Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
Articles
Displaced Ensemble Variational Assimilation Method to Incorporate Microwave Imager Brightness Temperatures into a Cloud-resolving Model
Kazumasa AONASHIHisaki EITO
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2011 Volume 89 Issue 3 Pages 175-194

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Abstract

The goal of the present study is to develop a method to assimilate Microwave Imager (MWI) brightness temperatures (TBs) into Cloud-Resolving Models (CRMs). To address the non-linear relationship of TBs to the state variables of CRM and the flow-dependency of the CRM forecast error covariance, we adopted an ensemble-based variational data assimilation method. However, there often exist large-scale displacement errors of rainy areas between the observation and CRM forecasts. In such cases, ensemble-based data assimilation can give erroneous analysis, particularly for observed rain areas without forecasted rain. In order to solve this problem, we propose ensemble-based assimilation that uses ensemble forecast error covariance with displacement error correction. Based on this idea, we developed a data assimilation method that incorporates the MWI TBs into the CRM developed by the Japan Meteorological Agency (JMANHM). This method consists of a displacement error correction scheme and an ensemble-based variational assimilation scheme. In the displacement error correction scheme, we obtained the optimum displacement that maximized the conditional probability of TB observation given the displaced CRM variables. In the assimilation scheme, we derived a cost function in the displaced ensemble forecast error subspace. Then, we obtained analyses of CRM variables by non-linear minimization of the cost function. In order to see the impact of the above MWI TB assimilation method on CRM analyses and forecasts, we performed assimilation experiments to incorporate TMI (TRMM Microwave Imager) low-frequency TBs (10, 19, and 21 GHz with vertical polarization) into the CRM for a typhoon case around Okinawa (9th June 2004). The results of the experiments show that the assimilation of TMI TBs alleviated the large-scale displacement errors and improved CRM forecasts. The displacement error correction also avoided misinterpretation of MWI TB increments due to precipitation displacements as those from other variables in the assimilation scheme.

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© 2011 by Meteorological Society of Japan
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