基于风险矩阵的重大活动气象风险评估          下载免费PDF全文

轩春怡  吴春艳  刘勇洪  舒文军 《大气科学学报》2022,45(5):791-800

基于风险矩阵理论,初步建立了重大活动气象风险评估方法与流程,并以北京某重大庆祝活动服务为例,在多部门联合共同确定活动期间气象风险源的基础上,利用长时间序列气象资料及短期测风雷达观测资料,分析了庆祝活动期间各类气象风险的可能性,结合对活动可能造成后果的严重程度,利用风险矩阵开展了气象风险评估及风险控制研究。评估结果表明,庆祝活动期间主要气象风险依次为降水/阴雨、白天大风、雾-霾、夜晚大风、高温、雷电、低温等7类。其中,降水/阴雨、白天大风、雾-霾为高风险,夜晚大风为中风险,其余均为低风险,根据评估结果建立的风险控制原则和具体措施成功应用于庆祝活动风险控制中。与现有气象业务服务中天气风险评估相比,本研究实现了重大活动气象风险评估由仅考虑高影响天气出现概率向基于影响的风险评估的转变。  相似文献   

2020年长江中下游地区梅汛期强降水特征及其与对流层上层斜压Rossby波的关系          下载免费PDF全文

孙思远  管兆勇 《大气科学》2022,46(5):1041-1054

2020年梅汛期（6～7月）长江中下游地区发生了严峻的汛情。2020年梅雨期长度和强度均远超历史平均水平。本文利用逐日NCEP/NCAR再分析资料和全球降水量网格数据集,研究了本次梅汛期降水特征及其与对流层上层斜压波动活动的联系。结果表明:本次梅汛期,长江中下游地区的总降水量和降水异常大值区位于安徽南部,共有7次连续的降水过程发生。长江中下游地区在对流层中低层辐合、高层辐散,且该地区上空有强的异常上升运动,有利于异常强降水的发生发展。同时,水汽自孟加拉湾和中国南海地区输送至长江中下游地区,为强降水的发生提供了充足水汽。利用小波分析研究该地区的逐日降水标准化时间序列时,发现其存在2～4天和6～14天的显著周期。高频（2～14天）扰动所显示的Rossby波动在对流层上层表现出向下游频散的特征,波动源于贝加尔湖附近。波扰动能量和通量所显示的波动向下游的传播过程与波包的传播过程较为一致,分别源于地中海和贝加尔湖附近的波扰能向东或向东南频散至长江中下游地区,有利于该地区扰动加强并进而有利于强降水的发生和维持。以上结果加深了人们对2020年超长“暴力梅”成因的认识并可为有效预测类似事件提供线索。  相似文献   

华南区域高分辨率数值模式前汛期预报初步评估     

林晓霞  冯业荣  陈子通  简云韬 《热带气象学报》2021,37(4):656-668

基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。   相似文献   

Numerical study of the barotropic responses to a rapidly moving typhoon in the East China Sea   总被引：2，自引：1，他引：1  

Yang Ding  Huaming Yu  Xianwen Bao  Liang Kuang  Caixia Wang  Wenjuan Wang 《Ocean Dynamics》2011,61(9):1237-1259

Barotropic responses of the East China Sea to typhoon KOMPASU are investigated using a high-resolution, three-dimensional, primitive equation, and finite volume coastal ocean model. Even the fact that the typhoon KOMPASU only brushed across the brink of China mainland without landing, it still imposed great influence across China's east coastal area, where storm surges ranging from 35 to 70 cm were intrigued during this event and a large wake of water setdown due to the outward radial transport driven by the cyclonic wind stress was generated after the KOMPASU traveled across the Yellow Sea. Analysis of the numerical results reveals that the barotropic waves propagating along the coast after the typhoon's landing can be identified as Kelvin wave and the currents associated with the storm are geostrophic currents. A series of model runs are initiated to diagnose the effects of wind stress, atmospheric pressure, and storm track variation on the surge's spatial distribution in the East China Sea. The barotropic waves affected by the atmospheric disturbance due to the typhoon in deep Pacific Ocean travel far more rapidly, arriving at the coastal regions at least 60 h ahead of the typhoon. The wave amplitudes are merely 0.2–0.4 cm and damp gradually due to friction. The model experiments also confirm that the surge levels in nearshore regions are highly dominated by winds, whereas the water level variations in deeper areas are controlled by the atmospheric pressure forcing during typhoon events in the East China Sea.  相似文献   

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling     

Kun-Hui Ye  Chi-Yung Tam  Wen Zhou  Soo-Jin Sohn 《大气科学进展》2015,32(5):680-689

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.  相似文献   

Accurate polynomial expressions for the density and specific volume of seawater using the TEOS-10 standard     

《Ocean Modelling》2015

A new set of approximations to the standard TEOS-10 equation of state are presented. These follow a polynomial form, making it computationally efficient for use in numerical ocean models. Two versions are provided, the first being a fit of density for Boussinesq ocean models, and the second fitting specific volume which is more suitable for compressible models. Both versions are given as the sum of a vertical reference profile (6th-order polynomial) and an anomaly (52-term polynomial, cubic in pressure), with relative errors of ∼0.1% on the thermal expansion coefficients. A 75-term polynomial expression is also presented for computing specific volume, with a better accuracy than the existing TEOS-10 48-term rational approximation, especially regarding the sound speed, and it is suggested that this expression represents a valuable approximation of the TEOS-10 equation of state for hydrographic data analysis. In the last section, practical aspects about the implementation of TEOS-10 in ocean models are discussed.  相似文献   

A verification framework for interannual-to-decadal predictions experiments   总被引：2，自引：1，他引：1  

L. Goddard  A. Kumar  A. Solomon  D. Smith  G. Boer  P. Gonzalez  V. Kharin  W. Merryfield  C. Deser  S. J. Mason  B. P. Kirtman  R. Msadek  R. Sutton  E. Hawkins  T. Fricker  G. Hegerl  C. A. T. Ferro  D. B. Stephenson  G. A. Meehl  T. Stockdale  R. Burgman  A. M. Greene  Y. Kushnir  M. Newman  J. Carton  I. Fukumori  T. Delworth 《Climate Dynamics》2013,40(1-2):245-272

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.  相似文献   

Comparison of the use of alternative UKCP09 products for modelling the impacts of climate change on flood frequency   总被引：3，自引：1，他引：2  

A. L. Kay  R. G. Jones 《Climatic change》2012,114(2):211-230

Climate change could have significant impacts on hydrology. This paper uses UK Climate Projections 09 (UKCP09) products to assess the impacts on flood frequency in Britain. The main UKCP09 product comprises conditional probabilistic information on changes in a number of climate variables on a 25?×?25?km grid across the UK (the Sampled Data change factors). A second product is a Weather Generator which produces time-series of current weather variables and future weather variables based on the Sampled Data and consistent with the change factors. A third product comprises time-series from a Regional Climate Model (RCM) ensemble which were used to downscale Global Climate Models (GCMs) on which the projections are based and whose outputs were used in the production of the Sampled Data. This paper compares the use of Sampled Data change factors, Weather Generator time-series, RCM-derived change factors and RCM time-series. Each is used to provide hydrological model inputs for nine catchments, to assess impacts for the 2080s (A1B emissions). The results show relatively good agreement between methods for most catchments, with the four median values for a catchment generally being within 10% of each other. There are also some clear differences, with the use of time-series generally leading to a greater uncertainty range than the use of change factors because the latter do not allow for the effects of, or changes in, natural variability. Also, the use of Weather Generator time-series leads to much greater impacts than the other methods for one catchment. The results suggest that climate impact studies should not necessarily rely on the application of just one UKCP09 product, as each has different strengths and weaknesses.  相似文献   

An asynchronous solver for systems of ODEs linked by a directed tree structure     

《Advances in water resources》2013

This paper documents our development and evaluation of a numerical solver for systems of sparsely linked ordinary differential equations in which the connectivity between equations is determined by a directed tree. These types of systems arise in distributed hydrological models. The numerical solver is based on dense output Runge–Kutta methods that allow for asynchronous integration. A partition of the system is used to distribute the workload among different processes, enabling a parallel implementation that capitalizes on a distributed memory system. Communication between processes is performed asynchronously. We illustrate the solver capabilities by integrating flow transport equations for a ∼17,000 km² river basin subdivided into 305,000 sub-watersheds that are interconnected by the river network. Numerical experiments for a few models are performed and the runtimes and scalability on our parallel computer are presented. Efficient numerical integrators such as the one demonstrated here bring closer to reality the goal of implementing fully distributed real-time flood forecasting systems supported by physics based hydrological models and high-quality/high-resolution rainfall products.  相似文献   

Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization     

Jieshun Zhu  Bohua Huang  Magdalena A. Balmaseda  James L. Kinter III  Peitao Peng  Zeng-Zhen Hu  Lawrence Marx 《Climate Dynamics》2013,41(9-10):2785-2795

Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.  相似文献