Characterization of Low Visibility and Forecasting Model in Chongqing Central Area

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By using the hourly visibility, temperature, pressure, humidity, wind, and atmospheric particulate concentration data in Chongqing from 2015 to 2023, the characteristics of low visibility (visibility <1000 m) in Chongqing and the influence of various factors on low visibility in Chongqing were analyzed. The visibility prediction model was established by using the neural network method, and the effect of introducing the PM2.5 concentration factor on low visibility prediction was analyzed and compared. Findings: Low visibility in Chongqing is dominated by precipitation low visibility (PLV), followed by fog low visibility (FLV), with the least proportion of fog‐haze mixed low visibility (FHLV). However, as visibility decreases further, the proportion of fog with low visibility increases significantly. The average visibility when fog occurs is lower than that when precipitation occurs and also much lower than that of fog‐haze mixed, indicating that low visibility is more affected by atmospheric water vapor. Over the past decade, as air pollutants have decreased each year, the proportion of fog and FHLV has also trended downward. The proportion of fog increases significantly in winter, and the low visibility below 200 m is mainly caused by fog in winter, while the increase of precipitation in June is the main cause of low visibility in this month. The diurnal variation of mean visibility under precipitation is relatively small. In contrast, the mean visibility during fog and fog‐haze mixed conditions is lower at night than during the day. The higher occurrence rate of these two types of low visibility conditions at night is a significant factor contributing to reduced visibility during nighttime. Atmospheric humidity, temperature, and particulate matter concentration are important factors affecting visibility, and visibility decreases significantly with the increase of PM2.5 when relative humidity (RH) is less than 70%, and PM2.5 has a lower effect on visibility when RH is greater than 70%. The forecast effect of introducing the PM2.5 concentration factor into the objective forecast model of visibility is better than that of not introducing the factor. The effect of introducing this factor is better than that of not introducing it, especially in the fall.