Research On Characterization Methods For Model And Radar Observation Errors In Convective-scale Ensemble Data Assimilation

Nowadays,convective-scale weather systems are still difficult to be forecasted timely and accurately due to the short life,small spatial scale,and complex three-dimensional structure of convection.The accuracy of forecasting and early warning is the focus of the current operational Numerical Weather Prediction(NWP)systems.The key to solving this problem is to explain and represent different kinds or error in convective-scale data assimilation,so as to optimize the background error covariance and observation error covariance.Therefore,based on the convective-scale data assimilation system,this paper explores the effects of microphysical uncertainty in model error on background error covariance,representation error due to unresolved scales and processes and radar observation operator error on observation error covariance to represent these errors in the data assimilation system from multiple angles,which provides a scientific basis for how to build a more reasonable and effective convective-scale data assimilation system.Firstly,based on ensemble sensitivity analysis which takes multiple spatial scales of convection into account,this paper carries on three experiments,including data assimilation experiment combined with ensemble sensitivity analysis,common data assimilation experiment without ensemble sensitivity analysis and experiment without data assimilation.The results show that although the combination experiment performed better in the ensemble spread and root mean square error of the model state variables,like the other two experiments,a high percentage of outliers was maintained throughout the forecast lead time.In the forecast of precipitation,the combination experiment and the common data assimilation experiment have similar forecasting performance,and both are better than the experiment without data assimilation.However,the forecast skills of moderate and light rain for three experiments are all relatively low,which proves the necessity of representing errors in the convective-scale data assimilation.Secondly,samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two-and one-moment schemes during a onemonth training period.The samples are then added to convective-scale ensemble data assimilation as additive noise and combined with large-scale additive noise based on samples from climatological atmospheric background error covariance.Two experiments,including the combination and large-scale error only,are conducted for a one-week convective period.The results reveal that the simulation with two-moment scheme triggers more convection and has larger ice-phase precipitation particles,which produce a stronger signal in the melting layer.During data assimilation cycling,although more water is introduced to the model,it is shown that the combination performs better for both background and analysis and significantly improves short-term ensemble forecasts of radar reflectivity and hourly precipitation.Thirdly,an approach based on samples for truncation error in radar observation space is used to approximate the representation error(RE)due to unresolved scales and processes and compare its statistics with the observation error(OE)statistics estimated by the Desroziers method.It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme,radar observation operator may also contribute,Finally,the impacts on the observation error statistics due to different types of errors in the forward operator(FE)for both radar reflectivity and radial wind data,in the context of convective-scale data assimilation in the summertime is investigated.A series of sensitivity experiments have been conducted with the Efficient Modular VOlume RADar Operator(EMVORADO),using the operational data assimilation system of the Deutscher Wetterdienst(DWD,German Weather Service).The investigated FEs are versatile,including errors caused by neglecting the terminal fall speed of hydrometeor,the reflectivity weighting,the beam broadening and attenuation effects as well as errors caused by different scattering schemes and formulations for melting particles.For reflectivity,it is found that accounting for the beam broadening effect evidently reduces the standard deviations,especially at higher altitudes.In comparison between the Rayleigh and the Mie schemes,the former one results in much smaller standard deviations for heights up to 4 km and aloft slightly larger standard deviations.For radial wind,positive impacts of considering the beam broadening effect on standard deviations and neutral impacts on correlations are observed.For both reflectivity and radial wind,whether taking the terminal fall speed of hydrometeor and the reflectivity weighting into account does not make remarkable differences in the estimated OE statistics.