The concept of reanalysis can be tracked back to synoptic analysis in the era of manual weather forecasting, in the era of numerical weather forecasting the data assimilation methods were used to provide initial conditions for numerical weather prediction models. However, due to the rapid updating of assimilation methods and numerical weather prediction models, the application of these data in climate research is limited. Therefore, the concept of reanalysis was proposed, whose core is to use fixed assimilation methods and fixed numerical models to reanalyze historical data. It interpolates the observed data based on physical model, and can provide estimates of unobserved variables, which solves the problem of sparse and irregularly distributed observation stations. In order to improve accuracy, the major global atmospheric reanalyses assimilate as much observational data as possible, that is, all input atmospheric reanalyses. Although the numerical models and assimilation methods are fixed, the observation system has been evolving, with major changes including modern sounding observations since the 1950s and satellite observations since 1979, as well as the upgrading of ground-based and satellite observation instruments. All of these have introduced inhomogeneity into global atmospheric reanalysis products. Therefore, there has been ongoing controversy over whether global atmospheric reanalysis can be applied to climate change research, which has also given rise to the sparse input reanalysis, such as 20th century reanalysis. In order to reduce the impact of observation system evolution, this type of reanalysis only assimilated a small amount of ground observations such as atmospheric pressure, which extended reanalysis to the mid-19th century.For global atmospheric reanalysis, atmospheric sounding observations are much more important than surface observations. Early data assimilation was mainly based on the simplest optimal interpolation method, assimilating the satellite retrievals of atmospheric temperature and humidity profiles. However, the vertical resolution of satellite observations was too low, and the effects of assimilation were low or negative. This is because optimal interpolation is based on linear theory and cannot handle and analyze observations related to variables in a nonlinear manner. The variational method allows for direct assimilation of satellite radiance observations, significantly improving the value of satellite observations. But the types of observations that can be assimilated are limited to variables that can be accurately simulated. In the late 1990s, a rapid radiative transfer model was developed to accurately simulate microwave brightness temperature observations under cloudy conditions, achieving full sky assimilation of microwave radiation. But only recently attempts have been made to assimilate key but discontinuous observation parameters, and only one global reanalysis has achieved assimilation of satellite aerosol optical depth. But so far, there is no global atmospheric reanalysis that can truly assimilate precipitation observations.Global atmospheric reanalysis surface analysis products may be the most widely used data. In order to improve the accuracy and spatiotemporal resolution of land surface analysis, many global atmospheric reanalysis systems also perform offline land surface analysis. In general, these land surface reanalysis use multi-source merged precipitation data to correct the precipitation products of the atmospheric reanalysis system, because the precipitation simulated by atmospheric models still has significant errors. But ERA5 Land did not perform such correction, likely because such correction affects the real-time performance of the product, as merged precipitation products are generally delayed by more than a month.However, the existing global atmospheric reanalysis systems exhibit significant differences in the surface analysis. The European Centre for Medium Range Weather Forecasts and Japanese Meteorological Agency reanalyzed and assimilated the observed surface air temperature and humidity observations collected at weather stations over global land, with the latter further assimilating land surface wind speed observations. The reanalysis series done by the United States agencies (i. e., the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA)) did not assimilate surface temperature, humidity, and wind speed observations collected by weather stations over global land. This results in lower consistency between reanalysis produced by the United States agencies and ground-based observations, while reanalysis produced by Europe and Japan agencies has higher consistency, making their applications more widespread. But until now all global atmospheric reanalysis have not included interannual variations in land cover, land use, and vegetation growth yet, which limits its accuracy in estimating surface variables. This article reviews the historical evolution of global atmospheric reanalysis done by Europe, the United States, Japan, and China, as well as the applicability of their surface analysis products in China.

The East Asian winter monsoon (EAWM) is a key component of the East Asian climate system, significantly influencing winter weather patterns. The El Nio-Southern Oscillation (ENSO) is recognized as a major driver of EAWM interannual variability. Although the ENSO-EAWM relationship has been extensively studied, uncertainties remain regarding its seasonal variation between early winter (November-December) and late winter (January-March), as well as the role of modulation by the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). To address inconsistencies arising from the use of traditional EAWM indices, which are often sensitive to regional definitions, this study employs a circulation-based approach using 850 hPa horizontal wind anomalies over East Asia and the western North Pacific. A 17-year sliding window regression between the Nio 3.4 index and the wind field is conducted for the period 1948—2014, separately for early and late winter. The resulting regression fields are subjected to multivariate empirical orthogonal function (EOF) analysis to identify dominant modes of variability and track the evolving ENSO-EAWM relationship independent of any predefined index.Interdecadal variations in this relationship are examined using NCEP-NCAR reanalysis and NOAA sea surface temperature datasets. A combination of multivariate EOF analysis, sliding correlation, and regression diagnostics is used to investigate the temporal evolution and mechanisms underlying changes in the ENSO-EAWM linkage. Results show that ENSO has a stronger influence on the EAWM in early winter, characterized by more pronounced southerly anomalies into northern East Asia. A notable weakening of the ENSO-EAWM relationship is observed after the early 1980s across both seasonal periods. Further analysis reveals the distinct roles of the PDO and AMO in modulating the ENSO-EAWM connection. During early winter, the formation of the PNA (Pacific-North American teleconnection) suppresses the development of the northwestern Pacific anomalous anticyclone typically associated with El Nio. As the PNA pattern tends to persist into late winter, the PDO exerts a stronger modulation during early winter, particularly during negative PDO phases. In these phases, positive height anomalies in the tropics extend westward into the subtropical western Pacific, intensifying the ENSO impact on the EAWM. Conversely, in late winter under negative PDO conditions, a weakened PNA allows for the resurgence of the anomalous anticyclone, thereby enhancing the ENSO-EAWM linkage. In contrast, the AMO modulates the ENSO-EAWM relationship primarily in late winter, with its influence confined to the tropics. Negative AMO phases enhance the ENSO signal, strengthening the EAWM response relative to positive AMO phases. This asymmetric modulation highlights the seasonally distinct impacts of decadal-scale oceanic variability on interannual ENSO-EAWM interactions.Additional factors influencing this relationship include the Arctic Oscillation (AO), whose negative phase strengthens anticyclonic anomalies over the northwestern Pacific, and sea ice variability in the Barents and Kara Seas. The diversity of ENSO types is also acknowledged as a potential contributor. These findings underscore the complex, seasonally dependent nature of the ENSO-EAWM relationship and emphasize the importance of accounting for both tropical and high-latitude influences when assessing East Asian winter climate variability.

In recent decades, with global warming, the Arctic region has exhibited a particularly significant warming trend. As a critical component of the high-latitude climate system, Arctic cyclones are closely linked to various weather phenomena in the Arctic, such as extreme winds and heavy precipitation events. Different from extratropical cyclones in midlatitude, the development and maintenance of some Arctic cyclones are influenced by the tropopause polar vortex (TPV). And the strengthening, maintenance, and vertical structure of these cyclones show significant differences due to effects of the TPV. The climate system in the Arctic region is complex and diverse. Therefore, to further explore the influence mechanism of Arctic cyclones while considering their unique physical mechanism, we aim to study the impact of stratospheric temperature anomalies on the development and maintenance of Arctic cyclones in summer.At three characteristic phases of cyclone lifecycle-initial formation, peak intensity, and maximum intensification rate-the distances between cyclone centers and TPV centers were systematically calculated. If the distance is less than 1000 km, it is considered that there is a connection between the cyclone and TPV, and it is a TPV cyclone. Conversely, it is determined to be a cyclone unrelated to TPV, that is, a non-TPV cyclone. Two representative intense cyclone cases were selected from the top 100 cyclones in the northern marginal of Eurasia region (NMER): one exhibiting strong dynamical coupling with the TPV and the other unrelated to TPV. Through the weather research and forecasting (WRF) numerical model, control and sensitivity experiments were designed and executed to conduct a comparative investigation into how stratospheric temperature anomalies modulate cyclones of contrasting structural types and their regionally correlated precipitation responses.The analysis reveals that cyclone cases closely associated with the TPV are influenced by the downward intrusion of high potential vorticity from the stratosphere into the troposphere, exhibiting a distinct quasi-barotropic structure with a warm-over-cold pattern. In the sensitivity experiments, after reducing the horizontal thermal differences in the upper troposphere and above, the folding characteristics of the tropopause are significantly weakened. Correspondingly, the stratospheric potential vorticity intrusion and absolute vorticity around the cyclone center are notably reduced, leading to a significant decrease in both the intensity of the cyclone center and the associated precipitation. In contrast, for another Arctic cyclone process unrelated to TPV activity, the formation and development are primarily driven by baroclinic instability. The horizontal thermal differences in the stratosphere would not show significant changes in the cyclone intensity or precipitation. The study primarily investigates the impacts of stratospheric temperature anomalies on Arctic cyclones, while there are relatively few discussions on other important factors that may affect Arctic cyclone activities and precipitation, such as ocean circulation, the feedback mechanism of the interaction between sea ice changes and the atmosphere. Future research requires expansion of cyclone case samples to enable more comprehensive investigations. Meanwhile, in order to refine the impact on precipitation more precisely, in the subsequent further study of the precipitation accompanying different types of Arctic cyclones, a higher resolution can be selected for WRF simulation to compare and analyze the possible precipitation differences caused by the resolution.

Hunan Province, located in central China, features a terrain dominated by mountains and hills, with plains enclosed by mountains on three sides. The region experiences a subtropical monsoon climate, with frequent high-temperature events during summer, particularly in the peak summer months of July and August. Research indicates that a rising trend in extreme heat events in Hunan, with the southeastern region experiencing the highest occurrence. Accurate fine-scale temperature forecasting remains a key challenge in regional weather prediction, while effective forecasting and timely warnings of severe weather are essential for disaster prevention and mitigation. Unlike short-term weather forecasts, extended-range forecasts (10—30 days) provide a longer decision-making window, allowing government authorities to implement proactive measures to enhance public safety and reduce disaster losses. However, current temperature forecasting studies in Hunan primarily focus on nowcasting and short-term model corrections, with limited research on extended-range forecasting. Furthermore, existing extended-range high-temperature forecasts in Hunan largely rely on sub-seasonal to seasonal (S2S) models, which often exhibit insufficient accuracy. Therefore, developing a dedicated forecasting model for extended-range high-temperature forecasting is crucial. The study aims to develop an extended-range forecasting model for heat wave events in Hunan Province during the peak summer period (July-August). The model integrates physical predictors derived from S2S model temperature forecasts and their corrections with a convolutional neural network (CNN) approach to enhance forecasting skill. Daily maximum temperature data from 97 meteorological stations in Hunan Province (1999—2022) and S2S model outputs from ECMWF and NCEP are utilized. Physical forecast factors are extracted from temperature and circulation forecast products using singular value decomposition (SVD) and the spatiotemporal projection model (STPM). These factors are then integrated into a CNN-based high-temperature prediction model (HTPM). Additionally, the maximum temperature forecasts from the S2S models undergo bias correction, and the corrected forecasts are combined with predictions from the HTPM to create an ensemble forecasting scheme. This approach aims to enhance the stability and accuracy of regional high-temperature forecasts. Results indicate that while the original S2S model forecasts exhibit low predictive skill, bias correction significantly improves their performance, though false alarm rates remain high. The CNN-based high-temperature forecasting model trained on ECMWF S2S data (HTPM-ECS2S) and NCEP S2S data (HTPM-NCEPS2S) effectively capture high-temperature events, demonstrating improved forecasting skill. The ensemble scheme successfully integrates multiple model outputs, further enhancing forecast accuracy and reliability.

Cloud microphysical processes are fundamentalto precipitation formation, involving complex interactions among cloud particles and their dynamic coupling with the surrounding atmosphere. Understanding the causal relationships underlying the information entropy of cloud microphysical quantities is crucial for elucidating the development of precipitating cloud systems and improving precipitation forecasting. This study investigates the multiscale causal relationships between the information entropy of total cloud water content (TWC) and that of relevant atmospheric variables, aiming to explore the self-organizational behavior and influencing mechanisms during cloud system evolution. A typical precipitating cloud event over northeastern China was selected for analysis. The degree of self-organization during the development of the cloud system was assessed through the information entropy of TWC, calculated based on the spatiotemporal distribution of cloud water content. This metric effectively captures the complexity and uncertainty of microphysical processes, where higher entropy values indicate greater disorder and lower values reflect more organized, potentially stable structures. To examine local coherence characteristics across different time scales, wavelet coherence analysis was employed to evaluate the nonlinear and timevarying relationships between TWC entropy and covariate entropies. Wavelet decomposition enabled the breakdown of information entropy time series into multiple scales, facilitating the identification of linear Granger causality relationships via a vector autoregression (VAR) model. The strength and direction of causal interactions were further quantified using the Liang-Kleeman information flow method.Results reveal that the TWC entropy increases initially and decreases as the cloud system matures, with a notable reduction during its mature stage, indicative of enhanced self-organization. On the 2-hour time scale, bidirectional Granger causality was observed between TWC entropy and all covariate entropies, suggesting mutual influence at this temporal resolution. At larger time scales (4 h and 8 h), the entropy of atmospheric precipitable water exerted the most substantial influence on TWC entropy, evidenced by the largest Liang-Kleeman information flow magnitude. Conversely, at shorter time scales (1 h and 2 h), the entropy of upward longwave radiation emerged as the dominnat driver. Radar reflectivity and vertical air velocity entropies also exhibited causal relationships with TWC entropy to varying degrees. In summary, atmospheric precipitable water and upward longwave radiation are key variables influencing changes in TWC information entropy across time scales. These findings offer new insights into the self-organization and evolution of precipitating cloud systems, emphasizing the necessity of multiscale and multi-variable approaches in studying cloud microphysics. Future work should focus on incorporating these casual insights into comprehensive cloud and precipitation models and exploring their applicability across different climatic regions.

Variations in cloud microphysical characteristics significantly impact weather and climate, making them a key focus in cloud precipitation physics research. Cloud droplet spectral dispersion is a crucial parameter for characterizing cloud microphysical processes and plays a fundamental role in studies of aerosol-cloud-precipitation interactions. Although extensive research has been conducted on cloud droplet spectral dispersion and its influencing factors, significant uncertainties remain due to variations in study regions, observation methods, and cloud developmental stages. These uncertainties pose challenges in addressing aerosol indirect effects. The northeastern Qinghai-Xizang Plateau is a climatically sensitive region where the plateau monsoon and the East Asian monsoon converge, making it highly susceptible to global climate change. A series of aircraft observation experiments have been conducted in this region, primarily focusing on cloud structural characteristics. However, limited attention has been given to cloud droplet spectral dispersion. Based on in situ aircraft observations of microphysical properties in typical supercooled stratiform clouds over the northeastern Qinghai-Xizang Plateau on April 27, 2022, this study analyzes the vertical distributions of cloud microphysical properties and cloud droplet spectral characteristics. The key findings are as follows: 1) The spectral width of cloud droplets was larger in the lower layers due to entrainment and weak activation processes. In the middle of the cloud, the activation of numerous aerosols led to competitive water vapor consumption among cloud droplets, limiting droplet growth and reducing spectral width. In the upper cloud layers, droplet condensation growth further decreased spectral width. 2) When the liquid water content and cloud droplet number concentration were below threshold values of 0.025 g·m－3 and 30 cm－3, respectively, cloud droplet spectral dispersion exhibited a wide range (0.30—0.85). However, above these thresholds, spectral dispersion decreased and remained within a narrower range (0.3—0.5). 3) Compared to the standard deviation of the cloud droplet spectrum, variations in aerosol concentration primarily influenced the mean cloud droplet radius, which dominated the negative correlation between aerosol concentration and cloud droplet spectral dispersion. 4) Cloud droplet spectral dispersion is a critical factor that must be considered in the parameterization of the cloud-to-rain autoconversion process. A positive correlation was observed between the automatic conversion rate and cloud droplet spectral dispersion, indicating that larger spectral dispersion facilitates the conversion of cloud water into rainwater. To further understand the mechanisms driving variations in cloud droplet spectral width, future research should focus on targeted aircraft observation experiments that simultaneously measure aerosol properties and cloud microphysical parameters.

Atmospheric humidity is a fundamental parameter in weather forecasting and atmospheric science, playing a critical role in weather analysis and numerical simulation. Providing specific humidity profiles with broad spatial coverage and high accuracy remains a key challenge for improving the performance of numerical weather prediction models. In this study, we propose a deep learning approach for retrieving atmospheric specific humidity profiles at multiple pressure levels across China, using microwave humidity sounder (MHS) data and a UNet3+neural network against ERA-5 reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). This method effectively mitigates the ill-posedness and uncertainty commonly encountered in traditional quantitative remote sensing retrievals, enabling robust and accurate humidity profile estimations across long-term series and multiple atmospheric layers. Experiments were conducted at the 700 hPa level, with data from 2011—2019 used for training, 2020 for validation, and 2021 for testing. Results show a slight underestimation of specific humidity compared with ERA-5, though seasonal differences in retrieval error were not statistically significant. Spatially, larger retrieval errors were observed in southern China and over land surfaces, while lower errors occurred in northern regions and over oceans. The root mean square error (RMSE) remained below 1.3 g-kg in all seasons, with the lowest average RMSE of 1.15 g-kg in winter. Temporal correlation coefficients exceeded 0.9, with an annual mean of 0.92, indicating strong spatial and temporal consistency with ERA-5. Further analysis was conducted across pressure levels from 300 hPa to 1 000 hPa. The retrieved specific humidity showed good agreement with ERA-5 in spatial patterns, with RMSE values across all levels remaining below 1.53 g-kg and correlation coefficients above 0.9. The retrieval accuracy improved with decreasing altitude, showing better agreement near the surface. Comparisons with radiosonde data confirmed these results, with an average annual RMSE of 0.91 g-kg from 300 hPa to 1 000 hPa. The inversion results were slightly lower than radiosonde observations overall, particularly above 700, while at near-surface levels (e. g., 1 000 hPa) a notable RMSE reduction of approximately 0.6 g-kg was observed. These findings demonstrate the effectiveness and high accuracy of the proposed deep learning-based inversion method for retrieving atmospheric specific humidity profiles from satellite microwave data.

Squall lines are a common form of severe convective weather, characterized by their abrupt onset, short duration, and localized spatial extent. Due to the limitations of conventional observational networks in capturing their detailed structural and dynamic characteristics, high-resolution numerical simulations are essential for indepth analysis. In mesoscale modeling, microphysics parameterization schemes exert a significant influence on the vertical distribution of temperature and humidity, making them a key factor in accurately reproducing extreme weather events. Therefore, evaluating the performance and mechanisms of different microphysics schemes under specific convective scenarios is critical for improving forecasting and early warning capabilities. Located in the East Asian monsoon region, Jiangsu Province frequently experiences severe convective weather in spring. On April 30, 2021, a squall line associated with a severe convective event impacted Nantong, Jiangsu, producing extreme surface winds, including a gust of 47.9 m·s－1 (Beaufort scale 15) in Tongzhou Bay. This study investigates the event using observational and reanalysis data, along with numerical simulations conducted with the Weather Research and Forecasting (WRF) model employing three microphysics schemes: Lin, Morrison-Gettelman (MG), and WSM6. The analysis focuses on the synoptic environment, structural characteristics, and physical mechanisms associated with the extreme winds and provides a comparative evaluation of the simulation performance. The results indicate the following: 1) The squall line developed under the influence of a deep upper-level cold vortex and a strong surface warm-moist low-pressure system, with instability and energy accumulation evident aloft. The system followed a broken-areal development pattern, evolving through initiation, maturity, and dissipation between 1200 and 1300 UTC. Radar observations revealed a bow echo and a V-notch signature. 2) The Lin scheme most accurately simulated the life cycle and vertical structure of the squall line, with maximum updrafts of 23.55 m·s－1 and downdrafts of－13.21 m·s－1. The MG scheme showed a temporal lag in simulating convective cell evolution, while the WSM6 scheme failed to reproduce a distinct squall-line echo. However, the MG scheme performed best in capturing the intensity and spatial distribution of extreme surface winds, successfully reproducing a maximum gust of 44.47 m·s－1 consistent with observations. The Lin and WSM6 schemes showed comparable overall performance, with the Lin scheme providing a more realistic thermodynamic structures. 3) At the surface, a mesoscale system comprising a rear-wake low, thunderstorm high, and pre-squall mesolow was identified near the squall line. These features, along with cold pool outflows, strong pressure gradients, and cold frontal passage, collectively contributed to the formation of damaging surface gusts. 4) Vertically, the convective system was characterized by upper-level divergence, low-level convergence, a mid-level warm layer, and a cold lower layer. Intense updrafts and latent heat release ahead of the squall line, in conjunction with strong vertical wind shear, created a favorable environment for the development of severe surface winds.This study provides insights into the dynamic and thermodynamic processes driving squall-line-induced damaging winds and assesses the capability of different microphysics schemes in simulating such events. The findings contribute to the advancement of numerical modeling of convective systems and offer reference values for future research. However, as the conclusions are based on a single case study, further validation using multiple events is required. Future work will examine the effects of model horizontal and vertical resolution on the simulation of gust front dynamics and associated thermodynamic processes.