Heavy rain is a kind of severe weather, often causing floods and serious soil erosion, leading to engineering losses, embankment rupture and crop flooding and other significant economic losses. Especially for some low...Heavy rain is a kind of severe weather, often causing floods and serious soil erosion, leading to engineering losses, embankment rupture and crop flooding and other significant economic losses. Especially for some low-lying terrain areas, rainwater cannot quickly vent caused by farm water and soil moisture being too saturated, so it will cause more geological disasters. This article combines live and forecast data, aiming at the results of the mid-rainstorm forecast in North China during the period of 7.19-2016, and compares with the actual situation of rainstorm. We carry out the mid-term forecast of the rainstorm. The atmosphere is a kind of medium with various fluctuation phenomena, and its physical properties and changes are studied by the analysis of volatility which is an important research method. It is important to improve the accuracy of such severe weather forecasting rainstorms and to take precautionary measures in a timely manner to minimize the losses caused by rainstorms.展开更多
A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybr...A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode （BGM）- three-dimensional variational （3DVAR） Data Assimilation （DA） scheme is designed on running the Advanced Research WRF （ARW WRF） model using the Advanced Microwave Sounder Unit A （AMSU-A） satellite radiance data. Results show that： the BGM ense- mble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clu- sters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.展开更多
The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and exten...The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.展开更多
文摘Heavy rain is a kind of severe weather, often causing floods and serious soil erosion, leading to engineering losses, embankment rupture and crop flooding and other significant economic losses. Especially for some low-lying terrain areas, rainwater cannot quickly vent caused by farm water and soil moisture being too saturated, so it will cause more geological disasters. This article combines live and forecast data, aiming at the results of the mid-rainstorm forecast in North China during the period of 7.19-2016, and compares with the actual situation of rainstorm. We carry out the mid-term forecast of the rainstorm. The atmosphere is a kind of medium with various fluctuation phenomena, and its physical properties and changes are studied by the analysis of volatility which is an important research method. It is important to improve the accuracy of such severe weather forecasting rainstorms and to take precautionary measures in a timely manner to minimize the losses caused by rainstorms.
基金supported by the National Natural Science Foundation of China (Grant No. 40975031)the National Science Foundation for Young Scientists of China (Grant No.41205074)
文摘A fine heavy rain forecast plays an important role in the accurate flood forecast, the urban rainstorm watedogging and the secondary hydrological disaster preventions. To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode （BGM）- three-dimensional variational （3DVAR） Data Assimilation （DA） scheme is designed on running the Advanced Research WRF （ARW WRF） model using the Advanced Microwave Sounder Unit A （AMSU-A） satellite radiance data. Results show that： the BGM ense- mble prediction method can provide an effective background field and a flow dependent background error covariance for the BGM- 3DVAR scheme. The BGM-3DVAR scheme adds some effective mesoscale information with similar scales as the heavy rain clu- sters to the initial field in the heavy rain area, which improves the heavy rain forecast significantly, while the 3DVAR scheme adds information with relatively larger scales than the heavy rain clusters to the initial field outside of the heavy rain area, which does not help the heavy rain forecast improvement. Sensitive experiments demonstrate that the flow dependent background error covariance and the ensemble mean background field are both the key factors for adding effective mesoscale information to the heavy rain area, and they are both essential for improving the heavy rain forecasts.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2013CB430106)China Meteorological Administration Special Public Welfare Research Fund(GYHY201206005)+1 种基金National Natural Science Foundation of China(41175087)National Fund for Fostering Talents(J1103410)
文摘The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.
