The Application of AMSR-E Soil Moisture for Improved Global Agricultural Assessment and Forecasting
ABSTRACT Soil moisture is estimated by the U. S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) by utilizing a modified two-layer Palmer water balance model derived from temperature and precipitation observations. It is envisaged that these soil moisture estimates can be improved by integrating passive microwave data which has greater temporal frequency and covers larger spatial domains than available in the past. By integrating direct observations from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the current PECAD soil moisture model, more accurate soil moisture and correspondingly crop yield estimates may be possible. This paper presents a methodology for soil moisture data assimilation using a simple bias correction and 1D Ensemble Kalman Filter data assimilation algorithm. An outline of the technical approach is presented.
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ABSTRACT: Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.Monthly Weather Review 01/2002; DOI:10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2 · 3.62 Impact Factor
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ABSTRACT: Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by wing spatial sampling with a 2 degree moving window we can obtain local statistics based on a one-year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.09/2004; DOI:10.1029/2004GL020938
Edited by Richard G. Allen, Luis S. Pereira, Dirk Raes, Martin Smith, 01/1998; FAO, Rome.