Enhancing model-based land surface temperature estimates using multiplatform microwave observations

Date

2013-01-01

Author

Holmes, Thomas R. H.
Crow, Wade T.
Yılmaz, Mustafa Tuğrul
Jackson, Thomas J.
Basara, Jeffrey B.

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Data assimilation, Soil-moisture, Carbon budget, Forest, Environment, Validation, Ecosystem

URI

https://hdl.handle.net/11511/41626

Collections

Department of Civil Engineering, Article