Search

This study investigates the impacts of grid spacing and station network on surface analyses and forecasts including temperature, humidity, and winds in Beijing Winter Olympic complex terrain. The high-resolution analyses are generated by a rapid-refresh integrated system that includes a topographic downscaling procedure. Results show that surface analyses are more accurate with a higher targeted grid spacing. In particular, the average analysis errors of surface temperature, humidity, and winds are all significantly reduced when the grid size is increased. This improvement is mainly attributed to a more realistic simulation of the topographic effects in the integrated system because the topographic downscaling at higher grid spacing can add more details in a complex mountain region. From 1 km to 100 m, 1-12-h forecasts of temperature and humidity are also largely improved, while the wind only shows a slight improvement for 1-6-h forecasts. The influence of station network on the surface analyses is further examined. Results show that the spatial distributions of temperature and humidity at a 100-m space scale are more realistic and accurate when adding an intensive automatic weather station network, as more observational information can be absorbed. The adding of a station network can also reduce forecast errors, which can last for about 6 h. However, although surface winds display better analysis skill when more stations are added, the wind at the mountaintop region sometimes encounters a marginally worse effect for both analysis and forecast. The results are helpful to improve the analysis and forecast products in complex terrain and have some implications for downscaling from a coarse grid size to a finer grid. [ABSTRACT FROM AUTHOR]

The PM2.5 (fine particulate matter with diameter ≤2.5 μm) concentration anomalies over the North China Plain (NCP) in early and late winter sometimes show a subseasonal reversal, which brings a great challenge for precise control of air pollution, and mechanisms are not well understood. This paper reveals the key role of the Barents Sea sea‐ice in this reversal. In early winter, a negative Scandinavian‐like pattern causes an anticyclonic anomaly over Northeast Asia and thus leads to the positive PM2.5 concentration anomaly over the NCP. In addition, anomalous warm advection associated with the positive North Atlantic Oscillation‐like pattern accelerates winter sea‐ice loss in the Barents Sea, especially in late winter, which increases the surface turbulent heating flux. These heating causes a negative Polar/Eurasian‐like pattern that induces a cyclonic anomaly over Northeast Asia and eventually leads to a negative PM2.5 concentration anomaly over the NCP in late winter. Vice versa. Plain Language Summary: In this study, we find that there is a significant seesaw pattern of PM2.5 concentration anomalies over the North China Plain (NCP) in early and late winter. Further results reveal that variations in Barents Sea sea‐ice play a crucial role. More specifically, a negative (positive) Scandinavian‐like pattern in early winter tends to induce an anticyclonic (a cyclonic) anomaly over Northeast Asia. This anticyclonic (cyclonic) anomaly leads to a higher (lower) PM2.5 concentration anomaly over the NCP in early winter. In addition, a North Atlantic Oscillation‐related warm (cold) temperature advection promotes (inhibits) the sea‐ice loss in the Barents Sea, especially in late winter. The Barents Sea‐ice loss (increment) in late winter tends to heat (cool) the lower‐level atmosphere and thus triggers an anticyclonic (a cyclonic) response in the upper troposphere there. The anticyclonic (cyclonic) response in the Barents Sea region as a part of the negative (positive) Polar/Eurasian‐like pattern induces a cyclonic (an anticyclonic) anomaly over Northeast Asia, which leads to a lower (higher) PM2.5 concentration anomaly over the NCP in late winter. These findings could be of great value for the subseasonal predictions of PM2.5 concentrations over the NCP in winter. Key Points: PM2.5 concentration anomaly over the North China Plain shows an visibly subseasonal reversal in early and late winter on interannual scaleThe Scandinavia‐like pattern and Polar/Eurasian‐like pattern are responsible for the seesaw pattern of PM2.5 concentration anomaliesThe sea‐ice variations in the Barents Sea play a key role in modulating such atmospheric circulations [ABSTRACT FROM AUTHOR]