台风“灿鸿”(1509)的适应性观测敏感区特征及其模拟观测同化研究

适应性观测可以改善资料同化和预报质量。本文利用集合卡尔曼变换适应性观测系统对2015年09号台风“灿鸿”进行了观测敏感区识别，并以第一目标时刻的观测敏感区为基础，运用观测系统模拟试验方法获取模拟的适应性观测资料。基于WRF中尺度同化和预报系统，开展了适应性观测敏感区模拟资料的同化和预报试验。研究发现，台风“灿鸿”（1509）的观测敏感区主要位于台风中心的东北侧及东南侧。同化敏感区内模拟观测资料比同化常规观测资料能更好地改善分析质量和高度、台风路径的预报质量，但对降水的预报改善较弱。     

适应性观测与集合变换卡尔曼滤波方法介绍

给出适应性观测理论和集合变换卡尔曼滤波方法及其研究现状的综述。重点介绍了集合变换卡尔曼滤波方法及其相关的一些问题。在数值预报领域,一种新的途径是利用数值预报系统信息在预报时效内确定出某些区域,如果在这些区域进行补充观测,可以最有效地改进预报技能。这种方法被称为适应性或目标观测,所确定的观测区域称为敏感区,敏感区内增加观测后分析质量将得到改善,对后续的预报技能可产生最大的预期影响。目前适应性观测研究已经成为世界气象组织(WMO)组织的THORPEX计划的一个子计划。集合变换卡尔曼滤波(The Ensemble Transform Kalman Filer,简称ETKF)是一种次优的卡尔曼滤波方案,最早是作为一种适应性观测算法提出的,现在还被用于集合预报初始扰动的生成。ETKF方法不仅可以同化观测资料,而且可以估计出观测对预报误差的影响。它与其它集合卡尔曼滤波方案不同之处在于:ETKF利用集合变换和无量纲化的思想求解与观测有关的误差协方差矩阵,可以快速估计出不同附加观测造成的预报误差协方差的减少量,预报误差减少最多的一组观测所对应的区域就是所寻找的敏感区。     

一次典型华南暴雨预报目标观测区的确定和敏感性试验

针对一次典型华南暴雨过程,利用适应性观测技术确定对华南暴雨预报起关键作用的敏感区,并设计一组试验方案,以目前国内实际地面观测站点分布为前提,在敏感区内增加不同数量的均匀随机分布的地面观测资料,通过MM5(The Fifth PSU/NCAR Mesoscale Model)三维变分(3DVAR)同化系统对已有的地面观测站点资料和敏感区内所增加的观测进行同化,考察地面观测站点的分布对关注区域内预报效果的影响。试验结果表明,各试验对降水的预报差异不大;而以扰动总能量的大小衡量预报水平,与CTRL试验相比,在敏感区内增加观测对短时临近数值预报效果的改善尤为明显,若敏感区内增加75%的地面观测,可使24 h后的关注区域内的数值预报水平提高12.6%,而随着敏感区内地面观测站点的增加,关注区域内的预报水平并没有得到相应提高,这说明布设过多的地面观测站点不但不能改善预报效果,反而造成资源浪费。因此,这一结果为充分利用有限资源、更好设计地面观测站网、最大限度提高华南暴雨的预报水平有很好的理论指导作用。      

基于集合卡尔曼变换的目标观测敏感区识别系统优化及影响试验

在基于集合卡尔曼变换(Ensemble Transform Kalman Filter,ETKF)方法的适应性观测系统的基础上,考虑湿度因子作用并增加对流层低层的大气运动信息,发展了更加适用于我国中尺度高影响天气系统敏感区识别的优化方案。针对环北京夏季暴雨和冬季降雪的高影响天气个例,分别设计4组试验进行观测敏感区识别试验,考察了优化方案目标观测敏感区识别质量,并对分析和预报结果进行了评估。结果表明:优化方案的目标观测敏感区识别效果最佳,对环北京夏季暴雨和冬季降雪天气的目标观测敏感区质量有明显改善,湿度因子可使最强观测敏感区更加集中,对夏季降水敏感区的影响比冬季降雪天气更加明显。低层大气信息的引入对最强观测敏感区的准确识别也具有重要的积极作用。目标观测敏感区的目标资料对分析和短期预报质量具有明显的正贡献。     

一种新型的适应性观测资料应用对台风预报的影响试验研究

近年来，气象界提出了一个“适应性（或目标）观测（adaptive or targeting observation）”的新概念，它是根据数值预报的敏感区在现有的、固定的气象观测网基础上增加的、有目标的特别观测。下投式探空仪（Dropsonde）是适应性观测的重要观测仪器。利用我国新一代数值预报系统GRAPES，初步研究了应用热带气旋适应性观测的下投式探空仪探测资料对台风（2003年杜鹃台风）预报的影响，并进行了敏感性试验。结果表明，数值天气预报中增加下投式探空仪观测资料之后，对台风的移动路径、台风中心位置和强度，以及其它要素的预报都有所改善；敏感性试验还指出，下投式探空仪观测资料的误差结构设置对台风的预报也有明显的影响，该类观测资料在修正背景场的过程中权重并不是越高越好     

基于TIGGE资料识别适应性观测敏感区的应用研究

基于TIGGE(THORPEX Interactive Grand Global Ensemble)资料,通过对比两类强降水过程,分析了集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)适应性观测敏感区识别方法在实际应用中的具体环节。试验中使用两种分辨率和不同范围的集合预报资料得到的信号方差空间分布和极大值区基本一致,而在实施计算中使用适当分辨率和范围的集合预报资料能够大大节省计算时间;使用可获得的最近时刻为初值的集合预报资料得到的敏感区识别结果更加可靠;使用不同中心集合预报资料得到的敏感区识别结果有一定差异,但对于比较典型的夏季主雨带降水过程,各中心资料得到的结果较一致,敏感区识别结果比较可靠;不同类型强降水过程的敏感区对选取的度量函数具有一定依赖性。     

应用奇异向量方法的适应性观测实例研究

近年来,通过适应性观测技术来减小预报误差已成为国际上数值预报中的一项关键技术,然而实施适应性观测对减小预报误差的影响评估是一个需要深入讨论的问题。文中利用奇异向量方法以2007年3月4日东北地区暴风雪天气过程为研究对象,考察了预报误差对不同观测区域观测资料的敏感性,在确定能量范数的基础上,分析了奇异向量的水平分和特征和垂直分布特征,利用奇异向量的空间结构确定了敏感区域。通过伪逆初始扰动场作为分析误差,研究验证区域的预报误差对不同区域增加观测的敏感性,试验结果表明,在敏感区域内进行补充观测来改善分析误差,能够最有效地提高验证区域内的预报水平;而减小非敏感区域内的分析误差对减小预报误差的贡献相对较小。这些结果表明,利用奇异向量法定义敏感区进行适应性观测,能够和有限的观测资源和计算资源的条件下,最大程度地减小验证区域的预报误差,从而达到提高验证区域预报准确率的目的。     

条件非线性最优扰动在长江中下游地区冬季暴雨中的应用研究

为了提高长江中下游地区高影响天气的数值预报,利用条件非线性最优扰动(CNOP)方法,对一次长江中下游地区冬季降水个例(高影响天气事件)进行目标观测研究,并通过观测系统模拟试验(OSSE)检验了该方法确定敏感区的有效性和可行性。试验结果表明,CNOP方法可有效识别对应于高影响天气事件的敏感区。通过对敏感区进行初始场修正后,可明显改善验证区内24 h累积降水预报误差和总能量预报误差。进一步分析发现,通过改善敏感区内的初始场信息(如水汽通量场和低层冷空气活动等),使得数值模式不仅能更真实刻画该天气系统的初始结构,还能更好模拟出该天气系统随时间的演变特征,因而减少了验证区内对该天气系统的预报误差。这一结果表明可以把CNOP方法应用于长江中下游地区高影响天气事件的目标观测研究或实践中。      

Lorenz 96模式中三种目标观测方法的有效性比较

目标观测是有效提升观测效能和观测质量的一种观测策略,其核心部分是敏感区的识别。本文在Lorenz-96模式上比较了奇异向量法(SVs)、集合变换卡尔曼滤波法(ETKF)和条件非线性最优扰动法(CNOP)识别敏感区的优劣,并尝试揭示ETKF方法性能不稳定的原因与机制。试验结果表明:在312h内的不同预报时刻,CNOP方法识别的敏感区范围较小且对预报效果的提升率最高;SVs方法识别的敏感区对72h内的预报有较好的改进,但72h后改进程度急剧下降,到120h后基本失效;ETKF方法识别的敏感区在72h内不如其他方法的效果好。此外,在ETKF方法识别的敏感区与随机选取的敏感区对比中发现,由于ETKF方法操作时采用顺序观测资料处理方案搜寻敏感区,本质上忽略了观测资料间的相关性,导致ETKF方法识别出的敏感区并不一定是全局信号方差最大的区域,对预报效果的改善有限,这也说明了如何优化敏感区搜寻方案是提高ETKF方法效能的关键。     

适应性观测及其策略问题

从适应性观测(目标观测)概念提出后,确定进行适应性观测的时间、敏感区域的方法,即适应性观测策略得到不断发展,本文介绍了目前最主要的几种适应性观测策略,其中包括奇异矢量、繁殖矢量、伴随敏感性、集合转换Kalman滤波等适应性观测策略,以及用于台风的适应性观测策略.总结了适应性观测及其策略的相关理论问题,以及各种适应性观测策略之间的相关关系和不同,讨论了适应性观测对预报改进的影响因素,如观测误差、同化方案、模式误差等.为了实施适应性观测的业务应用、比较不同的适应性策略的适用性,国际上针对不同的高影响天气过程,在不同地区开展了一系列适应性观测外场试验.本文总结了近几年来开展的适应性观测外场试验.这些试验结果表明,平均而言适应性观测可有效地改进高影响天气过程的数值天气预报,但实施高影响性天气的适应性观测业务仍然是一个挑战性任务.     

Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Niño–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.     

TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast

This study evaluates the impact of atmospheric observations from the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observing system on numerical weather prediction of hurricane Ike (2008) using three-dimensional data assimilation system for the Weather Research and Forecast (WRF) model (WRF 3D-Var). The TAMDAR data assimilation capability is added to WRF 3D-Var by incorporating the TAMDAR observation operator and corresponding observation processing procedure. Two 6-h cycling data assimilation and forecast experiments are conducted. Track and intensity forecasts are verified against the best track data from the National Hurricane Center. The results show that, on average, assimilating TAMDAR observations has a positive impact on the forecasts of hurricane Ike. The TAMDAR data assimilation reduces the track errors by about 30 km for 72-h forecasts. Improvements in intensity forecasts are also seen after four 6-h data assimilation cycles. Diagnostics show that assimilation of TAMDAR data improves subtropical ridge and steering flow in regions along Ike’s track, resulting in better forecasts.     

一种基于历史样本投影的三维变分同化方法

对于中尺度数值天气预报来说,初始条件的准确与否已成为影响预报技巧的主要因素之一。现有的大气观测资料在时空分布上的不均匀,以及存在的观测误差,使得我们必须引进资料同化方法,为中尺度数值模式提供最优的初始场。由于传统的三维变分同化(3DVar)方法缺乏模式约束以及背景误差协方差矩阵(B矩阵)不具有流依赖性,因此本文提出一种基于历史样本投影的3DVar(HSP-3DVar)方法,它不仅具有流依赖的B矩阵,而且比传统的3DVar简单易行。为了评价HSP-3DVar的同化性能,我们基于区域暴雨预报模式AREM(Advanced Regional Eta Model)对其进行了观测系统模拟试验(OSSE),结果表明:HSP-3DVar能够有效融合观测信息,模式初值在各层的均方根误差都显著地降低。     

GNSS反演资料在GRAPES_Meso三维变分中的应用

为了进一步提高GRAPES_Meso的分析和预报效果,该文在GRAPES_Meso三维变分同化系统中建立了同化GNSS/RO反演的大气资料的观测算子,实现了对GNSS/RO反演的大气资料的同化应用,并通过2013年7月1个月的同化和预报试验分析了GNSS/RO反演大气资料对GRAPES_Meso模式系统分析和预报的影响。结果表明:增加了GNSS/RO反演大气资料的同化后,GRAPES_Meso位势高度场的分析误差明显减小,平均分析误差减小约8%,预报误差略有减小,平均预报误差减小约1%;湿度场的分析误差和预报误差变化不明显,常规观测资料稀少的青藏高原地区的降水预报技巧有所提高,小雨到大雨的ETS (equitable threat score) 评分提高约0.01,对全国及其他分区的降水预报技巧总体上有正效果。     

The impact of observation systems on medium-range weather forecasting in a global forecast system

To investigate the impact of various types of data on medium-range forecasts, observing system experiments are performed using an assimilation algorithm based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) reanalysis system. Data-denial experiments for radiosonde, satellite, aircraft, and sea surface observations, and selected data experiments for radiosonde and surface data, are conducted for the boreal summer of 1997 and the boreal winter of 1997/1998. The data assimilation system used in this study is remarkably dependent on radiosonde data, which provides information about the three-dimensional structure of the atmosphere. As expected, the impact of radiosonde observations on medium-range forecasts is strongly positive over the Northern Hemisphere and tropics, whereas the satellite system is most beneficial over the Southern Hemisphere. These results are also found in experiments simulating historical changes in observation systems. Over the tropics, assimilation without radiosonde observations generates unbalanced analyses resulting in unrealistic forecasts that must be corrected by the forecast model. Forecasts based on analysis from the observation data before the era of radiosonde observation are found to be less meaningful. In addition, the impacts on forecasts are closely related to the geographical distribution of observation data. The memory of observation data embedded in the analysis tends to persist throughout forecasts. However, cases exist where the effect of forecast error growth is more dominant than that of analysis error, e.g., over East Asia in summer, and where the deficiency in observations is supplemented or the imbalance in analysis is adjusted by the forecast model during the period of forecasts. Forecast error growth may be related to the synoptic correction performed by the data assimilation system. Over data-rich areas, analysis fields are corrected to a greater extent by the data assimilation system than are those over data-poor areas, which can cause the forecast model to produce more forecast errors in medium-range forecasts. It is found that even one month per season is sufficient for forecast skill verification in data impact experiments. Additionally, the use of upper-air observations is found to benefit areas that are downstream of observation data-rich areas.     

Cloud and water vapour motion vectors in tropical cyclone track forecasting — A review

Summary Tropical cyclone track prediction remains a vexing problem in meteorology, particularly for numerical weather prediction. While there has been significant improvement in forecast skill in recent years, errors in prognosis, particularly for recurving cyclones still remain unacceptably high. Consistent with track prediction being to a significant extent an initial value problem, there has been, in recent years, cogent evidence that, a combination of high resolution numerical modelling, the use of appropriate assimilation techniques and the exploitation of high spatial and temporal resolution observations can improve the accuracy of tropical cyclone forecasts.Before landfall, tropical cyclones have their genesis and move over the data-sparse tropical oceans. Here the prediction of their movement is an application for which remotely sensed data are quintessential. In this context, this paper examines the increasingly important contribution of cloud and water vapour motion vectors to tropical cyclone prediction and evaluates their import to accurate prediction in terms of both the numerical modelling characteristics and the data assimilation techniques employed.Overall, it is shown that cloud and water vapour drift winds have made a significant contribution to the tropical cyclone track forecasting problem when used with conventional intermittent assimilation techniques, such as 6-hourly cycling, and, more recently, with continuous assimilation techniques such as 3- and 4-dimensional variational assimilation. These continuous assimilation schemes appear to have the potential to use near continuous asynoptic wind data in the most effective way.With 3 Figures     

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ～10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.     

资料同化对2017年登陆广东沿海台风的短期降水与路径预报影响

利用华南精细数值天气预报模式,设计了无同化资料(CTRL)、同化雷达反演水汽(EXP1)以及同化雷达反演水汽、地面和探空资料(EXP2)三个试验,对2017年登陆广东沿海的四个台风降水预报与路径预报进行模拟,以评估资料同化对登陆台风短期降水预报、路径预报的影响。分析结果如下:雷达反演水汽同化后对未来24小时降水预报技巧均有正的改善,对台风路径预报影响不大;在此基础上同化地面、探空资料后对台风路径预报有改进,对降水预报改进不明显(与EXP1比)。通过诊断分析台风“玛娃”,发现模式初值场水汽的增量配合对流上升区有利于短时间内成云致雨,从而提高短时降水预报;地面及探空资料同化有利于登陆台风的短时路径预报。