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NASA NTRS2010

NASA's Earth Observing System (EOS): Delivering on the Dream, Today and Tomorrow

This paper describes the successful operations of NASA's Earth Observing System (EOS) satellites over the past 10 years and the plans for the future. Excellent operations performance has been a key factor in the overall success of EOS. The EOS Program was conceived in the 1980s and began to take shape in the early 1990s. EOS consists of a series of satellites that study the Earth as an interrelated system. It began with the launch of Terra in December 1999, followed by Aqua in May 2002, and Aura in July 2004. A key EOS goal is to provide a long-term continuous data set to enable the science community to develop a better understanding of land, ocean, and atmospheric processes and their interactions. EOS has produced unprecedented amounts of data which are used all over the world free of charge. Mission operations have resulted in data recovery for Terra, Aqua, and Aura that have consistently exceeded mission requirements. The paper describes the ground systems and organizations that control the EOS satellites, capture the raw data, and distribute the processed science data sets. The paper further describes how operations have evolved since 1999. Examples of this evolution include (a) the implementation of new mission safety requirements for orbital debris monitoring; (b) technology upgrades to keep facilities at the state of the art; (c) enhancements to meet changing security requirements; and (d) operations management of the 2 international Earth Observing Constellations of 11 satellites known as the "Morning Constellation" and the "A-Train". The paper concludes with a view into the future based on the latest spacecraft status, lifetime projections, and mission plans.

Kelly, Angelita C.↗
NASA NTRS2010

Comparison of the MODIS Collection 5 Multilayer Cloud Detection Product with CALIPSO

CALIPSO, launched in June 2006, provides global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the Collection 5 scream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 km resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, we investigate the global performance of multilayer cloud detection algorithms (and thermodynamic phase).

Platnick, Steven↗
NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗
NASA NTRS2011

The Cloud-Aerosol Transport System (CATS): a New Lidar for Aerosol and Cloud Profiling from the International Space Station

Spaceborne lidar profiling of aerosol and cloud layers has been successfully implemented during a number of prior missions, including LITE, ICESat, and CALIPSO. Each successive mission has added increased capability and further expanded the role of these unique measurements in wide variety of applications ranging from climate, to air quality, to special event monitoring (ie, volcanic plumes). Many researchers have come to rely on the availability of profile data from CALIPSO, especially data coincident with measurements from other A-Train sensors. The CALIOP lidar on CALIPSO continues to operate well as it enters its fifth year of operations. However, active instruments have more limited lifetimes than their passive counterparts, and we are faced with a potential gap in lidar profiling from space if the CALIOP lidar fails before a new mission is operational. The ATLID lidar on EarthCARE is not expected to launch until 2015 or later, and the lidar component of NASA's proposed Aerosols, Clouds, and Ecosystems (ACE) mission would not be until after 2020. Here we present a new aerosol and cloud lidar that was recently selected to provide profiling data from the International Space Station (ISS) starting in 2013. The Cloud-Aerosol Transport System (CATS) is a three wavelength (1064, 532, 355 nm) elastic backscatter lidar with HSRL capability at 532 nm. Depolarization measurements will be made at all wavelengths. The primary objective of CATS is to continue the CALIPSO aerosol and cloud profile data record, ideally with overlap between both missions and EarthCARE. In addition, the near real time data capability of the ISS will enable CATS to support operational applications such as air quality and special event monitoring. The HSRL channel will provide a demonstration of technology and a data testbed for direct extinction retrievals in support of ACE mission development. An overview of the instrument and mission will be provided, along with a summary of the science objectives and simulated data.

Welton, Ellsworth J.↗
NASA NTRS2011

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

We have developed a relatively simple scheme for simulating retrieved cloud optical centroid pressures (OCP) from satellite solar backscatter observations. We have compared simulator results with those from more detailed retrieval simulators that more fully account for the complex radiative transfer in a cloudy atmosphere. We used this fast simulator to conduct a comprehensive evaluation of cloud OCPs from the two OMI algorithms using collocated data from CloudSat and Aqua MODIS, a unique situation afforded by the A-train formation of satellites. We find that both OMI algorithms perform reasonably well and that the two algorithms agree better with each other than either does with the collocated CloudSat data. This indicates that patchy snow/ice, cloud 3D, and aerosol effects not simulated with the CloudSat data are affecting both algorithms similarly. We note that the collocation with CloudSat occurs mainly on the East side of OMI's swath. Therefore, we are not able to address cross-track biases in OMI cloud OCP retrievals. Our fast simulator may also be used to simulate cloud OCP from output generated by general circulation models (GCM) with appropriate account of cloud overlap. We have implemented such a scheme and plan to compare OMI data with GCM output in the near future.

Joiner, J.↗
NASA NTRS2010

Global Variability of Mesoscale Convective System

Mesoscale convective systems (MCSs) in the tropics produce extensive anvil clouds, which significantly affect the transfer of radiation. This study develops an objective method to identify MCSs and their anvils by combining data from three A-train satellite instruments: Moderate Resolution Imaging Spectroradiometer (MODIS) for cloud-top size and coldness, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for rain area size and intensity, and CloudSat for horizontal and vertical dimensions of anvils. The authors distinguish three types of MCSs: small and large separated MCSs and connected MCSs. The latter are MCSs sharing a contiguous rain area. Mapping of the objectively identified MCSs shows patterns of MCSs that are consistent with previous studies of tropical convection, with separated MCSs dominant over Africa and the Amazon regions and connected MCSs favored over the warm pool of the Indian and west Pacific Oceans. By separating the anvil from the raining regions of MCSs, this study leads to quantitative global maps of anvil coverage. These maps are consistent with the MCS analysis, and they lay the foundation for estimating the global radiative effects of anvil clouds. CloudSat radar data show that the modal thickness of MCS anvils is about 4--5 km. Anvils are mostly confined to within 1.5--2 times the equivalent radii of the primary rain areas of the MCSs. Over the warm pool, they may extend out to about 5 times the rain area radii. The warm ocean MCSs tend to have thicker non-raining and lightly raining anvils near the edges of their actively raining regions, indicating that anvils are generated in and spread out from the primary raining regions of the MCSs. Thicker anvils are nearly absent over continental regions.

Yuan, J.↗
NASA NTRS2012

Top-of-the-Atmosphere Shortwave Flux Estimation from UV Observations: An Empirical Approach

Measurements of top of the atmosphere (TOA) radiation are essential to the understanding of Earth's climate. Clouds, aerosols, and ozone (0,) are among the most important agents impacting the Earth's short-wave (SW) radiation budget. There are several sensors in orbit that provide independent information related to the Earth's SW radiation budget. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze near-simultaneous data from several of these sensors. They include the Ozone Monitoring Instrument (OMI), on the NASA Aura satellite, that makes TOA hyper-spectral measurements from ultraviolet (UV) to visible wavelengths, and Clouds and the Earth's Radiant Energy System (CERES) instrument, on the NASA Aqua satellite, that makes broadband measurements in both the long- and short-wave. OMI measurements have been successfully utilized to derive the information on trace gases (e.g., 0 1, NO" and SO,), clouds, and absorbing aerosols. TOA SW fluxes are estimated using a combination of data from CERES and the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS). In this paper, OMI retrievals of cloud/aerosol parameters and 0 1 have been collocated with CERES TOA SW flux retrievals. We use this collocated data to develop a neural network that estimates TOA shortwave flux globally over ocean using data from OMI and meteorological analyses. This input data include the effective cloud fraction, cloud optical centroid pressure (OCP), total-column 0" and sun-satellite viewing geometry from OMI as well as wind speed and water vapor from the Goddard Earth Observing System 5 Modern Era Retrospective-analysis for Research and Applications (GEOS-5 MERRA) along with a climatology of chlorophyll content. We train the neural network using a subset of CERES retrievals of TOA SW flux as the target output (truth) and withhold a different subset of the CERES data to be used for validation.

Gupta, P.↗
NASA NTRS2012

The Cloud-Aerosol Transport System (CATS): A New Lidar for Aerosol and Cloud Profiling from the International Space Station

Spaceborne lidar profiling of aerosol and cloud layers has been successfully implemented during a number of prior missions, including LITE, ICESat, and CALIPSO. Each successive mission has added increased capability and further expanded the role of these unique measurements in wide variety of applications ranging from climate, to air quality, to special event monitoring (ie, volcanic plumes). Many researchers have come to rely on the availability of profile data from CALIPSO, especially data coincident with measurements from other A-Train sensors. The CALIOP lidar on CALIPSO continues to operate well as it enters its fifth year of operations. However, active instruments have more limited lifetimes than their passive counterparts, and we are faced with a potential gap in lidar profiling from space if the CALIOP lidar fails before a new mission is operational. The ATLID lidar on EarthCARE is not expected to launch until 2015 or later, and the lidar component of NASA's proposed Aerosols, Clouds, and Ecosystems (ACE) mission would not be until after 2020. Here we present a new aerosol and cloud lidar that was recently selected to provide profiling data from the International Space Station (ISS) starting in 2013. The Cloud-Aerosol Transport System (CATS) is a three wavelength (1064,532,355 nm) elastic backscatter lidar with HSRL capability at 532 nm. Depolarization measurements will be made at all wavelengths. The primary objective of CATS is to continue the CALIPSO aerosol and cloud profile data record, ideally with overlap between both missions and EarthCARE. In addition, the near real time (NRT) data capability ofthe ISS will enable CATS to support operational applications such as aerosol and air quality forecasting and special event monitoring. The HSRL channel will provide a demonstration of technology and a data testbed for direct extinction retrievals in support of ACE mission development. An overview of the instrument and mission will be provided, along with a summary of the science objectives and simulated data. Input from the ICAP community is desired to help plan our NRT mission goals and interactions with ICAP forecasters.

Welton, Ellsworth J.↗
NASA NTRS2012

Ozone and Aerosol Retrieval from Backscattered Ultraviolet Radiation

In this presentation we will discuss the techniques to estimate total column ozone and aerosol absorption optical depth from the measurements of back scattered ultraviolet (buv) radiation. The total ozone algorithm has been used to create a unique record of the ozone layer, spanning more than 3 decades, from a series of instruments (BUV, SBUV, TOMS, SBUV/2) flown on NASA, NOAA, Japanese and Russian satellites. We will discuss how this algorithm can be considered a generalization of the well-known Dobson/Brewer technique that has been used to process data from ground-based instruments for many decades, and how it differs from the DOAS techniques that have been used to estimate vertical column densities of a host of trace gases from data collected by GOME and SCIAMACHY instruments. The buv aerosol algorithm is most suitable for the detection of UV absorbing aerosols (smoke, desert dust, volcanic ash) and is the only technique that can detect aerosols embedded in clouds. This algorithm has been used to create a quarter century record of aerosol absorption optical depth using the buv data collected by a series of TOMS instruments. We will also discuss how the data from the OMI instrument launched on July 15, 2004 will be combined with data from MODIS and CALIPSO lidar data to enhance the accuracy and information content of satellite-derived aerosol measurements. The OMI and MODIS instruments are currently flying on EOS Aura and EOS Aqua satellites respectively, part of a constellation of satellites called the "A-train".

Bhartia, Pawan K.↗
NASA NTRS2012

Satellite Perspective of Aerosol Intercontinental Transport: From Qualitative Tracking to Quantitative Characterization

Evidence of aerosol intercontinental transport (ICT) is both widespread and compelling. Model simulations suggest that ICT could significantly affect regional air quality and climate, but the broad inter-model spread of results underscores a need of constraining model simulations with measurements. Satellites have inherent advantages over in situ measurements to characterize aerosol ICT, because of their spatial and temporal coverage. Significant progress in satellite remote sensing of aerosol properties during the Earth Observing System (EOS) era offers opportunity to increase quantitative characterization and estimates of aerosol ICT, beyond the capability of pre-EOS era satellites that could only qualitatively track aerosol plumes. EOS satellites also observe emission strengths and injection heights of some aerosols, aerosol precursors, and aerosol-related gases, which can help characterize aerosol ICT. After an overview of these advances, we review how the current generation of satellite measurements have been used to (1) characterize the evolution of aerosol plumes (e.g., both horizontal and vertical transport, and properties) on an episodic basis, (2) understand the seasonal and inter-annual variations of aerosol ICT and their control factors, (3) estimate the export and import fluxes of aerosols, and (4) evaluate and constrain model simulations. Substantial effort is needed to further explore an integrated approach using measurements from on-orbit satellites (e.g., A-Train synergy) for observational characterization and model constraint of aerosol intercontinental transport and to develop advanced sensors for future missions.

Yu, Hongbin↗
NASA NTRS2012

Correlation among Cirrus Ice Content, Water Vapor and Temperature in the TTL as Observed by CALIPSO and Aura-MLS

Water vapor in the tropical tropopause layer (TTL) has a local radiative cooling effect. As a source for ice in cirrus clouds, however, it can also indirectly produce infrared heating. Using NASA A-Train satellite measurements of CALIPSO and Aura/MLS we calculated the correlation of water vapor, ice water content and temperature in the TTL. We find that temperature strongly controls water vapor (correlation r =0.94) and cirrus clouds at 100 hPa (r = −0.91). Moreover we observe that the cirrus seasonal cycle is highly (r =−0.9) anticorrelated with the water vapor variation in the TTL, showing higher cloud occurrence during December-January-February. We further investigate the anticorrelation on a regional scale and find that the strong anticorrelation occurs generally in the ITCZ (Intertropical Convergence Zone). The seasonal cycle of the cirrus ice water content is also highly anticorrelated to water vapor (r = −0.91) and our results support the hypothesis that the total water at 100 hPa is roughly constant. Temperature acts as a main regulator for balancing the partition between water vapor and cirrus clouds. Thus, to a large extent, the depleting water vapor in the TTL during DJF is a manifestation of cirrus formation.

Flury, T.↗
NASA NTRS2013

Surface Irradiances Consistent With CERES-Derived Top-of-Atmosphere Shortwave and Longwave Irradiances

The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth's Radiant Energy System (CERES). This paper presents amethod to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, andModerate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthlymean irradiances calculated with 10 years of data is 4.7 (13.3) W/sq m for downward shortwave and 22.5 (7.1) W/sq m for downward longwave irradiances over ocean and 21.7 (7.8) W m22 for downward shortwave and 21.0 (7.6) W/sq m for downward longwave irradiances over land. The bias andRMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES,MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.

Kato, Seiji↗
NASA NTRS2013

Contemplating Synergistic Algorithms for the NASA ACE Mission

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval.

precipitation↗
NASA NTRS2013

New Directions: Emerging Satellite Observations of Above-cloud Aerosols and Direct Radiative Forcing

Spaceborne lidar and passive sensors with multi-wavelength and polarization capabilities onboard the A-Train provide unprecedented opportunities of observing above-cloud aerosols and direct radiative forcing. Significant progress has been made in recent years in exploring these new aerosol remote sensing capabilities and generating unique datasets. The emerging observations will advance the understanding of aerosol climate forcing.

satellite remote sensing↗
NASA NTRS2013

3D Radiative Transfer Effects in Multi-Angle/Multi-Spectral Radio-Polarimetric Signals from a Mixture of Clouds and Aerosols Viewed by a Non-Imaging Sensor

When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal--not noise--for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from "context" cameras, as was planned for NASA's ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.

APS↗
NASA NTRS2008

The Orbiting Carbon Observatory: NASA's First Dedicated Carbon Dioxide Mission

The Orbiting Carbon Observatory is scheduled for launch from Vandenberg Air Force Base in California in January 2009. This Earth System Science Pathfinder (ESSP) mission carries and points a single instrument that incorporates 3 high-resolution grating spectrometers designed to measure the absorption of reflected sunlight by near-infrared carbon dioxide (CO2) and molecular oxygen bands. These spectra will be analyzed to retrieve estimates of the column-averaged CO2 dry air mole fraction, X(sub CO2). Pre-flight qualification and calibration tests completed in early 2008 indicate that the instrument will provide high quality X(sub CO2) data. The instrument was integrated into the spacecraft, and the completed Observatory was qualified and tested during the spring and summer of 2008, in preparation for delivery to the launch site in the fall of this year. The Observatory will initially be launched into a 635 km altitude, near-polar orbit. The on-board propulsion system will then raise the orbit to 705 km and insert OCO into the Earth Observing System Afternoon Constellation (A-Train). The first routine science observations are expected about 45 days after launch. Calibrated spectral radiances will be archived starting about 6 months later. An exploratory X(sub CO2) product will be validated and then archived starting about 3 months after that.

Carbon Cycle↗
NASA NTRS2010

TPSAS-NF1676L-11609-DND

China and much of East Asia experienced a drought in the spring of 2010 that is said to be the worst in the past century. Strong winds (wind speed > 7 m/s) occurred in spring this year ~40% more than average in recent years. MODIS imagery indicates numerous major dust storms occurring in the Taklimakan and Gobi deserts. Intense, persistent dust was subsequently observed over North America by space-based, airborne and ground-based lidars during April 2010. Using CALIPSO lidar (CALIOP) measurements and air parcel back trajectories, we track the dust measured over North America back to East Asian source regions (mainly in the Tarim Basin). We also interpret the CALIOP and other A-Train observations using results from a 3D chemical transport model (GEOS-Chem) and investigate the meteorological context that gave rise to these dust storms.

Z Liu↗