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1.
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
2.
Quantitative precipitation forecasting (QPF) has been attempted over the Narmada Catchment following a statistical approach.
The catchment has been divided into five sub-regions for the development of QPF models with a maximum lead-time of 24 hours.
For this purpose the data of daily rainfall from 56 raingauge stations, twice daily observations on different surface meteorological
parameters from 28 meteorological observatories and upper air data from 11 aerological stations for the nine monsoon seasons
of 1972–1980 have been utilized. The horizontal divergence, relative vorticity, vertical velocity and moisture divergence
are computed using the kinematic method at different pressure levels and used as independent variables along with the rainfall
and surface meteorological parameters. Multiple linear regression equations have been developed using the stepwise procedure
separately with actual and square root and log-transformed rainfall using 8-year data (1972–1979). When these equations were
verified with an independent data for the monsoon season of 1980, it was found that the transformed rainfall equations fared
much better compared to the actual rainfall equations. The performance of the forecasts of QPF model compared to the climatological
and persistence forecasts has been assessed by computing the verification scores using the forecasts for the monsoon season
of 1980. 相似文献
3.
降雨预报信息作为洪水预报模型的输入,该信息的准确性直接影响洪水预报模型输出的准确性.为探究模型输入(降雨预报)误差与输出(洪水预报)误差之间的关系,以英那河流域为例,分析了不同雨量等级下,预报模型的输入误差与输出误差的分布规律,并定性分析了两种误差的相关关系.结果表明,降雨量等级若为无雨及小雨时,两种误差不相关;若为中... 相似文献
4.
aiNet- and GIS-based regional prediction system for the spatial and temporal probability of rainfall-triggered landslides 总被引：1，自引：0，他引：1
We developed a real-time forecasting system, aiNet-GISPSRIL, for evaluating the spatiotemporal probability of occurrence of
rainfall-triggered landslides. In this system, the aiNet (a kind of artificial neutral network based on a self-organizing
system) and GIS are merged for integrating the rainfall conditions into various environmental factors that influence the landslide
occurrence and for simulating the complex non-linear relationships between landslide occurrence and its related conditions.
Zhejiang Province (101,800 km2 in area), located in the southeast coastal region of China, is highly prone to the occurrence of landslides during intensive
rainfall. Since 2003, the aiNet-GISPSRIL has been used to predict landslides during the rainy seasons in the region. The aiNet-GISPSRIL
uses the regional 24-h forecast rainfall information and the real-time rainfall monitoring data from the rain-gauge network
as its inputs, and then provides 24-h forecast of the landslide probability for every 1 × 1-km grid cell within the region.
Verification studies on the performance of the aiNet-GISPSRIL show that the system has successfully predicted the dates and
localities of 304 landslides (accounting for 66.2% of reported landslides during the period). During the period from 2003
to 2007, because the system provided the probability levels of landslide occurrences up to 24-h in advance, gave locations
of potential landslides, and timely warned those individuals at high-risk areas, more than 1700 persons living in the risk
sites had been evacuated to safe ground before the landslides occurred and thus casualty was avoided. This highly computerized,
easy-operating system can be used as a prototype for developing forecasting systems in other regions that are prone to rainfall-triggered landslides. 相似文献
5.
Coupling typhoon rainfall forecasting with overland-flow modeling for early warning of inundation 总被引：1，自引：0，他引：1
Tsung-Yi Pan Lung-Yao Chang Jihn-Sung Lai Hsiang-Kuan Chang Cheng-Shang Lee Yih-Chi Tan 《Natural Hazards》2014,70(3):1763-1793
Taiwan suffers from an average of three or four typhoons annually, and the inundation caused by the heavy precipitation that is associated with typhoons frequently occurs in lowlands and floodplains. Potential inundation maps have been widely used as references to set up non-structural strategies for mitigating flood hazards. However, spatiotemporal rainfall distributions must be addressed to improve the accuracy of inundation forecasting for emergency response operations. This study presents a system for 24-h-ahead early warning of inundation, by coupling the forecasting of typhoon rainfall with the modeling of overland flow. A typhoon rainfall climatology model (TRCM) is introduced to forecast dynamically the spatiotemporal rainfall distribution based on typhoon tracks. The systematic scheme for early warning of inundation based on the spatiotemporal downscaling of rainfall and 2D overland-flow modeling yields not only the extent of inundation, but also the time to maximum inundation depth. The scheme is superior to traditional early warning method referring to the maximum extent and depth of inundation determined from conditional uniform rainfall. Analytical results show that coupling TRCM with an overland-flow model yields satisfactory inundation hydrographs for warning of the extent and peak time of inundation. This study also shows that the accuracy of forecasting spatiotemporal rainfall patterns determines the performance of inundation forecasting, which is critical to emergency response operations. 相似文献
6.
This paper deals with the presentation of a flood warning system (GFWS) developed for the specific characteristics of the Guadalhorce basin (3,200 km2, SE of Spain), which is poorly gauged and often affected by flash and plain floods. Its complementarity with the European flood alert system (EFAS) has also been studied. At a lower resolution, EFAS is able to provide a flood forecast several days in advance. The GFWS is adapted to the use of distributed rainfall maps (such as radar rainfall estimates), and discharge forecasts are computed using a distributed rainfall–runoff model. Due to the lack of flow measurements, the model parameters calibrated on a small watershed have been transferred in most of the basin area. The system is oriented to provide distributed warnings and fulfills the requirements of ungauged basins. This work reports on the performance of the system on two recent rainfall events that caused several inundations. These results show how the GFWS performed well and was able to forecast the location and timing of flooding. It demonstrates that despite its limitations, a simple rainfall–runoff model and a relatively simple calibration could be useful for event risk management. Moreover, with low resolution and long anticipation, EFAS appears as a good complement tool to improve flood forecasting and compensate for the short lead times of the GFWS. 相似文献
7.
Lawal Billa Hamid Assilzadeh Shattri Mansor Ahmed R. Mahmud Abdul H. Ghazali 《Central European Journal of Geosciences》2011,3(3):309-317
Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings. 相似文献
8.
宁镇地区是长江中下游地质灾害最严重的地区之一。镇江润州区虽然仅是宁镇地区的一个局部区域,但其气候和地质环境特征具有典型意义,所提出的地质灾害气象预警预报模型同样适用于整个宁镇地区,对长江中下游地区亦有借鉴作用。气候环境、降雨尤其是连续降雨或强降雨是诱发地质灾害的重要因素。润州区地质灾害主要与梅雨期总降雨量有关,其次与台汛期台风带来的降雨量有关,而与台汛期总降雨量无关。地质灾害预测预警方程应针对不同时期采用不同的预警模型：非梅雨期的预警方程采用预报日降雨量结合前5日降水之和的综合模型,梅雨期的预警方程采用梅雨期降雨总量模型。提出地质灾害气象预报预警等级应根据《国家突发公共事件总体应急预案》将等级统一划分为4级。该模型可作为完善我国现有地质灾害气象预警预报系统的参考。 相似文献
9.
An empirical approach for evaluation of the potential of debris flow occurrence in mountainous areas 总被引：3，自引：2，他引：1
Debris flows can occur relatively suddenly and quickly in mountainous areas, resulting in major structural damage and loss of life. The establishment of a model to evaluate the occurrence probability of debris flows in mountainous areas is therefore of great value. The influence factors of debris flows are very complex; they can basically be divided into background factors and triggering factors. Background factors include the mechanical characteristics of geo-materials, topography and landscape, and soil vegetation; and triggering factors include hydrological and rainfall conditions, and human activities. By assessing the dynamic characteristics of debris flows in mountainous areas, some important influence factors are selected here for analysis of their impacts on the occurrence probability of debris flow. A mathematical model for evaluation of the occurrence probability of debris flows is presented and combined with probability analysis. Matlab software is used for the numerical implementation of the forecasting model, and the influences of rainfall, lithology and terrain conditions on the occurrence probability of debris flows are analyzed. Finally, the presented model is applied to forecast the occurrence probability of debris flows in the mountainous area around Qingping Town; the simulation results show that many loose landslide deposits and heavy rainfall are the key factors likely to trigger debris flows in this region. 相似文献
10.
Desmond Manatsa Leonard Unganai Christopher Gadzirai Swadhin K. Behera 《Natural Hazards》2012,64(2):1187-1207
Farmers?? adaptation to climate change over southern Africa may become an elusive concept if adequate attention is not rendered to the most important adaptive tool, the regional seasonal forecasting system. Uptake of the convectional seasonal rainfall forecasts issued through the southern African regional climate outlook forum process in Zimbabwe is very low, most probably due to an inherent poor forecast skill and inadequate lead time. Zimbabwe??s recurrent droughts are never in forecast, and the bias towards near normal conditions is almost perpetual. Consequently, the forecasts are poorly valued by the farmers as benefits accrued from these forecasts are minimal. The dissemination process is also very complicated, resulting in the late and distorted reception. The probabilistic nature of the forecast renders it difficult to interpret by the farmers, hence the need to review the whole system. An innovative approach to a regional seasonal forecasting system developed through a participatory process so as to offer a practically possible remedial option is described in this paper. The main added advantage over the convectional forecast is that the new forecast system carries with it, predominantly binary forecast information desperately needed by local farmers??whether a drought will occur in a given season. Hence, the tailored forecast is easier for farmers to understand and act on compared to the conventional method of using tercile probabilities. It does not only provide a better forecasting skill, but gives additional indications of the intra-seasonal distribution of the rainfall including onsets, cessations, wet spell and dry spell locations for specific terciles. The lead time is more than 3?months, which is adequate for the farmers to prepare their land well before the onset of the rains. Its simplicity renders it relatively easy to use, with model inputs only requiring the states of El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) climate modes. The developed forecast system could be one way to enhance management of risks and opportunities in rain-fed agriculture among small-holder farmers not only in Zimbabwe but also throughout the SADC region where the impact of ENSO and/or IOD on a desired station rainfall is significant. 相似文献
11.
Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations
in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset
of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain
range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance.
The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir
Panjal range in HP) of the past 4 winters (2003–2004 to 2006–2007). Independent test results are reasonably good and suggest
that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over
different mountain ranges in NW-Himalaya. Significant difference in overall accuracy of the model is found for prediction
of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather
forecast categories respectively. In the same mountain range, significant difference is also found in overall accuracy of
the model for prediction of snow day and no-snow day for different areas. This can be attributed to their geographical position
and topographical differences. The analog weather forecast model performs better than persistence and climatological forecast
for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya. The developed analog weather forecast
model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges
in NW-Himalaya. 相似文献
12.
地质灾害气象预报预警方法是近年来地质灾害防范的热点。由于不确定因素较多,预报方法及预报精度上还有待提高。根椐我省实际情况,选取影响地质灾害发生的主要地质因素并设计了地质背景条件下各致灾因素概率模型;根椐地质灾害主要引发因素的降雨量和初步确定的降雨量临界值;选取BP神经网络模型算法进行数学运算,最终形成预报预警产品,分等级预报,在实际预报预警中取得较好的效果。 相似文献
13.
Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects 总被引：1，自引：0，他引：1
A K MITRA G R IYENGAR V R DURAI J SANJAY T N KRISHNAMURTI A MISHRA D R SIKKA 《Journal of Earth System Science》2011,120(1):27-52
Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific
task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range
has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However,
multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical
forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model
output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During
monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried
out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving
equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to
member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model
ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other
sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models
to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values,
the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products
could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual
member models. 相似文献
14.
选择开挖卸荷、爆破振动、岩体流变以及降雨作为影响边坡施工期变形的4个主要因素,深入分析不同影响因素对边坡变形的作用机制,结合边坡位移实测资料,建立了考虑变形机制的边坡稳定预测模型。在弹性理论的简化下推导边坡回弹变形公式计算开挖卸荷分量,基于Burger模型的蠕变本构方程计算流变分量,采用已有的经验公式计算爆破分量和降雨分量;综合4个变形分量形成考虑变形机制的边坡稳定预测模型,该模型可体现各影响因素对边坡变形的作用和相关程度,更加科学合理地解释边坡变形产生的力学机制。通过对锦屏一级水电站左岸边坡外观测点TP5变形监测资料的统计回归分析表明：相对于传统的统计模型,该模型的回归方程显著,方差比和复相关系数较大,预测误差较小,具有工程应用价值。 相似文献
15.
为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。 相似文献
16.
以延长洪水预见期、提高预报精度为目标,研究气象水文耦合机制,利用数值天气预报模式WRF(Weather Research and Forecasting)驱动分布式VIC(Variable Infiltration Capacity)水文模型,构建三峡库区陆气耦合洪水预报系统,并对2007~2008年期间四场暴雨洪水进行日滚动预报试验。结果表明,WRF模式在三峡库区内有着良好的短期降水预报精度,基于数值天气预报模式和分布式水文模型的陆气耦合洪水预报系统能有效延长三峡入库洪水预见期、提高洪水预报精度,具有较大的应用潜力。 相似文献
17.
18.
分析三峡水库入库流量短期预报结果,采用非参数方法估算预报相对误差的概率密度函数。以防洪为目标,同时兼顾发电、通航等要求,建立了防洪调度模型,采用差分进化算法求解,得到了汛期防洪优化调度图。通过比较不考虑预报、现有预报方案、准确预报3种情况的调度结果,分析了水文预报误差对防洪调度的影响。结果表明,无论考虑预报与否,与现有设计方案相比,汛期防洪优化调度图的发电量都增加,弃水减少,削峰率有所提高;而考虑预报信息有助于调控洪水,提高预报精度有助于增加水库抵御大洪水风险的能力;基于模拟现行作业预报方案,汛期防洪优化调度图比设计调度方案发电量增加5.3%,弃水减少5.9%,削峰率提高1.7个百分点,防御1000年一遇设计洪水的能力大大提高。 相似文献
19.
Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam 总被引：6，自引：2，他引：4
Dieu Tien Bui Biswajeet Pradhan Owe Lofman Inge Revhaug Øystein B. Dick 《Natural Hazards》2013,66(2):707-730
The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years. 相似文献
20.
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