An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) is cycled and evaluated for western North Pacific (WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone (TC) minimum sea level pressure (SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient variance for TC intensity. The 6-h ensemble priors from the WRF/EnKF system tend to overestimate the intensity for weak storms but underestimate the intensity for strong storms. The 5-d deterministic forecasts launched from the ensemble mean analyses of WRF/EnKF are compared to the NCEP and ECMWF operational control forecasts. Results show that the WRF/EnKF forecasts generally have larger track errors than the NCEP and ECMWF forecasts for all TC categories because the regional simulation cannot represent the large-scale environment better than the global simulation. The WRF/EnKF forecasts produce smaller intensity errors and biases than the NCEP and ECMWF forecasts for typhoons, but the opposite is true for tropical storms and severe tropical storms. The 5-d ensemble forecasts from the WRF/EnKF system for seven typhoon cases show appropriate variance for TC track and intensity with short forecast lead times but have insufficient spread with long forecast lead times. The WRF/EnKF system provides better ensemble forecasts and higher predictability for TC intensity than the NCEP and ECMWF ensemble forecasts.

Lili LEI;Yangjinxi GE;Zhe-Min TAN;Yi ZHANG;Kekuan CHU;Xin QIU;and Qifeng QIAN

Key Laboratory of Mesoscale Severe Weather/Ministry of Education,School of Atmospheric Sciences,Nanjing University,Nanjing 210063,China;National Meteorological Center,China Meteorological Administration,Beijing 100081,China

大气科学进展（英文版）

[1]Lili LEI;Yangjinxi GE;Zhe-Min TAN;Yi ZHANG;Kekuan CHU;Xin QIU;and Qifeng QIAN-.Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction)[J].大气科学进展（英文版）,2022(11):1816-1832

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