Evaluation of 3DVAR Data Assimilation with Automatic Weather Station Data for Heavy Rainfall Forecasting in Thailand

Data assimilation is a useful approach to obtain the most accurate estimate of the initial and boundary conditions for Numerical Weather Prediction (NWP). This study is to investigate data assimilation of Automatic Weather Station (AWS) data with Weather Research and Forecasting (WRF) Model for heavy rainfall forecasting in Thailand. Two heavy rainfall events occurred in the Northeast of Thailand, due to two tropical storms (i.e., SONCA; 24
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July, 2017 and PODUL; 28
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August, 2019) were simulated. Three assimilation experiments for each event were designed using two different AWS data sources provided by the Hydro-Informatics Institute (HII) and the Thai Meteorological Department (TMD) together with a combination of the AWS data sources. The results shown that the WRF model assimilated with the AWS observation simulates the extreme rainfall events more accurately than that of non-data assimilation of heavy rainfall in local areas. The WRF model shows better performance when assimilated with a combined AWS dataset with a positive bias score of less than 0.3 and POD larger than 0.6 in both heavy rainfall events.