Displaced Ensemble variational assimilation method to incorporate microwave imager brightness temperatures into a cloud-resolving model

We developed a data assimilation method that incorporates the microwave imager (MWI) brightness temperatures (TBs) into the cloud-resolving model (CRM) developed by the Japan Meteorological Agency (JMANHM). This method consisted 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 the analyses of CRM variables by non-linear minimization of the cost function. We applied this method to assimilate TMI (TRMM Microwave Imager) low-frequency TBs (10, 19, and 21 GHz with vertical polarization) for a Typhoon case around Okinawa (9 June 2004). The results of the assimilation experiments showed that the assimilation of TMI TBs alleviated the large-scale displacement errors and improved the CRM forecasts.