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1.
J C JOSHI TANKESHWAR KUMAR SUNITA SRIVASTAVA DIVYA SACHDEVA 《Journal of Earth System Science》2017,126(1):3
Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively. 相似文献
2.
Progress in Researches on Ensemble Forecasting of Extreme Weather Based on Numerical Models 总被引：1，自引：1，他引：0
Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events. 相似文献
3.
Snow avalanches are a major natural hazard for road users and infrastructure in northern Gaspésie. Over the past 11 years, the occurrence of nearly 500 snow avalanches on the two major roads servicing the area was reported. No management program is currently operational. In this study, we analyze the weather patterns promoting snow avalanche initiation and use logistic regression (LR) to calculate the probability of avalanche occurrence on a daily basis. We then test the best LR models over the 2012–2013 season in an operational forecasting perspective: Each day, the probability of occurrence (0–100%) determined by the model was classified into five classes avalanche danger scale. Our results show that avalanche occurrence along the coast is best predicted by 2 days of accrued snowfall [in water equivalent (WE)], daily rainfall, and wind speed. In the valley, the most significant predictive variables are 3 days of accrued snowfall (WE), daily rainfall, and the preceding 2 days of thermal amplitude. The large scree slopes located along the coast and exposed to strong winds tend to be more reactive to direct snow accumulation than the inner-valley slopes. Therefore, the probability of avalanche occurrence increases rapidly during a snowfall. The slopes located in the valley are less responsive to snow loading. The LR models developed prove to be an efficient tool to forecast days with high levels of snow avalanche activity. Finally, we discuss how road maintenance managers can use this forecasting tool to improve decision making and risk rendering on a daily basis. 相似文献
4.
随着全球气候变化、自然变迁及陆表生境改变,极端天气频发且呈现出多尺度时空变异特征,对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化,是当前预报强降水等极端事件的主要手段。从基于天气雷达0~3 h外推临近预报、融合数值模式0~6 h临近预报的发展历程梳理了临近预报的研究进展,阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展,指出"取长补短"的0~6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力,进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径,总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题,阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展,明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势,并讨论了未来的研究重点及发展方向。 相似文献
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宁夏降水型地质灾害气象条件等级预警系统 总被引：1，自引：0，他引：1
利用1982-2004年宁夏地质灾害与降水资料,在分析引发宁夏干旱区主要地质灾害的气象条件基础上,采用统计学方法分区建模,建立了宁夏降水型地质灾害潜势预报模型。依据该模型,在可视化高级编程语言DELPHl环境下,研究开发了一套自动化程度较高的降水型地质灾害气象条件等级预警系统;该系统可以通过网络,以协调一致的工作平台,将气象与地质等相关部门有机连接,实现了联合开展地质灾害预报及指导订正的业务流程。根据实时的雨情及降雨预报,依据所建的分区预报模型,对宁夏地质灾害的发生概率进行快速评价,实现对灾害发生的空间范围、强度及其分布概率的自动实时预警预报;通过人机交互订正,提供位图和GEOS（文本）2种格式的概率预报结论,同时实现了预报预警服务材料的自动化输出。2004,2005年的业务试运行表明,该预警系统基本能满足业务的需求,为新业务领域的拓展提供了技术支持。 相似文献
6.
Micha Werner Paolo Reggiani Ad De Roo Paul Bates Eric Sprokkereef 《Natural Hazards》2005,36(1-2):25-42
Application of recent advances in numerical weather prediction (NWP) has the potential of allowing delivery of flood warning to extend well beyond the typical lead times of operational flood warning at the river basin scale. A prototype system, a European Flood Forecasting System (EFFS) developed to deliver such pre-warnings, aiming at providing a pre-warning at lead times of between 5 and 10 days is described. Considerable uncertainty in the weather forecast at these lead times, however, means that resulting forecasts must be treated probabilistically, and although probabilistic forecasts may be easy to disseminate, these are difficult to understand. This paper explores the structure of operational flood warning, and shows that integration in the flood warning process is required if the pre-warning is to fulfil its potential. A simple method of summarising the information in the pre-warning is presented, and the system in hindcast mode is shown to give clear indication of an upcoming major event in the Rhine basin up to 10 days before the actual event. Finally recommendations on the use of data assimilation to embed the EFFS system within an operational environment are given. 相似文献
7.
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days. 相似文献
8.
David P. Bacon Nash’at N. Ahmad Thomas J. Dunn Michael C. Monteith Ananthakrishna Sarma 《Natural Hazards》2008,44(3):317-327
By definition, a crisis is a situation that requires assistance to be managed. Hence, response to a crisis involves the merging
of local and non-local emergency response personnel. In this situation, it is critical that each participant: (1) know the
roles and responsibilities of each of the other participants; (2) know the capabilities of each of the participants; and (3)
have a common basis for action. For many types of natural disasters, this entails having a common operational picture of the unfolding events, including detailed information on the weather, both current and forecasted, that may impact on either
the emergency itself or on response activities. The Consequences Assessment Tool Set (CATS) is a comprehensive package of
hazard prediction models and casualty and damage assessment tools that provides a linkage between a modeled or observed effect
and the attendant consequences for populations, infrastructure, and resources, and, hence, provides the common operational
picture for emergency response. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric
simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies
with numerical weather prediction to provide specific weather analysis and forecast capability that can be merged into the
geographic information system framework of CATS. This paper documents the problem of emergency response as an end-to-end system
and presents the integrated CATS–OMEGA system as a prototype of such a system that has been used successfully in a number
of different situations. 相似文献
9.
陕西省地质灾害-气象预报预警系统研制及应用 总被引：5，自引：0，他引：5
为了提高地质灾害-气象预报预警工作的自动化程度和产品的质量,文章从计算机系统制作的角度探讨了地质灾害-气象预报预警的方法,论述了陕西省地质灾害-气象预报预警系统的运行环境、软件功能、空间数据库、模型方法等内容,并介绍了陕西省汛期地质灾害-气象预报预警的应用实例。 相似文献
10.
Consistent seasonal snow cover depth and duration variability over the Western Himalayas (WH) 总被引：1，自引：0，他引：1
Precipitation in solid form, i.e., snow, during winter season over the Western Himalayas (WH) leads to the build-up of seasonal snow cover. Seasonal snow cover build-up (snow cover depth and duration) largely depends on atmospheric variables such as temperature, precipitation, radiation, wind, etc. Integrated (combined) influence of atmospheric variables on seasonal snow cover gets reflected in terms of spatial and temporal variability in seasonal snow cover build-up pattern. Hence spatial and temporal variability of seasonal snow cover build-up can serve as a good indicator of climate change in high altitude mountainous regions like the WH. Consistent seasonal snow cover depth and duration, delay days and early melt days of consistent seasonal snow cover at 11 stations spread across different mountain ranges over the WH were analyzed. Mean, maximum and percentiles (25th, 50th, 75th, 90th and 95th) of consistent seasonal snow cover depth and duration show decline over the WH in the recent past 2–3 decades. Consistent seasonal snow cover is found to melt early and snow cover build-up pattern is found to show changes over the WH. Decline in consistent seasonal snow cover depth, duration and changing snow cover build-up pattern over the WH in recent decades indicate that WH has undergone considerable climate change and winter weather patterns are changing in the WH. 相似文献
11.
基于不同积雪日定义的积雪资料比较分析 总被引：11，自引：4，他引：7
利用天气现象定义与积雪深度定义两种方法对全国884个台站的积雪日资料进行统计处理, 分别整理出每一台站各个积雪年的积雪日数、积雪深度、 初终雪间隔日数3个要素的两套数据, 并进行对比分析. 结果表明: 在全国东部大部分地区及新疆地区, 两种数据差别不大, 但在东北及青藏高原两套数据的差别较大. 在积雪日数的比较中, 两种数据在东北及青藏高原的差别基本都在10 d以上, 积雪深度的差别在0.4 cm以上, 初终雪间隔日数的差别以青藏高原最明显, 大部分地区的差别在15 d以上, 甚至有达到30 d以上的区域. 对青藏高原东北边坡代表站的积雪平均值进行M-K突变检验发现, 积雪深度定义的积雪日数与间隔日数减少趋势略大于天气现象定义统计的数值;而在积雪深度的比较中则相反. 两种定义的积雪间隔日数均在1987年出现突变. 相似文献
12.
In consideration of large uncertainties in severe convective weather forecast, ensemble forecasting is a dynamic method developed to quantitatively estimate forecast uncertainty. Based on ensemble output, joint probability is a post-processing method to delineate key areas where weather event may actually occur by taking account of the uncertainty of several important physical parameters. An investigation of the environments of little rainfall convection and strong rainfall convection from April to September (warm season) during 2009-2015 was presented using daily disastrous weather data, precipitation data of 80 stations in Anhui province and NCEP Final Analysis (FNL) data. Through ingredients-based forecasting methodology and statistical analysis,four convective parameters characterizing two types of convection were obtained, respectively, which were used to establish joint probability forecasting together with their corresponding thresholds. Using the ECMWF ensemble forecast and observations from April to September during 2016-2017, systematic verification mainly based on ROC and case study of different weather processes were conducted. The results demonstrate that joint probability method is capable of discriminating little rainfall convection and non-convection with comparable performance for different lead times, which is more favorable to identifying the occurrence of strong rainfall convection. The joint probability of little rainfall convection is a good indication for the occurrence of regional or local convection, but may produce some false alarms. The joint probability of strong rainfall convection is good at indicating regional concentrated short-term heavy precipitation as well as local heavy rainfall. There are also individual missing reports in this method, and in practice, 10% can be roughly used as joint probability threshold to achieve relative high TS score. Overall, ensemble-based joint probability method can provide practical short-term probabilistic guidance for severe convective weather. 相似文献
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14.
R K Giri Jagabandhu Panda Sudhansu S Rath Ravindra Kumar 《Journal of Earth System Science》2016,125(4):709-723
In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products. 相似文献
15.
多普勒雷达图像在地质灾害气象预警中的应用——以湖南省新邵县“2005．05．31”特大地质灾害气象预警为例 总被引：1，自引：0，他引：1
本文介绍了湖南地质灾害概况,通过对暴雨诱发地质灾害的机理分析和多普勒雷达系统监测暴雨的可能性分析,选取地质灾害频发的湖南省作为典型研究区,通过多普勒雷达图像地质灾害气象反演分析和灾害识别研究,精确地预报了湖南省新邵县“2005．05．31”特大地质灾害,论证多普勒雷达是监测突发性地质灾害的有效手段。 相似文献
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三种基于神经网络的洪水实时预报方案的比较研究 总被引：8，自引：1，他引：7
在总结神经网络应用的基础上,归纳了3种基于神经网络的洪水实时预报方案。第一种是神经网络水文模型的模拟模式加模拟误差的自回归校正模型,第二种是权重系数固定的神经网络实时预报方案,第三种是权重系数自动更新的神经网络实时预报方案。采用10个不同流域的日流量资料对这3种方案进行率定和校核。比较这3种方案的实时预报精度。结果发现,第三种方案不仅预报精度要高于其他两种方案,而且比第一种方案少了一个自回归校正模型,结构简洁。本文建议采用第三种洪水实时预报方案。 相似文献
19.
降雪含水比（Snow-to-liquid ratio,缩写为SLR）是降雪深度预报中将定量降水预报（Quantitative precipitation forecast,缩写为QPF）转化为雪深预报所必须的重要参数。利用2009-2017年冬半年辽宁省国家基本站逐小时降水量、积雪深度加密观测资料以及地面气温、地面温度、极大风速、天气现象等资料,通过制定适合本研究的质量控制标准,严格筛选降雪事件,分析辽宁省SLR的变化特征以及气温对降雪含水比的影响,研究结果表明：（1）辽宁省小时SLR的平均值为11,略高于经验值10,虽然SLR变化范围跨度很大,但主要集中在2~20内变化,而SLR大于30的极端值出现频率较低;（2）平均SLR在辽宁省不仅存在明显的空间分布差异,还存在显著的月变化特征;（3）地面气温与SLR有很好的相关性,平均SLR在不同气温区间变化明显,在-15℃附近SLR存在峰值,峰值前随气温降低平均SLR明显增大,而峰值后随着气温降低SLR突然减小。研究结果为今后辽宁冬季降雪深度预报中合理使用SLR这一重要参数提供参考。 相似文献
20.
Northern India is comprised of complex Himalayan mountain ranges having different altitude and orientations. This highly variable terrain is responsible for complexity of the weather systems passing over the region. During winter season, large amount of precipitation is received in this region due to eastward moving low pressure synoptic weather systems called Western Disturbances (WDs). Such heavy precipitation over the region lead to landslides and trigger avalanches in snow bound regions. This causes heavy damage to properties and human lives and therefore poses a great natural hazard threat. In this study an attempt is made to simulate a heavy precipitation event associated with an intense WD using a state of the art mesoscale model. Some important model simulated fields are compared with verifying analysis. Precipitation and circulation features associated with the intense WD are well simulated by the model. Forecast errors indicate that the high resolution mesoscale model could simulate the weather associated with the WD with reasonable accuracy. 相似文献