To assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of land-atmosphere coupled model, the satellite precipitation of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) was assimilated into the Weather Research Forecast (WRF) model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting element in the coupled model WRF-TOPX. Different in whether applied the 4D-Var data assimilation and evaporation, one control experiment and four experiments were performed to simulate a historical flood event happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the GPM IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF-TOPX; 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land-atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on the GPM IMERG could improve the Nash coefficient of the coupled model WRF-TOPX from 0.483 to 0.521 at hourly scale. These investigations can provide important implications for the land-atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with the GPM IMERG for flood simulation at large scale.

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction.

Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation measurement. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness-rain rate relationship. To solve these problems, a deep learning model using a spherical convolutional neural network was constructed to properly represent the Earth's spherical surface. With data inputted directly from IR band 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), the new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained, tested and validated. Compared to the commonly used IR-based precipitation product PERSIANN CCS (the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Network, Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE and CC, especially in the dry region and for extreme rainfall events. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.