The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 °C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

Polar amplification has been a research focus in climate research in recent decades. However, little attention has been paid to Antarctic amplification (AnA). We have examined the variations in annual and seasonal temperature over the Antarctic Ice Sheet and its amplification based on reconstruction covering the period 2002–2018. The results show the occurrence of annual and seasonal AnA, with an AnA index greater than 1.39 with seasonal differences, and that AnA is strong in the austral winter and spring. Moreover, AnA displays regional differences, with the greatest amplification occurring in East Antarctica, with an AnA index greater than 1.51, followed by West Antarctica. AnA is always absent in the Antarctic Peninsula. In addition, amplification in East Antarctica is most conspicuous in spring, which corresponds to the obvious warming in this season; and the spring amplification signal is weakest for West Antarctica. When considering the influence of the ocean, the AnA becomes obvious, compared to when only the land is considered. Southern Annular Mode (SAM), surface pressure and westerlies work together to affect the temperature change over Antarctica and AnA; and SAM and surface pressure are highly correlated with the temperature change over East Antarctica. The picture reflects the accelerated changes in Antarctic temperature.

Atmospheric water vapor is an important factor in the formation and evolution of extreme weather events, such as heavy rainfall, typhoons, and major droughts and floods. We analyzed the applicability of precipitable water vapor (PWV) values from the Global Navigation Satellite System Meteorology (GNSS/MET) stations in northeastern China. We examined the potential of PWV for precipitation forecasting using an enhanced bidirectional long short‐term memory (BiLSTM) network. Using radiosonde data, the accuracy of GNSS/MET PWV was evaluated, and the diurnal and seasonal differences in retrieval errors were analyzed. Results show that diurnal differences in retrieval errors are insignificant, while seasonal differences are pronounced, which can be attributed to the seasonal distribution of precipitation. Through case analyses of rainstorms and severe convective events, this study concludes that GNSS PWV varies several hours ahead of precipitation. Building on the earlier analyses of applicability assessment and precipitation warning signal identification, the improved BiLSTM framework is employed to investigate the application of GNSS PWV in hourly precipitation forecasting. Feature extraction and data resampling were utilized to enhance the physical interpretability of the binary nature of precipitation prediction and improve the model's generalization capability. Validation with 873 randomly split testing samples revealed a classification accuracy of 86.3% for precipitation prediction, with a regression RMSE of 2.73 mm. The intelligent precipitation forecasting methodology developed in this research can be applied to public‐sector precipitation monitoring and early warning services.

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models.

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS.