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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1997

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.

Foster, J. L.↗
NASA NTRS2000

The Estimation of Hydrometeor Profiles from Wideband Microwave Observations

Profiles of the microphysical properties of clouds and raincells are essential in many areas of atmospheric research and operational meteorology. In order to enhance the understanding of the nonlinear and underconstrained relationships between cloud and hydrometeor microphysical profiles and passive microwave brightness temperatures, estimations of cloud profiles for an anvil, a convective, and an updraft region of an oceanic squall were performed. The estimations relied on comparisons between radiative transfer calculations of incrementally estimated microphysical profiles and concurrent dual-altitude wideband brightness temperatures from the 22 February 1993 flight during TOGA-COARE. The wideband observations (10--220 GHz) are necessary for estimating cloud profiles reaching up to 20 km. The low frequencies enhance the rain and cloud water profiles, while the high frequencies are required to detail the higher altitude ice microphysics. A microphysical profile was estimated for each of the three regions of the storm. Each of the three estimated profiles produced calculated brightness temperatures within approximately 10 K of the observations. A majority, of the total iterative adjustment were to the estimated profile's frozen hydrometeor characteristics and were necessary to match the high frequency calculations with the observations. This indicates a need to validate cloud resolving models using high frequencies. Some difficulties matching the 37 GHz observation channels on the DC-8 and ER-2 aircrafts with the calculations simulated at the two aircraft heights (approximately 11 km and 20 km, respectively) were noted and potential causes presented.

Gail M Skofronick-Jackson↗
NASA NTRS2002

The Influence of Environmental State on Lightning and Convective Parameter Distributions

A merged and highly reduced database of TRMM level 1 (precipitation radar, microwave imager, lightning) and NCEP reanalysis (basic state, radiative and surface flux) data has been assembled for three years of the TRMM mission. This allows direct examination of the dependence of convective spectra (as observed through radar reflectivity, microwave brightness temperature and lightning flash rate) on environmental basic states and anomalies. Such analysis may be more physically justified and instructive than traditional geographic and/or seasonal binning. The dependence of convective spectra on several environmental forcing parameters is presented, including surface Bowen ratio (sensible heat to total turbulent flux), net atmospheric radiative flux convergence and net atmospheric enthalpy flux convergence. The latter are basic drivers of net moisture convergence in simple quasi-equilibrium models of tropical atmospheric convection.

Boccippio, D.↗
NASA NTRS2005

Retrieval of Dry Snow Parameters from Radiometric Data Using a Dense Medium Model and Genetic Algorithms

In this paper, GA-based techniques are used to invert the equations of an electromagnetic model based on Dense Medium Radiative Transfer Theory (DMRT) under the Quasi Crystalline Approximation with Coherent Potential to retrieve snow depth, mean grain size and fractional volume from microwave brightness temperatures. The technique is initially tested on both noisy and not-noisy simulated data. During this phase, different configurations of genetic algorithm parameters are considered to quantify how their change can affect the algorithm performance. A configuration of GA parameters is then selected and the algorithm is applied to experimental data acquired during the NASA Cold Land Process Experiment. Snow parameters retrieved with the GA-DMRT technique are then compared with snow parameters measured on field.

Tedesco, Marco↗
NASA NTRS1993

TOPEX/POSEIDON Microwave Radiometer (TMR): III. Wet Troposphere Range Correction Algorithm and Pre-Launch Error Budget

The sole mission function of the TOPEX/POSEIDON Microwave Radiometer (TMR) is to provide corrections for the altimeter range errors induced by the highly variable atmospheric water vapor content. The three TMR frequencies are shown to be near-optimum for measuring the vapor-induced path delay within an environment of variable cloud cover and variable sea surface flux background. After a review of the underlying physics relevant to the prediction of 5-40 GHz nadir-viewing microwave brightness temperatures, we describe the development of the statistical, iterative algorithm used for the TMR retrieval of path delay. Test simulations are presented which demonstrate the uniformity of algorithm performance over a range of cloud liquid and sea surface wind speed conditions...

Keihm, S. J.↗
NASA NTRS2006

Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm

While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world's oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25% uncertainty that cannot be detected by a radiometer alone. Of this value, 20% is attributed to changes in cloud morphology and microphysics, while 5% is a result of changes in the rain/no-rain thresholds. All but 2%-3% of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined.

Kummerow, Christian↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
NASA NTRS2010

Evaluation and Application of Remotely Sensed Soil Moisture Products

Whereas in-situ measurements of soil moisture are very accurate, achieving accurate regional soil moisture estimates derived solely from point measurements is difficult because of the dependence upon the density of the gauge network and the proper upkeep of these instruments, which can be costly. Microwave remote sensing is the only technology capable of providing timely direct measurements of regional soil moisture in areas that are lacking in-situ networks. Soil moisture remote sensing technology is well established has been successfully applied in many fashions to Earth Science applications. Since the microwave emission from the soil surface has such a high dependency upon the moisture content within the soil, we can take advantage of this relationship and combined with physically-based models of the land surface, derive accurate regional estimates of the soil column water content from the microwave brightness temperature observed from satellite-based remote sensing instruments. However, there still remain many questions regarding the most efficient methodology for evaluating and applying satellite-based soil moisture estimates. As discussed below, we to use satellite-based estimates of soil moisture dynamics to improve the predictive capability of an optimized hydrologic model giving more accurate root-zone soil moisture estimates.

Bolten, J.↗
NASA NTRS2010

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA will deploy the GPM Core Observatory carrying an advanced radar-radiometer system to serve as a physics observatory and a transfer standard for inter-calibration of constellation radiometers. The GPM Core Observatory is scheduled for launch in July 2013. NASA will provide a second radiometer to be flown on a partner-provided GPM Low-Inclination Observatory to enhance the near real-time monitoring of hurricanes and mid-latitude storms. JAXA will also contribute data from the Global Change Observation Mission-Water (GCOM-W) satellite. Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as cross-track scanning humidity sounders on operational satellites such as the NPP, POES, JPSS, and MetOp satellites, which are used to improve the precipitation sampling over land. Brazil has in its national space plan for a GPM low-inclination radiometer, and data from Chinese and Russian microwave radiometers could potentially become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide "next-generation" precipitation data products characterized by: (1) more accurate instantaneous precipitation measurement (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers including operational humidity sounders over land, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud/hydrometeor database constructed from GPM Core sensor measurements. As a science mission with integrated application goals, GPM will (1) provide new measurement standards for precipitation estimation from space, (2) improve understanding of precipitation physics, the global water cycle variability, and freshwater availability, and (3) advance weather/climate/hydrological prediction capabilities to directly benefit the society. An overview of the GPM mission concept, NASA program status, science activities in the United States, as well as a wide range of international scientific collaborations in radiometer inter-calibration, retrieval algorithm development, and ground validation will be presented.

Hou, Arthur Y.↗
NASA NTRS2011

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The cornerstone of the GPM mission is the deployment of a Core Observatory in a 65 deg non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for inter-calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The first space-borne dual-frequency radar will provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from passive microwave sensors. The combined use of DPR and GMI measurements will place greater constraints on radiometer retrievals to improve the accuracy and consistency of precipitation estimates from all constellation radiometers. The GPM constellation is envisioned to comprise five or more conical-scanning microwave radiometers and four or more cross-track microwave sounders on operational satellites. NASA and the Japan Aerospace Exploration Agency (JAXA) plan to launch the GPM Core in July 2013. NASA will provide a second radiometer to be flown on a partner-provided GPM Low-Inclination Observatory (L10) to improve near real-time monitoring of hurricanes and mid-latitude storms. NASA and the Brazilian Space Program (AEB/IPNE) are currently engaged in a one-year study on potential L10 partnership. JAXA will contribute to GPM data from the Global Change Observation Mission-Water (GCOM-W) satellite. Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as cross-track scanning humidity sounders on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), POES, the NASA/NOAA Joint Polar Satellite System (JPSS), and EUMETSAT MetOp satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. Relative to current data products, GPM's "nextgeneration" precipitation products will be characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers including operational humidity sounders over land, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory. An overview of the GPM mission concept, the U.S. GPM program status and updates on international science collaborations on GPM will be presented.

Hou, Arthur Y.↗
NASA NTRS2011

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to-7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Toure, Ally M.↗
NASA NTRS2012

Precipitation Estimation Using Combined Radar/Radiometer Measurements Within the GPM Framework

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. The GPM mission centers upon the deployment of a Core Observatory in a 65o non-Sun-synchronous orbit to serve as a physics observatory and a transfer standard for intersatellite calibration of constellation radiometers. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles needed for improving precipitation retrievals from microwave sensors. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha- Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide next-generation precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory.

Hou, Arthur↗
NASA NTRS2012

Global Precipitation Measurement (GPM) Mission: Overview and Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA will deploy a Core Observatory in 2014 to serve as a reference satellite to unify precipitation measurements from the constellation of sensors. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian MeghaTropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide "next-generation" precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory. GPM is a science mission with integrated applications goals. GPM will provide a key measurement to improve understanding of global water cycle variability and freshwater availability in a changing climate. The DPR and GMI measurements will offer insights into 3-dimensional structures of hurricanes and midlatitude storms, microphysical properties of precipitating particles, and latent heat associated with precipitation processes. The GPM mission will also make data available in near realtime (within 3 hours of observations) forocietal applications ranging from position fixes of storm centers, numerical weather prediction, flood forecasting, freshwater management, landslide warning, crop prediction, to tracking of water-borne diseases. An overview of the GPM mission design, retrieval strategy, ground validation activities, and international science collaboration will be presented.

Hou, Arthur Y.↗
NASA NTRS2012

Conceptual Problems in Land Surface Data Assimilation

A land data assimilation system (LDAS) merges observations (or satellite retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, several conceptual problems can interfere with realizing the potential improvements from data assimilation. Of particular concern is the frequent mismatch between the assimilated observations and the land surface model variables of interest. The seminar will discuss recent research with the ensemble-based NASA GEOS-S LDAS to address various aspects of this mismatch. These aspects include (i) the assimilation of coarse-scale observations into higher-resolution land surface models, (ii) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (iii) the forward modeling of microwave brightness temperatures over land for radiance-based land surface data aSSimilation, and (iv) the selection of the most relevant types of observations for the analysis of a specific water cycle variable (such as root zone soil moisture). At its core, the solution to the above challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Reichle, Rolf↗
NASA NTRS2013

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Land data assimilation↗
NASA NTRS2016

SMAP Data Assimilation at the GMAO

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days.

Land Surface Model↗
NASA NTRS2018

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Girotto, Manuela↗