The Key Supply Source of Long-Distance Moisture Transport for the Extreme Rainfall Event on July 21, 2012 in Beijing

In this study, the Weather Research and Forecasting (WRF) model and meteorological observation data were used to research the long-distance moisture transport supply source of the extreme rainfall event that occurred on July 21, 2012 in Beijing. Recording a maximum rainfall amount of 460 mm in 24 h, this rainstorm event had two dominant moisture transport channels. In the early stage of the rainstorm, the first channel comprised southwesterly monsoonal moisture from the Bay of Bengal (BOB) that was directly transported to north China along the eastern edge of Tibetan Plateau (TP) by orographic uplift. During the rainstorm, the southwesterly moisture transport was weakened by the transfer of Typhoon Vicente. Moreover, the southeasterly moisture transport between the typhoon and western Pacific subtropical high (WPSH) became another dominant moisture transport channel. The moisture in the lower troposphere was mainly associated with the southeasterly moisture transport from the South China Sea and the East China Sea, and the moisture in the middle troposphere was mainly transported from the BOB and Indian Ocean. The control experiment well reproduced the distribution and intensity of rainfall and moisture transport. By comparing the control and three sensitivity experiments, we found that the moisture transported from Typhoon Vicente and a tropical cyclone in the BOB both significantly affected this extreme rainfall event. After Typhoon Vicente was removed in a sensitivity experiment, the maximum 24-h accumulated rainfall in north China was reduced by approximately 50% compared with that of the control experiment, while the rainfall after removing the tropical cyclone was reduced by 30%. When both the typhoon and tropical cyclone were removed, the southwesterly moisture transport was enhanced. Moreover, the sensitivity experiment of removing Typhoon Vicente also weakened the tropical cyclone in the BOB. Thus, the moisture pump driven by Typhoon Vicente played an important role in maintaining and strengthening the tropical cyclone in the BOB through its westerly airflow. Typhoon Vicente was not only the moisture transfer source for the southwesterly monsoonal moisture but also affected the tropical cyclone in the BOB, which was a key supply source of long-distance moisture transport for the extreme rainfall event on July 21, 2012 in Beijing.     

Verification and Assessment of Real-time Forecasts of Two Extreme Heavy Rain Events in Zhengzhou by Operational NWP Models

In the present study, the performances of the NWP models on two heavy rainfalls on 20 July and 22 August 2021 over Henan Province were investigated. The impacts of the water vapor transport to the extreme rainfall were further discussed. The results showed that the regional model system in the Guangzhou Meteorological Service generally showed high scores on the extreme rainfall over Henan. The maximum 24h accumulative rainfall by the 24h forecasts by the CMA-GD reached 556 mm over Henan Province. The 24-h and 48-h Threat Score (TS) of heavy rainfall reached 0.56 and 0.64. The comparisons of the Fraction Skill Score (FSS) verifications of the heavy rainfall by CMA-GD and CMA-TRAMS at the radium of 40km reached 0.96 and 0.87. The water vapor transport to the extreme rainfall showed that the vertically integrated water vapor transport (IVT) of the whole layer before the occurrence of the heavy rainfall exhibited a double-eyes distribution in case 7 · 20. The north eye over Henan reached the same magnitude of IVT as the typhoon eye (Cempaka) over south China. The IVT over the lower troposphere (<500 hPa) showed an overwhelming magnitude than the upper level, especially in the planetary boundary layer (<700 hPa). More practical technical needs to be developed to improve its performances on the forecasting of extreme rainfall, as well as more experiments need to be conducted to examine the effects of the specific terrain and physical schemes on the extreme rainfall.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

两次强降水过程雨滴谱特征及差异

利用寿县观测站内的Parsivel激光雨滴谱仪结合观测站雨量数据及雷达基数据,分析了发生在2015年6月26—30日梅雨期间和2015年8月7—10日超强台风"苏迪罗"影响期间2次强降水过程的雨滴谱结构特征及其差异,拟合了雨强与雷达反射率因子之间的关系。结果表明:雨强的大小直接影响雨滴谱的特征参量,且随着雨强增大而增大;梅雨锋暴雨中1.0mm直径≤1.5mm的粒子所占比例最多,雨强贡献率最大;台风雨中0.75mm直径≤1.0mm的粒子所占比例最多,但1.0mm直径≤1.25mm的粒子对雨强的贡献最大,说明较大粒子对强降水的贡献较大。     

The Extra-Area Effect in 71 Cloud Seeding Operations during Winters of 2008–14 over Jiangxi Province,East China

Effects of weather modification operations on precipitation in target areas have been widely reported, but little is specifically known about the downwind (extra-area) effects in China. We estimated the extra-area effect of an operational winter (November-February) aircraft cloud-seeding project in northern Jiangxi Province in eastern China by using a revised historical target/control regression analysis method based on the precipitation data in winter. The results showed that the overall seasonal average rainfall at the downwind stations increased by 21.67%(p=0.0013). This enhancement effect was detected as far as 120 km away from the target area. Physical testing was used to compare the cloud characteristics before and after seeding on 29 November 2014. A posteriori analysis with respect to the characteristics of cloud units derived from operational weather radar data in Jiangxi was performed by tracking cloud units. Radar features in the target unit were enhanced relative to the control unit for more than two hours after the operational cloud seeding, which is indicative of the extra-area seeding effect. The findings could be used to help relieve water shortages in China.     

NUMERICAL PREDICTION OF AN EXTREME RAINSTORM OVER THE PEARL RIVER DELTA REGION ON 7 MAY 2017 BASED ON WRF-ENKF

An ensemble Kalman filter based on the Weather Research and Forecasting Model (WRF-EnKF) is used to explore the effectiveness of the assimilation of surface observation data in an extreme local rainstorm over the Pearl River Delta region on 7 May 2017. Before the occurrence of rainstorm, the signals of weather forecasts in this case are too weak to be predicted by numerical weather model, but the surface temperature over the urban area are high. The results of this study show that the wind field, temperature, and water vapor are obviously adjusted by assimilating surface data of 10-m wind, 2-m temperature, and 2-m water vapor mixing ratio at 2300 BST 6 May, especially below the height of 2 km. The southerly wind over the Pearl River Delta region is enhanced, and the convergence of wind over the northern Guangzhou city is also enhanced. Additionally, temperature, water vapor mixing ratio and pseudoequivalent potential temperature are obviously increased over the urban region, providing favorable conditions for the occurrence of heavy precipitation. After assimilation, the predictions of 12-h rainfall amount, temperature, and relative humidity are significantly improved, and the rainfall intensity and distribution in this case can be successfully reproduced. Moreover, sensitivity tests suggest that the assimilation of 2-m temperature is the key to predict this extreme rainfall and just assimilating data of surface wind or water vapor is not workable, implying that urban heat island effect may be an important factor in this extreme rainstorm.     

CWRF模式在中国夏季极端降水模拟的误差订正

利用1980—2015年6—8月我国逐日降水观测数据评估CWRF模式（Climate-Weather Research and Forecasting model）多种参数化方案对我国夏季日降水的模拟能力,并考察累积概率变换偏差订正法（CDFt）的订正效果。通过将广义帕累托分布（GPD）引入到偏差订正模型中,提出针对极端降水的累积概率变换偏差订正法（XCDFt）,检验和评估其对极端降水订正的适用性。结果显示:CWRF模式微物理过程选用Morrison-aerosol参数化方案组合对我国降水场的模拟较好,CDFt订正效果良好;XCDFt偏差订正模型能够较好地提取模式建模与验证时期变化信号,订正后相比订正前与观测极端降水的概率分布更为接近;经过XCDFt订正后华南、华中和华北地区20年一遇的极端降水重现水平较模拟值更接近观测值,可为CWRF模式提高极端降水的业务预测水平提供参考。     

基于DTW与K-means算法的河北场雨及雨型分区特征研究

深入挖掘气象站点的观测降雨数据,研究区域降雨的雨型规律,对于洪涝灾害预警和减灾措施制订有重要意义.本文基于河北省2005-2017年3189个站点逐小时降雨观测数据,进行"场雨"的划定,进而提取历史上各场雨的累积雨量、时长指标.采用数据挖掘技术中的DTW相似性算法进行场雨雨型的自动归类,将场雨分成Ⅰ-Ⅶ共7种雨型,包括...     

基于兰利法的泥石流临界雨量实验研究

分析了目前泥石流临界雨量确定常用方法存在的问题 ,以及泥石流临界雨量实验研究中实验降雨量确定的随意性和盲目性 ,提出以兰利 ( Langlie)法为指导进行实验 ,使实验降雨量的确定有了理论依据 ,减少了取得可靠实验结果所需实验次数 ,从而使泥石流临界雨量实验研究更趋完善和可行     

Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function(EOF) analysis. The predictors used in this study are Arctic sea ice concentration(ASIC) and regional sea surface temperature(SST) in selected optimal time periods. The most important role that Arctic sea ice(ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC(PA), SST(PS), and the two together(PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years(2005-2015). The correlation coefficient(COR) skills between the observed and cross-validation reforecast principal components(PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980-2004. The COR skill of the PA model during the independent forecast period of 2004-2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.