一类基于SVM/RBF的气象模型预测系统

利用广东湛江地区近30年月平均气温的气象数据作为数据集，建立了一种新的基于径向基核函数的支持向量机模型预测系统。通过适当选择模型参数，其平均绝对百分比误差只有5.61%，在绝对误差温度小于等于2℃的条件下，预测的准确率达到了95%，显示出所建立的支持向量机模型预测系统的有效性。通过分析发现了湛江海岸地区气候和全球气候变暖一致的事实。     

区域气候的时间序列数据研究

全球变暖是从二十世纪五十年代开始的全球平均气温上升现象.希望通过对不同时期各地区气候相关程度的研究,讨论其中的演变趋势,以及全球变暖效应在其中的影响.为此选择美国过去一百年间各地观测站的气温观测值,以十年为周期分为十组,并为每组数据构造观测站相关图.每组数据都经过周期分解的预处理,以消除日照周期在其中造成的周期性影响,然后运用高斯马尔可夫随机场模型,通过对逆协方差矩阵的统计估计,得到不同观测站之间的气温序列的条件依赖关系,并以此构建相关图.因为气候模型的时间序列特殊性,并对模型的假设和拟合方式进行了改进.最后从得到的相关图中发现了一百年间相继出现的两种相反演变趋势,从而对全球变暖是其主要原因的可能性加以讨论.     

利用相关网研究全球变暖现象

为研究全球变暖对气候多样性的影响,通过构建基于温度数据的相关网,对子图、团体结构进行统计学分析,发现网络越来越接近随机图。说明气候多样性逐渐消失,同时得到气候变暖并非全球普遍的结论。     

太阳活动影响全球变暖

一说到全球变暖,人们首先联想到的就是温室气体排放问题,似乎只有燃烧化石燃料、砍伐森林等人类活动才是全球变暖的罪魁祸首。不过美国国家大气研究中心（NCAR）的科学家们找到了影响全球气候的又一个重要因素——那就是我们头顶上的太阳,准确的说是太阳活动周期。他们的研究结果显示,最大太阳活动及其后续对地球都产生了积极的影响,具体的例子就是在热带太平洋上发生的拉尼娜现象和厄尔尼诺现象。这项研究使得人类在11年的太阳活动周期中有可能对某些时期的气温和降水模式进行预测。     

33计阻击全球变暖

在全球环境问题上,气候变暖是人类所面临的最大威胁。气候变暖本质上是由于空气中以二氧化碳为首的温室气体和以碳为主要元素的制造温室气体的元素大量聚集产生的。气候变暖导致全球气候严重变迁和由此引发难以预料、灾害突如其来的各种天气灾难。这种极度威胁人类家园的隐患和由此导致的各种灾难早已在很多影片中得到了再现,譬如《后天》就是其中的典型。针对全球变暖,各国政府、产业和各类环境组织等都已严肃面对,媒体也几乎每时每刻都通过各类新闻和话题呼吁全球公民携手阻击全球变暖。选择这33条方法一方面它们对于中国有更强的借鉴性,另一方面这33条都留给创意人士足够的回味空间。在我们看来,任何办法永远只适合其诞生的环境,可是办法背后的思路却有着广泛的施展空间和无数的创新可能。希望33计阻击全球变暖的思路能真正透过创新者的再思考和行动化作阻击全球变暖的,适合不同地区和环境的无数妙招。     

时间序列模型在降水量预测中的应用研究

研究准确预测降水量,可提高应对灾害的能力.降水量的变化既受大气环流、地形、气压、气候带等各种环境因子的影响,降水量的动态特征呈现复杂非线性,使得准确预测未来降水量的变化较为困难.为了提高预测精度,采用融合时间序列模型与支持向量回归提出了一种新的多因子影响降水量预测模型.首先用支持向量机进行环境因子的非线性选择,用时间序列模型进行模型阶数的确定,最后以最优阶模型一步预测法检验模型外推能力.应用于赤峰地区夏季降水量预测,仿真结果表明,改进方法预测精度高,用在旱涝预测方面具有较好的应用前景.     

组合模型在降雨量预测中的应用

研究降雨量准确预测问题,降水量的变化既受大气环流、地形、气压、气候带等各种环境因子的影响,降水量的动态特征呈现复杂非线性和各种干扰因素,预测不可能准确。传统预测模型难以对其进行准确预测,预测精度低。为提高降雨量的预测精度,提出一种组合模型的降雨量预测模型。首先采用小波分析将降雨量数据进行分解成线性和非线性部分,然后分别采用ARIMA和RBF神经网络模型对其进行预测,最后采用小波重构线性和非线性预测结果,得到降雨量最终预测结果。仿真结果表明,相对于传统预测模型,组合模型提高了降雨量预测精度,预测结果可以帮助农业、水利部门提高防治旱涝灾害的科学依据。     

森林场景可视化和林火模拟仿真技术研究综述

森林是生态环境系统的重要组成部分。随着气候变暖,恶劣气候气象条件造成全球森林火灾频繁发生,给国民经济和消防救援带来巨大挑战,森林火灾已成为全球主要的自然灾害。因此,森林场景可视化建模、3维场景仿真、林火模拟仿真、火场复现、预测和灾害评估成为林业虚拟仿真研究热点。本文对树木形态结构建模技术、森林场景大规模重建和实时渲染、森林场景可视化、林火模型和林火模拟仿真等前沿技术和算法进行综述。对相关的林木、植被的形态结构表达和真实感可视化建模方法进行归纳分类,并对不同可视化方法的算法优劣、复杂度、实时渲染效率和适用场景进行讨论。基于规则的林木建模方法和基于林分特征的真实场景重建方法对大规模森林场景重建技术进行分类,基于物理模型、经验模型和半经验模型对森林火灾的林火模型、单木林火、多木林火模拟和蔓延进行总结,对影响林火蔓延的不同环境气象因子（如地形地貌、湿度、可燃物等）和森林分布对林火发生、扩散和蔓延的影响进行分析,对不同算法的优劣进行对比、分析和讨论,对森林场景可视化和林火模拟仿真技术未来的发展方向、存在问题和挑战进行展望。本文为基于森林真实场景的森林火灾模拟仿真和数字孪生沉浸式互动模拟系统的构建提供了理论方法基础,该平台可以实现森林场景快速构建、不同火源林火模拟、火场蔓延模拟仿真以及不同气象影响条件的火场预测,可对森林火场救援指挥、火场灾害评估和火场复原提供可视化决策支持。     

基于混合模型的中长期降水量预测

针对中长期降水量预测精度较低的问题,提出了由改进集合经验模态分解方法、最小二乘法、核极限学习机和改进的果蝇优化算法构成的混合模型来对区域年度降水量序列进行预测。首先,通过改进集合经验模态分解方法将非平稳降水量时间序列分解为多个分解项。然后,根据不同分解项的特性分别采用最小二乘法和核极限学习机对其进行预测。由于核极限学习机均存在一定的参数敏感特性,因此提出使用改进的果蝇优化算法来对核极限学习机的相关参数搜索寻优,以提高其预测精度。最后,将各分解项的预测结果叠加,从而形成最终预测结果。以广东省7个地市1951－2015年的年度降水量为例,对所提方法进行了验证,结果表明:相比于自回归移动平均模型和核极限学习机模型,混合模型预测具有更高的预测精度。     

气温上升4℃，人类迎来大难？

《Geek》每期都在提全球变暖，那么地球变热了，到底对我们有些什么影响？英国政府拿出的这份气候地图就为人类温暖的未来做出了预测。这份4℃升温影响地图由英国气象局哈德利中心制作，阐述的是全球平均温度比工业革命前上升4℃所可能造成的部分影响。     

The influence of temperature and precipitation climate regimes on vegetation dynamics in the US Great Plains: a satellite bioclimatology case study

Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data are widely used in global‐change research, yet relationships between the NDVI and ecoclimatological variables are not fully understood. This study attempts to model climate‐driven vegetation dynamics through the integration of satellite‐derived NDVI data with climate data collected from ground‐based meteorological stations in the US Great Plains. Monthly maximum value composites of NDVI data (8‐km resolution) and monthly temperature and precipitation records from 305 stations were collected from 1982 to 2001. Analyses involving deseasonalized datasets supported temperature as the dominant climate regime, demonstrating a higher average NDVI–temperature correlation (r = 0.73) than the NDVI–precipitation relationship (r = 0.38). Cluster analysis was used to develop a climate regionalization scheme based primarily on temperature, and NDVI characteristics of each subregion were compared. In the context of global climate change, findings from this study emphasize the influence of temperature and precipitation variability over vegetation cover in the Great Plains region.     

sdsm — a decision support tool for the assessment of regional climate change impacts

General Circulation Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called ‘downscaling’ techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are currently able to provide and what impact assessors require. This paper describes a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. Statistical DownScaling Model (sdsm) facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. Additionally, the software performs ancillary tasks of predictor variable pre-screening, model calibration, basic diagnostic testing, statistical analyses and graphing of climate data. The application of sdsm is demonstrated with respect to the generation of daily temperature and precipitation scenarios for Toronto, Canada by 2040–2069.     

Climate change and the response of forests

Abstract

The global climate has warmed by over 0·5°C during the last 125 years. Models of the Earth's temperature reponse to increasing levels of atmospheric carbon dioxide and other greenhouse gases estimate that the average global surface temperature will rise about 4°C by the mid twenty-first century. High latitudes will warm more than lower latitudes and winters more than summers. Forests will undergo enormous changes as temperatures increase and precipitation patterns shift. It is doubtful that forest movement will meet the rate of climate change. Many sensitive hardwood tree species, such as paper and yellow birch, sugar maple and black ash, may die. Boreal forests will replace tundra and mixed hardwood forests will replace them. Much of the change expected in the location and composition of forests may be detected by means of remote sensing.     

Recent climate variability and its impact on precipitation,temperature, and vegetation dynamics in the Lancang River headwater area of China

The alpine ecosystem is one of the most fragile ecosystems threatened by global climate change. The impact of climate variability on the vegetation dynamics of alpine ecosystems has become important in global change studies. In this study, spatially explicit gridded data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) product (MOD11A1/A2), the Tropical Rainfall Measuring Mission (TRMM) rainfall product (3B43), and MODIS net primary productivity (NPP) product (MOD17A3), together with meteorological observation data, were used to explore the spatio-temporal pattern of climate variability and its impact on vegetation dynamics from 2000 to 2012 in the Lancang River headwater area. We found that the variation patterns of LST, precipitation, and NPP in the study area showed remarkable spatial differences. From the northwest to the southeast the spatial variation of average annual LST exhibited a decreasing–increasing–decreasing–increasing pattern. At the same time, most of the study area exhibited an increasing LST during the growing season. The annual precipitation increased in the semi-arid northern part, whereas it decreased in the semi-humid southern part. The precipitation variability during the growing season has a pattern similar to the annual precipitation variability. Although the majority of the regions have seen an NPP increase from 2000 to 2012, the responses of the vegetation to the varied climate factors were spatially heterogeneous. The alpine–subalpine meadows in the high-altitude areas were more sensitive to climate variability in the growing season. It is argued that satellite remote-sensing products have great potential in investigating the impact of climate variability on vegetation dynamics at the finer scale, especially for the Lancang River headwater area with complex surface heterogeneity.     

A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study

Downscaling techniques are used to obtain high-resolution climate projections for assessing the impacts of climate change at a regional scale. This study presents a statistical downscaling tool, SCADS, based on stepwise cluster analysis method. The SCADS uses a cluster tree to represent the complex relationship between large-scale atmospheric variables (namely predictors) and local surface variables (namely predictands). It can effectively deal with continuous and discrete variables, as well as nonlinear relations between predictors and predictands. By integrating ancillary functional modules of missing data detecting, correlation analysis, model calibration and graphing of cluster trees, the SCADS is capable of performing rapid development of downscaling scenarios for local weather variables under current and future climate forcing. An application of SCADS is demonstrated to obtain 10 km daily mean temperature and monthly precipitation projections for Toronto, Canada in 2070–2099. The contemporary reanalysis data derived from NARR is used for model calibration (1981–1990) and validation (1991–2000). The validated cluster trees are then applied for generating future climate projections.     

Change Characteristic of NDVI and Its Response to Climate Change in Inner Mongolia over the Past 30 Years

based on the third generation GIMMS NDVI time\|series datasets and meteorological datasets during 1982~2012,the change characteristic of NDVI and its response to climate change in Inner Mongolia in recent 30 years were analyzed by means of maximum value composite,trend analysis and correlation analysis.The results show that the overall trend of NDVI spatial and temporal distribution in Inner Mongolia shows an increasing tendency,and the change trend of NDVI shows a decreasing tendency only in areas which are the southwest of Hulun Buir,the northwest of XilinguoleMeng and the central of Ulanhot.Inner Mongolia responses significantly to global climate change.The change of average annual temperature and precipitation shows an increasing tendency,and the change rate of them are 0.2℃/10a,-10.7mm/10a,respectively.The correlation coefficients between NDVI and air temperature and precipitation shows spatial difference,and 17.6% areas are significantly related to precipitation,and only 0.4% of the areas are significantly related to air temperature.In addition,precipitation has more significantly effect on NDVI compared with air temperature.     

Using MODAWEC to generate daily weather data for the EPIC model

Although the EPIC model has been widely used in agricultural and environmental studies, applications of this model may be limited in the regions where daily weather data are not available. In this paper, a stand-alone MODAWEC model was developed to generate daily precipitation and maximum and minimum temperature from monthly precipitation, maximum and minimum temperature, and wet days. A case study shows that the crop yields and evapotranspiration (ET) simulated with the generated daily weather data compare very well with those simulated with the measured daily weather data with low normalized mean square errors (0.008–0.017 for crop yields and 0.003–0.004 for ET). The MODAWEC model can extend the application of the EPIC model to the regions where daily data are not available or not complete. In addition, the generated daily weather data can possibly be used by other environmental models. Associated with MODAWEC, the EPIC model can play a greater role in assessing the impacts of global climate change on future food production and water use.     

An atmosphere-ocean time series model of global climate change

Time series models of global climate change tend to estimate a low climate-sensitivity (equilibrium effect on global temperature of doubling carbon dioxide concentrations) and a fast adjustment rate to equilibrium. These results may be biased by omission of a key variable—heat stored in the ocean. A time series model of the atmosphere-ocean climate system is developed, in which surface temperature (atmospheric temperature over land and sea surface temperature) moves towards a long-run equilibrium with both radiative forcing and ocean heat content, while ocean heat content accumulates the deviations from atmospheric equilibrium. This model is closely related to Granger and Lee's multicointegration model. As there are only 55 years of observations on ocean heat content, the Kalman filter is used to estimate heat content as a latent state variable, which is constrained by the available observations. This method could be applied to other climate change problems where there are only limited observations on key variables. The final model adopted relates surface temperature to the heat content of the upper 300 m of the ocean. The resulting parameter estimates are closer to theoretically expected values than those of previous time series models and the estimated climate sensitivity to a doubling of carbon dioxide is 4.4 K.     

Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters

The dynamic nature of climate over Indian sub-continent is well known which influences Indian monsoon. Such dynamic variability of climate factors can also have significant implications for the vegetation and agricultural productivity of this region. Using empirical orthogonal function (EOF) and wavelet decomposition techniques, normalized difference vegetation index (NDVI) monthly data over Indian sub-continent for 18 years from 1982 to 2000 have been used to study the variability of vegetation. The present study shows that the monsoon precipitation and land surface temperature over the Indian sub-continent landmass have significant impact on the distribution of vegetation. Tropospheric aerosols exert a strong influence too, albeit secondary to monsoon precipitation and prove to be a powerful governing factor. Local climate anomaly is seen to be more effective in determining the vegetation change than any global teleconnection effects. The study documents the dominating influence of monsoon precipitation and highlights the importance of aerosols on the vegetation and necessitates the need for remedial measures. The present study and an earlier one point towards a possible global teleconnection pattern of ENSO as it is seen to affect a particular mode of vegetation worldwide.     

Short‐term response of arctic vegetation NDVI to temperature anomalies

The effects of climate change on northern vegetation productivity need to be fully understood in order to reduce uncertainties in predicting vegetation distributions under different climate warming scenarios. Knowledge of the relationship between northern climate and vegetation productivity will also help provide a better understanding of changes in vegetation distributions as an indicator of climate change and variability. Vegetation productivity and biomass have been monitored using long‐term satellite earth observations, mostly using the Normalized Difference Vegetation Index (NDVI), as a cumulative indicator of all effects resulting from processes related to climate change, including changes in temperature, precipitation, and disturbance. In this paper, the investigation is focused on the short‐term effect of temperature anomalies on arctic and tree‐line transition vegetation productivity in both dry and humid regions of Canada. The analysis shows that several land‐cover types composed mainly of trees and shrubs exhibit a significant increase in NDVI with higher‐than‐normal temperatures in the preceding 10–40‐day period, while land‐cover types consisting of lichen and moss growing on mostly barren surfaces show a significant NDVI decrease with increased temperature. These trends are consistent with results reported in plot‐warming experiments in the north, which have shown that certain vegetation communities increase, while others decrease in cover fraction and biomass in response to warming. When land cover is grouped into increasing and decreasing NDVI with temperature and stratified by dry and humid regions of Canada, much of the dry region of northern Canada does not exhibit significant NDVI response to preceding temperature anomalies. It is expected that in the absence of disturbance or other limiting factors, an increased frequency of elevated temperature anomalies may eventually contribute to changes in vegetation biomass. A map of land‐cover types that have the potential to increase in biomass with climate warming and those that are vulnerable to decline is presented.