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Enhancing model‐based land surface temperature estimates using multiplatform microwave observations
Author(s) -
Holmes Thomas R. H.,
Crow Wade T.,
Tugrul Yilmaz M.,
Jackson Thomas J.,
Basara Jeffrey B.
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50113
Subject(s) - data assimilation , environmental science , microwave , mean squared error , terrain , remote sensing , satellite , numerical weather prediction , amplitude , meteorology , temperature measurement , approximation error , computer science , algorithm , mathematics , statistics , geography , physics , telecommunications , cartography , quantum mechanics , astronomy
Land surface temperature plays an important role in surface processes and is a key input for physically based retrieval algorithms of soil moisture and evaporation. This study presents a framework for using independent estimates of land surface temperature from five microwave satellite sensors to improve the accuracy of land surface temperature output from a numerical weather prediction system in an off‐line (postprocessing) analysis. First, structural differences in timing and amplitude of the temperature signal were addressed. Then, satellite observations were assimilated into an auto‐regressive error model, formulated to estimate errors in the numerical weather prediction output. Errors in daily minimum and amplitude were treated separately. Results of this study provide new insights about potential added benefits of preprocessing and off‐line assimilation of microwave remote sensing‐based and model‐based temperature retrievals. It is shown that the satellite observations may be used to reduce errors in surface temperature, particularly for day‐time hours. Preprocessing is responsible for the bulk of this reduction in temperature error; data assimilation is shown to further reduce the random temperature error by a few tenths of a Kelvin, accounting for a 10% reduction in RMSE.