9.2 Evaluation of Passive Microwave Land Surface Emissivities for Improved Precipitation Retrievals Over Land for GPM-era Algorithms—Part II: Forward Modeling and Sensitivity to Land Surface Snowpack, Soil Moisture and Temperature

Thursday, 27 January 2011: 8:45 AM
611 (Washington State Convention Center)
Christa D. Peters-Lidard, NASA/GSFC, Greenbelt, MD; and R. R. Ferraro, G. Skofronick-Jackson, K. W. Harrison, Y. Tian, and L. S. W. Group

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group (LSWG). Specifically, and as a follow-up to the companion abstract submitted by Ferraro et al., we will demonstrate the performance of a forward approach for land surface microwave emission modeling using the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et al., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Delst, 2009). The PMM LSWG has selected 12 Targets/9 types of surfaces to intercompare surface microwave emission estimates from a variety of techniques at the frequencies relevant to GPM, with an initial study period defined as a single year, from 1 July 06 – 30 June 07. Several satellite datasets have been assembled, including AMSR-E, SSMI, SSMIS, TMI, AMSU, and WindSat, as well as ancillary satellite data including ISCCP, PR/VIRS, CloudSat, and model fields including GDAS, the GLDAS and NLDAS land surface modeling systems, and JCSDA emissivities. In addition to the evaluation of the forward model against the inverse approaches, we will demonstrate and evaluate the sensitivity of the surface emission, including polarization differences, to land surface states such as surface snowpack, soil moisture and soil temperature.
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