Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm⁻¹), 90 (755.625 cm⁻¹), and 417 (960 cm⁻¹) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.

Model error is an important source of numerical weather prediction (NWP) errors. Among model errors, the systematic diurnal bias plays an important role in high-resolution numerical weather prediction models. The main purpose of this study is to explore the characteristics of the systematic diurnal bias of a high-resolution NWP model in southern China and reduce the diurnal bias to improve the forecast results, hence providing a better background field for data assimilation. Based on the China Meteorological Administration Meso-scale (CMA-MESO) high-resolution NWP model, a 15-day sequential numerical weather prediction experiment was performed in southern China, and the forecast results were analyzed. A sequential bias correction scheme (SBCS) based on analysis increments was designed to reduce the systematic diurnal bias of the CMA-MESO model, and 15-day sequential comparative experiments were carried out. The analysis results showed that the CMA-MESO model has apparent systematic diurnal biases, and the characteristics differ among variables. A large diurnal bias was mainly found in the lower model layers, and it was concentrated in areas with a complex underlying surface for the horizontal distribution, such as the Qinghai-Tibet Plateau and South China Coast. The results based on the 15-day sequential experiment showed that the sequential bias correction scheme partly reduced the systematic diurnal biases of the CMA-MESO model. The mean biases of meridional wind, zonal wind, potential temperature, and water vapor mixing ratio were reduced by 45%, 35%, 20%, and 10%, respectively, and the root mean square errors (RMSEs) were reduced by approximately 5%. This study revealed the characteristics of the systematic diurnal bias of CMA-MESO model in southern China, which may be caused by the diurnal variation in the thermal and dynamic exchange on underlying surfaces. The effectiveness of the sequential bias correction scheme was also verified, and the results had good prospects for providing more reference information for high-resolution numerical prediction models and data assimilation.