Based on the PM_2.5 and PM₁₀ mass concentration data obtained from 51 national air quality monitoring stations and the corresponding rainfall intensity data in automatic meteorological stations in Hubei Province from 1 January 2015 to 31 December 2017, the impact of rainfall intensity on PM mass concentrations under relatively different humidity conditions was analyzed. The results showed that light rain occurred most frequently in the pollution process, with Xiangyang being affected for up to 587 h. PM concentration would not change drastically under the effect of precipitation. Mean rainfall intensity responsible for wet growth of PM₁₀ and PM_2.5 was mainly 1.4 mm/h) than that of PM₁₀ (>1.0 mm/h). Precipitation was more likely to produce a wet removal effect for a greater initial value of PM mass concentration, and on the contrary, a wet growth effect was more likely, with the threshold of PM₁₀ mass concentration being 150 μg/m³ and that of PM_2.5 mass concentration being 95 μg/m³. Wet removal played a leading role in lower humidity (∼60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity. As the precipitation level increased (rainfall ≥1.5 mm·h⁻¹), the wet removal to PM₁₀ mass concentration was enhanced more obviously. The variations of PM_2.5 had similar distributions to those of PM₁₀ under the effect of precipitation, but the wet removal effect of precipitation was weakened and the wet growth effect was enhanced.

Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.

The regional transport of air pollutants has been identified as a critical factor that affects air quality over downwind receptor areas; however, the regional transport patterns for air pollution episodes remain poorly understood. In this study, we identified two distinct patterns of regional transport: rapid transit transport (RTT) and stationary accumulation transport (SAT), both of which induced severe PM_2.5 pollution episodes in central China during 2015–2020. The differences and similarities between the two regional transport patterns of air pollution were characterized by their influencing meteorological factors. Similar meteorological conditions trigger the RTT and SAT, with a high-PM_2.5 concentration air mass from the upstream regions transported to the Twain-Hu Basin (THB) by the strong northerly winds with a southward advance of cold air mass. The average rate of increase in the PM_2.5 concentration of the RTT (12.5 μg/m³/h) is more than that of the SAT (5.7 μg/m³/h). However, meteorological conditions evolved differently in RTT and SAT. For RTT with the fast passage of cold air, the THB is located behind the strong cold front during the later stage, and these events rapidly ended owing to the northerly winds and wet scavenging of precipitation. For SAT with the slow cold air accumulation, the THB is mainly occupied by a weak high-pressure system with low wind speeds, strong descending airflow, and a low atmospheric boundary layer height, trapping the air pollutants accumulated in the THB and resulting in persistent heavy pollution incidents. This study provides new insights into the meteorological mechanism underlying the formation of severe air pollution episodes over a receptor region owing to the regional transport patterns of air pollutants.

Microphysics parameterization becomes increasingly important as the model grid spacing increases toward convection-resolving scales. Using observations from a field campaign for Mei-Yu rainfall in China, four bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) model were evaluated with respect to their ability to simulate precipitation, structure, and cloud microphysical properties over convective and stratiform regimes. These are the Thompson (THOM), Morrison graupel/hail (MOR_G/H), Stony Brook University (SBU_YLIN), and WRF double-moment six-class microphysics graupel/hail (WDM6_G/H). All schemes were able to predict the rain band but underestimated the total precipitation by 23%–35%. This is mainly attributed to the underestimation of stratiform precipitation and overestimation of convective rain. For the vertical distribution of radar reflectivity, many problems remain, such as lower reflectivity values aloft in both convective and stratiform regions and higher reflectivity values at middle level. Each bulk scheme has its advantages and shortcomings for different cloud regimes. Overall, the discrepancies between model output and observations mostly exist in the midlevel to upper level, which results from the inability of the model to accurately represent the particle size distribution, ice processes, and storm dynamics. Further observations from major field campaigns and more detailed evaluation are still necessary.

Regional PM_2.5 transport is an important cause of atmospheric environment change. However, the variations in regional PM_2.5 transport on a synoptic scale with meteorological drivers have been incomprehensively understood. Therefore, this study is targeted at the quasi-weekly oscillation (QWO) of regional PM_2.5 transport over central and eastern China (CEC) with the influence of synoptic-scale disturbance of the East Asian winter monsoon (EAWM) circulation. By constructing the data of daily PM_2.5 transport flux in CEC in the winters of 2015–2019, we utilize the extended empirical orthogonal function (EEOF) decomposition and other statistical methods to extract the moving spatial distribution of regional PM_2.5 transport over CEC, recognizing the QWO in regional PM_2.5 transport with the spatial–temporal variations over CEC. The source–receptor relationship in regional transport of PM_2.5 is identified with the 2 d lag effect of the North China Plain, as the upwind source region, on the PM_2.5 pollution change in the Twain-Hu Basin, as the downwind receptor region in central China. The QWO of regional PM_2.5 transport over CEC is regulated by the synoptic-scale disturbance of the EAWM circulation with the periodic activities of the Siberian high. These findings provide new insights into the understanding of regional PM_2.5 transport with the source–receptor relationship and the meteorological mechanism in atmospheric environment change.