In the context of increasingly frequent extreme weather events worldwide, waterlogging induced by flooding has emerged as a critical meteorological constraint on peanut production. According to precipitation characteristics during the growing season in the major peanut-producing areas of Henan, a field pot-culture experiment with waterlogging durations of 0, 3, 5, 7 d and 9 d (0 d serving as the control) is started on 29 August 2023, at Zhengzhou National Agrometeorological Experiment Station. Dynamic responses of photosynthetic and fluorescence parameters in peanuts under different waterlogging durations are systematically examined, and recovery experiments are subsequently conducted to explore the underlying physiological adaptation mechanisms and damage processes. Experimental results indicate that a moderate water increase during the early stage of waterlogging (3 d) temporarily enhanced photosynthetic performance. However, as the waterlogging period extended, sustained soil saturation inevitably led to root zone hypoxia, which significantly suppresses plant growth and progressively compromised photosynthetic function. Recovery experiments demonstrates that when waterlogging exceeded 7 days, photosynthetic function could only be restored to approximately 68% of the control level, indicating that complete recovery became difficult to achieve. Fluorescence imaging monitoring showes that, as waterlogging prolonged, the actual quantum efficiency of photosystem Ⅱ in leaves progressively decreases in a distinct spatial pattern from the leaf margin toward the base, with visible wilting symptoms appearing on the 7th and 9th day of treatment. Yield analysis indicates that short-term waterlogging of 3 d exerted a certain stimulatory effect on 100-pod weight and pod weight per plant. However, when waterlogging duration reached 5 d or longer, all measured yield parameters shift toward negative responses. Kernel weight per 100 seeds and full pod weight per plant begin to decline continuously from the very onset of waterlogging, with daily reduction rates reaching 1.65 g·d-1 and 5.73 g·d-1, respectively. It demonstrates that prolonged waterlogging persistently inhibits kernel development and the formation of high-quality pods. In summary, peanut response to waterlogging stress exhibits a distinct threshold effect: Short-term exposure (no more than 3 d) may induce compensatory growth via physiological adjustments, whereas prolonged stress (greater than 5 d) results in cumulative damage to the photosynthetic apparatus and yield formation processes, with such damage becoming largely irreversible beyond 7 d of waterlogging.
Microphysical characteristics and convective structures of heavy precipitation during"24·7"extreme rainstorm event in H enan Province are analyzed using disdrometers,dual-polarization radars,and conven-tional observations.This extreme rainstorm is successively influenced by the northwestern Pacific subtrop-ical high,a mid-latitude westerly trough and a sliding trough.Resulting extensive regions of heavy precipi-tation provide favorable environmental conditions for the development of complex and variable microphysi-cal characteristics.Heavy precipitation samples with precipitation intensity exceeding 20 mm·h-1 are classified into 4 distinct types using Gaussian Mixture Model(GMM)clustering algorithm.The type with an average precipitation intensity over 100 mm·h-1 is characterized by a larger mass-weighted mean diam-eter(Dm)and a higher normalized intercept parameter(N w).In addition,the variation trends of D and lgNw with increasing precipitation intensity are relatively complex.Notably,the precipitation of this type is primarily contributed by large raindrops,with their contribution rate being approximately twice that of medium raindrops.The convective system corresponding to this type is classified as moderate-intensity convection.Active ice-phase processes and highly efficient warm-rain processes within the system jointly lead to high concentrations and large mean particle sizes in the surface precipitation.On the contrary,the precipitation type with precipitation intensity below 50 mm·h-1 shows characteristics of raindrop size dis-tribution similar to maritime convective precipitation,where the increase of lgNw contributes more signifi-cantly to precipitation intensification,and the precipitation is predominantly contributed by small rain-drops.This type is associated with weak convective systems,characterized by relatively low convective cloud-top height,comparatively weaker ice-phase processes,and a dominance of warm-rain processes.Consequently,the surface precipitation is marked by small mean raindrop size and low concentrations of large raindrops.Across all precipitation types,the coalescence process within the warm rain area is identi-fied as the primary warm-rain mechanism responsible for generating heavy precipitation in the event.Com-parisons of heavy precipitation across different synoptic stages demonstrate that under the dominant influ-ence of the northwestern Pacific subtropical high,convective development is constrained to lower alti-tudes,resulting in smaller mean raindrop size.In contrast,during the stage influenced by the sliding trough,convection develops more deeply,and ice-phase processes are more active,leading to larger mean raindrop size.During the stage dominated by westerly trough,convection develops to relatively high alti-tudes with medium mean raindrop size.
A 3-d regional floating dust event occurs in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 13 April to 15 April in 2025. Multi-source observations, including meteorological and air quality measurements, meteorological gradient tower data, lidar, wind profile, FY-4B meteorological satellite products, and sounding data, are combined with synoptic analysis and HYSPLIT (Hybrid Single-particle Lagrangian Integrated Trajectory) model simulations to investigate causes for the formation and persistence of this event. Results show that during this floating dust event, visibility across GBA drops sharply from 25 km to below 10 km and remained in the range of 7-10 km for an extended period. Meanwhile, PM10 concentration surges by 10-20 times within 24 h, resulting in PM10 pollution recorded in all GBA cities. The hourly peak PM10 concentration in Guangzhou reaches 448 μg·m-3.HYSPLIT backward trajectory simulation indicates that the long-range transport of dust from the northern China is the direct cause of the floating dust event in GBA. The establishment of a low-level jet and the enhancement of subsidence provides dynamic conditions for the continuous transport of upstream dust into the region and its downward diffusion. Vertical aerosol variations further corroborate that over southern GBA (e.g., Shenzhen) between approximately 200 m and 1.2 km, subsequently accumulating mainly near the surface and causing a rapid rise in ground-level PM10 concentrations. The East Asian trough, Mongolia cyclone, and cold front, which guide the cold air southward rapidly, act as the key dynamic drivers of this long-range dust transport. The prolonged maintenance of the floating dust results from the combined influence of multiple factors. During the persistence stage, horizontal wind speeds at 520-m height in the boundary layer are generally below 8 m·s-1, and the atmosphere conditions remained stable and dry for approximately 36 h, which respectively suppressed horizontal and vertical diffusion. Additionally, sea-land breeze convergence further enhances the accumulation of particulate matter. Finally, the low-level easterly to southeasterly flow promotes the recirculation transport of marine-sourced floating dust aerosols, thereby delaying the dissipation of this floating dust event in GBA.
Radar wind observations, such as Doppler weather radar radial velocity and wind profiler data, are critical for initializing numerical weather prediction (NWP) models due to their high spatiotemporal resolution. However, their quantitative contribution to China Meteorological Administration Mesoscale Numerical Weather Prediction Assimilation System (CMA-MESO) hasn't yet been systematically evaluated. To address this gap, analysis sensitivity to observation (ASO) method is adopted for quantitative evaluation. Assimilation results from CMA-MESO system at 8 analysis times throughout 2024 are analyzed, incorporating wind profiler zonal and meridional winds, Doppler weather radar radial winds, aircraft reports, satellite-derived winds, and other conventional observations. Quantitative analysis are conducted through energy norm change in analysis increments, aggregated by variable type, altitude, season and analysis time, and their contributions are evaluated. Results demonstrate that wind observations contribute most significantly to the reduction of analysis error in CMA-MESO assimilation system. Based on these findings, the impact of wind variables from radar and other observation on the analysis innovation is analyzed. Collectively, radar wind observations reduce analysis errors by 23.72%, ranking third behind satellite winds (28.45%) and aircraft reports (28.49%). Temporally, contributions from radar exceed that of aircraft reports at 1800 UTC and 2100 UTC and during autumn seasons. This outcome is attributed to the stable error reduction induced by radar observation (root mean square error no greater than 0.63 m·s-1 vs. aircraft's 5.46 m·s-1), which compensates for the reduced availability of aircraft reports during convective periods. The combined contribution of Doppler weather radar and wind profiler to analysis error reduction is most pronounced at 700-800 hPa layer, where their total contribution reaches 4.1 times that of aircraft reports. Within 800-900 hPa layer, Doppler weather radar exhibits the greatest contribution, exceeding that of the other two observation types. Doppler weather radar's per observation contribution is constrained by its single-variable (radial wind) limitation, with high-impact stations concentrated in central-eastern China. In contrast, wind profilers exhibit higher per observation contribution than Doppler weather radar, particularly in eastern coastal regions and Beijing-Tianjin-Hebei Area. The spatial distribution of wind profiler contributions is influenced by both the volume of observations and per observation contribution efficiency, whereas that of Doppler weather radar is predominantly correlated with the volume of observational. It is confirmed that radar wind observations substantially optimize the initial fields of CMA-MESO system through two key advantages: Temporally stable error reduction, and a dominant impact in the mid-to-lower troposphere (700-900 hPa), which is critical for refining NWP initial fields.
The three-body scatter signature (TBSS) is a critical indicator for the detection of large hail (diameter is greater than 20 mm) using S-band Doppler weather radar. It is characterized by spurious echoes resulting from a triple-scattering processes: Initial scattering in the hail region, ground reflection, and secondary scattering back through the hail region. To improve hail warning accuracy and mitigate TBSS-induced artifacts in quantitative precipitation estimation, the development of automated TBSS identification algorithms is essential. A limitation in previous algorithmic is the imposition of an artificial constraint, whereby grid points with a horizontal polarization reflectivity factor (ZH) no more than 25 dBZ are classified as TBSS regions, and this inherently limits detection when the signature is obscured by genuine precipitation echoes exceeding this threshold.An innovative dual-polarization algorithm named TBSS-RFK is introduced, which synergistically combines random forest classification with K-means clustering to overcome these limitations. The methodology leverages a comprehensive dataset comprising 930 carefully validated cases (465 TBSS and 465 non-TBSS samples), derived from large hail events observed across Hunan Province from 2021 to 2025. For each event, three key dual-polarization parameters are extracted: The horizontal polarimetric reflectivity factor (ZH), differential reflectivity (ZDR), and correlation coefficient. The algorithm is designed to automatically identify TBSS grid points at a 1°×1 km resolution, followed by a comprehensive performance evaluation and application case analysis.Built upon a comprehensive understanding of TBSS formation mechanisms, polarimetric characteristics, and physical essence, TBSS-RFK algorithm successfully addresses the long-standing challenge of detecting TBSS obscured by genuine precipitation echoes. The algorithm achieves exceptional performance metrics with 96.6% probability of detection (POD), 6.5% false alarm ratio (FAR), and 90.5% critical success index (CSI). Compared with conventional single-polarization TBSS detection algorithms, TBSS-RFK demonstrates significant improvements by a 16% reduction in FAR and a 20.5% enhancement of CSI.Feature selection constitutes the cornerstone of TBSS-RFK algorithm's success. The availability of dual-polarization radar parameters enables robust identification of precipitation-embedded TBSS, with ZH and correlation coefficient identified as particularly diagnostic. Statistical analysis reveals that 50% of confirmed TBSS grid points are characterized by low-value thresholds: ZH not exceeding 15.0 dBZ, ZDR not exceeding 0.19 dB, and correlation coefficient not exceeding 0.84. Through rigorous examination of these polarimetric signatures and their physical interpretations, the algorithm strategically employs low-value regimes of ZH, ZDR, and correlation coefficient as primary detection features, with correlation coefficient emerging as the most statistically significant discriminator. This physics-informed feature engineering approach underpins the algorithm's superior performance.By transforming complex image recognition into an efficient binary classification task through meteorologically informed feature engineering, TBSS-RFK algorithm provides a robust operational solution and offers insights for developing lightweight machine learning algorithms. A primary limitation is that the algorithm's validation is based on 465 samples from Hunan Province, leaving its generalizability to other geographic/climatic regions unexplored. Future work will be directed toward assessing its stability with larger datasets.
High-resolution numerical models coupled with detailed cloud microphysical schemes are essential tools for in-depth investigation of hail cloud structure and formation mechanisms. Based on the dynamic framework of CMA-MESO 6.0 and its original double-moment cloud microphysical scheme, an explicit double-moment hail microphysical scheme is developed by incorporating more detailed hail physical processes. High-resolution numerical simulations are then conducted for two severe convective events: A multi-cell hailstorm in Jiangxi-Fujian on 22 March 2023 and a widespread severe wind and hail event in northeastern Chongqing on 24 March 2024. Simulations are conducted to evaluate the applicability of the newly developed scheme within CMA-MESO modeling system and to systematically analyze the dynamic, thermodynamic, and microphysical structural characteristics throughout the lifecycle of the hail clouds. Results indicate that CMA-MESO 6.0, when coupled with the new scheme, can reasonably reproduce the microphysical structure and its spatiotemporal evolution for both types of hail clouds, demonstrating explicit forecasting capability for hail mixing ratio and number concentration. The spatial distribution and temporal evolution of simulated rainfall and hail for both events are generally consistent with observations, despite some biases in reflectivity intensity exist, specifically an overestimation for Jiangxi-Fujian case and an underestimation for Chongqing case. The model successfully reproduces the developmental, mature, and dissipating stages of the multi-cell severe storm, elucidating the key dynamic-microphysical mechanisms, including the transport of supercooled water by the updraft, hail growth, and subsequent fallout. Microphysical analysis indicates that hail formation and evolution depend on the synergistic effects of supercooled liquid water content, ice-phase particle distribution, and vertical airflow, with 400-600 hPa layer identified as a critical region for hail particle generation and growth. Specifically, Jiangxi-Fujian case exhibits characteristics of a typical multi-cell severe storm, featuring deep, strong updrafts and abundant supercooled water. These conditions provide a favorable environment for hail growth, resulting in intense hailfall. In contrast, Chongqing case is characterized by weaker updrafts and a limited supply of supercooled water, leading to smaller hailstone size, a more dispersed spatial distribution, and hailfall being temporally concentrated and spatially widespread but of relatively lower intensity. These ressults provide a foundation for developing refined cloud microphysical parameterization schemes. Future efforts will focus on continued testing and optimization of the scheme by incorporating more observational data.
Accurate seasonal forecasting of summer precipitation is critical for climate adaptation and disaster risk reduction in China, yet remains challenging due to the variability of the East Asian summer monsoon. Dynamical climate models serve as the foundation for operational seasonal prediction but are constrained by systematic biases and uncertainties in initial conditions.To address this challenge, the prediction skill of a multi-model ensemble (MME) comprising advanced seasonal forecast systems is evaluated for summer (June-August) precipitation and key ocean-atmosphere precursor signals over China during 1993-2024. The analysis utilizes hindcasting data from eight seasonal prediction models. To ensure temporal consistency, an equally weighted multi-model ensemble mean (MME-mean) is constructed from seven models (excluding the shorter-record BCC-CPSv3, which is assessed separately). The verification is based on gridded observational datasets (CN05.1, GPCP), ERA5 reanalysis data, and HadISST sea surface temperature. Evaluation is conducted using standard metrics, including temporal correlation coefficient, anomaly correlation coefficient, and prediction score.Results reveal substantial spatiotemporal heterogeneity in the MME-mean skill for summer precipitation prediction. Predictive skill exhibits significant interannual variability, with higher skill found over the mid-lower reaches of the Yangtze, North China, and western Northwest China. In contrast, prediction skill is markedly lower over Northeast and Southwest China. Forecasts initialized closest to the target season (0-month lead) generally show the highest skill, underscoring the critical role of initial conditions. The MME-mean demonstrates proficient and stable skill in predicting large-scale circulation, particularly within the tropical and subtropical regions. The temporal evolution of the Philippine Sea anticyclone is captured with significant skill across all lead times. Conversely, predictive skill for mid-latitude circulation anomalies over Eurasia degrades rapidly with increasing lead time. Prediction skill for global sea surface temperature (SST) fields is notably high. The MME-mean also partially alleviates the characteristic "spring predictability barrier" for ENSO evident in individual models. However, a focused analysis for the mid-lower reaches of the Yangtze indicates that even the best-performing individual model fails to accurately predict precipitation anomalies in several extreme years, particularly flood years, which is attributed to more complex antecedent SST patterns.In summary, the MME-mean approach significantly enhances the predictive skill for large-scale precursor signals associated with summer precipitation over China. However, substantial challenges remain in accurately predicting regional precipitation anomalies, particularly for extreme precipitation events. Future efforts should also prioritize exploiting probabilistic forecasting reliability and integrating physically-constrained machine learning techniques for bias correction.
On 21 February 2024, a mesoscale convective system accompanied by hail developed across Jiangxi, Fujian, and Zhejiang. To investigate the evolution of dynamic structure and hydrometeor types of the persistent hailfall-producing cell during different stages, this event is analyzed based on S-band dual-polarization radar data. Results show that the synoptic conditions in the upper and lower levels constitute a typical elevated convective environment. During the mature stage, 3 radar parameters, the maximum reflectivity, echo top height and vertically integrated liquid water, show significant increases compared to the development stage. Large hail occurrence corresponds to higher values of maximum reflectivity, echo top height and vertically integrated liquid water. A sharp increase in vertically integrated liquid water content can serve as an indicator for the occurrence of large hail at the surface. Persistent and strong mesoscale convergent updrafts are identified as the primary dynamic mechanism responsible for the continuous hailfall. ZDR column appears consistently throughout both the development and mature stages, with its height gradually decreasing as the mesoscale convective system hailfall event progresses. During the development stage, ZDR column extends upward to -5 ℃ level. The updraft within the column rapidly transports raindrops from the warm sector into the cold sector, forming a hail embryo region above ZDR column. During the maturation stage, ZDR column persists with the sustained strong updrafts lifting more liquid droplets into the cold sector. This enhances the supply of supercooled water and hail embryos, which continuously form in the wet growth region between -20 ℃ and -10 ℃. Embryos grow into hailstones through the accretion and freezing of supercooled cloud water. When hail begins to descend, the drag effect caused by falling hail particles lowers ZDR column height to 0 ℃. The vertical configuration of radar polarimetric parameters varies during different stages of the mesoscale convective system and under hailfalls of different intensities. During the development stage, hail particles have already formed in the cold region, characterized by negative ZDR, low correlation coefficient and negligible KDP. This period represents favorable timing for artificial hail suppression. In the mature stage, the area extent of hail particles in the cold region expands significantly compared to the development stage, with particle phase becoming more uniform, reflected by an increase in the correlation coefficient. The negative ZDR zone extends vertically downward, while in the warm region, partial melting of descending hail particles along with raindrops results in a mixed phase region. This mixture leads to a decrease in the correlation coefficient and an expansion of the area characterized by absent KDP.
In order to achieve dynamic monitoring of drought conditions for summer maize across Huang-Huai-Hai Region, solar-induced chlorophyll fluorescence (SIF) data, the Self-calibrating Palmer Drought Severity Index (SCPDSI), and actual disaster records from 2001 to 2024 in this region are utilized to construct the drought dynamic monitoring thresholds for summer maize. To eliminate influences of solar radiation and canopy structure on SIF, the solar-induced chlorophyll fluorescence normalized by photosynthetically active radiation (SIFPAR) and yield of solar-induced chlorophyll fluorescence (Y-SIF) are calculated respectively. Response differences of SIF-derived indices to drought are compared using the method of correlation analysis. The indicator most sensitive to drought is then selected, and Gaussian fitting is utilized to construct fitting curves for different drought severity levels during the summer maize growth period. The average of fitting curves for adjacent drought levels is designated as the dynamic monitoring threshold curve for each drought level. The accuracy of the threshold curve is verified using typical drought records. These threshold curves are further applied in drought monitoring during the growth period of 2023 and 2024. Results indicate that SIF series indicators exhibit a single-peak trend throughout the summer maize growing season. Across all drought severity levels, the proportion of affected areas takes on a fluctuating yet decreasing trend in recent years. Both the spatial extent and occurrence frequency of drought events diminish as drought severity intensifies. SIF series indicators decrease as SCPDSI diminishes, exhibiting a positive correlation. During the summer maize growing period in Huang-Huai-Hai Region, the correlation coefficient between SIFPAR and SCPDSI is generally higher than that of SIF and Y-SIF. During the critical developmental stages in July and August, the spatial extent shows a positive correlation that exceeds 90%, showing that SIFPAR is particularly sensitive to drought conditions. Consequently, SIFPAR is selected as the indicator to monitor the functional changes of summer maize during drought periods. The dynamic drought monitoring threshold curve based on SIFPAR can accurately identify the occurrence and development of drought events, with the drought level identification accuracy rate of 87.93% compared to actual conditions. During 2023-2024 drought monitoring application, the dynamic monitoring thresholds consistently identified drought onset timing, severity, and spatial extent that closely aligned with actual disaster conditions. The dynamic monitoring thresholds can effectively capture the spatio-temporal evolution of drought across different developmental stages of summer maize. Results facilitate dynamic drought monitoring and provide data support for decision-making in Huang-Huai-Hai Region.
High-rise buildings not only increase urban land use efficiency, but also facilitate the attainment of conditions required for lightning leader initiation due to enhanced electric field distortion at the top, thereby increase lightning activity occurrence. To address the complexity of lightning strike process of high buildings, a 3-dimensional stochastic physical model of lightning strike to high-rise buildings is developed, based on a conventional physical model of lightning strike and considering downward negative ground flashes. The influence of building geometry on upward leader initiation and attachment process is numerically simulated. Five different geometric shapes (long lightning rod tower-shaped, cuboid-shaped, cuboid-tower-shaped, cylindrical-tower-shaped, slope-shaped) and different heights (100-500 m) of buildings are designed, and their effects on the initiation time of upward leader, the length and rate of upward leader and the striking distance are comparatively analyzed. The shape and height of a building have significant impacts on the initiation and attachment process of the upward leader. It is concluded that in the order of long lightning rod tower-shaped, cylindrical-tower-shaped, cuboid-tower-shaped, slope-shaped and cuboid-shaped buildings, the initiation time of a stable upward leader becomes progressively later, while the length of the upward leader and the striking distance are progressively reduced. Under the same conditions, the average initiation time of the stable upward leader of the cylindrical tower is about 0.26 ms earlier than that of the cuboid tower, the average length of the upward leader is increased by about 60 m, and the average striking distance is increased by about 30 m. A taller building height corresponds to an earlier initiation of the stable upward leader. For every 100-m increase in the height of a cuboid-tower structure, the electric field intensity at its tip is about doubled. Compared to a 100-m tall cuboid, the average upward leader length of a 500-m cuboid is extended by about 60 m, and the average striking distance increases by roughly 36 m. Tall buildings with sharp shapes at the top are not only easier to trigger the upward leader, but also promote faster leader development and increase the likelihood of attachment to a downward leader.
During the severe rainstorm event in North China from 23 July to 29 July in 2025, forecast performance of CMA-EPS, EC-EPS, CMA-GFS, EC-HR, Fengqing and AIFS are evaluated using synoptic verification, threat score (TS), and MODE (method for object-based diagnostic evaluation). Results indicate that EC-EPS successfully forecasts the distribution of two rain belts with cumulative precipitation exceeding 100 mm with a lead time of 6-13 d. CMA-EPS is also able to provide probability of precipitation exceeding 100 mm 4-11 d in advance, but its forecast stability decreases as the lead time shortens. Based on MODE spatial verification, the forecasted areas of cumulative precipitation exceeding 100 mm for CMA-GFS, EC-HR, and AIFS are all smaller than observation, while Fengqing forecast area is larger. Although the centroid position of EC-HR forecasts remains relatively stable, its deviation magnitude exceeds that of the other three models. CMA-GFS model exhibit the closest agreement with the observed precipitation days in both the edge and core regions of the western Pacific subtropical high, while EC-HR model overestimates the number of precipitation days in the edge zone. Fengqing and AIFS models exhibit significant false alarms in predicting convective precipitation days for both the edge and core regions. At medium-range lead times, all four models forecast a northward displacement in the spatial distribution of heavy precipitation days compared to observations, and the forecasts shift southward approaching reality as the forecast time decreases. Additionally, forecasts from Fengqing and AIFS demonstrate advantages in forecast stability. Forecasts of CMA-GFS, EC-HR, Fengqing and AIFS models all substantially underestimate the intensity of heavy precipitation centers compared to observations, and this bias persists even as the forecast lead time decreases. Furthermore, EC-EPS ensemble members exhibit greater dispersion than CMA-EPS, with some members in long-range forecasts predicting precipitation intensities closer to observations. As the forecast lead time decreases, however, the ensemble dispersion decreases, and the predicted precipitation center intensities became even weaker than the actual conditions. Regarding the temporal evolution of heavy precipitation in the regions from Baoding of Hebei to southwestern Beijing and from northeastern Beijing to Xinglong of Hebei, EC-HR model outperforms the other three models in forecasting the intensification phase and nocturnal rainfall characteristics. Notably, Fengqing and AIFS models show a significant decline in their ability to predict precipitation intensity and nocturnal rainfall features as the forecast lead time increases. The forecasting capability for the westward extension and northward shift of the subtropical high is superior to that for its eastward retreat and southward movement. The weakened positional forecasting skill during the eastward retreat of the subtropical high may partially explain the precipitation forecast biases.
To investigate the climate suitability of premium green tea in Jiangnan Tea Region and the climatic quality characteristics across different spring picking periods, this study systematically analyzes the climate adaptability for cultivating these green teas and the dynamic changes in climatic quality during 4 distinct spring picking stages. The research is conducted using two key datasets: Long-term meteorological observation records from Jiangnan Tea Region spanning 1961 to 2024, and high-resolution real-time grid data from CLDAS V2.0 covering 2015 to 2024. Methodologically, it is integrated fuzzy mathematics, k-means clustering algorithm, climatic quality index (Itcq) assessment, and Moran's spatial autocorrelation analysis to ensure comprehensive and reliable results. Findings indicate that the average climate suitability index of the Jiangnan Tea Region from 1961 to 2024 stood at 0.84, displaying a clear trend of initial increase followed by a gradual decrease. Specifically, the index peaks during 1991-2000 period and thereafter maintained fluctuations at a relatively high level. A spatial comparison between two time periods, 1961-1990 and 1991-2020, reveals that the regions most suitable for green tea cultivation has expanded notably toward the east and north.During the period of 2015-2024, the climatic quality index (Itcq) of tea in different spring picking stages, in descending order, is pre-rain tea (2.09), pre-Qingming tea (1.85), early spring tea (1.81), and late spring tea (1.74). Moreover, under the regulation of the spring temperature gradient, the high-value areas of the climatic quality index shows significant differences in spatial distribution. During this period, the climatic quality of pre-rain tea and pre-Qingming tea is highly stable, making them suitable for the layout of high-quality tea gardens, while the climatic quality stability of early spring tea is the poorest.Further analysis using Moran's index demonstrates that the climatic quality indices of teas across all spring picking periods exhibited significant positive spatial correlation (Moran's index ranging from 0.921 to 0.958, passing the test of 0.001 level), which confirms distinct spatial agglomeration characteristics. In local regions, the spatial correlation is primarily manifested as high-value clustering and low-value clustering. The research can provide scientific support for the differentiated cultivation and climate adaptability management of high-quality green tea in Jiangnan Tea Region.
The ongoing rise in greenhouse gas emissions is leading to a sharp increase in global surface temperatures and more frequent extreme weather events, which has intensified the fluctuation range of daily extreme temperatures and increased the difficulty of prediction. Research on forecasting changes in daily extreme temperature can provide reliable scientific data for assessing future disaster risks and support decision-making. Due to limitations in the performance and sensitivity, current global climate models (GCM) exhibit considerable uncertainty in predicting extreme temperatures, increasing the difficulty of predicting future trends. It is necessary to correct the direct prediction results of GCMs to obtain more reliable prediction results. Therefore, Siberian sea level pressure and the sea surface temperature of the Indian Ocean, both of which have significant impacts on the daily extreme temperature changes in China, are selected as physical factors for correction. Two methods, emergent constraints and Pareto optimal ensemble, are employed to correct GCM' predictions of daily extreme temperature changes in China under the SSP1-2.6 scenario for the middle of the 21st century. A comparison of results before and after correction reveals that both methods could effectively reduce the inter-model uncertainty of future daily extreme temperature changes. Among them, Pareto optimal ensemble scheme, which integrates three-variable factors-daily extreme temperature in China, Siberian sea level pressure, and the Indian Ocean sea surface temperature, proves most effective in minimizing inter-model uncertainty. The range of multi-model predictions of daily maximum (minimum) temperature changes in China for the mid-21st century, as corrected by the three-variable Pareto optimal ensemble scheme, is narrowed to 1.26 ℃ to 2.10 ℃ (1.12℃ to 2.06 ℃). The uncertainty range is reduced by approximately 36.8% (32.9%) compared to the uncorrected results. Moreover, the signal-to-noise ratio of the predicted daily extreme temperature changes increase in most areas of China, rising from below 1 without correction to above 1. At the same time, corrected results based on three-variable Pareto optimal ensemble scheme show significant regional differences, adjusting the magnitude of warming differentially over the Qinghai-Xizang Plateau, Northwest China, and Sichuan Basin. Overall, employing physical constraint derived from selected constraint factors to correct predictions of future daily extreme temperature changes in China is shown to be useful and feasible.
The applicability of different statistical approaches for estimating hourly precipitation thresholds across China is evaluated using data from 2464 manned weather station and 40437 unmanned weather station. Two categories of methods are compared: Extreme value theory models (EVT)-namely the Generalized Extreme Value (GEV) distribution and the Pearson type Ⅲ (P-Ⅲ) distribution, and empirical distribution-based techniques, including percentile ranking, percentile interpolation, the Z-index, and square/cube root transformations.Several important findings emerge from the analysis. First, the statistical characteristics of 1-h precipitation are found to be markedly right-skewed, reflecting the dominance of infrequent but intense rainfall events. Conventional normalization techniques, such as the Z-index or root transformations, are observed to enhance distributional normality only to a limited extent. The effectiveness of these methods improves under high truncation thresholds (e.g., 20 mm), yet in regions with frequent heavy rainfall events, their ability to approximate normality remains insufficient. Second, the spatial distribution of precipitation thresholds estimated by different methods is broadly consistent. High threshold values are mainly observed in South China and eastern North China. In contrast, a pronounced northwest-southeast oriented low-value belt is identified, extending from southern Henan through central Anhui to northern Zhejiang, while low values also dominate western North China and the arid northwest region. Third, systematic differences emerge between two methodological categories due to their underlying statistical principles. Thresholds derived from extreme value theory models are generally higher than those estimated from empirical methods. Among the EVT models, the GEV distribution shows greater sensitivity to the most extreme values, making it particularly suitable for characterizing rare and intense rainfall events. In contrast, the P-Ⅲ distribution, though effective, tends to be less responsive to the extremes. Empirical approaches are advantageous involving short data records or when no distributional assumptions can be justified, and they also demonstrate strong spatial transferability across regions. Finally, the selection of the truncation threshold is shown to be critical and should be determined according to research objectives. Low thresholds (e.g., 0.1 mm) are suitable for generalized climatological analyses, whereas higher thresholds are more appropriate for the study of extreme events. This flexibility underscores the importance of aligning methodological decisions with the intended application, whether for hydrological risk assessment, infrastructure planning, or broader climatological studies. For nationwide threshold estimation at individual stations, percentile interpolation is identified as the most suitable method due to its distribution-free nature and applicability to shorter records (e.g., more than 10 years), making it ideal for regional stations or gridded precipitation products with limited temporal coverage. In contrast, region-specific or seasonal analyses require careful selection of truncation thresholds based on local precipitation climatology to enhance representativeness-lower thresholds are sufficient for general climate studies, whereas higher thresholds are more effective in isolate extreme events, particularly in flood-prone regions.
Under the background of climate change, the frequency and intensity of extreme low-temperature events show an increasing trend. Low-temperature precipitation and freezing is a compound disaster characterized by the co-occurrence of cold air and precipitation, leading to more severe impacts than individual hazards. Therefore, accurate identification and assessment of such compound disasters are essential for disaster prevention and mitigation. An objective identification method for compound low-temperature precipitation and freezing disasters in southern China is developed, utilizing the latest observations from 1273 meteorological stations from 1961 to 2024, including daily minimum temperature and precipitation. The methodology is based on the spatiotemporal continuity of compound low-temperature precipitation and freezing disasters, employing a three-tier framework: Single-station detection using temperature and precipitation thresholds, regional aggregation with a 200-km neighborhood radius and 5% impacted area threshold, and process tracking with a minimum duration of 3 d. A comprehensive hazard assessment model is established, which incorporates key indicators including event duration, impacted area, minimum temperature, temperature drop amplitude, and precipitation. The entropy weight method is applied to determine objective weights for each factor. The hazard assessment framework includes comprehensive hazard index and spatial hazard levels, with the former characterizing the overall regional disaster features and the latter depicting the disaster impacts at individual stations. Applying the identification framework, 112 compound low-temperature precipitation and freezing disasters are identified in southern China for the period of 1961-2024. Identified events exhibit strong spatial consistency with actual freezing-rain occurrences. Particularly noteworthy, close correspondence is found between the top 10 events ranked by hazard index and historically documented catastrophic freezing disasters. Results reveal distinct spatiotemporal patterns of compound low-temperature precipitation and freezing disasters in southern China. Spatially, compound low-temperature precipitation and freezing disasters in southern China exhibit a zonal distribution pattern, characterized by higher frequency and hazard in the central region (Hunan, Guizhou, Jiangxi, and Hubei) but lower values in the northern and southern areas. Notably, the high-frequency zone shows a northeastward migration trend. Temporally, January emerges as the peak month, accounting for 41% of total frequency, while both the earliest onset and latest termination dates have advanced significantly. The annual frequency has increased to 1.2 times the baseline in recent 30 years. The hazard index demonstrates a phased variation: An initial increase followed by a decline and a recent resurgence. In recent years, both the hazard index and individual hazard factors (e.g., frequency, duration, impacted area and temperature drop) have demonstrated an upward trend. Three typical events occurring in January 2008, January 2023, and February 2024 are analyzed in detail. All have significant impacts on southern China, yet exhibited distinct differences in intensity, duration, spatial distribution, and disaster-causing mechanisms.
Gaoligong Mountain, a crucial ecological barrier in Southwest China, has experienced increasing frequency of lightning-induced fires in recent years, threatening ecosystems and human safety. To investigate the underlying meteorological mechanisms, a diagnostic analysis is conducted on two fire events in Gaoligong Mountain Nature Reserve, occurring on 15-16 March 2023, and 17-18 April 2024. Multi-source datasets are integrated, including VLF/LF (very low frequency/low frequency) 3-dimensional lightning location data, MODIS fire spots, ERA5 data, satellite-based vegetation and topography information.An improved lightning-induced fire identification algorithm is developed for Gaoligong Mountain, involving 3 steps: Spatio-temporal filtering, where the region is divided into 15 km×15 km grids and fire points are selected based on a maximum time lag of 24 h between lightning detection and fire ignition; population density filtering, which retains only areas with a density below 40 people per km; and low-probability verification, where repeated fires within 1 km grids are excluded based on the low recurrence probability (below 0.5%) of lightning-induced fires, thus effectively filtering out human-caused ignitions. Separately, mesoscale circulation signals are extracted using Barnes band-pass filter, with results validated against Himawari-8 satellite data.Regional analysis indicates a higher ground flash density in the southern part of the Reserve, while greater lightning current amplitudes are observed in the northern Nujiang River Basin. The land cover is dominated by coniferous, broad-leaved, and mixed forests. The complex terrain is found to modulate airflow, promote charge accumulation, and increase the probability of lightning occurrence.Meteorologically, large-scale circulation features an upper-level cold trough transporting cold air southward, and a mid-low level warm ridge bringing high temperatures. The atmosphere exhibits an unstable configuration with dry air overlaying moist air, providing energy sources for lightning-induced fire events. Mesoscale analysis indicates that both events are associated with a 700 hPa low vortex, with relative vorticity increasing approximately 3 h prior to the lightning occurrence. Fires are in the rear sector of 700 hPa low vortex, controlled by the easterly airflow. Under the influence of terrain, the airflow converged with the local valley wind circulation after crossing the mountain, triggering convection, which leads to the development of thunderstorm clouds and ground flashes. Small-scale conditions included 7 consecutive days without effective precipitation prior to fires, sustained temperatures above the monthly average, and dry surface fuels. The evaporation of precipitation particles in the lower troposphere enhances charge exchange between supercooled water droplets and ice crystals, resulting in ground flashes. These ground flashes with current amplitudes between 20 kA and 30 kA, are identified as the direct cause of the two lightning-induced fire events.Through these approaches, a multi-scale coupling mechanism for lightning fires is identified, involving unstable energy from large-scale circulation, convection driven by a mesoscale low vortex, and ignition enabled by small-scale dry fuels, thereby providing critical scientific support for regional prevention and early warning.
Based on lightning groups detected by lightning mapping imager (LMI) of FY-4A meteorological satellite and lightning electromagnetic pulse (LEMP) detected by DDW1 sensors of national lightning detection system from April to August during 2021-2023, spatial and temporal distributions and matching characteristics of these two datasets are analyzed for Beijing-Tianjin-Hebei Region. Results indicate that the frequency of DDW1-detected LEMP is roughly one order of magnitude higher than that of LMI-detected lightning groups, suggesting a generally higher detection efficiency of DDW1 in the target area. The spatial distribution of DDW1-detected LEMP is relatively concentrated with significant land-sea differences, while LMI-detected lightning groups are more dispersed and evenly distributed across the Bohai Sea and land areas. Monthly variations of lightning frequency detected by DDW1 and LMI are generally consistent, The diurnal variation of DDW1-detected LEMP peaks at 1600-1700 BT and reaches its minimum at 0900-1000 BT, while LMI-detected lightning groups are most frequent at 1900-2000 BT and least frequent at 1300-1400 BT. LMI exhibits significantly weaker efficiency during daytime compared to nighttime. Using spatiotemporal matching thresholds of 2 s and 50 km, 48.6% of LMI lightning groups match with DDW1 LEMP signals. The matched LMI-detected lightning groups exhibit higher average radiance (approximately twice that of unmatched groups) and larger current amplitudes in DDW1-detected cloud-to-ground lightning (CG). A significant negative correlation is observed between the radiance of matched LMI-detected lightning groups and the current intensity of matched DDW1-detected negative CG, indicating that higher radiance is associated with stronger negative current intensity. The matched LMI-detected lightning groups are predominantly concentrated in the eastern part of Beijing-Tianjin-Hebei Region, while unmatched lightning groups are frequently observed near the Taihang and Yanshan mountainous ranges, the central Bohai Sea and mountainous areas of central Shandong Province. The proportion of LMI-detected lightning groups that match with DDW1-detected LEMP is higher during daytime and lower at night. Although fewer lightnings are detected by LMI during daytime, they exhibit higher radiance and are often accompanied by strong lightning currents, making them more readily detectable by DDW1. Associated with weaker lightning currents, LMI-detected lightning groups have lower radiance at night, resulting in approximately half of them remaining undetected by DDW1.
In the analysis of high-impact extreme weather events, particularly in historical extreme value statistics, challenges such as inconsistent data sources, non-unified statistical methodologies, low computational efficiency for long time-series data, and overly technical data representations often lead to significant analytical errors, inefficiencies, insufficient impact assessments across sectors, and inadequate public communication. To address these bottlenecks, this study leverages the Meteorological Big Data Cloud Platform (Tianqing) to develop Extreme Value One-table Visualization Service System, a unified, visualization-driven analytical platform dedicated to historical ground-based meteorological extremes.The system focuses on core meteorological variables, including precipitation, temperature, and wind, from long-term hourly and daily observations at 2400 national ground stations across China. By integrating GBase 8a, an analytical database optimized for large-scale data processing, the platform enables efficient retrieval and statistical analysis of terabyte-level meteorological datasets, supporting real-time, rapid queries over extensive observational records. Its hierarchical query architecture allows seamless integration of real-time observations, facilitating station-wise extreme value ranking since each station's inception. Moreover, the system supports flexible queries by arbitrary spatial domains (e.g., administrative regions, river basins, or user-defined areas) and temporal windows (e.g., the past 5 or 10 years), catering to diverse research and operational needs. To enhance accessibility and visualization performance, the system adopts a browser/server (B/S) architecture. It features a responsive user interface built with the Vue.js framework, immersive 3D spatial pattern rendering via advanced web graphics technologies, and dynamic map interactions powered by WebGIS integration. These innovations significantly improve user experience, enabling the generation of high-resolution dynamic spatial distribution maps, multi-dimensional comparative analyses, and temporal trend visualizations, all with millisecond-level query response times.Since its nationwide operational deployment in 2023, the system has served both national and provincial meteorological agencies, supporting post-event analyses of major weather events. By the year of 2025, the platform has accumulated 9.42 million visits, demonstrating its scalability, stability, and practical value in meteorological operations. By synergizing big data infrastructure with user-centered visualization design, this research provides robust support for scientific decision-making on climate change and extreme weather among researchers and the public alike. Future development will focus on extending statistical services to massive gridded datasets and deeply integrating cutting-edge AI technologies to enable conversational, question-answering-style user interactions.
Lightning channel reactivation is intimately linked to channel decay and cutoff processes, yet the transition from a conductive plasma channel to a non-conductive state remains difficult to characterize. Due to limitations of existing observation techniques, numerical modeling is essential to elucidate the mechanisms of channel decay and reactivation. A self-sustained charge neutrality intra-cloud lightning parameterization model is used to simulate multiple intracloud lightnings in various tripolar thunderstorm charge environments. The channel cutoff threshold is systematically varied to investigate its impact on lightning development and reactivation.Simulation results indicate that lightning development is highly correlated with the channel cutoff threshold. Lower cutoff thresholds (10-3-10-2 S·m-1) produce flashes with longer durations, more complex branching, and higher reactivation initiation fields (more than 300 kV·m-1). In this regime, the channel still persists even when the channel conductivity is extremely low. Consequently, a high reactivation initiation field (more than 300 kV·m-1) is required to re-ionize the decayed channel. Under these conditions, fewer than 20 reactivation processes are observed, most of which are short attempts (less than 100 m) as the channel is nearly insulating. Specifically, the low residual conductivity impedes charge transport, thereby making re-breakdown difficult to achieve. In contrast, higher thresholds (1-10 S·m-1) lead to flashes that are shorter in duration and exhibit significantly fewer branches. Under these conditions, the channel is cut off while still maintaining significant residual conductivity, effectively serving as a primed path for subsequent discharges. Consequently, the reactivation initiation field decreases to 10-120 kV·m-1, whereas the reactivation frequency increases to several hundred events, with reactivated channel lengths often exceeding 200 m. The number of significant reactivation processes notably increases. Furthermore, increasing reactivation events along the main channel lead to a further accumulation of residual conductivity. When subsequent reactivation propagates along these existing paths, the re-breakdown electric field is lowered, which facilitates the long-distance propagation of reactivation.These results indicate that the lightning development process is intimately related to the channel cutoff threshold: Higher cutoff thresholds result in earlier channel extinction and shorter flash durations, but leave higher residual conductivity, thereby favoring reactivation. Conversely, lower thresholds prolong lightning evolution and promote spatial extension, yet the diminished residual conductivity suppresses reactivation. Frequent reactivation facilitates charge transfer and mitigates electric field enhancement, whereas limited reactivation allows for greater charge accumulation and higher field intensities. Therefore, the lightning development is regulated by the interaction between channel conductivity and reactivation processes.
At present, China's meteorological departments are gradually upgrading their lightning location networks from ADTD (Advanced TOA and Direction) system to DDW1 Total Lightning Location System. Based on lightning data from both DDW1 and ADTD in Jiangsu Province, the spatiotemporal distribution characteristics and current intensity of lightning events are comparatively analyzed to evaluate consistency and differences in detection performance between these two systems. Based on this comparison, the evolution of total lightning activity and the vertical distribution of cloud flashes during a squall line event on 4 July 2024 are examined through integrated analysis of S-band weather radar observations and Jiangsu Atmospheric Sounding Array (JASA) data, elucidating capabilities and constraints of DDW1 in detecting total lightning during severe convective weather. Results indicate that DDW1 and ADTD show consistent spatiotemporal patterns of cloud-to-ground (CG) lightning activity, both exhibiting a clear decreasing trend from south to north, with summer being the dominant season for CG lightning occurrence. Despite this similarity, DDW1 detects higher average density of CG lightning (especially positive CG lightning) with more spatially concentrated high-density regions compared to ADTD. The diurnal variation shows that CG lightning activity begins to increase at around 1000 BT in both systems, DDW1 exhibits a single-peak pattern in the afternoon, while ADTD displays a multi-peak distribution. Regarding current intensity, DDW1 generally reports lower current values than ADTD. It also detects low-magnitude currents (0-5 kA) over an area approximately 15 times larger than ADTD, indicating significantly higher sensitivity to weak lightning discharges. During the analyzed squall line event, lightning detected by DDW1 predominantly clustered within areas exhibiting strong radar reflectivity (exceeding 35 dBZ), with cloud flashes reaching maximum frequency at altitude between 9 and 11 km. Spatial and vertical variations of lightning activity correspond well with the evolution of intense radar echoes, while temporal variation in total flash frequency shows excellent agreement with JASA observations. These results confirm the operational reliability of DDW1 for total lightning monitoring during severe weather events. Although DDW1 shows notable advantages in lightning monitoring through enhanced detection sensitivity and expanded spatial coverage, its classification algorithms and cloud flash detection capabilities need further improvements.
