Minimize Aqua

Aqua Mission (EOS/PM-1)

Spacecraft     Launch    Mission Status     Sensor Complement    References

The Aqua mission is a part of the NASA's international Earth Observing System (EOS). Aqua was formerly named EOS/PM-1, signifying its afternoon equatorial crossing time. NASA renamed the EOS/PM-1 satellite to Aqua on Oct. 18, 1999. The Aqua mission is part of NASA's ESE (Earth Science Enterprise) program. 1) 2) 3)

The focus of the Aqua mission is the multi-disciplinary study of the Earth's water cycle, including the interrelated processes (atmosphere, oceans, and land surface) and their relationship to Earth system changes. The data sets of Aqua provide information on cloud formation, precipitation, and radiative properties, air-sea fluxes of energy, carbon, and moisture (AIRS, AMSU, AMSR-E, HSB, CERES, MODIS); and sea ice concentrations and extents (AMSR-E).

Aqua_Auto1E

Figure 1: Illustration of the Aqua satellite (image credit: NASA)

Spacecraft:

The Aqua spacecraft is based on TRW's modular, standardized AB1200 bus design (also referred to as T-330 platform) with common subsystems (Note: Northrop Grumman purchased TRW in Dec. 2002). The satellite dimensions are: 2.68 m x 2.47 m x 6.49 m (stowed) and 4.81 m x 16.70 m x 8.04 m (deployed). Aqua is three-axis stabilized, with a total mass of 2,934 kg at launch, S/C mass of 1,750 kg, payload mass =1,082 kg, propellant mass = 102 kg; power = 4.86 kW (EOL). Propulsion: hydrazine blow-down system; 4 pairs of thrusters. The design life is six years.

RF communications: X-band, S-band (TDRSS and Deep Space Network/Ground Network compatible). All communications are based on CCSDS protocols. Like the Terra mission, Aqua provides various means of payload data downlinks, among them Direct Broadcast (DB).

Aqua_Auto1D

Figure 2: The Aqua spacecraft in launch preparation at VAFB (image credit: NASA)


Launch: The Aqua spacecraft was launched on May 4, 2002 with a Delta-2 7920-10L vehicle from VAFB, CA. Aqua is the second satellite in NASA's series of EOS spacecraft. - Aura, the third of the three large satellites in the EOS series, was launched in July 2004 and is lined up behind Aqua, in the same orbit.

Orbit: Sun-synchronous circular orbit, altitude = 705 km (nominal), inclination = 98.2º, local equator crossing at 13:30 (1:30 PM) on ascending node, period = 98.8 minutes, the repeat cycle is 16 days (233 orbits).

The Aqua spacecraft is part of the “A-train” (Aqua in the lead and Aura at the tail, the nominal separation between Aqua and Aura is about 15 minutes) or “afternoon constellation” (a loose formation flight which started sometime after the Aura launch July 15, 2004). The objective is to coordinate observations and to provide a coincident set of data on aerosol and cloud properties, radiative fluxes and atmospheric state essential for accurate quantification of aerosol and cloud radiative effects.

The PARASOL spacecraft of CNES (launch on Dec. 18, 2004) is part of the A-train as of February 2005. The OCO mission (launch in 2009) will be the newest member of the A-train. Once completed, the A-train will be led by OCO, followed by Aqua, then CloudSat, CALIPSO, PARASOL, and, in the rear, Aura. 4)

Note: The OCO (Orbiting Carbon Observatory) spacecraft experienced a launch failure on Feb. 24, 2009 - hence, it is not part of the A-train.

Aqua_Auto1C

Figure 3: Illustration of Aqua in the A-train (image credit: NASA)

Figure 4: Anintroduction to Aqua (video credit: NASA)


Note: As of 12 March 2020, the previously single large Aqua file has been split into two files, to make the file handling manageable for all parties concerned, in particular for the user community.

This article covers the Aqua mission and its imagery in the period 2020

Aqua imagery in the period 2019-2002




Mission status and sample imagery

• March 26, 2020: Spring has arrived in Central Asia. And over the Aral Sea, that means there is dust in the air. The shrinking of this once-vast inland lake means winds more frequently pick up dust from the exposed lakebed. 5)

Aqua_Auto1B

Figure 5: On March 24, 2020, the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite captured this natural-color image of a dust storm over the once-vast inland lake. Much of the dust appeared to be coming from the Aralkum Desert, which has emerged as the Aral Sea has dried in recent decades. Dried lake beds are abundant sources of atmospheric dust because they are filled with light, fine-grained sediment that winds can easily lift (image credit: NASA Earth Observatory, image by Lauren Dauphin, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview. Story by Adam Voiland)

- Scientists use satellites to track when and where winds transport the most dust. One analysis of nearly a decade of data from the NASA’s Aura satellite found that dust transport from the Aral Sea peaks in the spring (March-May) and early winter (November-January). In the spring, northeasterly and southwesterly winds often prevail, with 42 percent of dust plumes blowing to the north, 32 percent to the south, and 26 percent to the west.

Figure 6: The Aral Sea has been shrinking since the 1960s, when the former Soviet Union undertook a major water diversion project on the arid plains of Kazakhstan, Uzbekistan, and Turkmenistan and diverted river water for crops that otherwise would have poured into the Aral Sea. As the lake dried, river runoff enriched the increasingly salty water with fertilizers and pesticides. As a result, the salty dust that blows from the exposed lake bed poses public health hazards and can degrade the fertility of soils in the surrounding area (image credit: NASA Earth Observatory)

• March 18, 2020: As observed during previous years in Southeast Asia, large numbers of seasonal agricultural fires and wildfires are creating clouds of smoke over northern Thailand and Burma (Myanmar). The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite acquired this natural-color image (Figure 7) on March 18, 2020. 6)

Aqua_Auto1A

Figure 7: The haze and smoke has resulted in unhealthy levels of air pollution, particularly in Chiang Mai in northern Thailand, which has had the most polluted air in the world several times this month (image credit: NASA Earth Observatory, image by Lauren Dauphin, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview. Text by Kasha Patel)

• March 12, 2020: Over the past few decades, the bison population in Yellowstone National Park has grown substantially. Some of that is due to longstanding conservation efforts, but some of it is also due to a greater abundance of food for the animals to graze on. A new study by a NASA scientist has found a link between the effects of climate change, the productivity of grasslands, and the proliferation of bison in Yellowstone. 7)

- Using 20 years of data from NASA’s Aqua and Terra satellites, earth scientist Christopher Potter examined the length of the growing season in Yellowstone—from snowmelt in springtime to the first snowfall in autumn—and the health and abundance of vegetation during that time. He found that the annual period of vegetation growth has been getting longer, likely due to the decreasing severity of winters and warming average temperatures (year-round) as a result of climate change.

- The National Park Service first approached NASA about ten years ago for insight into patterns of growth across Yellowstone’s grasslands. They thought tracking such growth could provide wildlife managers with clues about where bison and other animals might move. Potter became part of that effort.

- As the new research shows, the grass-growing season is getting longer and bison have more opportunities to feed, likely fueling their population growth. But as they follow the development of new and better grazing spots, they become more likely to leave the protected areas of the park. This creates challenges for the Park Service because bison can carry brucellosis, a bacterial disease that can spread among other wild species and domesticated livestock. Getting regular updates on grassland growth patterns can help wildlife managers better predict the movements of the bison herds.

- Scientists also see national parks as natural laboratories for climate research because land use is restricted, so the fingerprints of climate change are easier to detect than in settled areas. A decade ago, there was enough satellite data to make some projections about vegetation growth and animal movements, but not enough to draw conclusions about climate effects. Now the 20-year record allows for deeper insights about Yellowstone.

- MODIS instruments acquire images across nearly the entire surface of Earth every day. For his study, Potter compiled cloud-free images of Yellowstone across 20 years and also developed tools to make maps of vegetation and snow cover. Wildlife managers can check such maps to anticipate where and when confrontations between bison and human communities are likely to happen and to prepare appropriate conservation actions at the park boundaries.

- “By compiling daily satellite data, we created a near-real-time online tool that resource managers can consult much like a weather map,” said Potter, who is based at NASA’s Ames Research Center. “MODIS is the silver bullet for these large-scale wilderness situations, where daily updates make all the difference in planning a response.”

Aqua_Auto19

Figure 8: A new study has found a link between the effects of climate change, the productivity of grasslands, and the proliferation of bison in Yellowstone National Park. The map depicts one aspect of that change: how the end of the growing season fluctuated around Yellowstone National Park between 2001 and 2017. The map is based on measurements of vegetation greenness, the NDVI (Normalized Difference Vegetation Index), as observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Aqua and Terra (image credit: NASA Earth Observatory image by Joshua Stevens, using data from Potter, C. (2020). Story by Abby Tabor, NASA Ames Research Center, and Mike Carlowicz)

• March 9, 2020: It is not every day that a textbook high-pressure system, cold front, and warm front all reveal themselves on the same natural-color satellite image. But that is exactly what NASA research meteorologist Galina Wind recently noticed over the Eastern United States as she was browsing old satellite imagery. 8)

- On weather maps, the center of a high is generally marked with a blue H. “But in natural-color satellite images, you usually see clear skies because the downward movement of the air is strong enough to prevent any convective clouds from rising up as the Sun heats the surface,” explained Wind. “In this case, the descending air was weak enough that some fair-weather cumulus clouds were able to bubble up anyway.”

- The cold front, marked with blue triangles on a weather map, is visible here as a curving line of clouds with intense convection running through Minnesota, Iowa, Missouri, Kansas, and Oklahoma. While highs generally lead to sunny, calm conditions, cold fronts often produce turbulent, unsettled skies.

- “If you have ever seen a snowplow going at full speed, you will have a sense of how cold fronts work,” said Wind. “Cold air is heavier than warm air, so it literally shoves warm air up in front and over it.” As the displaced, warmer air rises and cools, water vapor condenses, yielding clouds, rain, and often thunderstorms.

Aqua_Auto18

Figure 9: This image is a mosaic based on data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPP on September 12, 2019. In the Southeast, thin rows of cumulus clouds trace the location of the high, with the center of circulation near Mississippi. High pressure systems typically have cool, descending air flow in their centers and winds that move in a clockwise direction in the Northern Hemisphere (image credit: NASA Earth Observatory, images by Joshua Stevens, using GOES-16 data from the NOAA-NASA GOES-R Mission, and MODIS and VIIRS data from NASA EOSDIS/LANCE and GIBS/Worldview and the Suomi National Polar-orbiting Partnership. Story by Adam Voiland)

Figure 10: This video, based on imagery from the Advanced Baseline Imager (ABI) on GOES 16 (GOES-East), shows how the weather system developed between 7 a.m. and 3 p.m. on September 12. The images were taken at 5 minute intervals (image credit: NASA Earth Observatory, using GOES-16 data from the NOAA-NASA GOES-R Mission)

- As storm clouds bubbled up around the cold front, clouds swirled in a counter-clockwise direction around an area of low-pressure centered on South Dakota. East of the cold front, notice how the winds circulating around the high-pressure area flowed in the opposite direction. Though clouds in the high were generally small, look for the larger, deeper clouds that developed as air was forced upward by the Appalachian Mountains.

- “Conditions can become quite fierce along cold fronts,” said Wind, noting that false-color imagery of the cold front does a particularly good job of highlighting the deep convection and thunderstorms that developed along it. (For instance, the pinks in this false-color view of the cold front show clouds that have grown tall enough for water droplets to turn into ice particles.)

- The warm front, normally shown with red half circles on a weather map, is marked here by the wide band of clouds through Minnesota, Wisconsin, and Michigan. “Warm front events are much calmer. They are basically just layers and layers of clouds,” said Wind. “Sometimes warm fronts can produce drizzle, but they generally come and go as gently as a lamb.”

- However, the leading wave of clouds that precedes a warm front can often indicate that more serious weather may be on the way. “If it is a clear, sunny day and you suddenly notice the arrival of large numbers of thin, feathery cirrus clouds, that is a clue that the weather could be changing soon,” said Wind.

• February 18, 2020: Scientists previously established that the world is greener than it was in the early 1980s. Updated maps show that the trend has continued, and researchers say reduced global warming is among the consequences. 9)

Aqua_Auto17

Figure 11: This map shows where greenness increased (green) and decreased (brown) across the planet between 2000 and 2018. Specifically, it shows the trend in the “leaf area index”—the amount of leaf area relative to ground area—during the growing season. The index is computed using data from the MODIS instrument on NASA’s Terra and Aqua satellites. White areas are where the land is barren, built upon, or covered with ice, wetlands, or water (image credit: NASA Earth Observatory, image by Joshua Stevens, using data from Shilong, P., et al. (2020). Story by Kathryn Hansen)

- Note that the map does not show overall greenness, which is why it does not exactly match heavily forested areas like the Amazon or the Congo Basin. Instead, the map shows how greenness has changed —a phenomenon most obvious in places like China and India where agriculture has intensified and governments have made efforts to conserve and expand forests.

- There is a clear greening trend in boreal and Arctic regions, a result of rising temperatures. For example, Svalbard in the high-Arctic has seen a 30 percent increase in greenness, according to Rama Nemani of NASA’s Ames Research Center, a co-author of the review paper in Nature Reviews Earth & Environment. The greening was concurrent with an increase in mean summer temperature from 2.9° to 4.7° Celsius (37.2° to 40.5° Fahrenheit) between 1986 and 2015.

Aqua_Auto16

Figure 12: Projected greening effect for the period 2081-2100 (image credit: NASA Earth Observatory, image by Joshua Stevens, using data from Shilong, P., et al. (2020). Story by Kathryn Hansen)

- The paper’s authors reviewed more than 250 published articles that have used satellite data, modeling, and field observations, to understand the causes and consequences of global greening. Among the key results, the authors noted that on a global scale greening can be attributed to the increase of carbon dioxide in the atmosphere. Rising levels of carbon dioxide increase the rate of photosynthesis and growth in plants.

- There is an interesting consequence of this global green up: as vegetation consumes some of the heat-trapping carbon dioxide it also performs evapotranspiration—a function similar to human sweating—which can have a cooling effect on the air. Scientists say that global greening since the early 1980s may have reduced global warming by as much as 0.2° to 0.25° Celsius (0.36° to 0.45° Fahrenheit). In other words, the world would be even warmer than it is if not for the surge in plant growth.

- “It is ironic that the very same carbon emissions responsible for harmful changes to climate are also fertilizing plant growth,” said co-author Jarle Bjerke of the Norwegian Institute for Nature Research, “which in turn is somewhat moderating global warming.”

- According to climate models, the future looks even greener. The map of Figure 12 shows what the green-up might look like in the future based on the Coupled Model Intercomparison Project (CMIP5) climate model, under a scenario in which increases in greenhouse gases lead to almost 5° Celsius (9° Fahrenheit) of warming by the end of the 21st century. Specifically, it shows the predicted change in the growing season’s “leaf area index” from 2081-2100 relative to 1981-2000.

Aqua_Auto15

Figure 13: This chart shows the predicted changes by latitude. Notice that high latitudes in the Northern Hemisphere are still expected to change the most (image credit: NASA Earth Observatory, image by Joshua Stevens, using data from Shilong, P., et al. (2020). Story by Kathryn Hansen)

• January 29, 2020: There is plenty of plant-like life around Antarctica; you just have to know when and where to look. During the austral spring and summer, coastal waters sometimes swirl with vibrant green color—the surface expression of a huge phytoplankton bloom. 10)

Aqua_Auto14

Figure 14: Off the coast of Antarctica, vibrant green phytoplankton swirls amidst the sea ice. The floating, microscopic plant-like organisms were abundant in Terra Nova Bay and McMurdo Sound on January 21, 2020, when the MODIS instrument on NASA’s Aqua satellite acquired this image. Such colorful swirls in coastal waters are sometimes caused by sediments stirred up by waves and currents. But scientists say the source of the color this month has a biological origin (image credit: NASA Earth Observatory, image by Joshua Stevens, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview. Text by Kathryn Hansen)

- “It is definitely a phytoplankton bloom,” said Kevin Arrigo, a biological oceanographer at Stanford University. “They tend to form every year in Terra Nova Bay around January.” Robert Dunbar, a fellow researcher at Stanford, agrees that the color is a phytoplankton bloom. “These kinds of features are common in Antarctica’s coastal polynyas in summer and late summer.”

- Polynyas are areas where winds sustain a persistent opening in the sea ice. Sunlight and near-surface nutrients are plentiful in these areas, making them havens for phytoplankton. Blooms can even occur in polynyas in late summer when the water is covered by thin, new frazil crystals and pancake ice. Research by Dunbar and colleagues has shown that late summer blooms can accumulate in this ice and turn it green. The phenomenon—most widespread in February and March—is visible in the photograph of Figure 15, shot by Dunbar in 2018.

Aqua_Auto13

Figure 15: Photo of the phytoplankton bloom in polynyas shot in 2018 (image credit: NASA Earth Observatory, photo by Robert Dunbar, Stanford University)

• January 21,2020: Among the most useful and longest-lived dust-monitoring sensors in space today is the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites. Terra MODIS has collected more than 20 years of dust data; Aqua MODIS has observed for 18 years. 11)

Aqua_Auto12

Figure 16: Changing winds and more vegetation are probably contributing to the trend. The image was acquired on 22 March 2019 with MODIS on Aqua (image credit: NASA Earth Observatory, image by Joshua Stevens, story by Adam Voiland)

- “Two decades is long enough to look for meaningful trends in atmospheric dust,” said NASA atmospheric scientist Hongbin Yu. In a new study in Atmospheric Chemistry and Physics, Yu and colleagues detailed their efforts to do just that. “We looked at the world’s six major dust outflow regions. We found lots of year-to-year variability, but in most areas we did not see obvious increases or decreases in dust. The one exception was the area we defined as the northwestern Pacific. In other words, the dust that blows east from deserts in western China and Mongolia, such as the Taklamakan and Gobi deserts.” 12)

Aqua_Auto11

Figure 17: In this area, dust activity has declined since the early 2000s. Over the course of the MODIS record, the researchers found a 1.5 percent decrease in the atmospheric dust detected by the sensor each year. “More detailed analysis showed that the trend was due to changes in the spring—March, April, and May,” said Yu. “Trends were negligible in other seasons.”(image credit: NASA Earth Observatory, image by Joshua Stevens, using VIIRS data from NASA EOSDIS/LANCE and GIBS/Worldview and the Suomi National Polar-orbiting Partnership, and data courtesy of Hongbin Yu, et al. (2020). Story by Adam Voiland)

- In this area, dust activity has declined since the early 2000s. Over the course of the MODIS record, the researchers found a 1.5 percent decrease in the atmospheric dust detected by the sensor each year. “More detailed analysis showed that the trend was due to changes in the spring—March, April, and May,” said Yu. “Trends were negligible in other seasons.”

- Scientists using other sensors and analysis techniques have noted the same pattern. Ground-based lidars in Japan detected a 4.3 percent decrease in spring dust. A team of Chinese scientists analyzed data from ground-based weather stations and an atmospheric model and concluded that spring dust storm frequency in arid and semiarid regions of China had decreased by 15 storms per year on average over a period of 25 years.

- Several research teams have addressed the obvious question: why? “There are three main factors that people have looked at: changes in winds, changes in vegetation cover, and changes in soil moisture,” said Yu. Changes in wind speeds, as well as wind shear, can affect how much dust winds can pick up. The presence of vegetation reduces how much dust winds can lift. And winds more easily lift dust from drier surfaces than wetter ones.

- “I haven’t personally tested which of these is the most important,” said Yu. “Based on what I have seen in the scientific literature, it is likely that a combination of all three is contributing to the decline.”

- For instance, some research shows a weakening in the polar vortex over the past few decades, a change that would limit the number of cold fronts (which elevate dust) and reduce maximum wind speeds of storm systems. Several studies also show increases in green vegetation and forests, likely due to global warming, reforestation projects, and efforts to prevent overgrazing.

• January 17, 2020: Afternoon storms are a typical phenomenon during summertime in Australia. They are a reminder that everyday natural events can be strikingly beautiful. 13)

- According to Bastiaan Van Diedenhoven, a researcher for Columbia University and NASA’s Goddard Institute for Space Studies, the cloud is a cumulonimbus—a type capable of producing thunderstorms and heavy rain.

- “Afternoon storms form often in the Austral summer as the land heats up, leading the air to rise and condense into clouds,” Van Diedenhoven said. “The storm looks very round from the top probably because there’s not much wind shear. When the top reaches its highest point near the tropopause, its top will expand horizontally.”

- While clouds like this are normal, they don’t turn up too often in MODIS or VIIRS imagery. The clouds usually grow large later in the afternoon, after the satellites have passed over and acquired their once-a-day imagery. Satellites with a geostationary orbit—giving a constant view of an area—can more easily capture full weather events. Watch the cumulonimbus evolve in this animation of images from Japan’s Himawari-8 satellite.

- Earlier in the week, thunderstorms had developed as a result Tropical Cyclone Claudia, which spun off the coast of Western Australia. And to the east, areas that previously saw thunderstorms triggered by the bush fires are now seeing the more typical type of thunderstorms, providing much-needed rain to the area.

- The cumulonimbus of Figure 18, however, was likely just a typical summer storm. Van Diedenhoven noted: “The cyclone or the fires should have nothing to do with it.”

Aqua_Auto10

Figure 18: These images show the evolution of a storm over Western Australia on January 14, 2020. Morning skies appear clear in the left image, acquired around 11 a.m. local time. The storm starts growing in the afternoon at about 1 p.m. (middle) and even larger an hour later (right image). The natural-color images were acquired (from left to right) with the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite; MODIS on NASA’s Aqua satellite, and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-NASA Suomi NPP satellite (image credit: NASA Earth Observatory image by Lauren Dauphin, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview and VIIRS data from NASA EOSDIS/LANCE and GIBS/Worldview and the Suomi National Polar-orbiting Partnership. Story by Kathryn Hansen)

• January 9, 2020: Satellite observations show how far winds normally spread North African dust particles before rain and gravity pull them down to the ocean. — Individual particles of dust are small and easy to ignore, but on a grand scale, atmospheric dust directly affects all of us, and the planet, in many ways. 14)

- Particles of dust are typically comprised of metal oxides, clays, and carbonates. They most often accumulate in low-lying desert basins, often dried rivers or lakes. When winds lift particles up, they can spread across thousands of miles, circling the planet and fertilizing key parts of oceans and rainforests with critical nutrients, notably iron. They also can spread diseases, trigger asthma and other respiratory health problems, and darken skies so much that they cause traffic accidents and problems for pilots.

- One of the most difficult to study—but far reaching—effects of dust is its influence on weather and climate. Dust particles can change air temperatures by blocking light, influence where clouds form and how they behave, and affect whether hurricanes or other types of storms break out. By fertilizing the sea, dust also helps influence the size and frequency of blooms of carbon dioxide-consuming phytoplankton, which could play a role in dialing global temperatures up or down.

- North Africa is the largest source of atmospheric dust in the world by far. “It has the key ingredients: plenty of strong winds, sediment piled up in basins, dry weather, and little vegetation,” explained NASA atmospheric scientist Hongbin Yu, noting the El Djouf region in Mauritania and Mali and the Bodélé Depression in northern Chad are the sources of much of the dust.

- Notice the seasonality in the distribution of dust. In December and January, strong northeasterly winds—the Harmattan—from West Africa carry dust southwestward, putting the highest concentrations over the Gulf of Guinea, and extending dust plumes west toward South America. Over the course of the year, as wind patterns and the location of the Intertropical Convergence Zone (ITCZ) shift, the most concentrated dust band moves north. From June through August, particles stream from West Africa toward the Caribbean.

- “Measuring the distribution of atmospheric dust is just the first piece of our research,” said Yu, who has been using satellites and other tools to study dust and other aerosol particles for two decades. “In order to understand the effect African dust has on the ocean, we have to determine how much dust gets removed from the atmosphere by rainfall.” That is where other NASA satellite sensors, like the Multi-angle Imaging SpectroRadiometer (MISR) on Terra and the lidar on the CALIPSO satellite, play a critical role. The CALIPSO lidar makes it possible to observe the height of different parts of dust plumes, which is key for estimating where dust drops into the sea. MISR excels at observing particle shapes in order to distinguish dust from other airborne material.

- By analyzing observations from all three sensors—as well as the European Infrared Atmospheric Sounding Interferometer (IASI)—Yu and colleagues have created maps showing how much dust gets deposited in the ocean and where. The most efficient removal of dust occurs in winter, when the dust is following the more southerly trajectory. The researchers found that it tends to be at low altitudes then and gets intercepted by rains along the ITCZ.

- “One of the most important things that comes out of this line of research is that we've realized that leading atmospheric circulation models like the Goddard Earth Observing System (GEOS) overestimate how fast dust is falling into the Atlantic ocean by 2 to 5 times,” Yu said. "Since in situ measurements [from ships or sensors on islands] of dust over the Atlantic Ocean are quite limited, satellites help fill data gaps, but models are still incredibly important for assessing the overall effects of dust.”

Aqua_AutoF

Figure 19: These maps, based on data analyzed by Yu and colleagues, show where the MODIS instrument observed dust moving out from Africa and over the Atlantic Ocean from 2007 to 2016. These optical depth observations are based on the amount of light dust particles scatter and absorb. The measurements capture dust throughout all levels of the atmosphere, not specific altitudes. The maps were first published in 2019 in the Journal of Geophysical Research–Atmospheres (image credit: NASA Earth Observatory, images by Joshua Stevens, using data courtesy of Yu, H., et al. (2019). Story by Adam Voiland)

• January 6, 2020: Forecasters predicted extreme fire conditions in southeastern Australia for the weekend of January 4–5, 2020, and they were correct in their assessment. The natural-color image of Figure 20 was acquired on January 4, 2020, by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua satellite. Smoke has a tan color, while clouds are bright white. It is likely that some of the white patches above the smoke are pyrocumulonimbus clouds—clouds created by the convection and heat rising from a fire. 15)

Aqua_AutoE

Figure 20: As predicted, the weekend brought extreme fire weather.(image credit: NASA Earth Observatory, image by Joshua Stevens, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview. Text by Michael Carlowicz)




Sensor complement: (AIRS, AMSU/HSB, AMSR-E, CERES, MODIS)

Aqua has six Earth-observing instruments on board, collecting a variety of global data sets. 16)

Note: The descriptions of CERES and MODIS can be found under Terra.

Instrument

Sponsor

Developer

Spectral resolution

Geophysical parameters

AIRS

NASA/JPL

BAE Systems

More than 2,300 spectral channels ranging from 0.4 µm to 15.4 µm

Atmospheric temperature and humidity, land and sea surface temperatures, cloud, radioactive energy flux

AMSR-E

JAXA

JAXA (Japan)

12 channels at six discrete frequencies from 6.9 GHz to 89 GHz

Precipitation rate, water vapor, surface moisture content, sea ice extent, snow extent

AMSU

NASA/GSFC

Aerojet

15 channels ranging from 50 GHz to 90 GHz

Atmospheric temperature and humidity

HSB

INPE

MMS, UK

Five channels ranging from 150 MHz to 183 MHz

Atmospheric humidity

CERES

NASA/LaRC

TRW

Cross-track and azimuthal scanners with three channels per scanner

Radiative energy flux

MODIS

NASA/GSFC

Raytheon (SBRS)

36 channels ranging from 0.4 µm to 14 µm

Cloud, radioactive energy flux, aerosols, land cover and land use change, vegetation dynamics, land surface temperature, sea surface temperature, ocean color, snow cover, atmospheric temperature and humidity, sea ice

Table 1: Overview of sensor complement on the Aqua spacecraft


AIRS (Atmospheric Infrared Sounder):

AIRS is a NASA/JPL instrument, PI: M. T. Chahine; prime contractor is BAE Systems (Infrared and Imaging Systems Division (LMIRIS) of BAE Systems, in Lexington, MA). AIRS, along with AMSU and HSB, is of HIRS and MSU heritage flown on the NOAA POES series. Objective: High-spectral-resolution measurement of global temperature/humidity profiles in the atmosphere in support of operational weather forecasting by NOAA. Measurement of the Earth's upwelling infrared radiances in the spectral range of 3.74 - 15.4 µm, simultaneously at 2378 frequencies (bands). Four visible wavelength channels are also present. 17) 18) 19) 20) 21) 22) 23)

Aqua_AutoD

Figure 21: Photo of the AIRS instrument (image credit: NASA)

The AIRS spectrometer is a pupil imaging, multi-aperture echelle grating design that utilizes a coarse 13 lines/mm grating at high orders (3-11) to disperse infrared energy across a series of detector arrays. The typical entrance slit of a spectrometer is subdivided into a series of eleven apertures, each of which is imaged onto the focal plane. The grating serves to spectrally disperse each image, which in turn is overlaid onto a HgCdTe detector array with each detector in the array viewing a unique wavelength by virtue of the grating dispersion. Rejection of overlapping grating orders and background photon suppression is provided by a series of IR bandpass filters located within the spectrometer and directly on the focal plane. Use of the grating in combination with the filter set provides a two-dimensional color map on the focal plane with a high degree of design flexibility in terms of color arrangement and spacing. Cooling of the spectrometer to 155 K is provided by a two stage passive radiator assembly with 10 Watt cooling capacity at 155 K.

Aqua_AutoC

Figure 22: Isometric view of the AIRS instrument (image credit: NASA/JPL)

Dispersed energy exiting the spectrometer is imaged onto a state-of-the-art hybrid PV/PC: HgCdTe focal plane assembly (FPA) consisting of a series of multi-linear arrays each associated with a specific entrance aperture. The assembly consists of 17 arrays arranged in 12 modules with each module individually optimized for wavelength and photon flux. The module set includes 10 photovoltaic (PV) modules covering the 3.7 - 13.7 µm region and 2 photoconductive (PC) modules for the 13.7 - 15.4 µm region. The more advanced PV modules include on-focal plane signal processing via a custom CMOS Readout IC (ROIC) specifically designed for AIRS temperature, photon flux and radiation conditions. The ROIC provides the first stage of signal integration at a 1.4 ms subsample rate, which are summed off focal plane in groups of 16 to meet full footprint dwell time requirements. The IR FPA provides simultaneous measurement of 2378 spectral samples across the 3.7 - 15.4 µm region with two samples per resolution element. Additionally, each PV sample is further divided by two in the cross-dispersed direction to provide increased yield and a measure of spectral redundancy. As a consequence, the IR FPA contains a total of 4482 active detectors. The complex FPA is packaged in a vacuum dewar maintained at the 155 K spectrometer operating temperature, with the IR FPA cooled to 58 K via a redundant, 1.5 W capacity Split Stirling pulse tube cryocooler.

Aqua_AutoB

Figure 23: Illustration of the FPA (Focal Plane Assembly), image credit: NASA/JPL)

Aqua_AutoA

Figure 24: The AIRS spectrometer assembly (image credit: NASA/JPL)

Aqua_Auto9

Figure 25: AIRS scan assembly (image credit: NASA/JPL)

Aqua_Auto8

Figure 26: Illustration of the cryocooler assembly (image credit: NASA/JPL)

The infrared region of 3.74-15.4 µm has a spectral resolution of 1200 (lambda/ delta lambda). The high spectral resolution permits the separation of unwanted spectral emissions and, in particular, provides spectrally clean “super windows,” ideal for surface observations. - This is supplemented by a VNIR photometer of four bands in the range between 0.4 and 1.0 µm. The VNIR channels are used to discriminate between low-level clouds and different terrain and surface covers, including snow and ice. The AIRS infrared bands have an IFOV of 1.1º and FOV = ± 49.5º scanning capability perpendicular to the spacecraft ground track (swath width = 1650 km, 13.5 km horizontal resolution in nadir, 1 km vertical). It takes 22.41 ms for each footprint of 1.1º in diameter (or 13.5 km). Each IR scan produces 90 footprints across the flight track and takes 2.67 s (see Figure 27). The VNIR channels have a footprint of 0.185º or about 2.3 km on the ground, nine VNIR footprints are within a 40 km swath. The VNIR photometer is boresighted to the spectrometer to allow simultaneous VNIR observations.

The VNIR photometer uses optical filters to define the four spectral bands. It operates at ambient temperatures (293-300 K). Inflight calibration is performed during each scan period. In addition, AIRS uses four independent cold-space views.

The major data products derived from AIRS are atmospheric temperature profiles, humidity profiles (from channels in the 6.3 µm water vapor band and the 11 µm windows, sensitive to the water vapor continuum), and land skin surface temperature.

AIRS is flown on the Aqua satellite with two operational microwave sounders: NOAA's AMSU and Brazil's HSB (Humidity Sounder Brazil). Together, the three sensors constitute constitute a possible advanced operational sounding system for future NOAA missions - offering increased accuracy of short-term weather predictions, improved tracking of severe weather events like hurricanes, and advances in climate research.

Instrument type

Multi-aperture, non-Littrow echelle array grating spectrometer configuration

Spectral coverage

3.74 - 15.4 µm for the array grating spectrometer (IR bands)
0.4 - 1.0 µm for photometer (4 VNIR bands at: 0.41-0.44, 0.58-0.68, 0.71-0.92, 0.49-0.94 µm)

Spectral resolution

1200 (lambda/delta lambda) array grating spectrometer, 2378 bands

Spatial resolution

13.5 km horizontal at nadir for IR bands (IFOV = 1.1º), 1 km vertical resolution, 2.3 km x 2.3 km for VNIR bands (IFOV = 0.185º)

IR detector cooling

Two-stage passive radiative cooler with retractable earth shield,

Swath width

1650 km (FOV= ± 49.5º) for IR bands; 40 km for VNIR bands

Instrument mass, power

177 kg, 220 W

Date rate, duty cycle

1.27 Mbit/s, 100%

Table 2: Overview of some AIRS parameters

Aqua_Auto7

Figure 27: Illustration of the AIRS scan geometry and coverage (image credit: NASA/JPL)


Some AIRS results in 2010:

The excellent sensitivity and stability of the AIRS instrument has recently allowed the AIRS team to successfully retrieve Carbon Dioxide (CO2) concentrations in the mid-troposphere (8-10 km) with a horizontal resolution of 100 km and an accuracy of better than 2 ppm. 24)

Originally designed to retrieve temperature and water vapor profiles for weather forecast improvement, the AIRS (Atmospheric Infrared Sounder) has become a valuable tool for the measurement and mapping of mid-tropospheric carbon dioxide concentrations. Several researchers have demonstrated the ability to retrieve mid-tropospheric CO2 from AIRS by different methods. The retrieval method selected for processing and distribution is called the method of “Vanishing Partial Derivatives” and results in over 15,000 CO2 retrievals per 24-hour period with global coverage and an accuracy better than 2 ppm.

The AIRS CO2 accuracy has been validated against a variety of mid-tropospheric aircraft measurements as well as upward looking interferometers (FTIR) from the ground.

Mid-tropospheric CO2 concentrations are an indicator for atmospheric transport and several interesting findings have resulted from analysis of the data.

- First is the non-uniformity of CO2, primarily caused by weather.

- Second is the ability to identify stratospheric-tropospheric exchange during a sudden stratospheric warming event.

- Third is the presence of a seasonally varying belt of enhanced CO2 concentrations in the Southern Hemisphere.

Aqua_Auto6

Figure 28: AIRS yields about 15,000 mid-tropospheric CO2 measurements per day (image credit: NASA/JPL)

Carbon dioxide turns out to be an excellent tracer gas since it does not react with other gases in the atmosphere. The project is finding that the AIRS mid-tropospheric CO2 is a good indicator of vertical motion in the atmosphere. It is a known fact that the majority of atmospheric CO2 is produced and absorbed near the surface and that there are no sources or sinks in the free troposphere. Thus elevated levels of mid-tropospheric CO2 are the result of airflow into the mid-troposphere from the near surface.

The most obvious finding from the AIRS retrievals is that the distribution of CO2 is not uniform as indicated in the models. Strong latitudinal and longitudinal gradients exist particularly over the large land masses in the Northern Hemisphere. This phenomenon is referred to as “CO2 weather”. The large variability in atmospheric circulation due to convection and global and mesoscale transport is responsible for most of the variability seen in the AIRS data. This implies that the AIRS CO2 data will be extremely useful for validating global scale transport in GCMs (Global Circulation Models).

Aqua_Auto5

Figure 29: AIRS mid-tropospheric CO2 is a tracer for atmospheric motion particularly in the vertical direction. July, 2010 monthly average (image credit: NASA/JPL)


AMSU/HSB

AMSU/HSB (Advanced Microwave Sounding Unit (NASA Instrument)/ (Humidity Sounder for Brazil), provided by INPE. Both instruments operate in conjunction.

AMSU was designed and developed by Aerojet of Azusa, CA (a GenCorp company). AMSU primarily provides temperature soundings, whereas HSB provides humidity soundings. AMSU is a 15-channel microwave radiometer. AMSU and HSB have a total of 19 channels, 15 are assigned to AMSU, each having a 3.3º beamwidth, and four are assigned to HSB, each having a beamwidth of 1.1º. AMSU comprises two separate units: AMSU-A1 (channels 3-15), and AMSU-A2 (channels 1 and 2). Channels 3 - 14 use the 50 to 60 GHz oxygen band to provide data for vertical temperature profiles up to 50 km. The “window” channels (1, 2, and 15) provide data to enhance the temperature sounding by correcting for surface emissivity, atmospheric liquid water, and total precipitable water. HSB channels 17 - 20 use the 183.3 GHz water vapor absorption line to provide data for the humidity profile. 25) 26)

AMSU-A1 measures temperature profiles from the surface up to 50 km in 15 channels. Temperature resolution: 0.25 - 1.2 K. The AMSU-A1 instrument has two 15 cm diameter antennas (reflectors with momentum compensation), each with a 3.3º nominal IFOV at the half power points or FWHM (Full width Half maximum). Each antenna provides a cross-track scan of ±49.5º from nadir with a total of 30 Earth views (scan positions) per scan line. The total scan period is eight seconds. The footprint (resolution) at nadir is 40 km. The swath width is approximately 1690 km. Internal calibration is performed with internal warm loads and cold space.

AMSU-A2 has a single 28 cm diameter antenna (reflector without momentum compensation) with a 3.3º nominal IFOV. All other instrument/observation parameters are the same as those of AMSU-A1.

AMSU parameters: mass = 91 kg (49 kg for AMSU-A1, 42 kg for AMSU-A2); power = 101 W; data rate = 2.0 kbit/s; thermal control by heater, central thermal bus, radiator; thermal operating range= 0-20º C.

Sensor

Channel

Center Frequency (GHz)

Bandwidth (MHz)

Sensitivity NEΔT (K)

AMSU-A2
(2 channels)

1
2

23.8
31.4

280
180

0.3
0.3

AMSU-A1
(13 channels)

3
4
5
6
7
8
9
10
11
12
13
14
15

50.300
52.800
53.596± 0.115
54.400
54.940
55.500
57.290344 = Flo
Flo ± 0.217
Flo ± 0.3222, (±0.048)
Flo ± 0.3222, (±0.022)
Flo ±0.3222, (± 0.010)
Flo ±0.3222, (± 0.0045)
89.000

180
400
170
400
400
330
330
78
36
16
8
3
6000

0.4
0.25
0.25
0.25
0.25
0.25
0.25
0.4
0.4
0.6
0.8
1.2
0.5

HSB
(4 channels)

17
18
19
20

150.0
183.31±1.00
183.31±3.00
183.31±7.00

2000
1000
2000
4000

1.0
1.1
1.0
1.2

Table 3: Spectral parameters of the AMSU-A and HSB instruments

Aqua_Auto4

Figure 30: View of AMSU-A1 (left) and AMSU-A2 (right), image credit: Aerojet

Parameter

AMSU-A1

AMSU-A2

Instrument size

72 cm x 34 cm x 59 cm

73 cm x 61 cm x 68 cm

Mass, power

49 kg, 72 W

42 kg

Data rate

1.3 kbit/s

0.4 kbit/s

Antenna size

15 cm (2 units)

31 cm (1 unit)

IFOV (Instantaneous Field of View)

3.3º

3.3º

Swath width

100º, 1650 km

100º, 1650 km

Pointing accuracy

0.2º

0.2º

No of channels

13

2

Table 4: Summary of AMSU instrument parameters


HSB (Humidity Sounder for Brazil):

HSB is an INPE-provided instrument of AMSU-B heritage (built by MMS (Matra Marconi Space) of Bristol, UK (now EADS Astrium Ltd) with participation of Equatorial Sistemas of Brazil), and sponsored by AEB (Brazilian Space Agency). HSB is a microwave radiometer with the objective to measure atmospheric radiation, to obtain atmospheric water vapor profile measurements and to detect precipitation under clouds with 13.5 km horizontal nadir resolution (humidity profiles for weather foresting). 27) 28) 29)

HSB is a four-channel self-calibrating instrument (passive sounder) providing a humidity profiling capability in the frequency range of 150 - 190 GHz, spanning the height from surface to about 42 km. The measured signals are also sensitive to a) liquid water in clouds (cloud liquid water content) and b) graupel and large water droplets in precipitating clouds (qualitative estimate of precipitation rate). HSB scans in the cross-track direction at a rate of 2.67 seconds in continuous mode. The instrument features a momentum-compensated scan mirror system. HSB is operated in combination with AMSU-A, they have a total of 19 channels: 15 are assigned to AMSU-A, each having a 3.3º beamwidth, and four assigned to HSB, each having a 1.1º beamwidth. The HSB receiver channels are configured to operate in DSB (Double Sideband).

The HSB collected valuable data for the first nine months of the mission but ceased operating in February 2003 (scanner anomaly).

Nr. of channels

4 (total), Ch 17 at 150 GHz, Ch 18: 183.31 ±1 GHz, Ch. 19: 183.31 ±3 GHz, and Ch 20: 183.31 ±7 GHz

Swath width, scan period

1650 km, 2.67 s

FOV

±49.5º cross track from nadir (+90º to -49.5º for calibration)

IFOV (spatial resolution)

1.1º (13.5 km at nadir)

Instrument pointing

Control = 3600 arcseconds, knowledge = 360 arcseconds,
stability = 74 arcseconds/s

Thermal control, operating range

Radiator, 13 - 35ºC

Instrument power

80 W average, 154 W peak

Instrument mass, size, data rate

51 kg, 70 cm x 65 cm x 46 cm, 4.2 kbit/s

Temperature accuracy (data profile)

1.0 - 1.2 K, coverage (twice daily) of land and ocean surfaces, resolution of 50 km (horiz.) and 1 km (vertical), up to 100 mb

Humidity accuracy (data profile)

20%, global coverage (twice daily), res. = 50 km, 1 km (vertical)

Radiance accuracy (data profile)

1-1.2 K, global coverage (twice daily), res. = 15 km (average)

Table 5: Specification of the HSB instrument

Aqua_Auto3

Figure 31: Photo of the HSB instrument (image credit: NASA)


AMSR-E (Advanced Microwave Scanning Radiometer-EOS):

AMSR-E is a JAXA/NASA cooperative instrument, of AMSR heritage, built by Mitsubishi Electronics Corporation (PIs: A. Shibata, R. W. Spencer). The objective is the measurement of geophysical parameters such as: cloud properties, radiative energy flux, precipitation, land surface wetness (moisture), sea ice, snow cover, sea surface temperature (SST), and sea surface wind fields. AMSR-E is a modified design of AMSR on ADEOS-II (Japan).

The AMSR-E instrument is a conically scanning total power passive microwave radiometer sensing microwave radiation (brightness temperatures) at 12 channels and 6 frequencies ranging from 6.9 to 89.0 GHz (6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz). Horizontally and vertically polarized radiation are measured separately at each frequency. 30) 31) 32)

AMSR-E consists of an offset parabolic reflector 1.6 m in diameter, fed by an array of six feedhorns. The reflector and feedhorn arrays are mounted on a drum which contains the radiometers, digital data subsystem, mechanical scanning subsystem, and power subsystem. The reflector/feed/drum assembly is rotated about the axis of the drum by a coaxially mounted bearing and power transfer assembly. All data, commands, timing and telemetry signals, and power pass through the assembly on slip ring connectors to the rotating assembly. The AMSR-E instrument has a mass of 314 kg, power = 350 W, a duty cycle of 100%, and an average data rate of 87.4 kbit/s.

Center frequency (GHz)

6.925

10.65

18.7

23.8

36.5

89.0

Bandwidth (MHz)

350

100

200

400

1000

3000

Sensitivity (K)

0.3

0.6

0.6

0.6

0.6

1.1

IFOV (km x km) footprint

75 x 43

51 x 29

27 x 16

31 x 18

14 x 8

6 x 4

Sampling rate (km x km)

10 x 10

10 x 10

10 x 10

10 x 10

10 x 10

5 x 5

Integration time (ms)

2.6

2.6

2.6

2.6

2.6

1.3

Main beam efficiency (%)

95.3

95.0

96.3

96.4

95.3

96.0

Beamwidth (º)

2.2

1.4

0.8

0.9

0.4

0.18

Polarization

Horizontal and Vertical

Incidence angle

55º

54.5º

Cross polarization

< - 20 dB

Swath width

> 1450 km

Dynamic range

2.7 - 340 K

Data quantization

12 bit

10 bit

Data rate

87.4 kbit/s

Antenna size, control unit

1.95 m x 1.7 m x 2.4 m, 0.8 m x 1.0 m x 0.6 m

Table 6: Performance parameters of AMSR-E

Aqua_Auto2

Figure 32: Schematic view of the AMSR-E instrument (image credit: NASA)

The AMSR-E instrument rotates continuously about an axis parallel to the local spacecraft vertical at 40 rpm. At an altitude of 705 km, it measures the upwelling scene brightness temperatures over an angular sector of ± 61º about the subsatellite track, resulting in a swath width of 1445 km. During a period of 1.5 seconds the S/C subsatellite point travels 10 km. Even though the IFOV for each channel is different, active scene measurements are recorded at equal intervals of 10 km (5 km for the 89 GHz channels) along the scan. The half cone angle at which the reflector is fixed is 47.4º which results in an Earth incidence angle of 55.0º.

Aqua_Auto1

Figure 33: Line drawing of the AMSR-E instrument (image credit: NASA)

Instrument calibration. The radiometer calibration accuracy budget, exclusive of antenna pattern correction effects, is composed of three major contributors: warm load reference error, cold load reference error, radiometer electronics nonlinearities and errors.

Some data products from AMSR-E are:

• Level 2A brightness temperatures

• Level 2 rainfall

• Level 3 rainfall

• Columnar cloud water over the oceans

• Columnar water vapor over the oceans

• Sea surface temperature (SST)

• Sea surface wind speed

• Sea ice concentration

• Sea ice temperature

• Snow depth on sea ice

• Snow-water equivalent on land

• Surface soil moisture

Aqua_Auto0

Figure 34: The Aqua spacecraft and instrument accommodations (image credit: NASA, JAXA)



1) C. L. Parkinson, “Aqua: An Earth-Observing Satellite Mission to Examine Water and other Climate Variables,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No 2, Feb. 2003, pp. 173-183, Note: The entire issue is devoted to the EOS Aqua Mission.

2) http://aqua.nasa.gov/

3) http://www.nasa.gov/pdf/151986main_Aqua_brochure.pdf

4) Eric J. Fetzer, “Observing Clouds and Water Vapor with NASA's A-Train,” Joint GCSS-GPCI/BLCI-RICO Workshop, NASA/GISS New York, USA, Sept. 18, 2006, URL: http://www.knmi.nl/samenw/rico/presentations/Fetzer_GCSS.pdf

5) ”A Dusty Day Over the Aral Sea,”

6) ”Smoke Blankets Thailand and Burma (Myanmar),” NASA Earth Observatory, 18 March 2020, URL: https://earthobservatory.nasa.gov/images
/146460/smoke-blankets-thailand-and-burma-myanmar

7) ”Satellite Observations Aid Bison Management,” NASA Earth Observatory, Image of the Day for 12 March 2020, URL: https://earthobservatory.nasa.gov/images
/146389/satellite-observations-aid-bison-management

8) ”A Textbook Weather Front,” NASA Earth Observatory, Image of the Day for 9 March 2020, URL: https://earthobservatory.nasa.gov/images/146392/a-textbook-weather-front

9) ”Global Green Up Slows Warming,” NASA Earth Observatory, Image of the Day for 18 February 2020, URL: https://earthobservatory.nasa.gov/images/146296/global-green-up-slows-warming

10) ”Bloom in McMurdo Sound,” NASA Earth Observatory, Image of the Day for 29 January 2020, URL: https://earthobservatory.nasa.gov/images/146213/bloom-in-mcmurdo-sound

11) ”A Decline in Asian Dust,” NASA Earth Observatory, Image of the Day for 21 January 2020, URL: https://earthobservatory.nasa.gov/images/146175/a-decline-in-asian-dust

12) Hongbin Yu, Yang Yang, Hailong Wang, Qian Tan, Mian Chin, Robert C. Levy, Lorraine A. Remer, Steven J. Smith, Tianle Yuan, and Yingxi Shi,”Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017,” Atmospheric Chemistry and Physics, Volume 20, pp: 139-161, Published: 3 January 2020, https://doi.org/10.5194/acp-20-139-2020, URL: https://www.atmos-chem-phys.net
/20/139/2020/acp-20-139-2020.pdf

13) ”Growth of a Summer Storm,” NASA Earth Observatory, Image of the Day for 17 January 2020, URL: https://earthobservatory.nasa.gov/images/146158/growth-of-a-summer-storm

14) ”A Dusty Journey,” NASA Earth Observatory, Image of the Day for 9 January 2020, URL: https://earthobservatory.nasa.gov/images/146122/a-dusty-journey

15) ”Fires and Smoke Engulf Southeastern Australia,” NASA Earth Observatory, 6 January 2020, URL: https://earthobservatory.nasa.gov/images/146110/fires-and-smoke-engulf-southeastern-australia

16) http://aqua.nasa.gov/about/instruments.php

17) H. H. Aumann, M. Chahine, C. Gautier, M. D. Goldberg, E. Kalnay, L. M. McMillin, H. Revercomb, P. W. Rosenkranz, W. L. Smith, D. H. Staelin, L. L. Strow, J. Susskind, “AIRS/AMSU/HSB on the Aqua Mission: Design, Science Objectives, Data Products, and Processing Systems,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No 2, pp. 253-264, February 2003, URL http://www.geog.ucsb.edu/~gautier/CV/pubs/Auman_et_al_2003.pdf

18) Aqua brochure of NASA/GSFC, March 2002, courtesy of Claire L. Parkinson, URL: http://aqua.nasa.gov/doc/pubs/Aqua_brochure.pdf

19) http://www-airs.jpl.nasa.gov/

20) M. H. Weiler, K. R. Overoye, J. A. Stobie, P. B. O'Sullivan, S. L. Gaiser, S. E. Broberg, D. A. Elliott, “Performance of the Atmospheric Infrared Sounder (AIRS) in the Radiation Environment of Low-Earth Orbit,” Proceedings of the SPIE Conference Optics and Photonics, San Diego CA, USA, July 31-Aug. 4, 2005, Vol. 5882

21) C. D. Barnet, M. D. Goldberg, L. McMillin, M. T. Chahine, “Remote sounding of trace gases with the EOS/AIRS instrument,” `Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective,' Edited by Huang, Hung-Lung A.; Bloom, Hal J. Proceedings of the SPIE, Vol. 5548, 2004, pp. 300-312

22) http://aqua.nasa.gov/about/instrument_airs.php

23) Stuart MacCallum, “The Atmospheric InfraRed Sounder,” 2005, URL: http://xweb.geos.ed.ac.uk/~stuart/Presentations/stuart_firbush2005.pdf

24) Thomas S. Pagano, Moustafa T. Chahine, Edward T. Olsen, “Seven years of observations of Mid-Tropospheric CO2 from the Atmospheric Infrared Sounder,” Proceedings of the 61st IAC (International Astronautical Congress), Prague, Czech Republic, Sept. 27-Oct. 1, 2010, IAC-10.B1.6.3

25) Eric Fetzer, Larry M. McMillin, David Tobin, Hartmut H. Aumann, Michael R. Gunson, W. Wallace McMillan, Denise E. Hagan, Mark D. Hofstadter, James Yoe, David N. Whiteman, John E. Barnes, Ralf Bennartz, Holger Vömel, VonWalden, Michael Newchurch, Peter J. Minnett, Robert Atlas, Francis Schmidlin, Edward T. Olsen, Mitchell D. Goldberg, Sisong Zhou, HanJung Ding, William L. Smith, and Hank Revercomb “AIRS/AMSU/HSB validation,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, Issue 2, Feb. 2003, pp. 418-431

26) Eric J. Fetzer, Edward T. Olsen, Luke Chen, Denise Hagan, “Validation of AIRS / AMSU / HSB retrieved products,” URL: http://trs-new.jpl.nasa.gov/dspace/bitstream/2014/38290/1/03-1851.pdf

27) Information provided by Janio Kono of INPE, Sao José dos Campos, Brazil

28) B. H. Lambrigtsen, R. V. Calheiros, “The Humidity Sounder for Brazil - an international partnership,” IEEE Transaction on Geoscience and Remote Sensing, Vol. 41, Issue 2, Feb. 2003, pp. 352-361

29) Ezio Castejon Garcia, Marcio Bueno dos Santos, “The Environmental Simulation of the Humidity Sounder for Brazil,” 54th Astronautical Congress of the IAF, Sept. 29 - Oct. 3, 2003, Bremen, Germany

30) http://www.ghcc.msfc.nasa.gov/AMSR/instrument_descrip.html

31) AMSR-E Data Users Handbook, 4th Edition, JAXA, March 2006, NCX-030021

32) http://nsidc.org/data/docs/daac/amsre_instrument.gd.html


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 (herb.kramer@gmx.net).

Spacecraft    Launch    Mission Status    Sensor Complement    References    Back to top