On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.
To understand the impacts of different combinations of planetary boundary layer (PBL), short-wave (SW) and longwave (LW) radiation schemes on the simulation results of meteorological variables and PM 2.5 concentrations under extremely heavy pollution conditions, the Weather Research and Forecasting model with Chemistry (WRF-Chem) model was applied in Beijing to investigate a high PM 2.5 pollution episode that occurred in January, 2013. Four PBL schemes, two SW schemes and three LW schemes with a total of 12 ensemble experiments were conducted in this study. The simulated meteorological variables including the temperature at 2 m (T2), the wind speed at 10 m (WS10) and the relative humidity (RH) were compared with their actual observations and the PM 2.5 concentrations. A correlation analysis between the PM 2.5 and T2, WS10 and RH values was also explored. The results indicated that there were no ideal scheme combinations that were most suitable for all meteorological variable simulations during this heavy pollution episode in Beijing. With the same emissions input, the simulation results of the WRF-Chem model that were configured with different physical parameterization schemes may vary significantly. As for the PM 2.5 simulation, the combination of the YSU PBL, Goddard SW and GFDL LW schemes showed the greatest consistency with the observed values. Although the PBL schemes have the dominant impacts on the simulations of meteorological variables, the selection of LW and SW schemes is of the same importance.
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