Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.

Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction

A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

Rainfall Assimilation Using a New Four-Dimensional Variational Method:A Single-Point Observation Experiment

Accurate forecast of rainstorms associated with the mei-yu front has been an important issue for the Chinese economy and society. In July 1998 a heavy rainstorm hit the Yangzi River valley and received widespread attention from the public because it caused catastrophic damage in China. Several numerical studies have shown that many forecast models, including Pennsylvania State University National Center for Atmospheric Research’s fifth-generation mesoscale model （MM5）, failed to simulate the heavy precipitation over the Yangzi River valley. This study demonstrates that with the optimal initial conditions from the dimension-reduced projection four-dimensional variational data assimilation （DRP-4DVar） system, MM5 can successfully reproduce these observed rainfall amounts and can capture many important mesoscale features, including the southwestward shear line and the low-level jet stream. The study also indicates that the failure of previous forecasts can be mainly attributed to the lack of mesoscale details in the initial conditions of the models.

Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea

The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.

An explicit four-dimensional variational data assimilation method

A new data assimilation method called the explicit four-dimensional variational (4DVAR) method is proposed. In this method, the singular value decomposition (SVD) is used to construct the orthogonal basis vectors from a forecast ensemble in a 4D space. The basis vectors represent not only the spatial structure of the analysis variables but also the temporal evolution. After the analysis variables are ex-pressed by a truncated expansion of the basis vectors in the 4D space, the control variables in the cost function appear explicitly, so that the adjoint model, which is used to derive the gradient of cost func-tion with respect to the control variables, is no longer needed. The new technique significantly simpli-fies the data assimilation process. The advantage of the proposed method is demonstrated by several experiments using a shallow water numerical model and the results are compared with those of the conventional 4DVAR. It is shown that when the observation points are very dense, the conventional 4DVAR is better than the proposed method. However, when the observation points are sparse, the proposed method performs better. The sensitivity of the proposed method with respect to errors in the observations and the numerical model is lower than that of the conventional method.

Forming proper ensemble forecast initial members with four-dimensional variational data assimilation method

A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generated by Monte Carlo forecast (MCF) or lagged average forecast (LAF). This method possesses significant statistical characteristic of MCF, and by virtue of LAF that contains multi-time information and its initial members are harmonic with

谱模式T63L9正规模初值化方案及试验

推导了全球谱模式T63L9的正规模，对求得的垂直和水平正规模做了分析，与其他文献进行了比较。在此基础上，为其资料四维同化系统研制了一套合适的绝热非线性正规模初值化方案，并进行了一系列试验。分析表明：方案的研制是成功的，它有效地消除了模式早期积分中虚假的高频振荡，明显改进了短期预报的效果；初值化不仅对随后的一次预报有明显的改进，而且通过同化循环，提高了整个资料同化和预报的质量。

PREDICTION OF ANNUAL FREQUENCY OF AFFECTING TROPICAL CYCLONE USING THE PRODUCTS OF A HYBRID COUPLED AIR-SEA MODEL

Better correlation exists between the activity of tropical cyclones affecting East China and Shanghai and the concurrent signals of SSTA in tropical Pacific. In an attempt to justify this statistic finding, a four-dimensional variational data assimilation system is established to optimize the initial fields of a hybrid air-sea coupled model. The prediction skill of tropical SSTA is improved. Long-term statistical models for predicting annual TC frequency affecting East China area and Shanghai city are developed based on 37-year products of this model and the forecast trials have achieved satisfactory results in 1998 and 1999.

Assimilation of All-Sky Radiance from the FY-3 MWHS-2 with the Yinhe 4D-Var System

Compared with traditional microwave humidity sounding capabilities at 183 GHz,new channels at 118 GHz have been mounted on the second generation of the Microwave Humidity Sounder(MWHS-2)onboard the Chinese FY-3C and FY-3D polar orbit meteorological satellites,which helps to perform moisture sounding.In this study,as the allsky approach can manage non-linear and non-Gaussian behavior in cloud-and precipitation-affected satellite radiances,the MWHS-2 radiances in all-sky conditions were first assimilated in the Yinhe four-dimensional variational data assimilation(YH4DVAR)system.The data quality from MWHS-2 was evaluated based on observation minus background statistics.It is found that the MWHS-2 data of both FY-3C and FY-3D are of good quality in general.Six months of MWHS-2 radiances in all-sky conditions were then assimilated in the YH4DVAR system.Based on the forecast scores and observation fits,we conclude that the all-sky assimilation of the MWHS-2 at 118-and 183-GHz channels on FY-3C/D is beneficial to the analysis and forecast fields of the temperature and humidity,and the impact on the forecast skill scores is neutral to positive.Additionally,we compared the impacts of assimilating the 118-GHz channels and the equivalent Advanced Microwave Sounding Unit-A(AMSUA)channels on global forecast accuracy in the absence of other satellite observations.Overall,the impact of the 118-GHz channels on the forecast accuracy is not as large as that for the equivalent AMSUA channels.Nevertheless,all-sky radiance assimilation of MWHS-2 in the YH4DVAR system has indeed benefited from the 118-GHz channels.

The adjoint-based Two Oceans One Sea State Estimate(TOOSSE)

An eddy-resolving four-dimensional variational(adjoint)data assimilation and state estimate was constructed for the low-to mid-latitude Pacifi c,Indian Oceans,and South China Sea based on the framework of“Estimating the Circulation and Climate of the Oceans(ECCO)”.It is named as the Two Oceans One Sea State Estimate(TOOSSE).It fi ts a model to a number of modern observations of 2015-2016,including the Argo fl oat temperature and salinity,satellite altimetric sea surface anomalies,by adjusting initial temperature and salinity,sea surface boundary conditions,and background diapycnal diff usivities.In total,~50%of the original model-data misfi ts have been eliminated,and the estimated state agreed well with a variety of independent observations at meso-to large scales,and on the intra-seasonal to interannual timescales.Mesoscale variability is systematically strengthened in TOOSSE and closer to observations than that without data assimilation,which is especially evidenced by the improved simulation of the mesoscale tropical instability waves(TIWs).Adjustments to ocean surface forcing parameters exhibit both large and frontal/mesoscale structures,and the magnitude reach 20%-100%of the fi rst guesses;the adjustments to diapycnal diff usivity exhibit an obvious elevation(decrement)in(below)the thermocline in the equatorial band.The results indicate that TOOSSE represents a dynamically and thermodynamically consistent ocean state estimate of the 2015-2016 Indo-Pacifi c Ocean,and can be widely utilized for regional process studies.

基于数值模拟与资料同化探究长三角地区冬季PM_(2.5)污染过程的气象影响

细颗粒物(PM_(2.5))累积主导着长三角地区冬季空气污染,其中,气象要素具有重要的作用.本文结合WRF-Chem模式和WRF-FDDA技术,针对2019年1月12—16日发生在长三角地区的一次典型PM_(2.5)污染过程进行数值模拟分析.通过敏感性试验,量化分析地面气象因素(温度、风速、相对湿度)对该地区PM_(2.5)浓度的影响,并利用对自动气象站观测资料的四维资料同化试验,探究气象场改进对PM_(2.5)模拟的改善.模拟结果表明,长三角地区PM_(2.5)污染受气象条件影响程度较为显著,PM_(2.5)浓度与风速和温度呈显著负相关,与相对湿度呈正相关.水平风速减少40%、温度增加3℃、相对湿度增加20%分别造成了+4.68%、-2.82%与+2.2%的PM_(2.5)浓度变化.而同化气象资料显著地改善了模拟的气象场精度,其均方根误差(RMSE)统计项中相对湿度减小9.68%,温度减小1.02℃,风速减小0.35 m·s^(-1),这也使得PM_(2.5)浓度的模拟效果有所改善,其中,模拟与观测PM_(2.5)浓度的相关系数提高了0.11,RMSE减小9.17μg·m^(-3).气象要素变化对大气污染物影响的量化研究,以及资料同化对PM_(2.5)模拟的改进,可促进大气污染的预报水平和有效控制.

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.