This paper presents an observing system simulation experiment (OSSE) study to examine the impact of dropsonde data assimilation (DA) on rainfall forecasts for a heavy rain event in Taiwan. The rain event was associated with strong southwesterly flows over the northern South China Sea (SCS) after a weakening tropical cyclone (TC) made landfall over southeastern China. The model simulates too strong southwesterly flows over the northern SCS in the experiment without dropsonde DA, resulting in over-forecasted rainfall in Taiwan. When synthetic dropsonde data over the northern SCS are assimilated, the model reproduces more realistic initial fields and a better simulated TC track that can help in producing improved low-level southwesterly flows and rainfall forecasts in Taiwan. It is also shown that dropsonde DA can aid the model in reducing the ensemble spread, thereby producing more converged ensemble forecasts. The sensitivity studies suggest that dropsonde DA with a 12-h cycling interval is the best strategy for deriving skillful rainfall forecasts in Taiwan. Increasing the DA interval to 6 h is not beneficial. However, if flight time is limited, a 24-h interval of DA cycling is acceptable because rainfall forecasts in Taiwan appear to be satisfactory. It is also suggested that 12 dropsondes with a 225-km separation distance over the northern SCS sets a minimum requirement for enhancing the model regarding rainfall forecasts. Although more dropsonde data can help to obtain better initial fields over the northern SCS, they do not provide more assistance to the forecasts of the TC track and rainfall in Taiwan.
|Effective start/end date||2019/08/01 → 2021/07/31|
- Data assimilation
- southwesterly flow
- rain forecast
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