数值天气预报业务模式现状与展望

基于新疆区域同化预报系统（简称“DOGRAFS”）,选取新疆伊犁河谷地区2016年7月31日—8月1日的强降水过程,利用常规观测资料和卫星微波辐射资料进行同化敏感试验,其中控制试验仅同化常规地面和探空资料,敏感试验在常规观测基础上分别增加微波温度（AMSU-A）、湿度（MHS）和温、湿度（AMSU-A、MHS）资料,并对2016年7月进行连续试验,以初步探究卫星微波温、湿度资料同化对新疆降水预报的影响。从关键要素增量的垂直和水平分布来看,仅同化AMSU-A资料与同时同化AMSU-A和MHS资料对初始场温度、位势高度和湿度的调整均比较显著,其低层温度和位势高度为正增量,中、高层为负增量,湿度场增量中心集中在800—600 hPa。而仅同化MHS资料对温度和位势高度的影响较小,对湿度场有所“微调”,但可更好地修正补充降水过程中的水汽信息。从降水预报和客观检验结果来看,同化AMSU-A资料总体为负效果;而同化MHS资料对于整个降水落区和大阈值降水的预报均有明显优势。

     

基于集合Kalman滤波数据同化与偏差修正的热带气旋强度集合预报研究

采用基于集合Kalman滤波数据同化和偏差修正方法的集合预报技术来研究热带气旋的强度预报问题.集合预报系统考虑初值误差和模式误差,利用MM5中尺度模式,采用Anthes-kuo、Grell和Betts-Miller等积云参数化方案和High-res-olution Blaekadar、Burlk-Thompson、MRF等边界层过程的9组不同的组合,分别进行45、60和75 min的短时预报.对9个预报结果采用"镜像法",得到18个集合成员.将蓝金涡旋作为同化的观测场,18个集合成员作为集合Kalman滤波的初始背景集合,采用ENSRF算法和逐点局地分析算法进行同化.同化后的结果作为集合预报的初值,预报过程对模式参数采用前述9种组合,进行72小时预报.通过求取偏差系数对预报结果进行修正,减小模式系统误差.选2003-2004年16个台风过程作为预报个例,讨论偏差修正前后对预报结果的影响.实验结果表明,基于集合Kalman滤波数据同化的热带气旋集合预报相对于非同化的集合预报对路径预报的改进效果优于强度预报.平均而言通过偏差修正,强度集合预报的潜力得到挖掘,绝对误差明显减小,通过偏差修正减小了强度集合预报均值的误差,进而使得预报概率密度函数均值向理论值靠近,从而提高了概率预报的精度和合理性,因此基于集合预报的偏差修正分析方法,是改善热带气旋强度预报水平的有效途径.     

Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction

A new data insertion approach is applied to the Derber and Rosati ocean data assimilation(ODA) system,a system that uses a variational scheme to analyze ocean temperature and provide ocean model corrections continuously.Utilizing the same analysis component as the original system,the new approach conducts analyses to derive model corrections intermittently at once-daily intervals.A technique similar to the Incremental Analysis Update(IAU) method of Bloom et al.is applied to incorporate the corrections into the model gradually and continuously.This approach is computationally more economical than the original.A 13-year global ocean analysis from 1986 to 1998 is produced using this new approach and compared with an analysis based on the original one.An examination of both analyses in the tropical Pacific Ocean shows that they have qualitatively similar annual and interannual temperature variability.Howerver,the new approach produces smoother monthly analyses.Moreover,compared to the independent observations from current meters,the new equatorial currents are significantly better than the original analyses,not only in maintaining the mean state but also in capturing the annual and interannual variations.     

面向WRF/Chem模式MOSAIC气溶胶方案的资料同化实现

CMA-CUACE-Haze化学天气模式是中国研发的气溶胶过程模拟和评估工具,但目前还没有配套的大气化学天气耦合同化分析系统。文中在CMA-MESO三维变分分析的基础上建立了区域化学天气耦合同化系统,该系统把变量之间不相关的PM_2.5 和PM_2.5-10作为控制变量,采用模型化的背景误差协方差,初步实现气溶胶观测PM_2.5和PM₁₀的同化分析。通过单站气溶胶观测的理想同化试验验证耦合同化系统设计的合理性,并针对2016年12月的重污染天气过程进行GTS传输的常规观测与气溶胶PM_2.5和PM₁₀观测资料同化与预报试验。试验结果表明,大气化学天气耦合同化系统可对气溶胶观测和天气变量观测同时进行极小化分析,大气化学变量和天气变量分析场互不影响;气溶胶观测资料的同化合理修正了大气化学背景场,PM_2.5和PM₁₀变量分析场更接近观测;气溶胶观测资料同化对污染物预报的影响可持续72 h。搭建的区域化学天气耦合同化系统能为CMA-CUACE-Haze化学天气模式提供更准确的化学初始场。     

Multi-species chemical data assimilation with the Danish Eulerian hemispheric model: system description and verification

Satellite retrievals of atmospheric composition provide a wealth of data on a global scale. These complement results from atmospheric chemistry-transport models (CTMs), and can be combined using data assimilation. We present two assimilation schemes coupled to the Danish Eulerian Hemispheric Model (DEHM), a three-dimensional, off-line CTM with full photochemistry: a variant on the ensemble Kalman filter and the three-dimensional variational scheme. The aim of this paper is to describe the two schemes and present an initial assessment of their impacts on model skill. Retrievals of multiple atmospheric trace gases are assimilated, namely: NO₂ tropospheric column densities, CH₄ total column densities, and partial column concentrations of O₃, CO and CH₄; these data are retrieved from four satellite sensors. Data for each species are assimilated independently of one another, and other species are only adjusted indirectly via the model’s chemistry and dynamics. Assimilation results are compared with measurements from surface monitoring stations and other satellite retrievals, and preliminary validation results are presented.Reference simulations (without assimilation) grossly underestimate surface CO concentrations, and both assimilation schemes eliminate this large and systematic model bias. The assimilation improves the spatial correlation of modelled CO with surface observations, and improves the spatial correlation between forecasts and retrievals for CO, NO₂ and O₃. Results for CH₄ show a loss of skill due to a mismatch in model bias between two assimilated CH₄ data-sets. Finally, we discuss differences in methodology and results between this paper and a recent study on multi-species chemical data assimilation. Joint optimisation of initial conditions and emission rates offers a promising direction for improving modelled boundary-layer concentrations.     

Variational data assimilation in the tropics using precipitation data part I: Column model

Summary This paper describes initial effort in the development of a four-dimensional variational data assimilation (4D-Var) in the tropics using precipitation data derived from remote sensing. The method of 4D-Var using precipitation data is formulated, and modifications to the parameterization schemes of moist processes to remove zeroth-order discontinuities are described. Variational data assimilation experiments are carried out using a column model to investigate the problems caused by discontinuities in parameterization schemes and assess the impact of assimilating precipitation data in the tropics.It is found that variational data assimilation with discontinuous parameterization schemes exhibits large fluctuations during the minimization process, slow convergence rates, and large analysis errors. The fluctuations become much more serious when precipitation data is assimilated. Precipitation data is very useful to estimate divergence in the tropics, provided that the temporal resolution of the data is sufficiently high. However, its impact on the analysis of temperature and moisture is not clear in the column model assimilation experiments, possibly due to the absence of horizontal advection.     

基于扰动模式的四维变分资料同化系统框架的设计完善和数值试验

为了建立一个应用于区域数值预报的四维变分资料同化（4DVar）系统,在近期开发的扰动预报模式GRAPES_PF基础上,开发完善增量四维变分同化系统框架。该框架中暂不包含物理过程（长短波辐射、边界层过程、对流参数化和云微物理等）。对比业务使用的GRAPES 3DVar系统,增加了温度控制变量。将无量纲Exner气压与流函数的线性风压平衡方程直接在地形追随垂直坐标面上求解,且通过广义共轭余差法（GCR）求解扰动亥姆霍兹（Helmholtz）伴随方程。利用人造“探空”资料对2015年10月台风“彩虹”进行了理想数值试验。试验结果表明,所开发的扰动四维变分同化框架得到了预期的结果,即同化更多资料并反复受到模式约束的四维变分同化系统能有效改善初值质量,进而改善区域数值预报。建立的区域四维变分同化框架合理可行,为进一步发展包含完整物理过程的区域四维变分同化系统奠定了研究基础。      

Development of an ensemble data assimilation system with LMDZ5 AGCM for regional reanalysis

Climate Dynamics - The present study describes a newly developed LMDZ–DART data assimilation system based on the Ensemble Kalman Filter (EnKF) with a stretched grid atmospheric model and...     

Experiments on the radar reflectivity data assimilation in the WRF-ARW model

Given are the results of numerical experiments on the radar reflectivity data assimilation in the nonhydrostatic WRF-ARW model. For this purpose, the procedure of three-dimensional variational data assimilation was used included as a separate block (WRF-3DVAR) in the WRF-ARW system. The experiments on the cyclic assimilation of radar information were carried out for ten days of summer of 2009. The data of six AKSOPRI complexes forming the Moscow ring of radar measurements were assimilated. The contribution of the radar reflectivity data assimilation to the quality of short-range precipitation forecasts was verified on the basis of the momentary and accumulated radar precipitation and total precipitation at the stations. It is obtained that the contribution of the radar reflectivity assimilation using the WRF-ARW algorithm to the precipitation forecast is close to neutral: the number of accurate forecasts increases by about 10%; however, the number of false alarms increases as well.     

天顶扫描模式在双偏振天气雷达Z_(dr)系统偏差订正中的应用

双偏振天气雷达Z_(dr)系统偏差是影响雷达数据质量的关键因素,本文利用南京信息工程大学C波段双偏振天气雷达,对90°仰角天顶扫描数据在Z_(dr)系统偏差订正中的应用进行了分析,同时增加90°仰角定点观测模式,讨论并确认了方位旋转关节对Z_(dr)测量结果的影响,在双偏振天气雷达雷达Z_(dr)系统偏差订正中加入了方位旋转关节因素。研究结果表明:选择合适的90°仰角数据,不仅可以对接收双通道不一致造成的Z_(dr)固定系统偏差进行订正,还能够进一步检测由方位旋转关节衰减不同引起的误差;利用90°仰角数据对Z_(dr)的系统偏差进行订正,稳定性和精度较高,满足双偏振天气雷达Z_(dr)精度要求。     

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.     

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.     

Atmosphere data assimilation system for the Siberian region with the WRF-ARW model and three-dimensional variational analysis WRF 3D-Var

The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis.     

天气雷达反射率资料订正前后在ARPS模式中的同化试验对比

以TRMM/PR反射率资料作参照,对常州、南京天气雷达反射率资料进行一致性订正,再利用中尺度模式ARPS及其数据同化系统ADAS对订正前、后的天气雷达反射率因子进行同化,模拟2013年6月25日和2010年8月24日江苏地区两次暴雨过程。两次暴雨过程均包括3组试验:控制试验和雷达资料订正前、后的同化试验。结果表明:(1)雷达反射率因子的同化很好地改进了模式初始湿度场,使降水预报在分布和强度上更接近实况;(2)雷达资料的订正进一步改进了反射率因子同化试验,并且通过调整初始湿度场和上升运动场(调整作用主要体现在前2~3 h内)改善了对降水的模拟预报,其结果证明雷达反射率因子的订正改善了雷达资料的质量。     

基于自动站资料的WRF-EnSRF陆面同化系统的效果检验及应用

基于集合平方根滤波方法(En SRF)同化方法和NOAH陆面模式的WRF-En SRF陆面同化系统,同化了江苏省70个自动站资料进行试验,研究加入不同的同化资料(地表温度、10 cm土壤温度、20 cm土壤温度)及初始扰动强度的大小对陆面数据同化系统性能的影响,以及对不同区域(降水大值区和降水小值区)的分析场进行效果对比,并且检验了同化系统在一次典型的梅雨锋暴雨的同化效果,证明了这个系统的有效性和可行性。对于资料选取试验,比较全场平均的同化时刻分析场模拟观测相对真实观测的均方根误差可以得到:同化地表温度资料并且初始扰动强度1 K的时候同化效果最理想。对于选定的降水大值区和降水小值区来讲,降水大值区的土壤温度和土壤湿度分析场更加接近于真实场。运用于一次梅雨锋暴雨的同化实验,对于最后一个同化时次的分析场作为背景场做集合预报,最终证明预报结果是有效的。土壤温度、土壤湿度、地表温度和近地面风场的预报结果都较用NCEP再分析资料直接做预报作为控制试验的结果有不同程度的改进。这说明该系统应用于实际同化中的性能较为良好,可以应用于实际土壤湿度与温度的预报。     

Parallel Computing of a Variational Data Assimilation Model for GPS／MET Observation Using the Ray-Tracing Method

The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate me-teorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with thevariational technique. Verified by radiosonde, including GPS/ME% observations into the analysis makesan overall improvement to the analysis variables of temperature, winds, and water vapor. However, thevariational model with the ray-tracing method is quite expensive for numerical weather prediction andclimate research. For example, about 4 000 GPS/MET refraction a;~gles need to be assimilated to producean ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time onthe NCEP‘s Cray C90 computer. Although efforts have been taken to reduce the computational cost, it isstill prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallelprocessors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and isimplemented by message passing. The authors present the principles for the code‘s design and examine theperformance on the state-of-the-art parallel computers in China. The results show that this parallel modelscales favorably as the number of processors is increased. With the Memory-IO technique implemented bythe author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to beuseful in numerical weather prediction (NWP) and climate studies.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

Interannual-to-decadal variability of the North Atlantic from an ocean data assimilation system

An ocean analysis, assimilating both surface and subsurface hydrographic temperature data into a global ocean model, has been produced for the period 1958–2000, and used to study the time and space variations of North Atlantic upper ocean heat content (HC). Observational evidence is presented for interannual-to-decadal variability of upper ocean thermal fluctuations in the North Atlantic related to the North Atlantic Oscillation (NAO) variability over the last 40 years. The assimilation scheme used in the ocean analysis is a univariate, variational optimum interpolation of temperature. The first guess is produced by an eddy permitting global ocean general circulation forced by atmospheric reanalysis from the National Center for Environmental Prediction (NCEP). The validation of the ocean analysis has been done through the comparison with objectively analyzed observations and independent data sets. The method is able to compensate for the model systematic error to reproduce a realistic vertical thermal structure of the region and to improve consistently the model estimation of the time variability of the upper ocean temperature. Empirical orthogonal function (EOF) analysis shows that an important mode of variability of the wintertime upper ocean climate over the North Atlantic during the period of study is characterized by a tripole pattern both for SST and upper ocean HC. A similar mode is found for summer HC anomalies but not for summer SST. Over the whole period, HC variations in the subtropics show a general warming trend while the tropical and north eastern part of the basin have an opposite cooling tendency. Superimposed on this linear trend, the HC variability explained by the first EOF both in winter and summer conditions reveals quasi-decadal oscillations correlated with changes in the NAO index. On the other hand, there is no evidence of correlation in time between the NAO index and the upper ocean HC averaged over the whole North Atlantic which exhibits a substantial and monotonic warming trend during the last two decades of the analysis period. The maximum correlation is found between the leading principal component of winter HC anomalies and NAO index at 1 year lag with NAO leading. For SST anomalies significant correlation is found only for winter conditions. In contrast, for HC anomalies high correlations are found also in the summer suggesting that the summer HC keeps a memory of winter conditions.     

REM模式伴随系统的建立及其四维变分资料同化初步试验

Regional-Eta-Coordinate-Model（REM）中尺度模式对中国区域性降水显示出公认的较高预报能力,建立其四维变分资料同化系统是完善该模式,进一步提高其预报效果的重要工作。本研究编写了REM模式的切线性模式和伴随模式,介绍了建立REM模式伴随系统的过程,并利用实际天气个例资料,分别对REM模式的切线性模式、伴随模式及定义的目标函数梯度进行了正确性检验,检验结果表明对REM模式的切线性模式及伴随模式编写是成功的。利用REM模式的伴随系统,对1998年06月08日00时到09日00时和2000年08月01日00时到02日00时两个实际天气个例进行了四维变分资料同化试验。从数值试验的结果分析可以看到,进行四维变分资料同化后,两个天气个例在预报结束时刻其预报结果对风场和湿度场的预报都有明显改善,对温度场和高度场的预报也有所改善。对于累积降水的预报,两个个例利用四维变分资料同化后得到的初始场进行的预报结果则有较大不同,在个例1中,变分同化后对降水中心的位置和降水强度的预报都有明显改善,预报结果更接近于观测场;个例2中,变分同化后对降水中心位置和强度的预报则没有改善,产生这种现象的原因可能是由于定义的目标函数中没有加进背景场项,也可能是由于采用的观测资料时次比较少,还需要进一步进行研究和试验。     

ENSO in a hybrid coupled model. Part II: prediction with piggyback data assimilation

A hybrid coupled model (HCM) for the tropical Pacific ocean-atmosphere system is employed for ENSO prediction. The HCM consists of the Geophysical Fluid Dynamics Laboratory ocean general circulation model and an empirical atmospheric model. In hindcast experiments, a correlation skill competitive to other prediction models is obtained, so we use this system to examine the effects of several initialization schemes on ENSO prediction. Initialization with wind stress data and initialization with wind stress reconstructed from SST using the atmospheric model give comparable skill levels. In re-estimating the atmospheric model in order to prevent hindcast-period wind information from entering through empirical atmospheric model, we note some sensitivity to the estimation data set, but this is considered to have limited impact for ENSO prediction purposes. Examination of subsurface heat content anomalies in these cases and a case forced only by the difference between observed and reconstructed winds suggests that at the current level of prediction skill, the crucial wind components for initialization are those associated with the slow ENSO mode, rather than with atmospheric internal variability. A “piggyback” suboptimal data assimilation is tested in which the Climate Prediction Center data assimilation product from a related ocean model is used to correct the ocean initial thermal field. This yields improved skill, suggesting that not all ENSO prediction systems need to invest in costly data assimilation efforts, provided the prediction and assimilation models are sufficiently close. Received: 17 April 1998 / Accepted: 22 July 1999