The Application of ARGO Data to the Global Ocean Data Assimilation Operational System of NCC

1. Introduction It is well-known that the state of ocean plays very important role in the climate change. But there is a paucity of the ocean observation data. The data distri- bution in the space, time and different components is very inhomogeneous, even in some areas, there are no any observation data. Hence, it brings some diffcul- ties to the scientists to study many problems relevant to ocean. This situation has been being changed since ARGO (Array for Real-time Geostrophic Oceanogra-…     

Impacts of XBT, TAO, Altimetry and ARGO Observations on the Tropical Pacific Ocean Data Assimilation

This study aims at assessing the relative impacts of four major components of the tropical Pacific Ocean observing system on assimilation of temperature and salinity fields. Observations were collected over a period between January 2001 through June 2003 including temperature data from the expendable bathythermographs （XBT）, thermistor data from the Tropical Ocean Global Atmosphere Tropical Atmosphere-Ocean （TOGA-TAO） mooring array, sea level anomalies from the Topex/Poseidon and Jason-1 altimetry （T/P-J）, and temperature and salinity profiles from the Array for Real-time Geostrophic Oceanography （ARGO） floats. An efficient three-dimensional variational analysis-based method was introduced to assimilate the above data into the tropical-Pacific circulation model. To evaluate the impact of the individual component of the observing system, four observation system experiments were carried out. The experiment that assimilated all four components of the observing system was taken as the reference. The other three experiments were implemented by withholding one of the four components. Results show that the spatial distribution of the data influences its relative contribution. XBT observations produce the most distinguished effects on temperature analyses in the off-equatorial region due to the large amount of measurements and high quality. Similarly, the impact of TAO is dominant in the equatorial region due to the focus of the spatial distribution. The Topex/Poseidon-Jason-1 can be highly complementary where the XBT and TAO observations are sparse. The contribution of XBT or TAO on the assimilated salinity is made by the model dynamics because no salinity observations from them are assimilated. Therefore, T/P-J, as a main source for providing salinity data, has been shown to have greater impacts than either XBT or TAO on the salinity analysis. Although ARGO includes the subsurface observations, the relatively smaller number of observation makes it have the smallest contribution to the assimilation syst     

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.     

The REMO Ocean Data Assimilation System into HYCOM （RODAS_H）： General Description and Preliminary Results

The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.     

The Impact of Soil Moisture on Dispersion-Related Characteristics

This study investigates the impact of soil moisture availability on dispersion-related characteristics: surface friction velocity (u* ), characteristic scales of temperature and humidity (T * and q * ), the planetary boundary layer height (h) and atmospheric stability classified by Monin-Obukhov length (L), Kazanski-Monin parameter (μ) and convective velocity scale (w* ) during daytime convective condition using a one-dimensional primitive equation with a refined soil model.     

Evaluation of the tropical variability from the Beijing Climate Center’s real-time operational global Ocean Data Assimilation System

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center(BCC_GODAS2.0) has been run daily in a pre-operational mode.It spans the period 1990 to the present day.The goal of this paper is to introduce the main components and to evaluate BCC_GODAS2.0 for the user community.BCC_GODAS2.0 consists of an observational data preprocess,ocean data quality control system,a three-dimensional variational(3DVAR) data assimilation,and global ocean circulation model[Modular Ocean Model 4(MOM4)].MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction.Satellite altimetry data,SST,and in-situ temperature and salinity data are assimilated in real time.The monthly results from the BCC_GODAS2.0 reanalysis are compared and assessed with observations for 1990-2011.The climatology of the mixed layer depth of BCC-GODAS2.0 is generally in agreement with that of World Ocean Atlas 2001.The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales.Performances in predicting variations in the SST using BCC_GODAS2.0 are evaluated.The standard deviation of the SST in BCC-GODAS2.0 agrees well with observations in the tropical Pacific.BCC-GODAS2.0 is able to capture the main features of E1 Nino Modoki I and Modoki Ⅱ,which have different impacts on rainfall in southern China.In addition,the relationships between the Indian Ocean and the two types of E1 Nino Modoki are also reproduced.     

Application of Nonlinear Balance Scheme in Three-Dimensional Variational Data Assimilation

The dynamical constrains in three-dimensional variational data assimilation are discussed when consid- ering the impact of stream divergence and convergence on the pressure and wind fields.For the analysis of severe tropical cyclone,frontal structures,and other rapidly changing structures,the geostrophic balance and linear balance cannot properly represent the relationship between wind and pressure fields.However,the nonlinear balance incremental equation takes into account the information of flow-dependent background, and makes response to the flow-dependent background covariance in the 3D-Var system.Results indicate that the application of the nonlinear balance equation to 3D-Var system improves the quality of severe trop- ical cyclone assimilation system,which has some positive effects on intensity prediction of tropical cyclones.     

Comparative Experiments on the Effect of Radar Data Assimilation and Increasing Horizontal Resolution on Short-term Numerical Weather Prediction

To examine the effect of radar data assimilation and increasing horizontal resolution on the short-term numerical weather prediction, comparative numerical experiments are conducted for a Huabei (North China) torrential rainfall event by using the Advanced Regional Prediction System (ARPS) and ARPS Data Analysis System (ADAS). The experiments use five different horizontal grid spacings, i.e., 18, 15, 9, 6, and 3 km,respectively, under the two different types of analyses: one with radar data, the other without. Results show that, when radar data are not used in the analysis (i.e., only using the conventional observation data), increasing horizontal resolution can improve the short-term prediction of 6 h with better representation of the frontal structure and higher scores of the rainfall prediction, particularly for heavy rain situations. When radar data are assimilated, it significantly improves the rainfall prediction for the first 6 h, especially the locality and intensity of precipitation. Moreover, using radar data in the analysis is more effective in improving the short-term prediction than increasing horizontal resolution of the model alone, which is demonstrated by the fact that by using radar data in the analysis and a coarser resolution of the 18-km grid spacing, the predicted results are as good as that by using a higher resolution of the 3-km grid spacing without radar data. Further study of the results under the radar data assimilation with grid spacing of 18-3 km reveals that the rainfall prediction is more sensitive to the grid spacing in heavy rain situations (more than 40 mm) than in ordinary rain situations (less than 40 mm). When the horizontal grid spacing reduces from 6 to 3 km, there is no obvious improvement to the prediction results. This suggests that there is a limit to how far increasing horizontal resolution can do for the improvement of the prediction. Therefore, an effective approach to improve the short-term numerical prediction is to combine the radar data assimilation with an optimal horizontal resolution.     

The Impact Cooling of the Storm-Induced SST on Hurricane Intensity

The effects of storm-induced sea surface temperature （SST） cooling on hurricane intensity are investigated using a 5-day cloud-resolving simulation of Hurricane Bonnie （1998）. Two sensitivity simulations are performed in which the storm-induced cooling is either ignored or shifted close to the modeled storm track. Results show marked sensitivity of the model-simulated storm intensity to the magnitude and relative position with respect to the hurricane track. It is shown that incorporation of the storm-induced cooling, with an average value of 1.3℃, causes a 25-hPa weakening of the hurricane, which is about 20 hPa per 1℃ change in SST. Shifting the SST cooling close to the storm track generates the weakest storm, accounting for about 47% reduction in the storm intensity. It is found that the storm intensity changes are well correlated with the air-sea temperature difference. The results have important implications for the use of coupled hurricane-ocean models for numerical prediction of tropical cyclones.     

Study on the Variational Assimilation Technique for the Retrieval of Wind Fields from Doppler Radar Data

This paper introduces a variational assimilation technique for the retrieval of wind fields from Doppler radar data. The assimilated information included both the radial velocity (RV) and the movement of radar echo. In this assimilation technique, the key is transforming the movement of radar echo to a new radar measuring variable- "apparent velocity" (AV). Thus, the information of wind is added, and the indeterminacy of recovering two-dimensional wind only by AV was overcome effectively by combining RV with AV. By means of CMA GRAPES-3Dvar and CINRAD data, some experiments were performed. The results show that the method of retrieval of wind fields is useful in obtaining the construction of the weather system.     

Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model

The Hybrid Coordinate Ocean Model(HYCOM) uses different vertical coordinate choices in different regions. In HYCOM, the prognostic variables include not only the seawater temperature, salinity and current fields, but also the layer thickness. All prognostic variables are usually adjusted in the assimilation when multivariate data assimilation methods are used to assimilate sea surface temperature(SST). This paper investigates the effects of SST assimilation in a global HYCOM model using the Ensemble Optimal Interpolation multivariate assimilation method. Three assimilation experiments are conducted from 2006–08. In the first experiment, all model variables are adjusted during the assimilation process. In the other two experiments, the temperature alone is adjusted in the entire water column and in the mixed layer. For comparison, a control experiment without assimilation is also conducted. The three assimilation experiments yield notable SST improvements over the results of the control experiment. Additionally, the experiments in which all variables are adjusted and the temperature alone in all model layers is adjusted, produce significant negative effects on the subsurface temperature. Also, they yield negative effects on the subsurface salinity because it is associated with temperature and layer thickness. The experiment adjusting the temperature alone in the mixed layer yields positive effects and outperforms the other experiments. The heat content in the upper 300 m and 300–700 m layers further suggests that it yields the best performance among the experiments.     

A Time-Averaged Covariance Method in the EnKF for Argo Data Assimilation

In the ensemble Kalman filter (EnKF), ensemble size is one of the key factors that significantly affects the performance of a data assimilation system. A relatively small ensemble size often must be chosen because of the limitations of computational resources, which often biases the estimation of the background error covariance matrix. This is an issue of particular concern in Argo data assimilation, where the most complex state-of-the-art models are often used. In this study, we propose a time-averaged covariance method to estimate the background error covariance matrix. This method assumes that the statistical properties of the background errors do not change significantly at neighbouring analysis steps during a short time window, allowing the ensembles generated at previous steps to be used in present steps. As such, a joint ensemble matrix combining ensembles of previous and present steps can be constructed to form a larger ensemble for estimating the background error covariance. This method can enlarge the ensemble size without increasing the number of model integrations, and this method is equivalent to estimating the background error covariance matrix using the mean ensemble covariance averaged over several assimilation steps. We apply this method to the assimilation of Argo and altimetry datasets with an oceanic general circulation model.

Experiments show that the use of this time-averaged covariance can improve the performance of the EnKF by reducing the root mean square error (RMSE) and improving the estimation of error covariance structure as well as the relationship between ensemble spread and RMSE.

RÉSUMÉ [Traduit par la rédaction] Dans le filtre de Kalman d'ensemble (EnKF), la taille de l'ensemble est l'un des facteurs clés qui ont une influence importante sur la performance d'un système d'assimilation de données. Il faut souvent choisir une taille d'ensemble assez petite à cause des limites des ressources informatiques, ce qui biaise souvent l'estimation de la matrice de covariance de l'erreur de fond. Cette question revêt une importance particulière pour l'assimilation des données Argo, qui fait souvent appel à des modèles de pointe très complexes. Dans cette étude, nous proposons une méthode de covariance moyennée dans le temps pour estimer la matrice de covariance de l'erreur de fond. Cette méthode suppose que les propriétés statistiques des erreurs de fond ne changent pas de façon importante d'une étape d'analyse à la suivante durant un court laps de temps, ce qui permet d'utiliser dans les étapes courantes les ensembles générés aux étapes précédentes. Ainsi, on peut construire une matrice d'ensembles conjoints combinant les ensembles des étapes précédentes et courantes pour former un plus grand ensemble dans le but d'estimer la covariance de l'erreur de fond. Cette méthode peut accroître la taille de l'ensemble sans augmenter le nombre d'intégrations du modèle; elle équivaut à estimer la matrice de covariance de l'erreur de fond en utilisant la covariance moyenne de l'ensemble calculée sur plusieurs étapes d'assimilation. Nous appliquons cette méthode à l'assimilation des ensembles de données Argo et d'altimétrie avec un modèle de circulation océanique générale.

Des essais montrent que l'emploi de cette covariance moyennée dans le temps peut améliorer la performance de l'EnKF en réduisant l’écart-type et en améliorant l'estimation de la structure de la covariance de l'erreur de même que la relation entre l'étalement et l'écart-type l'ensemble.     

Argo大洋观测资料的同化及其在短期气候预测和海洋分析中的应用

国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0～2000 m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候预测、海洋监测分析和预报能力的一个关键所在.通过开发非线性温—盐协调同化方案和利用同化高度计资料来调整模式的温度和盐度场,建立了可同化包括Argo等多种海洋观测资料的全球海洋资料变分同化系统,提高了对全球海洋的监测分析能力.实现了海洋资料同化系统与全球海气耦合模式的耦合,显著提高了短期气候预测水平.利用Argo资料改进了海洋动力模式中的物理过程参数化方案,有效提高了海洋模式对真实大洋的模拟能力和对厄尔尼诺/拉尼娜的预测能力.开发了利用Argo浮标漂流轨迹推算全球海洋表层和中层流的方法,提高了推算的全球表层流、中层流资料质量,有效弥补了洋流观测的匮乏.     

集合Kalman滤波资料同化技术及研究现状

针对国内集合Kalman滤波资料同化领域的研究空白,对该技术的背景、理论、优势以及存在的问题做了简要描述,对目前国际上的主要研究成果做了介绍,并给出了该方法可能的发展方向。     

亚印太交汇区的海洋再分析系统

An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.     

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.     

观测资料与模式的协调性对同化效果的影响

针对资料同化时模式与观测资料不协调导致的同化失效问题, 利用国家气候中心NCCo简化海气耦合模式, 基于一种提取观测资料中与模式相协调信息分量 (也称模式可协调信息) 的资料重构方法, 探讨了观测资料和模式的协调性对资料同化效果的影响。结果发现: 利用简单海气耦合模式同化海表温度距平资料时, 由于模式与资料不匹配使同化后的初始场中产生与模式动力特征不协调的小尺度扰动, 这些小扰动在预报阶段会迅速增长, 污染预报结果, 使得预报失败。研究还发现, 无论在耦合或不耦合同化形式下, 对海温资料中的模式可协调分量进行同化时, 预报效果明显好于对原观测资料的同化。其缘于对观测海温中模式可协调信息分量同化生成的初始场中, 消除了与模式动力特征不协调的小尺度扰动, 突出了原始资料中与模式协调的ENSO尺度信息分量, 改善了初始场与模式的协调性, 从而提高了模式的预报技巧。     

资料同化方法研究进展

数值天气预报模式的不断完善和大气观测探测资料（特别是卫星、雷达等非常规探测资料）的大量涌现,推动着资料同化方法的逐步发展.文章主要回顾了资料同化方法研究的发展过程、目前的应用现状以及对未来同化方法的展望.随着人们对资料同化含义的深入理解,对于资料同化的研究由初始的探索阶段逐步发展到以经验性为主的同化方法,主要包括SCM和nudging;统计方法的引入,成为资料同化方法研究发展道路上具有重要意义的一个里程碑,从而出现了多元统计插值同化,比如OI,3D-Var以及PSAS;针对背景误差协方差固定不变与实际情况的差异,考虑时间维的四维资料同化方法成为目前国际上较为主流的同化研究方法,其中以4D-Var和Kalman滤波为代表;随着计算机技术的进步,更加合理的四维资料同化方法将会成为未来业务预报中主要的资料同化方法.     

基于集合Kalman滤波数据同化的热带气旋路径集合预报研究

构建了一个基于集合Kalman滤波数据同化的热带气旋集合预报系统,通过积云参数化方案和边界层参数化方案的9个不同组合,采用MM5模式进行了不同时间的短时预报。对预报结果使用“镜像法”得到18个初始成员,为同化提供初始背景集合。将人造台风作为观测场,同化后的结果作为集合预报的初值,通过不同参数组合的MM5模式进行集合预报。对2003～2004年16个台风个例的分析表明,初始成员产生方法能够对热带气旋的要素场、中心强度和位置进行合理扰动。同化结果使台风强度得到加强,结构更接近实际。基于同化的集合路径预报结果要优于未同化的集合预报。使用“镜像法”增加集合成员提高了预报准确度,路径预报误差在48小时和72小时分别低于200 km和250 km。