Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6–48 h in Mumbai, using mesoscale (20–200 km) and synoptic scale (200–2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6–48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.     

Extreme precipitation events identified using detrended fluctuation analysis (DFA) in Anhui,China

Extreme weather events include unusual, severe or unseasonal weather, and weather at the extremes of the historical distribution. They have become more frequent and intense under global warming, especially in mid-latitude areas. They bring about great agricultural and economic losses. It is important to define the threshold of extreme weather event because it is the starting point of extreme weather event research, though it has been of seldom concern. Taking extreme precipitation events in Anhui, China as an example, the detrended fluctuation analysis (DFA) method is introduced to define the threshold of extreme weather events. Based on it, the spatial and temporal distributions of extreme precipitation events are analyzed. Compared to the traditional percentile method, DFA is based on the long-term correlation of time series. Thresholds calculated by DFA are much higher than the 99th percentile and the values are higher in the south and lower in the north. This spatial pattern is similar to the annual precipitation spatial pattern. There is an obvious increasing trend in the number of days with extreme precipitation, especially after the 1980s. This observation supports the point that more extreme events happen under global warming.     

Quantifying Changes in Extreme Weather Events in Response to Warmer Global Temperature

Global warming is expected to affect both the frequency and severity of extreme weather events, though projections of the response of these events to climate warming remain highly uncertain. The range of changes reported in the climate modelling literature is very large, sometimes leading to contradictory results for a given extreme weather event. Much of this uncertainty stems from the incomplete understanding of the physics of extreme weather processes, the lack of representation of mesoscale processes in coarse-resolution climate models, and the effect of natural climate variability at multi-decadal time scales. However, some of the spread in results originates simply from the variety of scenarios for future climate change used to drive climate model simulations, which hampers the ability to make generalizations about predicted changes in extreme weather events. In this study, we present a meta-analysis of the literature on projected future extreme weather events in order to quantify expected changes in weather extremes as a function of a common metric of global mean temperature increases. We find that many extreme weather events are likely to be significantly affected by global warming. In particular, our analysis indicates that the overall frequency of global tropical cyclones could decrease with global warming but that the intensity of these storms, as well as the frequency of the most intense cyclones could increase, particularly in the northwestern Pacific basin. We also found increases in the intensity of South Asian monsoonal rainfall, the frequency of global heavy precipitation events, the number of North American severe thunderstorm days, North American drought conditions, and European heatwaves, with rising global mean temperatures. In addition, the periodicity of the El Niño–Southern Oscillation may decrease, which could, in itself, influence extreme weather frequency in many areas of the climate system.     

USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.     

1957-2005年河南省降水和温度极端事件变化

利用河南省1957-2005年逐日降水,最高、最低和日平均气温资料,分析了近50 a河南省极端天气事件的变化趋势。结果表明：1957-2005年河南省暴雨日数、极端降水事件和严重干燥事件发生频率都在增加,但其线性趋势并不显著;暴雨和极端降水的变化趋势呈明显的南北差异;异常高温事件增加而异常低温事件减少,暖冬的趋势比较显著;高温日数和低温日数都显著减少,其变化趋势的空间分布具有很好的一致性。     

1957-2005年河南省降水和温度极端事件变化

 利用河南省1957-2005年逐日降水,最高、最低和日平均气温资料,分析了近50 a河南省极端天气事件的变化趋势。结果表明：1957-2005年河南省暴雨日数、极端降水事件和严重干燥事件发生频率都在增加,但其线性趋势并不显著;暴雨和极端降水的变化趋势呈明显的南北差异;异常高温事件增加而异常低温事件减少,暖冬的趋势比较显著;高温日数和低温日数都显著减少,其变化趋势的空间分布具有很好的一致性。     

Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions

Daily and sub-daily weather data are often required for hydrological and environmental modeling. Various weather generator programs have been used to generate synthetic climate data where observed climate data are limited. In this study, a weather data generator, ClimGen, was evaluated for generating information on daily precipitation, temperature, and wind speed at four tropical watersheds located in Hawai??i, USA. We also evaluated different daily to sub-daily weather data disaggregation methods for precipitation, air temperature, dew point temperature, and wind speed at M??kaha watershed. The hydrologic significance values of the different disaggregation methods were evaluated using Distributed Hydrology Soil Vegetation Model. MuDRain and diurnal method performed well over uniform distribution in disaggregating daily precipitation. However, the diurnal method is more consistent if accurate estimates of hourly precipitation intensities are desired. All of the air temperature disaggregation methods performed reasonably well, but goodness-of-fit statistics were slightly better for sine curve model with 2?h lag. Cosine model performed better than random model in disaggregating daily wind speed. The largest differences in annual water balance were related to wind speed followed by precipitation and dew point temperature. Simulated hourly streamflow, evapotranspiration, and groundwater recharge were less sensitive to the method of disaggregating daily air temperature. ClimGen performed well in generating the minimum and maximum temperature and wind speed. However, for precipitation, it clearly underestimated the number of extreme rainfall events with an intensity of >100 mm/day in all four locations. ClimGen was unable to replicate the distribution of observed precipitation at three locations (Honolulu, Kahului, and Hilo). ClimGen was able to reproduce the distributions of observed minimum temperature at Kahului and wind speed at Kahului and Hilo. Although the weather data generation and disaggregation methods were concentrated in a few Hawaiian watersheds, the results presented can be used to similar mountainous location settings, as well as any specific locations aimed at furthering the site-specific performance evaluation of these tested models.     

Aerosol physical characteristics at Bhubaneswar, East coast of India

Physical characterization of atmospheric aerosols was carried out using various equipments like Grimm's spectrophotometer, Aetholometer and Microtops-II at Bhubaneswar, a coastal city in the east coast of India. Meteorological parameters were recorded on-line with an automatic weather station, which showed weather relatively free from extreme events with high humidity during the period. The pre-monsoon months showed an increase in aerosol mass in the higher size ranges. The black carbon (BC) showed maximum values during winter which may be due to various anthropogenic activities like biomass burning and forest fire as well as dry conditions conducive to transport from far off places. The α values representing aerosol size distribution and β values showing the total aerosol concentration in vertical air column rose simultaneously in pre-monsoon months to attain maximum values during February–March 2008. The AOD was also correlated with PM-10 and BC concentrations.     

1955—2014年杭州极端气温和降水指数变化特征

根据杭州市1955—2014年降水量、气温逐日资料,采用国际通用的极端天气指数和线性倾向估计、M-K检验等方法,分析了杭州市近60 a极端气温和降水的变化特征。结果表明：1）杭州市近60 a的气温呈一致升高趋势,且变化显著,表现为极端高温阈值和极端低温阈值的升高及极端高温日数的增多;极端冷事件显著减少,暖事件显著增多。2）极端降水指数中只有强降水量的增加较明显,主要贡献为夏季和冬季强降水量的增强。3）冬季平均气温、极端低温阈值、霜冻日数等极端冷事件的突变发生于20世纪80年代初中期,夏季平均气温、极端高温阈值、高温日数等极端暖事件的突变发生于20世纪末21世纪初,与全国范围内的气候增暖进程基本一致。另外,降水强度、极端降水阈值等极端降水指数的突变时间在2008年左右,即2008年后气温升高和降水强度的增加突变期叠加,尤其在夏季和冬季表现更突出,可能诱发更多的异常天气。     

Evaluating the Dependence of Vegetation on Climate in an Improved Dynamic Global Vegetation Model

The capability of an improved Dynamic Global Vegetation Model (DGVM) in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land ModelDGVM (CLM3.0-DGVM) with a submodel for temperate and boreal shrubs, as well as other revisions such as the two-leaf scheme for photosynthesis and the definition of fractional coverage of plant functional types (PFTs). Results show that the revised model may correctly reproduce the global distribution of tempera...     

土壤湿度年际变化对中国区域极端气候事件模拟的影响研究 I. 基于CAM3.1的模式评估

利用NCAR大气模式CAM3.1对中国区域近40年的极端气候事件进行了模拟试验;在此基础上, 利用1961～2000年中国区域452站的逐日最高、最低气温和降水资料, 从气候平均、年际变化和长期变化趋势等方面全面评估了该模式对中国极端气候事件的模拟能力。结果表明:(1)模式对中国区域极端气候指数气候平均态的大尺度空间分布特征具有一定的模拟能力;模式对极端降水指标空间分布的模拟能力较好, 而对极端气温指标的模拟较差;模式对极端气候指标的模拟存在系统性的偏差, 模拟的极端降水的系统性偏差要远大于对极端温度的模拟。(2)模式对极端气温指数的年际变化特征具有较强的模拟能力, 而对极端降水指数的年际变化基本没有模拟能力;模式模拟的各极端降水指标的年际变幅与观测存在较大的偏差。(3)模式较好地模拟出了暖夜和暖昼指数在中国大部分区域的增加趋势, 但变幅较实测偏小;模式对热浪持续指数长期趋势的模拟则相对略差。模式对极端气温指标长期趋势的模拟能力总体优于对极端降水指标的模拟。模式对极端降水频次和中雨日数长期趋势的模拟尚可, 但对持续湿期长期趋势的空间分布模拟较差。研究结果可为该模式用于极端气候的模拟研究提供一定参考。     

Possible impacts of climate change on extreme weather events at local scale in south–central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.     

The construction of oxygen isotope chronologies from tree-ring series sampled at different temporal resolution and its use as climate proxies: statistical aspects

Tree-ring oxygen stable isotope data series from conifers growing on the Dachstein Plateau (Austrian Alps) were selected to demonstrate the applicability of the serial pooling method using shifted 5-year tree-ring blocks for summer temperature reconstruction. The addressed method allows the construction of long isotope chronologies with significant climate correlation and well preserved climate sensitivity applying the irreducible sample replication of five trees. The linear regression model for temperature reconstruction is verifiable and the predicted data are well correlated with instrumental data, especially reproducing the long-term temperature trend. However, the reduced mean variance leads to loss of extreme years, which can be regulated by the combination of one data series in annual resolution with five shifted 5-year block data series. This significantly improves the variance of the mean chronology, sufficiently to identify extremes. Therefore, we recommend the use of mixed data sets as a compromise between essential sample replication and economic considerations.     

Stochastic precipitation generator with hidden state covariates

Time series of daily weather such as precipitation, minimum temperature and maximum temperature are commonly required for various fields. Stochastic weather generators constitute one of the techniques to produce synthetic daily weather. The recently introduced approach for stochastic weather generators is based on generalized linear modeling (GLM) with covariates to account for seasonality and teleconnections (e.g., with the El Niño). In general, stochastic weather generators tend to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this overdispersion, we incorporated time series of seasonal dry/wet indicators in the GLM weather generator as covariates. These seasonal time series were local (or global) decodings obtained by a hidden Markov model of seasonal total precipitation and implemented in the weather generator. The proposed method is applied to time series of daily weather from Seoul, Korea and Pergamino, Argentina. This method provides a straightforward translation of the uncertainty of the seasonal forecast to the corresponding conditional daily weather statistics.     

Mapping the shadow of experience of extreme weather events

Climate change will increase the frequency and/or intensity of certain extreme weather events, and perceived experience with extreme weather may influence climate change beliefs, attitudes, and behaviors. However, the aspects of extreme events that influence whether or not people perceive that they have personally experienced them remain unclear. We investigate (1) the correspondence of reported experience of extreme weather events with documented events, and (2) how characteristics of different extreme events shape the geographic area within which people are likely to report they have experienced it—the event’s perceived “shadow of experience.” We overlay geocoded survey responses indicating personal experience with hurricanes, tornadoes, and drought—from a 2012 nationally representative survey (N?=?1,008) of U.S. residents—on maps of recorded event impacts. We find that reported experiences correspond well with recorded event impacts, particularly for hurricanes and tornadoes. Reported experiences were related to event type, proximity, magnitude and duration. The results suggest locations where disaster preparedness efforts and climate change education campaigns could be most effective after an extreme weather event.     

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

CWRF模式对长江流域极端降水气候事件的模拟能力评估

基于1980—2016年长江流域站点观测降水,评估了CWRF区域气候模式对长江流域面雨量和极端降水气候事件的模拟能力.结果表明:CWRF模式能较好地再现1980—2016年长江流域及不同分区降水空间分布及月/季面雨量年际变率,且在冬、春季表现较好,夏、秋季次之.CWRF模式对长江流域面雨量存在系统性高估,对面雨量的模拟...     

副热带极端事件查询与环流检索一体化平台及其应用

基于实时基本气象要素、大气环流客观分析场及历史相关资料,结合副热带地区重要天气气候事件指标和异常相似型的客观划定,设计了具有查询和检索2个模块的副热带极端事件查询与环流检索一体化平台。平台基于ASP.NET技术,使用C#语言开发而成,实现了重要天气气候事件及异常查询、气象要素监测绘图、近期环流检索和自定义环流检索等功能。平台通过采用Browser/Server系统结构,进一步提高了查询的便捷性。文中给出了业务应用的实例,检验了系统的实用性,实践表明该平台可以作为副热带地区长—中—短一体化预报业务的重要支撑。     

State of China's Climate in 2020

2020年,全国平均气温10.25℃,较常年偏高0.7℃;全国平均降水量694.8毫米,比常年偏多10.3％.总体上涝重于旱,夏季南方地区发生1998年以来最严重汛情,暴雨洪涝灾害重;干旱呈区域性阶段性特征,灾害损失偏轻;春夏季节转换早,高温出现早,极端性强,夏季南方高温持续时间长;登陆台风偏少,影响时段和地域集中;冷...