Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

Variational Assimilation of Satellite Cloud Water/Ice Path and Microphysics Scheme Sensitivity to the Assimilation of a Rainfall Case

Hydrometeor variables （cloud water and cloud ice mixing ratios） are added into the WRF three-dimensional variational assimilation system as additional control variables to directly analyze hydrometeors by assimilating cloud observations. In addition, the background error covariance matrix of hydrometeors is modeled through a control variable transform, and its characteristics discussed in detail. A suite of experiments using four microphysics schemes （LIN, SBU-YLIN, WDM6 and WSM6） are performed with and without assimilating satellite cloud liquid/ice water path. We find analysis of hydrometeors with cloud assimilation to be significantly improved, and the increment and distribution of hydrometeors are consistent with the characteristics of background error covariance. Diagnostic results suggest that the forecast with cloud assimilation represents a significant improvement, especially the ability to forecast precipitation in the first seven hours. It is also found that the largest improvement occurs in the experiment using the WDM6 scheme, since the assimilated cloud information can sustain for longer in this scheme. The least improvement, meanwhile, appears in the experiment using the SBU-YLIN scheme.

Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform

Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.

Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

The high observation efficiency,scanning speed and observation frequency of the Fengyun-4A(FY-4A)satellite indicates the progress of Chinese geostationary meteorological satellites.The characteristics of FY-4A atmospheric motion vectors(AMVs)derived from the high-level water vapor(WV-High)channel,mid-level water vapor(WV-Mid)channel,and infrared(IR)channel of FY-4A are analyzed,and their corresponding observation errors estimated.Then,the impacts of single-channel and multi-channel FY-4A AMVs on RMAPS-ST(the Rapid-refresh Multi-scale Analysis and Prediction System-Short Term)are evaluated based on one-month data assimilation cycling and forecasting experiments.Results show that the observation errors of FY-4A AMVs from the three channels have an explicit vertical structure.Results from the cycling experiments indicate that the assimilation of AMVs from WV-High produces more apparent improvement of the wind in the upper layer,while a more positive effect in the lower layer is achieved by the assimilation of AMVs from IR.Furthermore,the assimilation of AMVs from IR is more skillful for medium and moderate precipitation than from other channels owing to the good quality of data in the lower layer in the AMVs from IR.Assimilation of FY-4A AMVs from the three channels could combine the advantages of assimilation from each individual channel to improve the wind in the upper,middle and lower layers simultaneously.

Ensemble-based diurnally varying background error covariances and their impact on short-term weather forecasting

背景场误差协方差在资料同化系统中具有非常重要的作用,目前业务变分同化系统中常采用静态背景场误差协方差,未考虑其具体的日变化特征.为构建更为合理且便于业务系统应用的日变化背景误差协方差,本文构建了高分辨率集合预报样本的日变化背景场误差协方差,揭示了其日变化特征,并应用到了CMA-BJ业务系统中,开展了基于业务框架的批量循环同化预报试验.结果表明,背景场误差存在明显的日变化特征,采用集合日变化背景场误差协方差能够改进模式的预报效果.