An approach to refining the ground meteorological observation stations for improving PM_2.5 forecasts in Beijing-Tianjin-Hebei region

Abstract

Abstract. The paper investigates how to refine the ground meteorological observation network for greatly improving the PM2.5 concentration forecasts by identifying the sensitive areas for targeted observations 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 non-linear 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 surrounding 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 surrounding 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 achieve the forecasting skill comparable to, even higher than that obtained by assimilating all ground station observations. This network sheds light on that some of the current ground stations within and surrounding the BTH region are very useless for improving the PM2.5 forecasts in the BTH region and can be greatly scattered to avoid the thankless work.

Analysis of Precipitation Zone Forecasts and Examination of Numerical Forecasts for Two Heavy Rainfall Processes in June 2019 in Jiangxi, China 2019

“…In both of these heavy rainfalls, the 850 hPa shear lines were situated around 28 • N, in close proximity to each other. A low-level jet (LLJ) is typically present on the southern side of the shear in most cases [28,35]. Usually, the precipitation zones of heavy rainfalls occur between the southern side of the shear and the exit area of the low-altitude flow.…”

Spatiotemporal distribution of negative air ion and PM2.5 in urban residential areas

“…To a certain extent, the higher relative humidity promoted the absorption of moisture and clumping of fine PM. 23,24 Correlation analysis of NAI with respect to temperature and relative humidity Previous studies have shown that temperature and relative humidity have an important influence on the NAI concentration. 25 Therefore, based on the measured data under different weather conditions, a correlation analysis between NAI, temperature and relative humidity was carried out as shown in Table 2.…”

The Sensitive Area for Targeting Observations of Paired Mesoscale Eddies Associated With Sea Surface Height Anomaly Forecasts

“…With the realization of satellite technology, the altimeter provides SSHA observation data on a nearly global scale, and then assimilating these data becomes an effective way to reduce the uncertainty of SSHA prediction (Oke et al., 2013). It is worth noting that assimilating additional observations in regions highly sensitive to the initial values of forecasts leads to a significant improvement in forecasting skill; conversely, in areas with weak sensitivity, it may yield only modest improvements or, in certain instances, even poorer forecasting skills due to additional errors induced by imperfect assimilation procedures or unsolved processes in models (Janjic et al., 2018; Yang et al., 2023). Hence, even if there are sufficient observations, it remains crucial to prioritize the specific areas where the observations should be preferentially assimilated to attain better forecasting skills.…”