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Altimetric Data Assimilation by EnOI and 3dvar in a Tropical Pacific Model:Impact on the Simulation of Variability

Advances in Atmospheric Sciences 2012年第4期29卷 823-837页

作者：符伟伟 Center for Ocean and Ice Danish Meteorological Institute ICCES Institute of Atmospheric PhysicsChinese Academy of Sciences

When altimetric data is assimilated, 3dvar and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3dvar, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3dvar. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3dvar in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3dvar generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3dvar appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.

关键词： EnOI 3dvar SLA assimilation Tropical Pacific variability

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Assimilation of HY-2A scatterometer sea surface wind data in a 3dvar data assimilation system-A case study of Typhoon Bolaven

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Frontiers of Earth Science 2015年第2期9卷 192-201页

作者： Yi YU Weimin ZHANG Zhongyuan WU Xiaofeng YANG Xiaoqun CAO Mengbin ZHU National University of Defense Technology Changsha 410073 China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100101 China

The scatterometer （SCAT） on-board China＇s HY-2A satellite has the capability to provide high resolution wind vector information over the global ocean surface. These wind vector data produced by the HY-2A scatterometer （HY-2A SCAT） are available to the data assimilation system with real-time information of high accuracy. In this paper, two experiments are designed to investigate the impact of HY-2A SCAT data in the three- dimensional variational assimilation system for the Weather Research and Forecast model （WRF 3dvar）. The powerful Typhoon Bolaven, which struck South Korea in August 2012, is selected for this case study. The results clearly demonstrate that HY-2A SCAT data can effectively complement the scarce observations over the ocean surface and improve the prediction of the wind and pressure fields of a typhoon. The case study of Typhoon Bolaven exhibits the significant and positive impact of HY- 2A SCAT data on the numerical prediction of the tropical cyclone track.

关键词： HY-2A scatterometer data assimilation seasurface wind 3dvar

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The WRF 3dvar System Combined with Physical Initialization for Assimilation of Doppler Radar Data

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Acta meteorologica Sinica 2009年第2期23卷 129-139页

作者：杨毅邱崇践龚建东黄静 Key Laboratory of Semi-Arid Climate Change of the Ministry of Education College of Atmospheric SciencesLanzhou University National Meteorological Center of China

The three-dimensional variational data assimilation （3dvar） system of the Weather Research and Forecasting （WRF） model （WRF-Var） is further developed with a physical initialization （PI） procedure to assimilate Doppler radar radial velocity and reflectivity observations. In this updated 3dvar system, specific humidity, cloud water content, and vertical velocity are first derived from reflectivity with PI, then the model fields of specific humidity and cloud water content are replaced with the modified ones, and finally, the estimated vertical velocity is added to the cost-function of the existing WRF-Var （version 2.0） as a new observation type, and radial velocity observations are assimilated directly by the method afforded by WRF-Var. The new assimilation scheme is tested with a heavy convective precipitation event in the middle reaches of Yangtze River on 19 June 2002 and a Meiyu front torrential rain event in the Huaihe River Basin on 5 July 2003. Assimilation results show that the increments of analyzed variables correspond well with the horizontal distribution of the observed reflectivity. There are positive increments of cloud water content, specific humidity, and vertical velocity in echo region and negative increments of vertical velocity in echo-free region where the increments of horizontal winds present a clockwise transition. Results of forecast experiments show that the effects of adjusting cloud water content or vertical velocity directly with PI on forecast are not obvious. Adjusting specific humidity shows better performance in forecasting the precipitation than directly adjusting cloud water content or vertical velocity. Significant improvement in predicting precipitation as well as in reducing the model＇s spin-up time are achieved when radial velocity and reflectivity observations are assimilated with the new scheme.

关键词： 3dvar physical initialization assimilation Doppler radar

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A PRELIMINARY STUDY ON THE 3dvar ASSIMILATION OF THE AMSU-A DATA IN SPACE-TIME MULTISCALE ANALYSIS SYSTEM

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Journal of Tropical Meteorology 2017年第3期23卷 314-322页

作者：刘瑞霞谢元富刘杰 Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite China Meteorological Administration National Satellite Meteorological Center NOAA Earth System Research Laboratory China Meteorological Press

Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA's STMAS(Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data.Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5-11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0-6h precipitation forecast significantly.

关键词： multigrid 3dvar space-time multiscale analysis system numerical experiments

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Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application

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Science China Earth Sciences 2022年第10期65卷 1961-1971页

作者： Zengliang ZANG Yanfei LIANG Wei YOU Yi LI Xiaobin PAN Zhijin LI College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China No.32145 Unit of PLA Xinxiang 453000China Department of Atmospheric and Oceanic Sciences Fudan UniversityShanghai 200433China Joint Institute for Regional Earth System Science and Engineering University of California Los AngelesCalifornia 91109USA

With an increasing number of air quality monitoring stations installed around the Chinese mainland,high-resolution aerosol observations become available,allowing improvements in air pollution monitoring and aerosol forecasting.However,the multi scales(especially small-scale)information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method(3dvar).This study attempted to extend the traditional 3dvar to a multi-scale 3dvar with two iteration steps,two-scale-3dvar(TS-3dvar),to improve the effectiveness of assimilating high-resolution observations.In TS-3dvar,the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples.The data assimilation(DA)analysis field generated by TS-3dvar is more accurate than 3dvar in reproducing the field’s multi-scale characteristics,which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting.Particulate matter with an aerodynamic diameter of less than 2.5μm(PM_(2.5))and 10.0μm(PM_(10)) from the surface air quality monitoring stations from November 01 to November 30,2018 at 00:00 were assimilated daily to verify the effects of TS-3dvar and 3dvar on the aerosol analysis and forecast accuracy.The results showed that TS-3dvar better constrained both large-scale and small-scale,especially the spatial wavelengths in a range of 54-216 km and those above 351 km.The average power spectra of the TS-3dvar assimilation increment in the two wavelength ranges were 71.70%and 35.33%higher than those of 3dvar.As a result,the TS-3dvar was more effective than 3dvar in improving the accuracy of the initial chemical field,and thereby the forecasting capability for PM_(2.5).In the initial chemical field,the 30-

关键词： Multi-scale 3dvar Data assimilation Aerosol forecast WRF-Chem

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GRAPES Hybrid-3dvar对台风苏迪罗的数值试验

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大气科学 2020年第5期44卷 1076-1092页

作者：庄照荣李兴良陈静孙健国家气象中心北京100081 中国气象局数值预报中心北京100081

为了把反映天气形势变化的背景误差协方差引入到变分分析系统中来提高分析质量,本文在GRAPES区域三维变分框架的基础上通过扩展控制变量方法实现动态与静态背景误差协方差耦合,建立混合三维变分分析系统(GRAPES Hybrid-3dvar)。通过控... 详细信息

为了把反映天气形势变化的背景误差协方差引入到变分分析系统中来提高分析质量,本文在GRAPES区域三维变分框架的基础上通过扩展控制变量方法实现动态与静态背景误差协方差耦合,建立混合三维变分分析系统(GRAPES Hybrid-3dvar)。通过控制变量扰动产生的集合样本进行单点观测分析试验验证Hybrid-3dvar及其局地化方案的合理性,并针对台风苏迪罗进行实际观测资料同化和数值预报试验,结果表明:用集合样本描述的背景误差协方差是随着天气流型变化的,动力场和质量场的离散度在台风中心处最大,因而混合同化的分析增量包含更多细微结构和中小尺度信息;其分析和24 h内预报要素质量优于3dvar,24 h内降水强度和落区预报也更准确,混合同化分析改善了3dvar分析的降水空报问题;同时混合同化分析的24 h内台风路径预报也最接近实况,台风强度预报在48 h之内都比3dvar更接近观测。

关键词： Hybrid-3dvar 3dvar GRAPES 局地化台风苏迪罗

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利用WRF-3dvar同化常规观测资料对近地层风速预报的改进试验

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高原气象 2014年第3期33卷 675-685页

作者：张飞民王澄海兰州大学大气科学学院/甘肃省干旱气候变化与减灾重点实验室兰州730000

利用WRF 3dvar同化系统和Evenson提出的方法在分析时刻添加扰动形成扰动型初始场,并在此基础上同化常规观测资料(包括固定站点的探空观测和常规地面观测),通过比较WRF3dvar系统对整层大气的初始(分析)场及预报场的改进,检验了同化常规... 详细信息

利用WRF 3dvar同化系统和Evenson提出的方法在分析时刻添加扰动形成扰动型初始场,并在此基础上同化常规观测资料(包括固定站点的探空观测和常规地面观测),通过比较WRF3dvar系统对整层大气的初始(分析)场及预报场的改进,检验了同化常规观测资料后WRF模式对研究区域内近地层风速的预报效果。结果表明,同化常规观测资料对初始场有改进,且对低层大气风场和温度场的改进较FNL资料明显;GFS同化常规观测资料后生成的初始场和FNL资料提供的初始场对风速和温度的预报在不同气压层存在不同的预报时效,低层(1000 hPa)风速和温度预报在前36 h改善明显,而在较高的500 hPa和200 hPa上风速预报在前36 h改进明显,温度预报则只在前12 h得到了改善;无论是采用FNL资料还是同化常规观测资料作为初始场,对近地层风速预报均在前36 h有改进,表明对于近地面风速这种短时间内变化较大的变量,同化与之时间和空间上较为接近的资料可能改善其预报效果。

关键词：近地层风速 WRF 3dvar 常规观测资料

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HY-2A SCAT资料在3dvar和Hybrid同化方法中的应用研究

HY-2A SCAT资料在3DVAR和Hybrid同化方法中的应用研究

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作者：刘晓燕国家海洋环境预报中心

学位级别：硕士

21世纪以来,混合(Hybrid)同化方法以其显著的优势逐渐成为同化研究热点,该方法在三维变分(3dvar)同化方法的基础上引入了“流依赖”的背景误差协方差。由于大气系统的真实背景误差是随天气流型而变的,而3dvar同化方法的背景误差协方差... 详细信息

21世纪以来,混合(Hybrid)同化方法以其显著的优势逐渐成为同化研究热点,该方法在三维变分(3dvar)同化方法的基础上引入了“流依赖”的背景误差协方差。由于大气系统的真实背景误差是随天气流型而变的,而3dvar同化方法的背景误差协方差是静态的,Hybrid同化方法弥补了该不足,因此理论上优于3dvar。卫星散射计资料以其时空分辨率高、覆盖面积广、观测可连续等优点成为全球海面风场资料的重要来源,HY-2A卫星散射计海面风场资料是由我国自主研发的HY-2A卫星搭载的散射计提供,该资料的出现丰富了稀缺的海面风场资料,为充分利用有限的海面风场资料来改进数值预报初始场,选取了该资料作为本文观测资料。本文旨在研究Hybrid和3dvar两种同化方法同化HY-2A卫星散射计风资料是否能够改善初始场以及提高模式预报能力,同时对比分析两种同化方法哪种更优。基于以上研究目的,本文开展了相应工作,并得到了初步结论,如下：(1)使用FNL全球分析资料和浮标资料分别对观测数据HY-2A卫星散射计风资料进行了检验,检验结果表明,HY-2A卫星散射计风数据精度满足同化试验要求。此外,还对该散射计风资料进行了有效的质量控制。(2)选取了“苏力(Soulik,201307)"和“菲特(Fitow,201323)’’为本文试验个例,分别设计了三组对比试验：控制试验(Ctrl试验)为不同化观测资料,直接使用集合平均作为初始场进行72小时预报;3dvar和Hybrid试验分别是以集合平均作为背景场,使用3dvar和Hybrid同化方法同化HY-2A SCAT风数据,然后进行了 72h的预报试验。试验结果表明：(a)初始场的调整结果表明,使用两种同化方法对初始场进行调整所产生的效果在不同的个例中、对于不同的诊断量不尽相同。整体而言,HY-2A卫星散射计风资料的引入对于“苏力”的初始场起到了一定的改善作用,对于“菲特”的初始场改善不大,同时Hybrid同化效果要优于3dvar方法。(b)台风预报结果显示,采用两种方法同化HY-2A SCAT风数据得到的初始场对台风路径和强度预报均起到了正效应。在两个台风个例试验中,对于移动路径的改善均较为明显,改进率可高达40%以上,且Hybrid同化方法的效果均优于3dvar;对于台风强度(最低压和最大风速)的改善相对较弱,改进率多数低于10%,且3dvar的同化方法产生的效果优于Hybrid。可见,HY-2A SCAT风数据的加入对路径以及强度预报均起到了较好的优化作用,但Hybrid同化方法中的“流依赖”的背景误差协方差的引入只能够较好地改善路径预报结果,对强度预报没有优化作用。(c)本文分别采用了三种随机扰动生成方法(Monte Carlo、Random-CV和ETKF)生成集合扰动,进而得到三种集合平均。试验结果表明,三种扰动生成方案产生的效果虽各不相同,但差异都不大。

关键词：数值预报 WRF模式集合平均 3dvar Hybrid 随机扰动

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A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

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Science China Earth Sciences 2020年第9期63卷 1366-1380页

作者： Daichun WANG Wei YOU Zengliang ZANG Xiaobin PAN Hongrang HE Yanfei LIANG Institute of Meteorology and Oceanography National University of Defense TechnologyNanjing 211101China No.94595 Unit of PLA Weifang 216500China No.32145 Unit of PLA Xinxiang 453000China

A three-dimensional variational(3dvar)data assimilation(DA)system is presented here based on a size-resolved sectional aerosol model,the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC)within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem)model.The use of this approach means that both gaseous pollutants such as SO2,NO2,CO,and O3 as well as particulate matter(PM2.5,PM10)observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted,one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC)and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts.Results show that,in the first place,use of the DA system can provide a more accurate model initial field.The root-mean-square error of PM2.5,PM10,SO2,NO2,CO,and O3 mass concentrations in analysis field fell by 29.27μg m-3(53.5%),34.5μg m-3(50.9%),30.36μg m-3(64.2%),8.91μg m-3(39.5%),0.46 mg m-3(47.4%),and 15.11μg m-3(51.0%),respectively,compared to a background field without assimilation.At the same time,mean fraction error was reduced by 42.6%,53.1%,45.2%,43.1%,69.9%,and 48.8%,respectively,while the correlation coefficient increased by 0.51,0.55,0.48,0.38,0.47,0.65,respectively.Secondly,the results of this analysis reveal variable benefits from assimilation on different pollutants.DA significantly improves PM2.5,PM10,and CO forecasts leading to positive effects that last more than 48 h.The positive effects of DA on SO2 and O3 forecasts last up to 8 h but that remains relatively poor for NO2 forecasts.Thirdly,the influence of assimilation varies in different areas.It is possible that the positive effects of DA on PM2.5 and PM10 forecasts can last more than 48 h across most regions of China.Indeed,DA significantly improves SO2 forecasts within 48 h over north China,and much longer CO assimilation benefits(4

关键词： WRF-Chem Aerosol Gaseous pollutant 3dvar Data assimilation

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Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

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Advances in Atmospheric Sciences 2011年第4期28卷 758-774页

作者： Seung-Woo LEE Dong-Kyou LEE School of Earth and Environmental Sciences Seoul National University

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.

关键词： 3dvar background error covariances retrieved satellite data assimilation ensemble forecasts.

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