This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 hours. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 hours lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 hours. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

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The fidelity of gridded rainfall datasets is important for the characterization of rainfall features across the globe. This study investigates the climatology, interannual variability, and spatial-temporal variations of seasonal rainfall over Thailand during the 1970–2007 period using station data obtained from the Thai Meteorological Department (TMDstn). In addition, the performance of three gridded rainfall datasets, namely APHRODITE, CRU, and GPCC, in reproducing these seasonal rainfall features were intercompared and further validated with the results derived from the TMDstn. Results show that the gridded datasets can reproduce the spatial distribution of the TMDstn’s summer mean rainfall. However, large systematic underestimation is seen in APHRODITE, while GPCC shows better agreement with TMDstn as compared to others. In the winter, the spatial distribution of the seasonal mean of rainfall is well captured by all gridded data, especially in the upper part of Thailand, while they failed to capture high rainfall intensity in the south and the eastern parts of Thailand. Meanwhile, all the gridded datasets underestimated the interannual variability of summer and winter season rainfall. Using EOF analysis, we demonstrate that all the gridded datasets captured the first two dominant modes of summer rainfall, while they underestimated the explained variance of EOF-1. In the winter season, a good agreement is found between the first two modes of the TMDstn and the gridded datasets for both the spatial pattern and temporal variation. Overall, the GPCC data show relatively better performance in reproducing the spatial distribution of rainfall climatology and their year-to-year variation over Thailand. Furthermore, the performance of the gridded datasets over Thailand is largely dependent on the season and the complexity of the topography. However, this study indicates the existence of systematic bias in the gridded rainfall datasets when compared with TMDstn. Therefore, this indicates the need for users to pay attention to the reliability of gridded rainfall datasets when trying to identify possible mechanisms responsible for the interannual variability of seasonal rainfall over Thailand.