Abstract
By using the dropsonde data collected by the Hong Kong Observatory over the northern South China Sea in 2020 and the South China Regional Fine Forecast Model, the impact of these dropsonde data on the forecast of two tropical cyclones (Higos, and Nangka) was assessed. The results indicated the following: (1) The effect of the data assimilation on the wind and humidity fields lasted up to 24 h, whereas the assimilation did not exert a noticeable impact on the temperature forecast. (2) The mean track forecast errors of the two tropical cyclones, Higos and Nangka, were reduced by 34% and 24%, respectively, after the assimilation of the dropsonde data, but no fundamental improvement was made in intensity forecasts, such as in minimum sea level pressure. (3) The sensitivity tests of the two tropical cyclones showed that the assimilation of the observed wind field had the greatest impact on the cyclone track forecast. Sensitivity tests revealed that the assimilation of data altered the cyclone’s steering flow, thereby changing the movement path and improving the forecast performance. Assimilation of the observed humidity alone has no substantial impact on the track forecasts.
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Data availability
Dropsonde data were provided by the Hong Kong Observatory. The best tracks are available from China Meteorological Administration tropical cyclone data center (available online at https://tcdata.typhoon.org.cn/ywgc_zl.html). The real time typhoon position reports were provided by National Meteorological Center of the China Meteorological Administration. The ERA5 reanalysis data were downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview and https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview.
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Acknowledgements
This research was supported by the Science and Technology Program of Guangdong Province (Grant no. 2202A1515011870), the Science and Technology Innovation project of Guangdong provincial Water Resources Department (grant no. 2022-01), and the National Natural Science Foundation of China under Grant 41675099 and U1811464.
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Zhang, C., PakWai, Chan et al. Impact of dropsonde data on two tropical cyclone forecasts in the South China Sea. Meteorol Atmos Phys 135, 23 (2023). https://doi.org/10.1007/s00703-023-00963-4
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DOI: https://doi.org/10.1007/s00703-023-00963-4