SEARCH · Search NASA

Results for “Land Surface Model Data”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

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↗

DOE OSTI2021

Development of observation-based global multilayer soil moisture products for 1970 to 2016

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

NASA-ISRO Synthetic Aperture Radar (NISAR) Mission

NISAR is a multi-disciplinary Earth-observing radar mission that makes global measurements of land surface changes that will greatly improve Earth system models. NISAR data will clarify spatially and temporally complex phenomena, including ecosystem disturbances, ice sheet collapse, and natural hazards including earthquakes, tsunamis, volcanoes, and landslides. It provides societally relevant data that will enable better protection of life and property. The mission, a NASA-ISRO partnership, uses two fully polarimetric SARs, one at L-band (L-SAR) and one at S-band (S-SAR), in exact repeating orbits every 12 days that allows interferometric combination of data on repeated passes. NASA provides the L SAR; a shared deployable reflector; an engineering payload that supports mission-specific data handling, navigation and communication functions; science observation planning and L SAR data processing. ISRO provides the S-SAR, spacecraft, launch vehicle, satellite operations, and S-SAR data processing. The mission will be launched from the Satish Dhawan Space Centre, Sriharikota, India. Mission development has addressed many unique challenges and incorporates many “firsts” for a jointly-developed free-flyer radar science mission.

Sarma, C V H S↗

NASA NTRS2023

Diverging Trends in Rain-on-Snow Over High Mountain Asia

Rain-on-snow (ROS) over snow-dominated regions such as High Mountain Asia (HMA) modulates snowmelt and runoff and is key contributor in influencing water availability and hazards (e.g., floods and landslides). We studied the trends in ROS in HMA over the past two decades from 2001 to 2018 using the land surface model Noah-MP driven by an ensemble precipitation data set. Our results show that changes in precipitation phase and rainfall are altering ROS. Because of the strong physical heterogeneity and atmospheric dynamics of HMA, ROS characteristics and trends are region-dependent and ROS occurs predominantly over the Indus, Ganges-Brahmaputra, and northwestern basins. In the Indus, ROS representing ∼5% of the annual precipitation and ∼20% of the annual snowmelt, has an increasing trend. This is contrary to the Ganges-Brahmaputra characterized by decreasing ROS trends, where it represents ∼11% of the annual precipitation and ∼60% of the annual snowmelt. In the northwestern basins, ROS has bidirectional trends due to elevation patterns and trends in rainfall, and it constitutes ∼5 to ∼10% of the annual precipitation. Increasing trends in ROS over Indus contribute to reducing the snowpack in late summer, with concerns of reduced water availability and increased groundwater exploitation. Similarly, because of its high amount and contribution to snowmelt, the decreasing ROS trends in the Ganges-Brahmaputra will have consequences of decreased recharge from the headwaters and exacerbated use of groundwater unless increasing trends in rainfall compensate for the decreasing snowmelt. These results provide new insights on ROS-driven changes in the hydrological cycle over HMA.

rain-on-snow↗

NASA NTRS1994

An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

Diak, George R.↗

DOE OSTI2022

Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado

The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.

2D Geospatial Maps↗

NASA NTRS1998

Real Time Land-Surface Hydrologic Modeling Over Continental US

The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

Houser, Paul R.↗

NASA NTRS2019

Hydrological Land Surface Data and Services at NASA GES DISC

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelveNASA Earth Observing System (EOS) data centers that process, archive, document, and distributedata from Earth science missions and related projects. The GES DISC hosts a wide range ofremotely-sensed and model data and provides reliable and robust data access and services to usersworldwide. This presentation, focusing on hydrological land surface data, provides a summary tablefor the hydrological data holdings, along with discussions of recent updates to data and data services.

Rui, Hualan↗

NASA NTRS2008

Global Land Data Assimilation System (GLDAS) Products from NASA Hydrology Data and Information Services Center (HDISC)

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. New data formats (e.g., netCDF), temporal averaging and spatial subsetting will be available in the future. The HDISC has the capability to support more hydrology data products and more advanced analysis tools. The goal is to develop HDISC as a data and services portal that supports weather and climate forecast, and water and energy cycle research.

Fang, Hongliang↗

NASA NTRS2009

Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.

Fang, Hongliang↗

NASA NTRS2008

Customer-oriented Data Formats and Services for Global Land Data Assimilation System (GLDAS) Products at the NASA GES DISC

Fang, Hongliang↗

NASA NTRS2016

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

soil moisture↗

NASA NTRS1984

Prescription of land-surface boundary conditions in GISS GCM 2: A simple method based on high-resolution vegetation data bases

A simple method was developed for improved prescription of seasonal surface characteristics and parameterization of land-surface processes in climate models. This method, developed for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM II), maintains the spatial variability of fine-resolution land-cover data while restricting to 8 the number of vegetation types handled in the model. This was achieved by: redefining the large number of vegetation classes in the 1 deg x 1 deg resolution Matthews (1983) vegetation data base as percentages of 8 simple types; deriving roughness length, field capacity, masking depth and seasonal, spectral reflectivity for the 8 types; and aggregating these surface features from the 1 deg x 1 deg resolution to coarser model resolutions, e.g., 8 deg latitude x 10 deg longitude or 4 deg latitude x 5 deg longitude.

Matthews, E.↗

NASA NTRS2011

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and land surface hydrology through simulation, downscaling, and data assimilation. An overview of the GPM mission, science status, and synergies with HyMex activities will be presented

Hou, Arthur Y.↗

NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domainscale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospherichydrological process within a land surface model for use in HMA.

Yuan Xue↗

NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domain-scale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospheric-hydrological process within a land surface model for use in HMA.

Yuan Xue↗

NASA NTRS2000

The Shuttle Radar Topography Mission: A Global DEM

Digital topographic data are critical for a variety of civilian, commercial, and military applications. Scientists use Digital Elevation Models (DEM) to map drainage patterns and ecosystems, and to monitor land surface changes over time. The mountain-building effects of tectonics and the climatic effects of erosion can also be modeled with DEW The data's military applications include mission planning and rehearsal, modeling and simulation. Commercial applications include determining locations for cellular phone towers, enhanced ground proximity warning systems for aircraft, and improved maps for backpackers. The Shuttle Radar Topography Mission (SRTM) (Fig. 1), is a cooperative project between NASA and the National Imagery and Mapping Agency (NIMA) of the U.S. Department of Defense. The mission is designed to use a single-pass radar interferometer to produce a digital elevation model of the Earth's land surface between about 60 degrees north and south latitude. The DEM will have 30 m pixel spacing and about 15 m vertical errors.

Farr, Tom G.↗

NASA NTRS2020

Impact of Gauge-Based Precipitation Corrections on the Skill of SMAP Level-4 Soil Moisture Estimates

The NASA Soil Moisture Active Passive (SMAP) mission provides observations of L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations at a resolution of ~40 km globally every 2-3 days. These observations are routinely assimilated into the NASA Catchment land surface model to generate the Level-4 Soil Moisture (L4_SM) product, which provides global estimates of surface and root-zone soil moisture, soil temperature, and surface fluxes (among others) at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment land surface model in the L4_SM algorithm is driven with 0.25Â°, hourly surface meteorological forcing data from the NASA Goddard Earth Observing System (GEOS) "forward-processing" product. Outside of Africa and the high latitudes, the GEOS precipitation forcing is corrected using the Climate Prediction Center Unified (CPCU) gauge-based, 0.5Â°, daily precipitation product.Soil moisture estimates from the L4_SM product were previously shown to improve over land model-only estimates that do not benefit from the assimilation of Tb observations, thereby demonstrating the value of assimilating SMAP observations for soil moisture estimation. In this presentation, we further isolate the contribution of the gauge-based precipitation corrections to the skill of the L4_SM soil moisture estimates. Specifically, we compare the skill of the L4_SM soil moisture to that of separate model-only and assimilation estimates obtained without the benefit of the gauge-based precipitation corrections.Preliminary results suggest that the soil moisture skill added by the CPCU-based precipitation corrections primarily depends on the quality of the CPCU precipitation product and is greatest in regions where the CPCU gauge network is dense and reliable. Conversely, in regions where the CPCU product is known to be of poor quality, for example in central Australia, the assimilation of SMAP Tb observations provides the most benefit. The presentation will provide an in-depth evaluation of the soil moisture skill of the model-only and assimilation estimates vs. independent in situ and satellite measurements.

Reichle, Rolf↗