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NASA NTRS2022

History and Development of the USAF Agriculture Meteorology Modeling System and resulting USAF-NASA Strategic Partnership

The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations’ modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations.

John Eylander↗

NASA NTRS2001

Comparison of Jovian and Terrestrial Lightning as Observed from Space

We compare the images of Jovian lightning taken by Galileo spacecraft with the images of terrestrial lightning observed by Lightning Imaging Spectrometer (LIS) onboard the TERRA spacecraft. Both data sets have good spatial resolution: Galileo's pixel is 25 km, or about half the atmospheric scale height, LIS pixel is 3-6 km, also about half of the scale height. This good resolution allows us to see that both Jovian and terrestrial lightning spots look diffuse because of the scattering in the clouds above. Previously we used the appearance of the diffuse spots on Jupiter to model lightning depths and the opacity and shape of the overlying clouds (Dyudina and Ingersoll, 2000). The comparison with LIS data allowed us to verify that the model is valid for terrestrial lightning. The irregular shapes of large terrestrial lightning suggests 30-km scale horizontal bolts. On Jupiter the spots, projected onto the horizontal plane, are nearly circular suggesting that the large size of the spots is mostly due to the horizontal diffusion of the photons scattered in the clouds. Unlike the Galileo observations, LIS has fine temporal resolution of 2 ms, or about 250 frames per single lightning flash. We will discuss the temporal evolution of terrestrial flashes and its implications for Jupiter.

Dyudina, Ulyana A.↗

NASA NTRS2006

Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor: Clustering Algorithm - Part 2

We describe the clustering algorithm used by the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) for combining the lightning pulse data into events, groups, flashes, and areas. Events are single pixels that exceed the LIS/OTD background level during a single frame (2 ms). Groups are clusters of events that occur within the same frame and in adjacent pixels. Flashes are clusters of groups that occur within 330 ms and either 5.5 km (for LIS) or 16.5 km (for OTD) of each other. Areas are clusters of flashes that occur within 16.5 km of each other. Many investigators are utilizing the LIS/OTD flash data; therefore, we test how variations in the algorithms for the event group and group-flash clustering affect the flash count for a subset of the LIS data. We divided the subset into areas with low (1-3), medium (4-15), high (16-63), and very high (64+) flashes to see how changes in the clustering parameters affect the flash rates in these different sizes of areas. We found that as long as the cluster parameters are within about a factor of two of the current values, the flash counts do not change by more than about 20%. Therefore, the flash clustering algorithm used by the LIS and OTD sensors create flash rates that are relatively insensitive to reasonable variations in the clustering algorithms.

Mach, Douglas M.↗

NASA NTRS2009

Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S.

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within physics parameterizations, model resolution limitations, as well as uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and temperature, ground fluxes, and vegetation are necessary to better simulate the interactions between the land surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection. The NASA Short-term Prediction Research and Transition (SPORT) Center has been conducting studies to examine the impacts of high-resolution land surface initialization data generated by offline simulations of the NASA Land Informatiot~ System (LIS) on subsequent numerical forecasts using the Weather Research and Forecasting (WRF) model (Case et al. 2008, to appear in the Journal of Hydrometeorology). Case et al. presents improvements to simulated sea breezes and surface verification statistics over Florida by initializing WRF with land surface variables from an offline LIS spin-up run, conducted on the exact WRF domain and resolution. The current project extends the previous work over Florida, focusing on selected case studies of typical pulse convection over the southeastern U.S., with an emphasis on improving local short-term WRF simulations over the Mobile, AL and Miami, FL NWS county warning areas. Future efforts may involve examining the impacts of assimilating remotely-sensed soil moisture data, and/or introducing weekly greenness vegetation fraction composites (as opposed to monthly climatologies) into ol'fline NASA LIS runs. Based on positive impacts, the offline LIS runs could be transitioned into an operational mode, providing land surface initialization data to NWS forecast offices in real time.

Case, Jonathan L.↗

NASA NTRS2011

The 13 years of TRMM Lightning Imaging Sensor: From Individual Flash Characteristics to Decadal Tendencies

How often lightning strikes the Earth has been the object of interest and research for decades. Several authors estimated different global flash rates using ground-based instruments, but it has been the satellite era that enabled us to monitor lightning thunderstorm activity on the time and place that lightning exactly occurs. Launched into space as a component of NASA s Tropical Rainfall Measuring Mission (TRMM) satellite, in November 1997, the Lighting Imaging Sensor (LIS) is still operating. LIS detects total lightning (i.e., intracloud and cloud-to-ground) from space in a low-earth orbit (35deg orbit). LIS has collected lightning measurements for 13 years (1998-2010) and here we present a fully revised and current total lightning climatology over the tropics. Our analysis includes the individual flash characteristics (number of events and groups, total radiance, area footprint, etc.), composite climatological maps, and trends for the observed total lightning during these 13 years. We have identified differences in the energetics of the flashes and/or the optical scattering properties of the storms cells due to cell-relative variations in microphysics and kinematics (i.e., convective or stratiform rainfall). On the climatological total lightning maps we found a dependency on the scale of analysis (resolution) in identifying the lightning maximums in the tropics. The analysis of total lightning trends observed by LIS from 1998 to 2010 in different temporal (annual and seasonal) and spatial (large and regional) scales, showed no systematic trends in the median to lower-end of the distributions, but most places in the tropics presented a decrease in the highest total lightning flash rates (higher-end of the distributions).

Albrecht, R. I.↗

NASA NTRS2011

Improving Numerical Weather Predictions of Summertime Precipitation Over the Southeastern U.S. Through a High-Resolution Initialization of the Surface State

It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high resolution models. This paper presents model verification results of a case study period from June-August 2008 over the Southeastern U.S. using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the NASA Land Information System (LIS) and sea surface temperature (SST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spin-up run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer, but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS/MODIS data substantially impact surface and boundary layer properties.

Case, Jonathan L.↗

NASA NTRS2011

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Kumar, Sujay V.↗

NASA NTRS2015

SMOS Soil Moisture Data Assimilation in the NASA Land Information System: Impact on LSM Initialization and NWP Forecasts

Land surface models are important components of numerical weather prediction (NWP) models, partitioning incoming energy into latent and sensitive heat fluxes that affect boundary layer growth and destabilization. During warm-season months, diurnal heating and convective initiation depend strongly on evapotranspiration and available boundary layer moisture, which are substantially affected by soil moisture content. Therefore, to properly simulate warm-season processes in NWP models, an accurate initialization of the land surface state is important for accurately depicting the exchange of heat and moisture between the surface and boundary layer. In this study, soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) satellite radiometer are assimilated into the Noah Land Surface Model via an Ensemble Kalman Filter embedded within the NASA Land Information System (LIS) software framework. The output from LIS-Noah is subsequently used to initialize runs of the Weather Research and Forecasting (WRF) NWP model. The impact of assimilating SMOS retrievals is assessed by initializing the WRF model with LIS-Noah output obtained with and without SMOS data assimilation. The southeastern United States is used as the domain for a preliminary case study. During the summer months, there is extensive irrigation in the lower Mississippi Valley for rice and other crops. The irrigation is not represented in the meteorological forcing used to drive the LIS-Noah integration, but the irrigated areas show up clearly in the SMOS soil moisture retrievals, resulting in a case with a large difference in initial soil moisture conditions. The impact of SMOS data assimilation on both Noah soil moisture fields and on short-term (0-48 hour) WRF weather forecasts will be presented.

Blankenship, Clay↗

NASA NTRS2021

Illinois Disasters: Utilizing NASA Earth Observations to Enhance Drought Monitoring in Illinois

Drought and flooding in Illinois have severe impacts on the communities and ecosystems of the state. Soil moisture is a valuable indicator of drought and flood vulnerability but can be difficult to measure since in situ monitoring is limited to discrete stations throughout the state. The team created a framework to compare in situ, modeled, and NASA satellite soil moisture measurements to increase the spatial coverage of soil moisture monitoring. The team partnered with the Illinois State Weather Survey, USDA Midwest Climate Hub, NOAA Regional Climate Services of the Central Region, NOAA National Integrated Drought Information System’s Midwest Drought Early Warning System, and the NOAA North Central River ForecastCenter. The team standardized and compared soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission, modeled soil moisture outputs from NASA’s SPoRT Land Information System (SPoRT-LIS), and in situ measurements from the Illinois State Weather Survey’s Water and Atmospheric Resources Monitoring (WARM) program. Compared to the WARM data, the satellite and modeled data showed seasonally variable differences and bias. The difference was highest in the winter months and lowest in the late summer and early fall months for the SMAP and SPoRT-LIS data products. SPoRT-LIS produced lower seasonal variability and SMAP demonstrated higher correlation values and lower differences. These analyses suggest that both SMAP and SPoRT-LIS products offer unique strengths and limitations when used for soil moisture monitoring

Joshua Green↗

NASA NTRS1990

Lightning observations from space shuttle

The experimental program of the Earth Sciences and Applications Division at NASA/MSFC includes development of the Lightning Imaging Sensor (LIS) for the NOAA Earth Observing System (EOS) Polar Platform. The research plan is to use existing lightning information to generate simulated data for the LIS experiment. Navigation algorithms were used to transform pixel locations to latitude and longitude values. The simulated data would then be used to test and develop algorithms for the analysis of LIS data. Individual frames of video imagery obtained from Space Shuttle Missions provide the raw data for the simulation. Individual video frames were digitized to get the pixel locations of lightning flashes. The pixel locations will be used to locate the geographical position of the event. Because of a lack of detailed knowledge of camera orientation with respect to the Space Shuttle, video scenes that contain identifiable city lights were chosen for analysis. A method for locating the payload bay camera axis was developed and tested. Two measurements are needed: the pixel location of the apparent horizon and a timed siting of a known location passing the principal line of the image. Individual video frames were navigated and lightning illuminated clouds were located on the map. Satisfactory agreement in location was achieved for cities and LLP lightning locations. Ground truth measurements were compared to satellite observations. A vertical lightning event was identified on the horizon. Very low frequency (VLF) transmission on this particular occassion shows a strong response to negative cloud to cloud flashes.

Boeck, William L.↗

NASA NTRS1993

Laser-initiated ordnance for air-to-air missiles

McDonnell Douglas Missile Systems Company (MDMSC) has developed a laser ignition subsystem (LIS) for air-to-air missile applications. The MDMSC subsystem is designed to activate batteries, unlock fins, and sequence propulsion system events. The subsystem includes Pyro Zirconium Pump (PZP) lasers, mechanical Safe & Arm, fiber-optic distribution system, and optically activated pyrotechnic devices (initiators, detonators, and thermal batteries). The LIS design has incorporated testability features for the laser modules, drive electronics, fiber-optics, and pyrotechnics. Several of the LIS have been fabricated and have supported thermal battery testing, integral rocket ramjet testing, and have been integrated into integral rocket ramjet flight test vehicles as part of the flight control subsystem.

Sumpter, David R.↗

NASA NTRS2003

Preliminary Optical And Electric Field Pulse Statistics From Storm Overflights During The Altus Cumulus Electrification Study

The Altus Cumulus Electrification Study (ACES) was conducted during the month of August, 2002 in an area near Key West, Florida. One of the goals of this uninhabited aerial vehicle (UAV) study was to collect high resolution optical pulse and electric field data from thunderstorms. During the month long campaign, we acquired 5294 lightning generated optical pulses with associated electric field changes. Most of these observations were made while close to the top of the storms. We found filtered mean and median 10-10% optical pulse widths of 875 and 830 microns respectively while the 50-50% mean and median optical pulse widths are 422 and 365 microns respectively. These values are similar to previous results as are the 10-90% mean and median rise times of 327 and 265 microns. The peak electrical to optical pulse delay mean and median were 209 and 145 microns which is longer than one would expect from theoretical results. The results of the pulse analysis will contribute to further validation of the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS) satellites. Pre-launch estimates of the flash detection efficiency were based on a small sample of optical pulse measurements associated with less than 350 lightning discharges collected by NASA U-2 aircraft in the early 1980s. Preliminary analyses of the ACES measurements show that we have greatly increased the number of optical pulses available for validation of the LIS and other orbital lightning optical sensors. Since the Altus was often close to the cloud tops, many of the optical pulses are from low-energy pulses. From these low-energy pulses, we can determine the fraction of optical lightning pulses below the thresholds of LIS, OTD, and any future satellite-based optical sensors such as the geostationary Lightning Mapping Sensor.

Mach, D. A.↗

NASA NTRS2007

Observations of Lightning on Earth from the Lunar Surface

The NASA Optical Transient Detector (OTD) launched into a 70deg inclination orbit in April 1995 aboard the MicroLab-1 satellite and the Lightning Imaging Sensor (LIS) launched into a 35deg inclination orbit in November 1997 (and still operating today) aboard the Tropical Rainfall Measuring Mission have produced the most comprehensive global observations of lightning activity on Earth. The OTD collected data for 5-yr from an altitude of 740 km while the LIS, in its 10th year of operations, is still collecting data from its current altitude of 402 km. From these altitudes the OTD observes an individual storm within its field of view for approx.3 min and the LIS for approx.90 sec as the satellites orbit the earth. Figures 1-4 show the combined LIS/OTD distribution of lightning for day and night during the Northern Hemisphere warm season from April through August (Fig. 1,2) and the cool season from October through February (Fig. 3,4) as might be observed from the lunar surface (12-h daylight and 12-h nighttime observations). The day and night plots are for the twelve hour periods centered on local noon and midnight. The total viewtime of the global lightning activity is 200 hours or less, depending on latitude (Fig. 5). Most of the observed lightning occurs over the northern hemisphere land areas as reported in previous studies. More lightning activity is seen at the higher northern latitudes during the day. The greatest lightning maxima occurs in the southeastern US, during the day. The corresponding region at night shows much less lightning activity. In contrast, there is a maxima in lightning activity at night over the high Plains area of the U.S. This region had lower lightning rates during the daytime period. During the cold season, the southern hemisphere has significantly more lightning. The maxima in Central Africa is still present, and a secondary maxima is observed in South Africa. In South America, the maxima in Argentina occurs at night in association with large-scale mesoscale convective storm complexes. This is the region on the earth having the greatest frequency of extreme storms with flash rates exceeding 1000 flashes/min. daytime maxima is seen extending from Northern Argentina to Brazil. In the US., the Gulf of Mexico and the Gulf Coast states exhibit a maximum in lightning activity both day and night.

Goodman, S. J.↗

NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗

NASA NTRS2009

Modeling and Observational Framework for Diagnosing Local Land-Atmosphere Coupling on Diurnal Time Scales

Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph A., Jr.↗

NASA NTRS2008

Total Lightning Observations of Extreme Weather Events over the Contiguous United States

The overall objective is to investigate total lightning characteristics of extreme weather events over the contiguous United States (CONUS) using TRMM (Tropical Rainfall Measuring Mission) LIS (Lightning Image Sensor) and OTD (Optical Transient Detector) satellite observations. A large LIS (10+ years) and OTD (5 years) data base is available to study the instantaneous total or cloud-to-ground (CG) plus intracloud (IC) lightning characteristics of extreme weather events. More specifically, the LIS and OTD data are combined with National Lightning Detection Network (NLDN) observations to examine the total and CG lightning flash rate and density, the IC:CG ratio, and positive CG percentage. These instantaneous lightning characteristics can be used for basic science studies to better understand the physical and dynamical linkages between lightning and precipitation and their environmental controls. They can also provide a first-look of extreme weather events leading up to future satellite observations (e.g., NOAA GOES-R Geostationary Lightning Mapper [GLM]) for use in climate studies and the short-term prediction and warning process. Extreme weather events are defined by the NOAA Storm Data reports of tornadoes, large hail (at least 0.75 inch) and strong straight-line winds (at least 50 kts). Over CONUS, there are over 70,000 severe storm reports in the TRMM spatial domain (< 35 N) from 1998-2007 and over 100,000 storm reports in the OTD spatial domain (5/1995-4/2000). Temporal co-location is on the order of 1% (i.e., 1000 s of coincident overpasses), providing a statistically significant sample of instantaneous total lightning properties. This instantaneous behavior of lightning in extreme weather is then compared to that of typical thunderstorm events, or randomly sampled LIS/OTD events in which the extreme events have been eliminated from the population. Results describing the instantaneous behavior of total lightning within a large sample of extreme and typical thunderstorms over CONUS will be presented. When possible, coincident VHF lightning observations from the ground-based Northern Alabama Lightning Mapping Array (LMA) are compared to the LIS/OTD optical lightning signatures, providing a validation source for instantaneous space-based optical lightning properties and a means to extend lightning inferences over the life-cycle of extreme weather.

Carey, Lawrence D.↗

NASA NTRS2010

Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

Kumar, Sujay V.↗

NASA NTRS2011

Global Assimilation of Multi-Sensor Snow Observations for Improved Characterization of Snow Processes

Snow conditions on the land surface are recognized to be key components of the global hydrological cycle as they play a critical role in the determination of local and regional climate. In many mid-latitude and high-latitude regions, the seasonal water storage and associated spring snowmelt dominate the local hydrology. The contribution to the runoff and moisture conditions from snow is vital in supporting agriculture and in determining water resources management practices. Consequently, accurate characterization of snow properties becomes important for both end-use applications and weather and climate research. Recently a joint effort between the u.S. Air Force and NASA has enabled a blended, multi-sensor snow product known as the AFWA NASA Snow Algorithm (ANSA). This global snow dataset has been generated by utilizing the Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) datasets. ANSA product includes estimates of snow cover extent, snow water equivalent (SWE) and SWE-derived snow depth fields. The MODIS-based products enable snow cover mappings under cloud-free conditions whereas the passive microwave data from AMSR-E provides measurements under cloudy conditions. These remotely-sensed snow observations are further augmented with the information from ground-based snow measurements through data fusion techniques. The resulting ANSA products are employed in the NASA Land Information System (LIS) data assimilation framework, which provides a comprehensive environment for integrating community land surface models, ground and satellite-based observations, and ensemble-based data assimilation tools. LIS incorporates the multisensor ANSA snow retrievals with the land surface model estimates to generate spatially and temporally continuous estimates of snow states, through data assimilation. A suite of experiments to assimilate ANSA snow cover, SWE and snow depth estimates with different land surface models in LIS are conducted and the resulting estimates of snow conditions are evaluated against a number of in-situ observational datasets, over several regions of the world. These evaluations are used to compare and contrast the advantages and disadvantages of these multi-sensor snow observations.

Kumar, Sujay↗