|HAIN, C - National Oceanic & Atmospheric Administration (NOAA)
|ZHAN, X - National Oceanic & Atmospheric Administration (NOAA)
|WARDLOW, B - University Of Nebraska
|SVOBODA, M - University Of Nebraska
|MECIKALSKI, J - University Of Alabama
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/2011
Publication Date: 12/14/2011
Citation: Hain, C., Crow, W.T., Anderson, M.C., Zhan, X., Wardlow, B., Svoboda, M., Mecikalski, J. 2011. Dual assimilation of microwave and thermal-infrared satellite observations of soil moisture into NLDAS for improved drought monitoring [abstract]. Meeting Abstract. 2012 CDROM.
Technical Abstract: Our research group is currently developing an operational data assimilation (DA) system for the optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time green vegetation fraction (GVF) into the Noah land-surface model component of the National Land Data Assimilation System (NLDAS). NLDAS produces the hydrologic products (e.g. soil moisture, evapotranspiration, and runoff) used by NCEP for operational drought monitoring, but these products are sensitive to model input errors in soil texture (affecting infiltration rates) and prescribed precipitation rates. Periodic updates of SM state variables in LSMs achieved by assimilating diagnostic moisture information retrieved using satellite remote sensing have been shown to compensate for model errors and result in improved hydrologic output. The work proposed here will build on a project currently funded under the Climate Test Bed Program entitled “A GOES Thermal-Based Drought Early Warning Index for NIDIS”, which is developing an operational TIR SM index (Evaporative Stress Index; ESI) based on maps of the ratio of actual to potential ET (fPET) generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm. The research team has demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can significantly reduce SM drifts in LSMs such as Noah. The two different SM retrievals have been shown to be quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25 to 60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root-zone, reflected in the observed canopy temperature. The added value of TIR over MW alone is most significant in areas of moderate to dense vegetation cover where MW retrievals have very little sensitivity to SM at any depth. Finally, climatological estimates of GVF currently used in the operational NLDAS are not always representative of observed seasonal and intra-seasonal GVF conditions, especially in regions experiencing drought conditions. A detailed methodology of the assimilation system will be presented along with an analysis of initial results, with an emphasis on comparisons with in-situ SM observations and standard drought metrics.