: air-sea interface processes & surface flux modeling; ocean mixed layer salinity changes
6) Land Processes: land-atmosphere interface processes & surface flux modeling; integrated surface radiation-energy-water-carbon budget process modeling
7) Coupled Cloud-Radiation Models: diagnosis of cloud dynamics, macrophysical/microphysical processes, & response of 3D radiation field; parameterization of microphysics & radiative transfer in non-hydrostatic mesoscale cloud resolving models
8) Retrieval/Validation/Synthesis: physical retrieval of precipitation & latent heating; algorithm calibration & products normalization; algorithm validation & quantification of uncertainty; synthesis of validation for algorithm improvement
9) Applications/Outreach: weather forecasting; flash flood forecasting; news media products; educational tools.
Ground segment of GPM Core spacecraft:
The GPM ground system architecture builds on the lessons learned from and the experiences of TRMM. Specifically, the ground system supports the generation of radiometer precipitation products from the GMI within one hour of observation and combined radar/radiometer swath products within three hours of observation. The ground system consists of fully integrated elements supporting flight operations, data processing and distribution, and ground validation.
The MOC (Mission Operations Center) is highly automated and staffed 8x5 (8 hours, five days a week) as the GPM instruments operate in survey mode and require very little ground commanding. It interfaces with PPS (Precipitation Processing System) to deliver 5-minute duration science instrument files, 5-minute duration housekeeping data files, metadata associated with data processing and delivery, and ancillary data to support science product generation. The MOC also interfaces with PPS to receive instrument commands and command requests as needed (Ref. 105).
Figure 79: Overview of the GPM Core spacecraft ground segment (image credit: NASA)
The PPS (Precipitation Processing System) is based on an evolution of the TSDIS (TRMM Data Information System) permitting algorithm and other prototyping to aid in the GPM data system development. Its function is to create higher-level science data products, deliver science data products to the user community, provide interface to the instrument science teams, and deliver instrument commands and instrument team command requests to the MOC.
Figure 80: Overview of the GPM Core spacecraft ground segment at JAXA (image credit: JAXA)
Figure 81: Data flow of the GPM Core Observatory and the constellation satellites to the Precipitation Processing System at GSFC (image credit: NASA, Ref. 54)
Table 8: Description of GPM data products (Ref. 18)
GPM Ground Validation:
The GPM mission supports a vigorous Ground Validation (GV) program for pre-launch algorithm development and post-launch product evaluation. Based lessons learned from the traditional approach to ground validation is to use ground-based observations to directly assess the quality of satellite products, GPM is establishing joint GV sites with partner agencies and a series of pre- and post-launch field campaigns to carry out one or more of the following three types of validation activities (Ref. 105):
• Direct Validation: These activities facilitate statistical comparisons of GPM satellite precipitation products with ground measurements provided by national networks of radars and rain gages from GPM partners around the world. The purpose of these activities is to identify potential discrepancies between spaceborne and ground-based estimates of precipitation that may require more in-depth studies.
• Precipitation Physics Validation: These activities focus on collecting intensive, targeted, airborne and ground-based measurements of precipitation processes and ancillary observations to provide the basis for developing, testing, and refining satellite retrieval algorithms using both model-simulated and observation-derived microphysical databases. The broad aim of these activities is to gain further insights into the physical relationships between clouds/precipitating particles and simulated microwave radiances at different frequencies to refine the interpretation of GPM satellite measurements and retrieval algorithms.
• Integrated Hydrological Validation: These activities follow the paradigm of an “end to end” assessment of GPM multi-satellite precipitation products using hydrological basins as a time-and-area-integrated measure of data quality in terms of coupled hydrologic and land-surface modeling and prediction.
Accordingly, four field campaigns were conducted or are planned in 2010-2012 to be held in different climatic regimes:
• Light Precipitation Validation Experiment (LPVEx) - This joint CloudSat-GPM collaboration is concentrating on light rain in shallow melting layer situations. It is covering the Helsinki Testbed and the Gulf of Finland during September and October 2010. It involves the FMI (Finnish Meteorological Institute) and Environment Canada in addition to NASA. 143)
• Mid-Latitude Continental Convective Clouds Experiment (MC3E) – This is a NASA-DOE (Department of Energy) field campaign at the DOE-ASR Central Facility in Oklahoma. It is planned for April 15 -June 1, 2011. 144)
• High-Latitude Cold-Season Snowfall Campaign: This GPM-Environment Canada campaign concentrated on snowfall retrieval in Ontario, Canada. The project was conducted from January 15, 2012 until March 3, 2012. However, much of the ground instrumentation was installed during November, 2011.
GCPEx (GPM Cold Season Precipitation Experiment) was carried out in the winter of 2011/012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager onboard the GPM core satellite, and radiometers on constellation member satellites. 145)
• Japan's GV: Japan is developing the DPR based on the excellent heritage of the TRMM PR development. Japan's GV focuses on the ground experiment relevant to DPR. An airborne experiment is planned (Ref. 109). 146)
Figure 82: Illustration of field campaign locations in the time frame 2010-2012 (image credit: NASA)
GV (Ground Validation) site in Okinava: NICT (National Institute of Information and Communications Technology) has three facilities on the main island of Okinawa. The main office is the Okinawa Electromagnetic Technology Center (NICT Okinawa), and two radar sites are the Ogimi Wind profiler Facility (NICT Ogimi) and the Nago Precipitation Radar Facility (NICT Nago) as shown in Figure 83. 147)
• COBRA (C-band polarimetric Radar) is installed at the NICT Nago site. The COBRA system is a ground-based, monostatic pulse Doppler radar using a single wave (5340 MHz) in the C-band. The maximum observation range is a radius of approximately 300 km, although this depends on the repetition frequency and the transmitted pulse. The spatial resolution is 37.5–600 m, depending on the pulse width and the over-sampling rate.
• 400-MHz Wind Profiler: The 400-MHz Wind Profiler (400-MHz WPR) is installed at the NICT Ogimi site. The 400-MHz WPR is able to observe simultaneously the atmospheric turbulence echo and the echo from precipitation. Hence, by analyzing the echo power spectrum of the received signal, the rain drop size distribution can be estimated for which the effects of wind speed, the intensity of atmospheric turbulence, and background winds have been removed.
Using these vertical and ground-based measurements of raindrop size distributions, the extinction cross-section and the back scattering cross section can be processed by the Mie scattering theory. Then, the specific attenuation (k) and the radar reflectivity (Z) for Ku-band are estimated. The vertical variations and characteristics (depending on rain type) of rain attenuation for Ku-band can be analyzed. The GPM/DPR for the Ku- and Ka-band algorithm can be also evaluated using this ground validation observation network in Okinawa.
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The information compiled and edited in this article was provided by Herbert J. Kramer from his documentation of: ”Observation of the Earth and Its Environment: Survey of Missions and Sensors” (Springer Verlag) as well as many other sources after the publication of the 4th edition in 2002. - Comments and corrections to this article are always welcome for further updates (firstname.lastname@example.org).