Aqua: AIRS, AMSU, HSB, AMSR-E, CERES, MODIS

This brochure provides an overview of the Aqua spacecraft, instruments, science, and data products Aqua, Latin for water, is a NASA Earth Science satellite mission named for the large amount of information that the mission is collecting about the Earth's water cycle, including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice. Additional variables also measured by Aqua include radiative energy fluxes, aerosols, vegetation cover on the land, phytoplankton and dissolved organic matter in the oceans, and air, land, and water temperatures. Note: this guide was produced before Aqua was launched; for the most recent information on Aqua, go to http://aqua.nasa.gov. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional, Informal education

The Effects of Different Footprint Sizes and Cloud Algorithms on the Top-Of-Atmosphere Radiative Flux Calculation from the Clouds and Earths Radiant Energy System (CERES) Instrument on Suomi National Polar-Orbiting Partnership (NPP)

Only one Clouds and Earths Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W/sq. m for SW (shortwave) and less than 1.0 W/sq. m for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W/sq. m when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W/sq. m between them; the biases of daytime and night- time LW flux are close to zero with RMS errors of 0.8 and 0.2 W/sq. m. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W/sq. m in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W/sq. m and the RMS error increases to 5.2 W/sq. m. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W/sq. m in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record

Overview of CERES Cloud Properties Derived From VIRS AND MODIS DATA

Simultaneous measurement of radiation and cloud fields on a global basis is recognized as a key component in understanding and modeling the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. The NASA Clouds and Earth s Radiant Energy System (CERES) Project (Wielicki et al., 1998) began addressing this issue in 1998 with its first broadband shortwave and longwave scanner on the Tropical Rainfall Measuring Mission (TRMM). This was followed by the launch of two CERES scanners each on Terra and Aqua during late 1999 and early 2002, respectively. When combined, these satellites should provide the most comprehensive global characterization of clouds and radiation to date. Unfortunately, the TRMM scanner failed during late 1998. The Terra and Aqua scanners continue to operate, however, providing measurements at a minimum of 4 local times each day. CERES was designed to scan in tandem with high resolution imagers so that the cloud conditions could be evaluated for every CERES measurement. The cloud properties are essential for converting CERES radiances shortwave albedo and longwave fluxes needed to define the radiation budget (ERB). They are also needed to unravel the impact of clouds on the ERB. The 5-channel, 2-km Visible Infrared Scanner (VIRS) on the TRMM and the 36-channel 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua are analyzed to define the cloud properties for each CERES footprint. To minimize inter-satellite differences and aid the development of useful climate-scale measurements, it was necessary to ensure that each satellite imager is calibrated in a fashion consistent with its counterpart on the other CERES satellites (Minnis et al., 2006) and that the algorithms are as similar as possible for all of the imagers. Thus, a set of cloud detection and retrieval algorithms were developed that could be applied to all three imagers utilizing as few channels as possible while producing stable and accurate cloud properties. This paper discusses the algorithms and results of applying those techniques to more than 5 years of Terra MODIS, 3 years of Aqua MODIS, and 4 years of TRMM VIRS data

Consistency of Global Modis Aerosol Optical Depths over Ocean on Terra and Aqua Ceres SSF Datasets

Aerosol retrievals over ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua platforms are available from the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) datasets generated at NASA Langley Research Center (LaRC). Two aerosol products are reported side-by-side. The primary M product is generated by sub-setting and remapping the multi-spectral (0.47-2.1 micrometer) MODIS produced oceanic aerosol (MOD04/MYD04 for Terra/Aqua) onto CERES footprints. M*D04 processing uses cloud screening and aerosol algorithms developed by the MODIS science team. The secondary AVHRR-like A product is generated in only two MODIS bands 1 and 6 (on Aqua, bands 1 and 7). The A processing uses the CERES cloud screening algorithm, and NOAA/NESDIS glint identification, and single-channel aerosol retrieval algorithms. The M and A products have been documented elsewhere and preliminarily compared using 2 weeks of global Terra CERES SSF Edition 1A data in which the M product was based on MOD04 collection 3. In this study, the comparisons between the M and A aerosol optical depths (AOD) in MODIS band 1 (0.64 micrometers), tau(sub 1M) and tau(sub 1A) are re-examined using 9 days of global CERES SSF Terra Edition 2A and Aqua Edition 1B data from 13 - 21 October 2002, and extended to include cross-platform comparisons. The M and A products on the new CERES SSF release are generated using the same aerosol algorithms as before, but with different preprocessing and sampling procedures, lending themselves to a simple sensitivity check to non-aerosol factors. Both tau(sub 1M) and tau(sub 1A) generally compare well across platforms. However, the M product shows some differences, which increase with ambient cloud amount and towards the solar side of the orbit. Three types of comparisons conducted in this study - cross-platform, cross-product, and cross-release confirm the previously made observation that the major area for improvement in the current aerosol processing lies in a more formalized and standardized sampling (and most importantly, cloud screening) whereas optimization of the aerosol algorithm is deemed to be an important yet less critical element

Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched aboard the Terra spacecraft on December 18, 1999 and Aqua spacecraft on May 4, 2002. A comprehensive set of remote sensing algorithms for the retrieval of cloud physical and optical properties have enabled over twelve years of continuous observations of cloud properties from Terra and over nine years from Aqua. The archived products from these algorithms include 1 km pixel-level (Level-2) and global gridded Level-3 products. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Results include the latitudinal distribution of cloud optical and radiative properties for both liquid water and ice clouds, as well as latitudinal distributions of cloud top pressure and cloud top temperature. MODIS finds the cloud fraction, as derived by the cloud mask, is nearly identical during the day and night, with only modest diurnal variation. Globally, the cloud fraction derived by the MODIS cloud mask is approx.67%, with somewhat more clouds over land during the afternoon and less clouds over ocean in the afternoon, with very little difference in global cloud cover between Terra and Aqua. Overall, cloud fraction over land is approx.55%, with a distinctive seasonal cycle, whereas the ocean cloudiness is much higher, around 72%, with much reduced seasonal variation. Cloud top pressure and temperature have distinct spatial and temporal patterns, and clearly reflect our understanding of the global cloud distribution. High clouds are especially prevalent over the northern hemisphere continents between 30 and 50 . Aqua and Terra have comparable zonal cloud top pressures, with Aqua having somewhat higher clouds (cloud top pressures lower by 100 hPa) over land due to afternoon deep convection. The coldest cloud tops (colder than 230 K) generally occur over Antarctica and the high clouds in the tropics (ITCZ and the deep convective clouds over the western tropical Pacific and Indian sub-continent)

RADIATIVE FLUXES AND ALBEDO FEEDBACK IN POLAR REGIONS

The Arctic is experiencing an unprecedented increase in surface temperature and decrease in sea ice extent. Discussion as to the causes that contribute to the Arctic warming is still ongoing. The ice-albedo feedback has been proposed as a possible mechanism for polar amplification of such warming. It states that more open water leads to more solar heat absorption, which results in more ice melting and more open water. In order to study this relationship there is a need for accurate information on the solar heat input into the Arctic Oceans. I have developed and improved inference schemes for shortwave radiative fluxes that respond to the needs of Polar Regions utilizing most recent information on atmospheric and surface states. A Moderate Resolution Imaging Spectroradiometer (MODIS) approach has been optimized for Polar Regions and implemented at 1° for 2002-2010 and at 5-km for 2007. A methodology was developed to derive solar fluxes from the Advanced Very High Resolution Radiometer (AVHRR) and implemented at 0.5° for 1983-2006. Evaluation against ground measurements over land and ocean at high latitudes shows that the MODIS shortwave flux estimates are in best agreement with ground observations as compared to other available satellite and model products, with a bias of -3.6 Wm-2 and standard deviation of 23 Wm-2 at a daily time scale. The AVHRR estimates agree with ground observations with a bias of -4.7 Wm-2 and a standard deviation of 41 Wm-2 at a daily time scale. The ice-albedo feedback was evaluated by computing the solar heating into the Arctic Ocean using the improved satellite flux estimates. A growth at a rate of 2 %/year in the trend of solar heating for 2003-09 was found at a 75 % confidence level; the trend for 1984-2002 was only 0.2 %/year at a 99 % confidence level. The ice retreat is correlated to the solar energy into the ocean at 0.7 at a 75 % confidence level. An increase in the open water fraction resulted in a maximum 300 % positive anomaly in solar heating in 2007 located where the maximum sea ice retreat is

Comparison Of Ceres-Modis Derived Polar Cloud Properties With Cloudsat/calipso And Ground-Based Measurements

Passive satellites often face difficulty when detecting clouds over snow and ice covered surfaces beneath them. The recent launches of active satellites, which directly measure cloud properties, have allowed scientists to gain a firsthand look at the complex cloud profiles across polar regions. To help quantify the differences between passive and active satellite retrievals, cloud properties derived for the Clouds and Earth\u27s Radiant Energy System (CERES) project using MODerate Resolution Imaging Spectroradiometer (MODIS) data are compared with combined measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat (CC), and Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) observations at the North Slope of Alaska site, from July 2006 to June 2010. The study was then extended to include the entire Arctic and Antarctic. During the 4-year period, monthly mean cloud fractions (CFs) between ARM and CC differ by 5%. While CERES-MODIS CF retrievals agree well with ARM and CC during warm months (May-October), retrievals during the cold season (November-April) significantly underestimate CF. Annual mean cloud-base heights derived from ARM and CC agree within 200 m, while their cloud-top heights (Htop) differ by an average of 1.2 km, due largely to CC detecting more upper-level clouds during the warm months. Effective cloud heights from CERES-MODIS retrievals fall between CC and ARM cloud bases and tops, as expected. Cloud fractions and heights across the span of the Arctic depict similar features as those shown at the ARM NSA site. During summer months, cloud fractions between CERES-MODIS and CC agree well, differing by no more than 10% across most regions of the Arctic. During this same season, however, cloud heights vary by as much as 5.2 km. This is largely due to multi-layer cloud systems, where CC measures the uppermost cloud layer, and CERES-MODIS detects lower cloud layers, resulting in lower CERES-MODIS cloud heights, but equal cloud fractions. Winter shows a contrast to the similarities in cloud fraction detected in summer, with CERES-MODIS underestimating CF by as much as 59%. Cloud heights between the two platforms, however, show better agreement during the cold months, when fewer high clouds occur. The largest differences in CERES-MODIS and CC cloud fractions and heights occur during the cold season (JJA) in the Antarctic. During this time period, CC detects cloud fractions as much as 43% higher than CERES-MODIS, over regions coupled with cloud heights up to 12.3 km higher than CERES-MODIS. These extreme differences are caused by the presence of polar stratospheric clouds, which occur at altitudes between 15 and 25 km, and are nearly impossible for the CERES-MODIS sensor to detect. Finally, single-layered low-level stratus cloud effective radius (re), liquid water path (LWP), and optical depth (τ) retrieved from CERES-MODIS and surface-based retrievals at the ARM NSA site were investigated. When surface snow and sea ice are not present, ARM and CERES-MODIS retrieved cloud droplet re, LWP, and τ agree well. However, when snow and sea ice are introduced, CERES-MODIS retrieved re values are higher than ARM results, while optical depths are lower. These differences suggest that CERES-MODIS cloud fraction retrieval algorithms during polar night and microphysical retrieval algorithms over snow and ice covered surfaces need future improvement

A multi-satellite climatology of clouds, radiation and precipitation in southern West Africa and comparison to climate models

Southern West Africa (SWA) has a large population that relies on highly variable monsoon rainfall, yet climate models show little consensus over projected precipitation in this region. Understanding of the current and future climate of SWA is further complicated by rapidly increasing anthropogenic emissions and a lack of surface observations. Using multiple satellite observations, the ERA-Interim reanalysis, and four climate models, we document the climatology of cloud, precipitation and radiation over SWA in June-July, highlight discrepancies among satellite products, and identify shortcomings in climate models and ERA-Interim. Large differences exist between monthly mean cloud cover estimates from satellites, which range from 68 to 94 %. In contrast, differences among satellite observations in top of atmosphere outgoing radiation and surface precipitation are smaller, with monthly means of about 230 W m–2 of longwave radiation, 145 W m–2 of shortwave radiation and 5.87 mm day–1 of precipitation. Both ERA-Interim and the climate models show less total cloud cover than observations, mainly due to underestimating low cloud cover. Errors in cloud cover, along with uncertainty in surface albedo, lead to a large spread of outgoing shortwave radiation. Both ERA-Interim and the climate models also show signs of convection developing too early in the diurnal cycle, with associated errors in the diurnal cycles of precipitation and outgoing longwave radiation. Clouds, radiation and precipitation are linked in an analysis of the regional energy budget, which shows that inter-annual variability of precipitation and dry static energy divergence are strongly linked

Assessment of the Visible Channel Calibrations of the TRMM VIRS and MODIS on Aqua and Terra

Several recent research satellites carry self-calibrating multispectral imagers that can be used for calibrating operational imagers lacking complete self-calibrating capabilities. In particular, the visible (VIS, 0.65 m) channels on operational meteorological satellites are generally calibrated before launch, but require vicarious calibration techniques to monitor the gains and offsets once they are in orbit. To ensure that the self-calibrating instruments are performing as expected, this paper examines the consistencies between the VIS channel (channel 1) reflectances of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites and the Version 5a and 6 reflectances of the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission using a variety of techniques. These include comparisons of Terra and Aqua VIS radiances with coincident broadband shortwave radiances from the well-calibrated Clouds and the Earth s Radiant Energy System (CERES), time series of deep convective cloud (DCC) albedos, and ray-matching intercalibrations between each of the three satellites. Time series of matched Terra and VIRS data, Aqua and VIRS data, and DCC reflected fluxes reveal that an older version (Version 5a, ending in early 2004) of the VIRS calibration produced a highly stable record, while the latest version (Version 6) appears to overestimate the sensor gain change by approx.1%/y as the result of a manually induced gain adjustment. Comparisons with the CERES shortwave radiances unearthed a sudden change in the Terra MODIS calibration that caused a 1.17% decrease in the gain on 19 November 2003 that can be easily reversed. After correction for these manual adjustments, the trends in the VIRS and Terra channels are no greater than 0.1%/y. Although the results were more ambiguous, no statistically significant trends were found in the Aqua MODIS channel-1 gain. The Aqua radiances are 1% greater, on average, than their Terra counterparts, and after normalization are 4.6% greater than VIRS radiances, in agreement with theoretical calculations. The discrepancy between the two MODIS instruments should be taken into account to ensure consistency between parameters derived from them. With the adjustments, any of the three instruments can serve as references for calibrating other satellites. Monitoring of the calibrations continues in near-real-time and the results are available via the world wide web

Satellite remote sensing of vegetation dynamics in the context of climate change

Vegetation is a key component of the Earth's climate system. Understanding vegetation dynamics in a changing climate requires both in situ and remote sensing data. Satellite remote sensing is especially indispensible for continuous monitoring of vegetation over large areas. This dissertation is focused on investigation of vegetation dynamics in the broader context of climate change using satellite data over two critical regions: the arctic-boreal area in the northern high latitudes and Amazonia in South America. The northern high latitudes have experienced amplified warming. We found the response of the arctic-boreal vegetation to this warming to be different between North America and Eurasia during a 30-year period since 1982: the relationship between vegetation green-up and temperature rise was stable over Eurasia, but in North America, the amount of vegetation green-up per unit amount of warming has decreased since the beginning of 21st century. This could partly be explained by the unmatched northward movements of temperature and precipitation patterns in North America. The Amazonian rainforests have highly dense canopies of green leaves. In such dense media, reflection of solar radiation tends to saturate. Thus, the satellite measurements are weakly sensitive to vegetation changes. At the same time, the data are strongly influenced by changing sun-sensor geometry. This makes it difficult to discriminate between vegetation changes and sun-sensor geometry effects. We developed a new physically based approach to detect changes in dense forests. Analyses of several years of data from three sensors on two satellites under a range of sun-sensor geometries provide robust evidence for a sunlight driven seasonal cycle in structure and greenness of Amazonian rainforests. The 2005 and 2010 dry-season droughts decreased the photosynthetic activity of Amazonian rainforests. We demonstrate that satellite data capture such decreases. Furthermore, we show that in 2004 and 2007, when there was lower wet-season water abundance compared to normal years, the photosynthetic activity of Amazonian forests also decreased. Potentially frequent water deficits over Amazon in the future, irrespective of whether they occur in the dry or wet season, will decrease the photosynthetic activity of Amazonian forests, and provide a positive feedback to global warming