Evaluation on the applicability of ERA5 reanalysis dataset to tropical cyclones affecting Shanghai

Abstract

Based on the 16 historical tropical cyclones (TCs) affecting Shanghai from 2007 to 2019, the suitability of ERA5 for studying TCs affecting Shanghai is systematically evaluated from the perspective of TC track, intensity, 10-m and upper-level wind, using TC best-track data of China Meteorological Administration and surface observations and sounding data. Corresponding to tropical storm (TS), strong tropical storm (STS), typhoon (TY), strong typhoon (STY) and super typhoon (SuperTY), the median TC track bias is 68.1, 52.9, 42.5, 25.4, and 18.2 km, respectively, the median maximum 10-m wind speed (VMAX_10m) bias is –3.7, –6.5, –11.4, –21.7, and –32.2 m·s^–1, respectively, and the median minimum mean sea level pressure (MSLP_min) bias is 2.2, 5.6, 8.1, 28.2, and 48.7 hPa, respectively. With the increase of TC intensity, the median TC track bias decreases, while the median VMAX_10m and MSLP_min bias increase. In general, VMAX_10m in ERA5 is lower than observed, and MSLP_min is larger than observed. Under influence of TS, STS, TY and STY, the median 10-m wind speed (V_10m) bias in the city is 3.2, 4.2, 4.7, and 5.4 m·s^–1, respectively, and is 4.4–5.2 m·s^–1 near the east coast, respectively. V_10m is mostly biased high, showing an “M” type pattern with the distance between TC and Shanghai. The median 10 m wind direction (WD_10m) bias is in a range of –7º to +7º. The median upper-level wind speed (V_upper) bias decreases with height, with a maximum of ~5 m·s^–1 at 975 hPa. Below 900 hPa V_upper in ERA5 is typically larger than the radiosonde observation, and its mean bias error (MBE) increases with TC intensity. The upper-level wind direction (WD_upper) matches the sounding data well, with a maximum bias of a few degrees only. The results provide a reference for the application of ERA5 to coastal cities affected by TCs.

Keywords

ERA5 reanalysis / tropical cyclone / wind field / urban

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41875059, 41875071, and 4175049), Shanghai Natural Science Foundation (No. 21ZR1457700).

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