Online Model Parameter Estimation With Ensemble Data Assimilation in the Real Global Atmosphere: A Case With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data

Abstract

AbstractThis 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 B1 parameter of Berry's parameterization. We conducted an online estimation of the B1 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 B1 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 B1 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 B1, and more constraints will be required to obtain a suitable value for climatological simulations.