An operational nonhydrostatic mesoscale model has been developed by the Numerical Prediction Division (NPD) of the Japan Meteorological Agency (JMA) in partnership with the Meteorological Research Institute (MRI). The model is based on the MRI/NPD unified nonhydrostatic model (MRI/NPD-NHM), while several modifications have been made for operational numerical weather prediction with a horizontal resolution of 10 km. A fourth-order advection scheme considering staggered grid configuration is implemented. The buoyancy term is directly evaluated from density perturbation. A time-splitting scheme for advection has been developed, where the low-order (second order) part of advection is modified in the latter half of the leapfrog time integration. Physical processes have also been revised, especially in the convective parameterization and PBL schemes. A turbulent kinetic energy (TKE) diagnostic scheme has been developed to overcome problems that arise to predict TKE. The model performance for mesoscale NWP has been verified by comparison with a former operational hydrostatic mesoscale model of JMA. It is found that the new nonhydrostatic mesoscale model outperforms the hydrostatic model in the prediction of synoptic fields and quantitative precipitation forecasts.
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.
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