トップページ    計算科学研究センターとは    人材育成    計算科学eラーニング    データ同化    International Symposium/School on Data Assimilation    International Symposium on Data Assimilation-Online (ISDA-Online)    "Assimilation of GPM DPR Spectral Latent Heating using Vertical Correlated Observation Error Covariance in Kalman Gain"

The International Symposium on Data Assimilation Online (ISDA-Online) Feb 5, 2021
"Satellite Data Assimilation"
International Symposium on Data Assimilation - Online

The observation error covariance matrix is often approximated with a diagonal matrix when assimilating observation data. However, observations about the vertical distribution of precipitation such as space-borne radars have an observation error correlation which cannot be ignored in the vertical. In addition, the structure of the correlation matrix depends on the environment of precipitation such as deep convection and stable stratification.

To incorporate this correlation and dependency, we directly calculated the Kalman gain including the correlated observation error using the Moor-Penrose inverse matrix for each precipitation type, and investigated the impact of full or diagonal observation error covariance matrix in a data assimilation system. In this study, we investigated the impact of assimilating GPM DPR Spectral Latent Heating (GPM-SLH) by a nudging method with Kalman gain including the correlated observation error. The NWP model used in the experiment was Local Forecast Model (LFM) operated by JMA for short range precipitation forecasts and aviation weather forecasts. As a result of the assimilation experiments, we found that the observation error covariance matrix of SLH has a characteristic structure depending on the precipitation type and plays an important role in the assimilation of dense observation data without vertical thinning. Assimilation of SLH significantly improves the forecast of deep convective precipitation in the summer season. However, it was shown ineffective for shallow convective clouds over the ocean in the winter season. In this presentation, we will show the characteristics of the SLH observation error covariance matrix and demonstrate the
detail of impacts on assimilation and prediction results.

## 講師プロフィール

Yasutaka IKUTA

Meteorological Research Institute, Japan Meteorological Agency

（2021年2月5日）