资源环境科技发展态势分析平台

This study aims to improve precipitation forecasts by estimating model parameters of a numerical weather prediction model with an ensemble-based data assimilation method. We implemented the parameter estimation algorithm into a global atmospheric data assimilation system NICAM-LETKF, which incorporates Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study estimated a globally uniform model parameter of a large-scale condensation scheme known as the B-1 parameter of Berry's parameterization. We conducted an online estimation of the B-1 parameter using the Global Satellite Mapping of Precipitation (GSMaP) data and successfully reduced NICAM's precipitation forecast bias relative to the GSMaP data, especially for weak rains. The estimated B-1 parameter evolved toward the optimal value obtained by manual tuning. The parameter estimation also mitigated a dry bias for the lower troposphere in the Tropics. However, the estimated B-1 intensified biases for cloud water mixing ratio and outgoing long-wave radiation in the regions where shallow clouds are dominant. This is because only precipitation data were used to estimate the optimal value of B-1, and more constraints will be required to obtain a suitable value for climatological simulations.