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Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system. [ABSTRACT FROM AUTHOR]

Low visibility event, as a disastrous weather, has great impacts on traffic and transportation, aircraft, and people's daily life, etc. Timely and accurate forecasts of low visibility events are urgently needed and meaningful. The reasonable algorithm of atmospheric extinction in atmospheric chemistry models is the basis for quantitatively predicting low visibility. The revised IMPROVE algorithm (RIMP) of atmospheric extinction is incorporated into the chemistry-weather interacted model GRAPES_Meso5.1/CUACE CW V1 to improve the prediction of low visibility events (LVEs) in the urban agglomerations in eastern China, which is compared with the original IMPROVE algorithm (OIMP) used in this model. The study results show that the RIMP effectively reduces the overestimation of low visibility prediction by OIMP in general, leading to a decrease of root-mean-square errors (RMSEs) and an increase of Threat Score (TS) of visibility <3 km, 5 km, and 10 km overall both at regional and city scales in varying degrees due to its more detailed processing of aerosols' size, optical feature and hygroscopic growth; The improvements of visibility prediction of LVEs by RIMP depends on the combined contribution of high relative humidity (RH) and PM 2.5 instead of single high RH or PM 2.5. The relative contributions of RH and PM 2.5 concentration on different levels of low visibility are different in Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions due to their different RH and PM 2.5 , which leads to the different improvement of RIMP in the two regions. The larger improvements by RIMP occur for visibility <5 km in BTH, while in YRD, the larger improvements by RIMP occur for visibility <10 km and >5 km. Moreover, the improvements by RIMP were more evident with higher RH conditions in both regions. The uncertainty created by the extinction algorithm is one important factor of the multiple factors affecting LVEs prediction; accurate modeling of high RH near saturation is also very important for LVEs prediction. • The revised IMPROVE (RIMP) algorithm is applicated to the atmospheric chemistry model to improve low visibility prediction. • RIMP generally improves low visibility prediction than the original IMPROVE algorithm (OIMP). • The improvements in low visibility prediction by RIMP vary depending on different aerosol and humidity conditions. [ABSTRACT FROM AUTHOR]

Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model GRAPES_Meso5.1/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m−2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models. •ACI increases COT and CLWP when low-cloud and haze coexist. • ACI further leads to decreased SDSR, lower temperature, and higher RH. • ACI-induced changes improve mesoscale NWP to a certain extent. • The ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude. [ABSTRACT FROM AUTHOR]