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  • 1
    Publication Date: 2019-07-12
    Description: Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC.JA.5386.2011
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  • 2
    Publication Date: 2019-07-19
    Description: Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC.ABS.6719.2012 , Pan-GASS Conference; Sep 10, 2012 - Sep 14, 2012; Boulder, CO; United States
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  • 3
    Publication Date: 2019-07-19
    Description: 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.
    Keywords: Earth Resources and Remote Sensing
    Type: International Workshop on Data Assimilation for Operational Hydrological Forecasting and Water Resources Management; Oct 30, 2010 - Nov 04, 2010; Delft; Netherlands
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  • 4
    Publication Date: 2019-07-13
    Description: Several independent measurements of warmseason soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of landatmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operated in both a freerunning mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its freerunning counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. These results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN48412 , Journal of Geophysical Research: Atmospheres (ISSN 2169-897X) (e-ISSN 2169-8996); 122; 21; 11,524-11,548; November 16, 2017
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  • 5
    Publication Date: 2019-07-13
    Description: Evapotranspiration (ET) is a critical component of the Earth's water budget, a critical modulator of land-atmosphere (L-A) interactions, and also plays a crucial role in managing the Earth's energy balance. In this study, the feasibility of generating spatially-continuous daily evaporative fraction (EF) and ET from minimal remotely-sensed and meteorological inputs in a trapezoidal framework is demonstrated. A total of four variables, Normalized Difference Vegetation Index (NDVI), Land surface temperature (T(sub s)), gridded daily average temperature (T(sub a)) and elevation (z) are required to estimate EF. Then, ET can be estimated with the available soil heat flux (G) and net radiation (Rn) data. Firstly, the crucial model variable, T(sub s)-T(sub a), is examined how well it characterizes the variation in EF using in situ data recorded at two eddy correlation flux towers in Southern Great Plains, U.S.A. in 2011. Next, accuracy of satellite-based T(sub s) are compared to ground-based T(sub s). Finally, EF and ET estimates are validated. The results reveal that the model performed satisfactorily in modeling EF and ET variation at winter wheat and deciduous forest during the high evaporative months. Even though the model works best with the observed MODIS-T(sub s) as opposed to temporally interpolated T(sub s), results obtained from interpolated T(sub s) are able to close the gaps with reasonable accuracy. Due to the fact that T(sub s)-T(sub a), is not a good indicator of EF outside the growing season when deciduous forest is dormant, potential improvements to the model are proposed to improve accuracy in EF and ET estimates at the expense of adding more variables.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN46896 , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (ISSN 1939-1404) (e-ISSN 2151-1535); 11; 1; 12-23
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  • 6
    Publication Date: 2019-07-13
    Description: We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN50582 , Journal of Hydrometeorology (ISSN 1525-755X) (e-ISSN 1575-7541); 19; 2; 375–392
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  • 7
    Publication Date: 2019-09-11
    Description: Heterogeneity in warm-season (May-August) land-atmosphere (LA) coupling is quantified with the long-time, multiple-station measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and the moderate-resolution imaging spectroradiometer (MODIS) satellite remote sensing at the Southern Great Plains (SGP). We examine the coupling strength at 7 additional locations with the same surface type (i.e., pasture/grassland) as the ARM SGP central facility (CF). To simultaneously consider multiple factors and consistently quantify their relative contributions, we apply a multiple linear regression method to correlate the surface evaporative fraction (EF) with near-surface soil moisture (SM) and leaf area index (LAI). The observations show moderate to weak terrestrial segment LA coupling with large heterogeneity across the ARM SGP domain in warm-season. Large spatial variabilities in the contributions from SM and LAI to the EF changes are also found. The coupling heterogeneities appear to be associated with differences in land use, anthropogenic activities, rooting depth, and soil type at different stations. Therefore, the complex LA interactions at the SGP cannot be well represented by those at the CF/E13 based on the metrics applied here. Overall, the LAI exerts more influence on the EF than does the SM due to its overwhelming impacts on the latent heat flux. This study complements previous studies based on measurements only from the CF and has important implications for modeling LA coupling in weather and climate models. The multiple linear regression provides a more comprehensive measure of the integrated impacts on LA coupling from several different factors.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN60272 , Journal of Geophysical Research: Atmospheres (ISSN 2169-897X) (e-ISSN 2169-8996); 123; 15; 7867-7882
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  • 8
    Publication Date: 2019-11-06
    Description: This study evaluates the impact of assimilating soil moisture data from NASA's Soil Moisture Active Passive (SMAP) on short-term regional weather and air quality modeling in East Asia during the Korea-US Air Quality Study (KORUS-AQ) airborne campaign. SMAP data are assimilated into the Noah land surface model using an ensemble Kalman filter approach in the Land Information System framework, which is semi-coupled with the NASA-Unified Weather Research and Forecasting model with online chemistry (NUWRF-Chem). With SMAP assimilation included, water vapor and carbon monoxide (CO) transport from northern-central China transitional climate zones to South Korea is better represented in NUWRF-Chem during two studied pollution events. Influenced by different synoptic conditions and emission patterns, impact of SMAP assimilation on modeled CO in South Korea is intense (〉30 ppbv) during one event and less significant (〈8 ppbv) during the other. SMAP assimilation impact on air quality modeling skill is complicated by other error sources such as the chemical initial and boundary conditions (IC/LBC) and emission inputs of NUWRF-Chem. Using a satellite-observation constrained chemical IC/LBC instead of a free-running, coarser-resolution chemical IC/LBC reduces modeled CO by up to 80 ppbv over South Korea. Consequently, CO performance is improved in the middle-upper troposphere whereas degraded in the lower troposphere. Remaining negative CO biases result largely from the emissions inputs. The advancements in land surface modeling and chemical IC/LBC presented here are expected to benefit future investigations on constraining emissions using observations, which can in turn enable more accurate assessments of SMAP assimilation and chemical IC/LBC impacts.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN60274 , Journal of Geophysical Research: Atmospheres (ISSN 2169-897X) (e-ISSN 2169-8996); 123; 18; 10,732-10,756
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  • 9
    Publication Date: 2019-07-12
    Description: Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.
    Keywords: Earth Resources and Remote Sensing
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  • 10
    Publication Date: 2019-07-13
    Description: 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.
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN44100 , Journal of Hydrometeorology (ISSN 1525-755X) (e-ISSN 1525-7541); 17; 2; 517-540
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