黑潮流域海温适应性观测敏感区诊断方法研究

敏感区诊断是适应性观测的关键问题,集合变换卡尔曼方法(EnsembleTransformKalmanFilter,ETKF)是目前主要的诊断方法之一。将集合变换卡尔曼方法应用于海洋环境适应性观测,根据ROMS海洋模式数据构建海表温度集合预报,以黑潮流域宫古海峡附近海域为验证区进行敏感区诊断计算,分析不同间隔时间条件下敏感区分布情况,结合模拟系统观测试验验证在敏感区进行适应性观测对预报质量的提升效果。结果表明,在诊断所得敏感区内添加观测能够提升预报质量;随时间间隔增大,敏感区向上游区域平移且预报质量提升效果减小;与在验证区整体添加观测相比,敏感区观测对预报质量提升效果基本相同并且观测成本明显减少。     

Impact of Assimilating Radiances with the WRFDA ETKF/3DVAR Hybrid System on Prediction of Two Typhoons in 2012

The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform ...     

局地化方法在集合转换卡尔曼滤波同化的适用性研究

为了探索协方差局地化（Covariance Localization,CL）方法在集合转换卡尔曼滤波（Ensemble Transform Kalman Filter,     

基于集合卡尔曼变换的区域集合预报初步研究

为了深入研究集合卡尔曼变换(Ensemble Transform Kalman Filter,ETKF)初值扰动方法,提高集合预报质量,从全球大集合预报资料中提取初始扰动场,建立区域模式的ETKF初值扰动方案,对2008年7月22日发生在中国东部的一次暴雨过程进行集合预报试验,并分析ETKF方案构造的扰动场特征和集合预报效果。结果表明,由ETKF初始扰动方案产生的扰动场大小与分布合理,能够反映观测站点的空间分布,能够保持所有正交、不相关方向的误差方差。集合预报降水落区相对控制预报有所改善,集合平均小雨和中雨TS评分和BS评分总体优于控制预报。24h集合预报的Talagrand分布优于36h预报。试验结果揭示了ETKF初值扰动方案的基本性质及利用ETKF方法进行区域集合预报的可行性。     

Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model

Based on a simple coupled Lorenz model, we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics. Four initial perturbation approaches are used in the ensemble forecasting experiments: the random perturbation(RP), the bred vector(BV), the ensemble transform Kalman filter(ETKF), and the nonlinear local Lyapunov vector(NLLV) methods. Results show that,regardless of the method used, the ensemble ave...     

Study on ETKF-Based Initial Perturbation Scheme for GRAPES Global Ensemble Prediction

Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper, ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System (GRAPES) global ensemble prediction is developed in terms of the ensemble transform Kalman filter (ETKF) method.A new GRAPES global ensemble prediction system (GEPS) is also constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based in ation, are carried out for about two months. The structure characters and perturbation amplitudes of the ETKF initial perturbations and the perturbation growth characters are analyzed, and their qualities and abilities for the ensemble initial perturbations are given. The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect the perturbation amplitudes.The initial perturbations and the spread are reasonable. The initial perturbation variance, which is approximately equal to the forecast error variance, is found to respond to changes in the observational spatial variations with simulated observational network density. The perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained up to 96-h lead time. The statistical results for 52-day ensemble forecasts show that the forecast scores ofensemble average for the Northern Hemisphere are higher than that of the control forecast. Provided that using more ensemble members, a real-time observational network and a more appropriate inflation factor,better effects of the ETKF-based initial scheme should be shown.     

基于集合卡尔曼变换的目标观测敏感区识别系统优化及影响试验

在基于集合卡尔曼变换(Ensemble Transform Kalman Filter,ETKF)方法的适应性观测系统的基础上,考虑湿度因子作用并增加对流层低层的大气运动信息,发展了更加适用于我国中尺度高影响天气系统敏感区识别的优化方案。针对环北京夏季暴雨和冬季降雪的高影响天气个例,分别设计4组试验进行观测敏感区识别试验,考察了优化方案目标观测敏感区识别质量,并对分析和预报结果进行了评估。结果表明:优化方案的目标观测敏感区识别效果最佳,对环北京夏季暴雨和冬季降雪天气的目标观测敏感区质量有明显改善,湿度因子可使最强观测敏感区更加集中,对夏季降水敏感区的影响比冬季降雪天气更加明显。低层大气信息的引入对最强观测敏感区的准确识别也具有重要的积极作用。目标观测敏感区的目标资料对分析和短期预报质量具有明显的正贡献。     

Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model

This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes.     

ASSIMILATION OF REAL OBSERVATIONAL DATA WITH THE GSI-HYBRID DATA ASSIMILATION SYSTEM TO IMPROVE TYPHOON FORECAST

A hybrid GSI (Grid-point Statistical Interpolation)-ETKF (Ensemble Transform Kalman Filter) data assimilation system has been recently developed for the WRF (Weather Research and Forecasting) model and tested with simulated observations for tropical cyclone (TC) forecast. This system is based on the existing GSI but with ensemble background information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution (27 km) is used. A case study of typhoon Muifa (2011) is performed to assimilate real observations including conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble covariance shows significant improvements with respect to track forecast compared to the standard GSI system which in theory is three dimensional variational analysis (3DVAR). By comparing the analyses, analysis increments and forecasts, the hybrid system is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better describe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon initial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is tested and similar results are revealed with those from cycled GSI-ETKF approach.     

ETKF初值扰动方法中真实观测及扰动调节因子研究

目前国家气象中心业务GRAPES区域集合预报系统中集合变换卡尔曼滤波（ETKF）方法采用的是模拟观测信息，为进一步完善ETKF方法，拟对ETKF初值扰动通过引入真实探空观测资料，使扰动场能够代表真实观测的不确定信息，改善区域集合预报技巧。真实观测资料的引入会使得每日的观测数目和分布发生变化，这对ETKF方法而言可能会引起扰动振幅的不稳定，因此在引入真实观测资料的基础上设计了新的扰动振幅调节因子，通过格点空间中离散度和均方根误差关系来对初值扰动振幅进行自适应调整。从初值扰动结构、概率预报技巧以及降水预报效果等方面对比分析了基于模拟观测、真实观测以及真实观测结合新型调节因子的ETKF方案的差异，结果表明:真实探空资料能够有效应用于GRAPES区域集合预报系统中，真实观测资料与模拟观测资料相比较为稀疏，可以获得更大量级的初值扰动振幅；真实观测资料有助于提高区域集合的离散度，但对集合预报准确度以及概率预报结果的提高有限，对于降水预报效果提高也有限；新型的扰动振幅调节因子可以有效获得稳定的初值扰动振幅，并保持ETKF扰动结构，真实观测资料与扰动振幅自适应调节因子相结合，可以有效提高区域集合的概率预报结果，并有效提高降水预报效果。