An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing-Tianjin-Hebei region

This paper investigates how to refine the ground meteorological observation network to greatly improve the PM2.5 concentration forecasts by identifying sensitive areas for targeted observations that are associated with a total of 48 forecasts in eight heavy haze events during the years of 2016-2018 over the Beijing-Tianjin-Hebei (BTH) region. The conditional nonlinear optimal perturbation (CNOP) method is adopted to determine the sensitive area of the surface meteorological fields for each forecast, and a total of 48 CNOP-type errors are obtained including wind, temperature, and water vapor mixing ratio components. It is found that, although all the sensitive areas tend to locate within and/or around the BTH region, their specific distributions are dependent on the events and the start times of the forecasts. Based on these sensitive areas, the current ground meteorological stations within and around the BTH region are refined to form a cost-effective observation network, which makes the relevant PM2.5 forecasts starting from different initial times for varying events assimilate fewer observations, but overall, it achieve the forecasting skill comparable to and even higher than that obtained by assimilating all ground station observations. This network sheds light on the idea that some of the current ground stations within and around the BTH region are very useless for improving the PM2.5 forecasts in the BTH region and can be greatly scattered to avoid unnecessary work.

Automated summary

This paper investigates how to refine the ground meteorological observation network to greatly improve the PM2.5 concentration forecasts. By identifying sensitive areas, a cost-effective observation network is formed, achieving forecasting skill comparable to and even higher than assimilating all ground station observations. The study reveals the uselessness of some current ground stations in the Beijing-Tianjin-Hebei region for improving PM2.5 forecasts.