Ensemble-Based Data Assimilation Method Study Of Lightning And Radar Data For Mesoscale And Small-Scale Weather Process

China is one of the countries most severely affected by severe convective weather and typhoons.Forecasting of severe convective weather and typhoons relies heavily on numerical weather prediction(NWP).This study aims to improve the most accurate initial field for NWP by assimilating multisource observations and thus to improve the forecasting for severe convective weather and typhoon processes.Radar and lightning data are the most commonly used assimilated data in current scientific research and operations due to their high spatial and temporal resolution.The biggest difficulty in lightning data assimilation is that the lightning observations are not model variables.Based on the stream-dependent and easily implemented ensemble square root filter(EnSRF)assimilation method,this study proposes data assimilation methods using the maximum vertical velocity and water vapor as lightning proxy variables and compares the effectiveness of the two lightning assimilation methods for improving the forecasting of severe convective processes.Then,aiming at the problem of spurious convection existing in numerical prediction,the assimilation method of weak reflectivity is improved,and the assimilation method of "0" maximum vertical velocity is proposed.Finally,a scheme of fusion and assimilation of lightning,radar data and "0" maximum vertical velocity is established to provide a new idea for the development of NWP.The main conclusions are as follows:“Increasing” the “missing” small-scale and mesoscale information in the background field is improant for improving severe convection forecasting.Upward motion and adequate water vapor conditions are important factors for the occurrence of strong convective activities.First,this study proposes an assimilation method using the two-dimensional maximum vertical velocity as lightning proxy observation,which is a new attempt and exploration compared with traditional three-dimensional data assimilation.The forecast results show that lightning assimilation not only enhances the convergences at the lower level and divergences at the upper level,but also increases the water vapor and hydrometeors in the background field,which provides a warm and moist conditions for the development of convection.Lightning data assimilation also helps to enhance the intensity of the ground cold pool and low-level vertical wind shear ahead of the convection.The combination of the cold pool and vertical wind shear helps to trigger new convection ahead of the convective activity,leading to a rapid southward movement of the convection,which can improve the forecasting of severe convection.Then,this study proposes an assimilation method using the water vapor mixing ratio as the lightning proxy variable and compares this assimilation method with the assimilation method using the maximum vertical velocity as the lightning proxy observation.The results show that lightning data assimilation can improve the forecasting effect to a certain extent for both 9 km resolution and 3 km resolution,for both space-based FY-4A detected total lightning and ground-based detected total lightning,and for both large-scale severe convection process and small-scale local convection process.Comparing the two assimilation schemes,when the maximum vertical velocity is used as a proxy observation,the vertical localization function can affect the whole layer of the model,which makes the adjustment of the dynamic and thermal fields of the model background field more adequate.The lightning data assimilation method using maximum vertical velocity as proxy observation provides a more significant improvement in forecasting and is a more promising method for lightning data assimilation.“Decreasing” the “redundant” small-scale and mesoscale information in the background field is another difficulty in NWP.To address this problem,this study proposes a method to assimilate the composite reflectivity factor in the weak reflectivity region and compares this method with the traditional three-dimensional weak reflectivity factor assimilation method.The results show that in the area where the observed reflectivity factor is weak but the forecasting reflectivity is strong,the amount of observed reflectivity factor data in the vertical direction is small,which leads to inadequate adjustment of the model background field in the traditional weak reflectivity assimilation.Assimilating the two-dimensional composite reflectivity factor can increase the lower layer divergence and upper layer convergence,which reduces the upward motion in the weak reflectivity region.This makes the lower layer temperature higher and the middle layer temperature lower,which is not conducive to the development of the ground cold pool and the maintenance of spurious convection.In addition,assimilating the two-dimensional composite reflectivity factor can reduce the water vapor,which can effectively suppress spurious precipitation.In addition,assimilating the composite reflectivity can reduce the assimilation computation by approximately three quarters compared with assimilating the 3D reflectivity factor,which is a cost-effective assimilation scheme.To suppress spurious convection more accurately and effectively,this study further proposes a method to assimilate "0" maximum vertical velocity in the spurious convection region.Two severe convective events with different characteristics are studied to verify the robustness of this assimilation method.The results show that the assimilation of "0" maximum vertical velocity can suppress the upward motion and reduce the water vapor and hydrometeors of spurious convection region in the background field,and thus effectively suppressing the development of spurious convection.In addition,the assimilation of "0" maximum vertical velocity can reduce the water vapor transport from the surrounding area to the spurious convection area and weaken the near-surface cold pool,which is not conducive to the regeneration and maintenance of the spurious convection.The assimilation of "0" maximum vertical velocity can effectively suppress both the strong spurious rainband and the weak precipitation around the real rainband,significantly reducing the false prediction ratio of composite reflectivity factor and accumulation precipitation.Compared with the composite reflectivity factor assimilation method in the weak reflectivity region,the assimilation the "0" maximum vertical velocity avoids the problem of the unsatisfactory suppression of spurious convection caused by the default data in clear-sky areas,and it is a more applicable method.Finally,combined with the above research results,an ensemble-based data assimilation study is conducted for Typhoon Mangkhut.Radar data are assimilated on land,and in the far ocean,where radar detection is limited,lightning data are used as a supplement to achieve multisource data assimilation of radar,lightning and "0" maximum vertical velocity.This study significantly improves typhoon track forecasting,reduces the typhoon intensity forecasting error,clarifies the contribution of multisource data in typhoon forecasting,and provides a useful reference for the continuous development of NWP in the future.