Displaced Ensemble Variational Assimilation Method to Incorporate Microwave Imager Brightness Temperatures into a Cloud-resolving Model

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 (9 th 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.

The JMA Nonhydrostatic Model and Its Applications to Operation and Research

“…As described in the previous section, high resolution data assimilation methods (e.g., 4D-VAR and LETKF) have been developed and applied to case studies of cloud resolving forecast experiments of precipitation. In addition, a displaced ensemble variational assimilation method has also been developed and tested for a data assimilation experiment on satellite microwave imager data (Aonashi and Eito, 2011).…”

Typhoon-Position-Oriented Sensitivity Analysis. Part I: Theory and Verification

“…TyPOS is based on the ensemble-based sensitivity analysis recently developed by several researchers (Martin and Xue 2006;Ancell and Hakim 2007;Torn andHakim 2008, 2009;Liu et al 2008;Gombos and Hansen 2008;Sugiura 2010;Aonashi and Eito 2011). Here, we briefly outline this sensitivity analysis technique in a generalized manner.…”

“…This technique is based on the ensemble-based sensitivity analysis method (Martin and Xue 2006;Ancell and Hakim 2007;Torn andHakim 2008, 2009;Liu et al 2008;Gombos and Hansen 2008;Sugiura 2010;Aonashi and Eito 2011). It has been applied to the sensitivity analysis of the minimum sea level pressure (SLP) and the rootmean-square error of SLP of TCs (Torn and Hakim 2009) as well as the 1000-hPa potential vorticity (Gombos et al 2012).…”

“…It has been applied to the sensitivity analysis of the minimum sea level pressure (SLP) and the rootmean-square error of SLP of TCs (Torn and Hakim 2009) as well as the 1000-hPa potential vorticity (Gombos et al 2012). The ensemble-based sensitivity analysis has also been used to correct the displacement error suitable for real-world observations using the data assimilation technique (Aonashi and Eito 2011). These approaches motivate us to calculate the sensitivity field by employing TC position directly as a metric.…”

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

“…To reduce this error, spatial localization should be used in EnVAR as well as in EnKF. In some EnVAR systems (e.g., Buehner 2005;Liu et al 2009;Aonashi and Eito 2011), spatial localization has been applied to non-diagonal components of the forecast error covariance matrix (B localization). To improve the performance of parallel processing in making calculations, however, it is better to apply observation localization, the practice adopted in local ensemble transform Kalman filter (LETKF, Hunt et al 2007) method.…”