Research Papers

Assimilation of rain-affected radiances with an adjoint model

[+] Author Affiliations
Clark Amerault

Marine Meteorology Division, Naval Research Laboratory, 7 Grace Hopper Ave, Monterey, CA 93943

Xiaolei Zou

The Department of Meteorology, Florida State University, Room 404 Love Building, Tallahassee, FL 32306-4520

James Doyle

Marine Meteorology Division, Naval Research Laboratory, 7 Grace Hopper Ave, Monterey, CA 93943

J. Appl. Remote Sens. 3(1), 033531 (May 21, 2009). doi:10.1117/1.3153332
History: Received October 14, 2008; Revised April 24, 2009; Accepted May 12, 2009; May 21, 2009; Online May 21, 2009
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Abstract

The ability to assimilate microwave radiance observations of the Earth's atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Clark Amerault ; Xiaolei Zou and James Doyle
"Assimilation of rain-affected radiances with an adjoint model", J. Appl. Remote Sens. 3(1), 033531 (May 21, 2009). ; http://dx.doi.org/10.1117/1.3153332


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