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# Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

• 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.
摘要: 本文搭建了结合WRF模式和集合Kalman滤波 (WRF/EnKF)的同化系统，对2016年西北太平洋台风季进行了循环同化和预报，并对结果进行了评估。该WRF/EnKF同化系统使用80个集合成员，每6小时同化一次常规观测资料、卫星资料、及热带气旋(TC)的最低海平面气压。WRF/EnKF同化系统的6小时集合预报对TC路径有较为合适的方差，但对TC强度的方差估计不足；同时WRF/EnKF同化系统会高估弱TC的强度，但低估强TC的强度。将由WRF/EnKF同化系统的集合平均分析场起报的5天确定性预报与NCEP和ECMWF的控制预报相比可得，WRF/EnKF预报比NCEP 和 ECMWF 预报有更大的TC路径误差，这是因为相对于全球模式，区域模式不能更好地模拟大尺度环境。对于强TC，WRF/EnKF的预报比NCEP和ECMWF预报强度预报误差更小，但对于弱TC则相反。针对7个台风个例，WRF/EnKF同化系统所得的5天集合预报在短预报时效上对TC路径和强度均有合适的离散度，但在长预报时效上却离散度不足；并且WRF/EnKF同化系统的集合预报优于NCEP和ECMWF，对TC强度有更高的可预报性。
• Figure 1.  Tropical cyclone tracks for each of the 21 TCs studied here. See Table 1 for a detailed list of storms. The colors are used to differentiate the TC tracks.

Figure 2.  (a) 6-h forecast ensemble mean RMS error (dark gray bar) and ensemble spread (light gray bar) of TC positions as a function of TC intensity. The number of verification times is given along the top. (b) 6-h forecast ensemble mean bias of TC positions for tropical storm (TS, blue line), severe tropical storm (STS, green line), typhoon (TY, red line), and all storms (ALL, black line). The range rings denote 10-km intervals. Error bars denote the 5% and 95% percentiles determined from bootstrap resampling.

Figure 3.  (a) 6-h forecast ensemble mean RMS error (dark gray bar) and ensemble spread (light gray bar) of TC minimum SLP as a function of TC intensity. The number of verification times is given along the top. (b) 6-h forecast ensemble mean bias of TC minimum SLP as a function of TC intensity. (c) As in (a), but for the TC maximum wind speed. (d) As in (b), but for the TC maximum wind speed. Error bars denote the 5% and 95% percentiles determined from bootstrap resampling.

Figure 4.  RMS error of TC positions for 5-d forecast as a function of forecast hour for (a) TS, (b) STS, (c) TY, and (d) ALL. The blue solid line denotes the WRF/EnKF forecast launched from ensemble mean analysis, and the red and black solid lines are the NCEP GFS forecast and ECMWF forecast, respectively. The number of verification times for WRF/EnKF and NCEP is given by the first row along the top, and the number of verification times for ECMWF is given by the second row along the top. Error bars denote the 5% and 95% percentiles determined from bootstrap resampling.

Figure 5.  Biases of TC positions from 5-d forecasts for (a) TS, (b) STS, and (c) TY. The blue dot, green plus, and red square denote WRF/EnKF forecasts launched from the ensemble mean analyses, NCEP GEFS control forecasts, and ECMWF EPS control forecasts, respectively. The range rings denote 200-km intervals.

Figure 6.  Same as Fig. 4, except for RMS error of TC minimum SLP.

Figure 7.  Same as Fig. 4, except for bias of TC minimum SLP.

Figure 8.  Same as Fig. 4, except for RMS error of TC maximum wind speed.

Figure 9.  Same as Fig. 4, except for bias of TC maximum wind speed.

Figure 10.  Profiles of the differences of the mean specific humidity between WRF/EnKF and ECMWF 48-h forecasts for (a) STS and (b) TY. For each forecast, the mean specific humidity is the averaged specific humidity over an outer circle centered around each vortex with a 5° radius minus that over an inner circle with a 2° radius.

Figure 11.  The mean absolute error (solid) and ensemble spread (dashed) from ensemble forecasts of typhoons listed in Table 2 as a function of forecast hour for (a) track, (b) minimum SLP, and (c) maximum wind speed. The blue lines denote the WRF/EnKF forecast, and the red and black lines displays the NCEP GEFS and ECMWF EPS forecasts, respectively.

Figure 12.  5-d ensemble forecast (a) tracks, (b) minimum SLP, and (c) maximum wind speed for typhoon Meranti from 0000 UTC 10 September. The thin blue, red, and green lines show the forecast values of the WRF/EnKF, NCEP, and ECMWF ensemble forecasts, respectively; and the thick lines denote the according ensemble mean. The black line denotes the observed value.

Figure 13.  Same as Fig. 12, except for typhoon Sarika from 1200 UTC 12 October.

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## Manuscript History

Manuscript revised: 02 August 2022
Manuscript accepted: 12 August 2022
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

###### Corresponding author: Zhe-Min TAN, zmtan@nju.edu.cn;
• 1. Key Laboratory of Mesoscale Severe Weather/Ministry of Education, School of Atmospheric Sciences, Nanjing University, Nanjing 210063, China
• 2. National Meteorological Center, China Meteorological Administration, Beijing 100081, China

Abstract: 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.

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