Abstract:Based on hourly precipitation observations from 75 national meteorological stations in Zhe-jiang Province and ECMWF ERA5 reanalysis data(0.25°×0.25° horizontal resolution)from 2010 to 2022,this study develops a pattern-classification-based ingredient approach for spring regional rainstorm forecasting.Three dominant synoptic regimes were identified:stationary front rainband,warm-sector shear line,and trough-cold front patterns.For each regime,key environmental predictors were selected,and the random forest algorithm was applied to identify critical ingredients and construct a composite in-gredient index,establishing regime-specific rainstorm forecast models.The models were evaluated through historical hindcast and real-time verification during spring 2023-2024,and compared against ECMWF,GRAPES_GFS,and Zhejiang Provincial OCF(Objectively Corrected Forecast)ensemble products.Results demonstrate that the"pattern-classification first,ingredient-selection second"strategy significantly improves rainstorm hit rates while reducing false alarm ratio and miss rate.In real-time fore-casting during 2023-2024,the models achieved a 95.7%hit rate,with substantially lower miss rates and significantly higher Threat Scores than the reference products.

Abstract:Lightning,as a common atmospheric discharge phenomenon in strong convective weather systems,poses significant challenges for accurate early warning due to its transient nature.Within short observation windows,the limited representativeness of lightning location data often fails to accurately re-flect the probability of lightning occurrence in the target area.To address these issues,this study propo-ses a method for constructing a thunderstorm index,based on ADTD lightning location data and lightning stroke current data,which integrates spatial weighting and standardization.This index is then used to achieve graded lightning warnings.A comparative test between this algorithm and the national standard GB/T 38121-2023 algorithm yields the following results:(1)The thunderstorm index effectively over-comes the discreteness limitations of raw lightning data.Areas with high index values show strong spatial consistency with intense radar echo areas,indicating that the index reliably identifies the center of thun-derstorm intensity.Furthermore,the dynamic changes in the thunderstorm index can characterize the spa-tial migration law and intensity development trend of thunderstorm activities.A rapid increase in the in-dex can serve as a key signal for the initiation,development,or movement of thunderstorm cloud clusters toward the center of the target area.(2)Compared to the national standard algorithm,which relies on predefined warning areas and fixed lightning count thresholds,the thunderstorm index algorithm offers greater flexibility.By optimizing spatial weighting parameters or adjusting index thresholds,it can better balance the Probability of Detection(POD)and the Effective Alert Rate(EAR)to meet the needs of different application scenarios.(3)Both algorithms meet the requirement of achieving a 10-minute lead time Probability of Detection(POD10min)exceeding 80%.However,the national standard algorithm ex-hibits relatively low Effective Alert Rates for 10-minute lead time(EAR10min),specifically 50.78%,38.83%,and 19.33%for Level 3,Level 2,and Level 1 warnings,respectively.In contrast,the thun-derstorm index algorithm improves these rates to 60.85%,53.02%,and 45.41%,demonstrating a sig-nificant advantage.(4)Regarding warning timeliness,the thunderstorm index algorithm maintains an Ef-fective Alert Rate for 20-minute lead time(EAR20min)of 53.22%while achieving a Probability of Detec-tion for 20-minute lead time(POD20min)of 80%.This indicates the method's potential for providing lon-ger effective warning times.Future integration of artificial intelligence time-series algorithms is expected to further enhance warning accuracy and effective warning duration.

Abstract:The South China Sea is rich in wind energy resources.It is of great significance to carry out scientific and accurate assessment for the future development and utilization of its offshore wind energy potential.This study employed a random forest algorithm to correct the ASCAT satellite-derived wind speed data over the South China Sea from 2011 to 2020,and the wind energy density was estimated based on the Weibull distribution.A model for wind turbine energy utilization efficiency was established using observations.Based on the satellite wind speed and ERA5 significant wave height data,a typhoon impact index was constructed by using the information entropy method.The spatial distribution of wind energy resources in the South China Sea was evaluated by combining the wind energy density,wind energy utili-zation efficiency and the typhoon impact index.The results are as follows.(1)Wind energy density in most areas of the South China Sea was high,with primary high-value areas located in the southern Taiwan Strait and the Luzon Strait.A non-significant decreasing trend in wind power density was observed from 2011 to 2020,primarily driven by a reduction in spring.(2)The wind energy utilization efficiency in the South China Sea was generally high,and the interannual variation remained basically stable,but the wind energy utilization efficiency in the high wind energy density area was low.(3)The high impact area of ty-phoon was located in the northern South China Sea and the eastern sea area of Hainan Island.The impact of typhoon showed an upward trend from 2011 to 2020,and had experienced two upward stages.(4)The evaluation index of wind energy resources in the northeast,central,southwest and southern waters of the South China Sea was high,and it was suitable to use islands to carry out offshore wind power generation.

Abstract:This study systematically analyzes the anomalous characteristics of the extreme high tem-perature and low precipitation event in Zhangjiajie during August 2024 using multi-source meteorological observation data,NCEP reanalysis data,and ocean monitoring products,and reveals its physical mecha-nisms from the perspective of multiple factors such as atmospheric circulation,water vapor transport,and air-sea interaction.The results are as follows:(1)The Northern Hemisphere 500 hPa geopotential height field exhibited a dipole pattern,with a"two-trough-one-ridge"circulation configuration over the mid-to-high latitudes of Eurasia.The eastward propagation of split short-wave troughs from the Ural Mountains low trough,together with the anomalously strong,westward-extending,and northward-shifting Western Pacific Subtropical High(WPSH),constituted the key circulation systems responsible for this event.(2)A vertical circulation cell,characterized by low-level convergence and ascent over the eastern Si-chuan-Chongqing-western Hunan region and upper-level divergence and subsidence from the Hetao area to North China,enhanced the subsidence warming effect.(3)The vertically integrated water vapor trans-port in the troposphere exhibited a"northward convergence and southward divergence"pattern,with cen-tral China acting as a net water vapor outflow region,thereby exacerbating the drought conditions.(4)The WPSH area,intensity,ridge line,and western extension ridge point indices all showed significant anomalies,with the ridge line and western extension ridge point being significantly positively correlated with the daily maximum temperature in Zhangjiajie.(5)This event occurred during the decay year of an eastern-Pacific type El Niño,and its climatic response differed markedly from historical years of the same type,highlighting the increasing complexity of ENSO impacts under global warming.(6)Although the South China Sea summer monsoon was delayed and weaker,the Walker circulation intensified phase-wise in August,exerting a positive feedback on the high temperature and drought by strengthening the subtrop-ical high.This study elucidates the driving mechanisms of extreme events in climate-sensitive regions through multi-factor synergy,providing a scientific basis for predicting such events and informing disaster response strategies.

Abstract:The 150 pilot reports （PIREPs） of icing conditions in Shandong province were collected. Aircraft icing diagnosing and forecasting experiments were carried out separately with simulation data of physical quantity field and three icing algorithms （ Ic, RAP, RAOB） , and the accuracy rates of the three algorithms were verified. To get better accuracy rate and lower missing rate, the Ic and RAOB algorithms were get improved by readjusting icing temperature and humidity thresholds, or corresponding coefficient. The accuracy rate of the new Ic algorithm was nearly increased by 10% in condition of keeping a lower missing rate, and the missing rate of the new RAOB algorithm was nearly decreased by 12% in condition of keeping a good accuracy rate.