Water vapor transport characteristics of a rainstorm process leading to se-vere urban waterlogging in Wuhu
This study aims to accumulate prediction experience of local rainstorm process,enhance understanding of heavy rainfall water vapor characteristics,and improve rainstorm prediction ability.Analyzing precipitation data from automatic meteorological observation stations,the China precipitation dataset,ERA5 reanalysis data,and NCEP/NCAR reanalysis data,we investigate the water vapor transport characteristics of a severe waterlogging rainstorm process in Wuhu City during the early morning of June 5,2022.Utilizing methods such as water vapor budget analysis,HYSPLIT backward trajectory tracking,and water vapor transport contribution rate analysis,our findings reveal that the rainstorm occurred in the 200 hPa diverging area and the left front of the 850 hPa low-lev-el jet stream.The continuous impacts of the 500 hPa cold air on the low-level jet stream triggered convective cloud clusters,resulting in strong precipitation.The high-level divergence and low-level convergence enhanced horizontal convergence and vertical transport of water vapor,while the southwest low-level jet stream strengthened and transported water vapor to the rainstorm area,providing the necessary conditions for the occurrence of the rainstorm.Consequently,Wuhu City had abundant water vapor before the heavy rainfall event,with a deep and continuously humidified wet layer.The total column water increased by 4.1 kg·m-2 in 6 hours,with the rainstorm occurring at the highest value of continuous water vapor increase.During the rainstorm,key parameters such as to-tal column water,specific humidity of 850 hPa,water vapor flux,and water vapor flux divergence reached signifi-cant levels,correlating well with the intensity of rainstorm.Our analysis attributes these conditions to the strength-ening southwest low-level jet stream from the Bay of Bengal to the lower reaches of the Yangtze River,continu-ously transporting water vapor to Wuhu City.After the rainstorm,water vapor quantities decreased significantly.The results of the water vapor budget and water vapor tracking analysis showed that before the rainstorm oc-curred,water vapor inflow mainly occurred at the western and southern boundaries of the lower troposphere.The inflow layer was deep,and there was upward vertical transport of water vapor.The quantity of water vapor inflow into the entire layer was approximately 56.0×107 t·h-1,with a main inflow height of 850-700 hPa.The maxi-mum quantity of water vapor inflow into a single layer could reach 9.0×107 t·h-1,originating mainly from 1 000 meters above the Bay of Bengal and the South China Sea,with their water vapor channel trajectory accounting for 32.0％and their water vapor transport contribution rate of 55.4％.The other two sources of water vapor in the northwest passage were 7 000 meters above the Baltic Sea and 3 000 meters below the Ural River.When the rain-storm occurred,the main inflow layer decreased to 850 hPa,and the southern boundary turned to outflow at 700 hPa.The inflow of the entire layer decreased to about 21.0×107 t·h-1,weakening vertical water vapor transport,with the total net inflow height of water vapor concentrated at 850 hPa and a net inflow quantity of about 1.0× 107 t·h-1.The water vapor mainly originated from 1 000 meters above the South China Sea,with their water vapor channel trajectory accounting for 46.0％and their water vapor transport contribution rate of 60.3％.The other two sources of water vapor in the northwest passage were over 2 000 meters east of the lower reaches of the Ural Riv-er and over 7 000 meters above the Norwegian Sea.The Bay of Bengal and the South China Sea were the main sources of water vapor during the rainstorm.The study underscores that while water vapor conditions are essential for rainstorm formation,other factors such as trigger mechanisms and impact system locations are crucial in un-derstanding waterlogging events.Future research should focus on integrating multi-source detection data to enhance rainstorm prediction and early warning capabilities.
万方数据
维普
