Radiance Data Assimilation for Binary Typhoon Cases

The predictability of track and intensity for binary tropical cyclones (TCs) is relatively low due to the interaction between two TCs. In this study, radiance data were assimilated using the Three Dimensional Variational (3D-Var) data assimilation method to improve track and intensity forecasts of binary TCs. In detail, a total of three binary TC cases over the Northwestern Pacific from 2010 to 2015, which affected the Korean Peninsula, were selected. Infrared and microwave radiance data from multiple instruments and satellites were assimilated with the appropriate treatments of quality control, channel selection, bias correction, and cloud detection. The Weather Research and Forecasting (WRF) model was used as a forecasting model, and the resolution of the innermost domain was high (2 km) to capture the structure and intensity of TCs. Background error covariance was calculated using the National Meteorological Center (NMC) method, where background error statistics were from the differences between 24-h and 12-h forecasts for a month-long period. Overall, track and intensity errors of three binary TCs are reduced through the assimilation of radiance data. Especially, track forecasts are improved significantly because large-scale environments such as the North Pacific High and mid-latitude trough/ridge are simulated well. Analysis increment and its evolution are investigated to reveal the reason for the improvement of each binary TC's forecast, focusing on TC's internal structure and its environment. Additionally, effects of other observational data (e.g. radiosonde, satellite wind) are analyzed through the sensitivity experiments.