甘肃省天水地区暴雨时空分布及天气学分型

利用1965—2019年常规观测资料及2008—2019年区域站加密资料，分析了天水地区暴雨时空分布特征，同时选取30个具有代表性的暴雨样本进行天气学分型及高低空物理量特征分析，确立了预报指标。结果表明：（1）近55 a来天水地区暴雨年均发生2.75站次，整体呈增多趋势，增加幅度为0.2站次?(10 a)-1，其中20世纪60年代后期及80—90年代呈减少趋势，20世纪70年代及21世纪以来呈增多趋势。（2）天水地区暴雨4月中旬—10月下旬都有出现，但集中在夏季7—8月，7月频次最高，月分布呈单峰型特征，旬分布呈三峰型特征，与副高位置密切相关。（3）天水地区暴雨空间分布自西向东依次增多，并存在多个分布中心。（4）天水地区暴雨环流形势分为低槽（涡）东移型和副高西北侧西南气流型两种，其高低空物理量配置各有不同。     

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM_2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m³ to –1.6 µg/m³. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM_2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM_2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM_2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.     

论四川盆地下中三叠统(T1j-T2L)地下水的形成

四川盆地下中三叠统嘉陵江组—雷口坡组(T1j-T2L)地下水分布广泛,资源丰富,类型各异。其展布规律及水文地球化学景观系在漫长的地质发展历史阶段中,经沉积、掺杂、淋滤、火山活动、生物化学及剥蚀淋滤等多种因素综合作用形成。对其形成分布的研究论述,有助于对地下水资源的开发利用。