PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

A data assimilation (DA) system using ground PM₁₀ observation for Asian Dust Aerosol Model version 2 (ADAM2), which is the operational dust forecasting model of Korea Meteorological Administration (KMA), has been developed with the optimal interpolation (OI) method. The observations are provided by the PM₁₀ network operated by KMA. Three DA experiments are performed to simulate a dust event observed in Korea from 1 March to 31 May 2009 with different assimilation cycles of 24 (DA24), 12 (DA12), and 06 hours (DA06). 48-hour forecasts from the adjusted Initial Condition (IC) of dust concentration are compared with control simulation (CTL) and observation from independent stations. It is found that CTL simulates spatial patterns of dust emitted and transported associated with a developing low pressure system over the dust source regions quite well, compared with satellite measurement. However, it appears that there is considerable uncertainty in estimating the concentration of dust. With IC adjustment, the model simulates improved dust concentration, showing considerably reduced RMSE, particularly for the prediction within 12 hours of forecast. At the same time, it is shown that the time interval of DA affects the predictability of ADAM2, so that DA06 appears to have better predictability within a 12-hour simulation, reducing RMSE by 50% compared with CTL. This suggests that assimilating PM₁₀ to the dust prediction model using OI has the potential to predict air quality in Korea when the cycle of assimilation is sufficiently short.     

Development of an Operational Hybrid Data Assimilation System at KIAPS

This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.     

The Korean Integrated Model (KIM) System for Global Weather Forecasting

The Korea Institute of Atmospheric Prediction Systems (KIAPS) began a national project to develop a new global atmospheric model system in 2011. The ultimate goal of this 9-year project is to replace the current operational model at the Korea Meteorological Administration (KMA), which was adopted from the United Kingdom’s Meteorological Office’s unified model (UM) in 2010. The 12-km Korean Integrated Model (KIM) system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and a state-of-the-art physics parameterization package, has been launched in a real-time forecast framework, with initial conditions obtained via the advanced hybrid four-dimensional ensemble variational data assimilation (4DEnVar) over its native grid. A development strategy for KIM and the evolution of its performance in medium-range forecasts toward a world-class global forecast system are described. Outstanding issues in KIM 3.1 as of February 2018 are discussed, along with a future plan for operational deployment in 2020.     

A new DRP-4DVar-based coupled data assimilation system for decadal predictions using a fast online localization technique

Climate Dynamics - A new coupled data assimilation (CDA) system based on dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) for decadal predictions is developed...     

Assimilation of Global Navigation Satellite Radio Occultation Observations in GRAPES: Operational Implementation

This paper presents the design of an observation operator for assimilation of global navigation satellite system (GNSS) radio occultation (RO) refractivity and the related operational implementation strategy in the global GRAPES variational data assimilation system. A preliminary assessment of the RO data assimilation effect is performed. The results show that the RO data are one of the most important observation types in GRAPES, as they have a significant positive impact on the analysis and forecast at all ranges, especially in the Southern Hemisphere and the global stratosphere where in-situ measurements are lacking. The GRAPES model error cannot be controlled in the Southern Hemisphere without RO data being assimilated. In addition, it is found that the RO data play a key role in the stable running of the GRAPES global assimilation and forecast system. Even in a relatively simple global data assimilation experiment, in which only the conventional and RO data are assimilated, the system is able to run for more than nine months without drift compared with NCEP analyses. The analysis skills in both the Northern and Southern Hemispheres are still relatively comparable even after nine-month integration, especially in the stratosphere where the number of conventional observations decreases and RO observations with a uniform global coverage dominate gradually.     

Assimilation of Global Navigation Satellite Radio Occultation Observations in GRAPES:Operational Implementation

This paper presents the design of an observation operator for assimilation of global navigation satellite system(GNSS) radio occultation(RO) refractivity and the related operational implementation strategy in the global GRAPES variational data assimilation system.A preliminary assessment of the RO data assimilation effect is performed.The results show that the RO data are one of the most important observation types in GRAPES,as they have a significant positive impact on the analysis and forecast at all ranges,especially in the Southern Hemisphere and the global stratosphere where in-situ measurements are lacking.The GRAPES model error cannot be controlled in the Southern Hemisphere without RO data being assimilated.In addition,it is found that the RO data play a key role in the stable running of the GRAPES global assimilation and forecast system.Even in a relatively simple global data assimilation experiment,in which only the conventional and RO data are assimilated,the system is able to run for more than nine months without drift compared with NCEP analyses.The analysis skills in both the Northern and Southern Hemispheres are still relatively comparable even after nine-month integration,especially in the stratosphere where the number of conventional observations decreases and RO observations with a uniform global coverage dominate gradually.     

Multi-resolution Hybrid Data Assimilation Core on a Cubed-sphere Grid (HybDA)

This study illustrates the characteristics of the data assimilation system at the Korea Institute of Atmospheric Prediction Systems (KIAPS), based on the cubed-sphere grid system. The most interesting feature is the use of spherical harmonic functions defined on cubed-sphere grid points, which makes it possible to control the allowable physical wavenumber for the analysis increments. The relevant computational costs and parallel scalability are represented. The multiple-resolution approach is a distinguishable aspect of this data assimilation system. The wavenumber, up to which the analysis is conducted, increases as the outer iteration progresses. This multiresolution strategy is based on an investigation into the change of spectral components of analysis increments. The multi-resolution outer-loop provides cost-effective analysis-improvement, by explicitly controlling the analysis increments entered into the observation operator. To utilize the high-resolution deterministic forecast as a background state, it is subtracted from the forecast ensemble, to produce ensemble forecast perturbation that is hybridized with static background error covariance. Based on the cycled analysis experiments, the higher-resolution deterministic forecast is shown to preserve the high-frequency feature of the analysis increment relative to the ensemble mean forecast.     

江苏省公路网络复杂性分析

利用美国研制的WRF-Var同化系统以及中国的云迹风资料,对2008年6月10日长江流域的一次暴雨过程做了三维和四维变分同化试验。试验结果表明：上述两种方案均能提高模式对大到暴雨预报的准确率,特别是四维方案在提高大暴雨的准确率方面表现更好,这是降水预报的重要方面;两个同化试验均能在一定程度上改善对要素场的预报,使用四维同化方案能够更有效地调整初始场、降低各要素场的预报误差。诊断结果表明：此次大暴雨过程是在高空低槽和低层切变线的共同作用下发生的,并有高、低空急流与之配合,具有较好的动力条件;低空的西南急流,不但为产生暴雨提供了所需的水汽输送,也为造成暴雨强对流所需的位势不稳定提供了必要的能量。     

Observation system experiments for Typhoon Jangmi (200815) observed during T-PARC

In this study, the impact of various types of observations on the track forecast of Tropical Cyclone (TC) Jangmi (200815) is examined by using the Weather Research and Forecasting (WRF) model and the corresponding three-dimensional variational (3DVAR) data assimilation system. TC Jangmi is a recurving typhoon that is observed as part of the THORPEX Pacific Asian Regional Campaign (T-PARC). Conventional observations from the Korea Meteorological Administration (KMA) and targeted dropsonde observations from the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) were used for a series of observation system experiments (OSEs). We found that the assimilation of observations in oceanic areas is important to analyze environmental flows (such as the North Pacific high) and to predict the recurvature of TC Jangmi. The assimilation of targeted dropsonde observations (DROP) results in a significant impact on the track forecast. Observations of ocean surface winds (QSCAT) and satellite temperature soundings (SATEM) also contribute positively to the track forecast, especially two- to three-day forecasts. The impact of sensitivity guidance such as real-time singular vectors (SVs) was evaluated in additional experiments.     

The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment

A four dimensional variational data assimilation (4DVar) based on a dimension-reduced projection (DRP-4DVar) has been developed as a hybrid of the 4DVar and Ensemble Kalman filter (EnKF) concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjoint-based 4DVar and three-dimensional variational data (3DVar) assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix (simply B-matrix) during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.     

基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究

传统变分同化方法中使用各向同性和均质的背景场误差协方差,忽略了背景场误差协方差的天气系统依赖性,而在变分框架下引入集合流依赖的背景场误差协方差还需要额外的集合预报.为在变分同化中引入更合理的背景场误差协方差,通过引入云指数构建"云依赖"背景场误差协方差,提出了一种云依赖背景场误差协方差的同化方案,并应用于雷达等多源观测...     

使用卫星资料进行边界层四维变分同化研究综述

数值天气预报是当前天气预报的重要手段之一,而同化工作一直是数值预报研究的重点。在查阅了近年来国内外相关文献的基础上,对使用卫星举行资料边界层四维变分同化的研究进展进行了论述,并对最优插值法和变分法2种主要的研究方法进行了简单介绍。研究结果认为,变分方法对于初始场的改善具有明显的效果,与纯粹的统计插值方法相比,变分法具有十分明显的优势;理论上变分法能够同化所有类型大气探测资料,对非常规探测的包容能力极强,不仅能最大限度地获得观测中的信息量,而且避免了各种反演不适定问题。     

Initialization and simulation of a landfalling typhoon using a variational bogus mapped data assimilation (BMDA)

Summary Recently, a new data assimilation method called “3-dimensional variational data assimilation of mapped observation (3DVM)” has been developed by the authors. We have shown that the new method is very efficient and inexpensive compared with its counterpart 4-dimensional variational data assimilation (4DVar). The new method has been implemented into the Penn State/NCAR mesoscale model MM5V1 (MM5_3DVM). In this study, we apply the new method to the bogus data assimilation (BDA) available in the original MM5 with the 4DVar. By the new approach, a specified sea-level pressure (SLP) field (bogus data) is incorporated into MM5 through the 3DVM (for convenient, we call it variational bogus mapped data assimilation – BMDA) instead of the original 4DVar data assimilation. To demonstrate the effectiveness of the new 3DVM method, initialization and simulation of a landfalling typhoon – typhoon Dan (1999) over the western North Pacific with the new method are compared with that with its counterpart 4DVar in MM5. Results show that the initial structure and the simulated intensity and track are improved more significantly using 3DVM than 4DVar. Sensitivity experiments also show that the simulated typhoon track and intensity are more sensitive to the size of the assimilation window in the 4DVar than that in the 3DVM. Meanwhile, 3DVM takes much less computing cost than its counterpart 4DVar for a given time window.     

Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields. In particular, variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study,governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions(CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. Then, these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) system. Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution. Furthermore,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR system. Subsequent case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature). Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution, especially at the middle and lower levels.     

同化IASI资料对台风“红霞”和“莫兰蒂”预报的影响研究

红外高光谱大气探测仪IASI可提供高精度的大气垂直温度和湿度信息,能够探测台风结构特征,有效弥补台风影响区域观测资料稀缺的不足。以WRFDA三维变分同化系统为基础构建IASI同化试验平台,实现McNally提出的MW云检测方法,并调整参数形成大阈值的LMW云检测方法,以超强台风“红霞”（1506）和“莫兰蒂”（1614）为试验个例,对IASI观测资料进行同化对比试验。对于台风“红霞”,MW云检测方案对于高层通道299保留的观测数目仅为大阈值LMW云检测的16.2%和WRFDA系统默认的MMR云检测的9.2%,对于底层通道921分别为3.3%和2.6%。但是MW试验分析场强度最强,获得的72 h台风路径预报最接近真实路径,路径误差最小。两个台风个例试验结果相似,表明有效的云检测过程能提高IASI资料同化分析场的准确性,同化IASI资料有利于改善台风预报技巧。      

The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter

A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous threehourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements(RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours.     

Strategies to Overcome Filter Divergence in the DRP-4-DVar Approach

This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.     

A global ocean temperature and altimeter data assimilation system for studies of climate variability

 An ocean data assimilation (ODA) system which can assimilate both temperature and altimeter observations has been applied to the global ocean and tested between January 1993–October 1996. A statistical method has been used to convert sea surface height (SSH) anomalies observations from TOPEX/POSEIDON into synthetic temperature profiles. The innovative aspect of this method is the introduction of time dependency in the correlations used to transform the altimeter observations into temperature corrections. The assimilation system is based on a univariate variational optimal interpolation scheme applied to assimilate both in situ and synthetic temperature profiles. In addition, a longer global analysis for the upper-ocean temperature starting from January 1979 and ending November 1997, has been produced to examine the skill of sea temperature assimilation with a rather simple and practical method. The temperature analysis shows encouraging improvement over a corresponding ocean simulation when compared to independent (not assimilated) temperature data both at seasonal and interannual time scales. However, the univariate data assimilation of hydrographic data does not result in an improvement of the velocity field. In fact the assimilation of sparse in situ data can introduce unrealistic spatial variability in the temperature field which affects the velocity field in a negative way. This deficiency is partially overcome when we also assimilate altimeter observations since the coverage is complete and uniform for this data. In particular, our study shows that temperature corrections due to the altimeter signal have a positive impact on the current system in the tropical Pacific. Received: 28 May 2000 / Accepted: 6 November 2000     

TOPEX/Poseidon高度计资料在全球海洋环流模式中的同化及分析

利用一个新的四维变分海洋资料同化系统LICOM-3DVM对TOPEX／Poseidon高度计资料进行了同化。该同化系统是在LASG／IAP气候海洋模式LICOM1．0的基础上建立起来的,所用的同化方法为三维变分映射资料同化方法3DVM。高度计观测资料是采取间接的方式进行同化,即先建立起二维海面高度距平场与三维温度场的统计关系,并由此通过观测的海面高度距平信息反演出“观测”的三维温度场,然后利用LICOM-3DVM四维变分同化系统将此反演的温度场同化到海洋模式中。作者设计了两组试验并对结果进行了比较分析,积分时间从1993年1月至2001年12月共9年时间。结果表明,由于上混合层相关系数较小,因此同化后海温没有改进;而在温跃层以及更深层次,同化后的海温均有很大程度的改善。从对赤道太平洋地区海温的气候态、季节变化和年际变化以及Nino3区的Nino指数的模拟情况来看,由于同化时将海面高度异常和海温异常之间的相关参数取为常数,没有考虑其季节和年际变化,因此,同化后对于赤道太平洋的年际变化没有改善。对于黑潮地区,由于模式的分辨率较低,同化之前没有很好地模拟出温度锋面,温度和盐度梯度都偏小,流速也偏弱;而同化后使得温度锋面和盐度梯度与WOA01更加吻合,流速增强。     

往返式探空观测资料的质量控制及不确定性分析

为了推动新型探测资料在数值预报模式中的应用,本文进行了往返式探空资料同化应用前重要的基础性研究工作。基于国内首次往返式探空观测资料,首先建立了面向业务化应用的往返式探空资料质量控制方案,通过对比和分析质量控制前后观测样本的统计特征,论证了质量控制方案的合理性,质量控制后探测要素抽样分布更为合理,要素间一致性得到提高。进而以数值天气预报高时间分辨率的模式预报场和同站址业务常规探空观测资料为参考,分析质量控制后资料的不确定性,结果表明往返探空探测精度达到了世界气象组织WMO（World Meteorological Center）规定的突破目标,部分探测要素甚至实现了理想目标,探测资料具有可用性。最后结合数值模式背景场探讨往返探空资料的可同化性,研究表明往返探空的风场观测和夜间温度观测满足变分同化系统的高斯、无偏假定,可直接同化;气压、湿度和日间温度观测在资料同化前需要开展偏差订正工作,从而更有效的发挥资料价值。本文的研究工作为今后往返探空资料在模式中的同化应用奠定了基础。