广西甘蔗秋旱灾害风险评估技术初步研究

为增强对广西甘蔗秋旱灾害的风险评估和应急管理能力,利用气象、植被、基础地理信息和社会经济数据,根据风险三角形理念,从广西甘蔗秋旱灾害的危险度、受灾可能性和承灾体脆弱度3个方面,选择因子构建甘蔗秋旱灾害风险评估的指标体系,采用层次分析法构造判断矩阵以确定各指标和因子的权重,构建评估模型,并计算广西甘蔗秋旱灾害风险指数,再基于GIS绘制广西甘蔗秋旱灾害风险区划,结果显示:高风险区和较高风险区主要分布在来宾和崇左等市的局部地区,低风险区主要分布在桂东南地区。利用灾情数据进行验证表明:广西甘蔗秋旱灾害风险分布与甘蔗灾情损失空间分布情况基本一致。     

驻马店地区严重伏,秋旱的分析和预报

给出了综合考虑降水量、总少雨时段和降水时段及少雨时段分布的生旱指数公式。以此计算严重伏、秋旱指数，较以往诸多文献中仅考虑降水量计算干旱指数更为客观、合理地确定了驻马店地区严重伏、秋旱年。     

2019年浙江省秋旱大气环流特征分析

2019年秋季浙江出现全省性旱情,浙中西地区旱情最为明显,多地降水量破历史最低纪录,气温则较常年偏高1.5℃以上,基于浙江省67站降水、气温及综合气象干旱指数资料和NCEP再分析资料,分析2019年浙江秋旱成因.结果表明:降水持续偏少、气温持续偏高是秋旱发生的直接原因.500 hPa极涡主体偏于西半球,亚洲区冷空气强度...     

贵州省秋旱时空分布规律研究

依据秋旱标准的有关计算方法,对贵州省78个站1971～2000年秋旱发生规律、类型划分、分布特征及区域性秋旱发生规律和阶段变化等进行系统分析,揭示贵州省秋旱的发生及分布规律.     

玉林地区秋旱气候特征分析及防御对策

利用玉林地区５个测站１９５８～１９９６年降雨量资料,统计分析了玉林地区秋旱天气的气候规律,并在此基础上,提出秋旱的防御对策,为做好秋旱预报、减少秋旱灾害给农业生产造成的损失提供可靠的依据。     

玉林地区秋旱气候特征分析及防御对策

利用玉林地区５个测站１９５８￣１９９６年降雨量资料,统计分析了玉林地区秋旱天气的气候规律,并在此基础上,提出秋旱的防御对策,为做好秋旱预报、减少秋旱灾害给农业生产造成的损失提供可靠的依据。     

遂溪县干旱的发生规律及防御对策

根据干旱的农业气象指标,应用近50年的逐日降水资料,分析了遂溪县干旱的气候规律,并提出一些防御干旱的对策。分析表明,遂溪县出现春旱或秋旱的机率高达93%,基本是十年九旱,同时出现中等以上程度春旱和秋旱的机率为20%;春旱重于秋旱,春旱高发期的相隔时间明显缩短、频率加大,秋旱相隔时间较长,连续2年出现重秋旱的机率较小。     

干旱气候对白银市秋粮生产的影响

本文对白银市前一年秋季 ,当年春季、秋季大气干旱指数与秋粮产量进行了对比分析和相关分析 ,分析结果表明 ,秋粮产量与当年伏旱关系最密切 ,其次是前一年的秋旱和当年的春旱。从服务于农业生产的角度出发 ,用这 3个旱段的干旱指数构造了服务指数 ,其与秋粮产量关系更加密切     

重庆市秋旱时空分布规律研究

依据有关干旱计算方法，对重庆市1961-2000年秋旱时空分布规律、类型划分及区域性秋旱发生规律和阶段变化等进行了系统地分析、研究，全面地揭示了重庆市秋旱的发生分布规律。     

中国西南和华南地区秋季干旱变化规律

利用我国西南和华南地区131个测站1961~2010年近50 a降水和NECP资料,采用线性趋势分析、合成分析、功率谱分析等方法,基于秋季降水距平百分率,研究分析了近50 a我国西南和华南地区各级别秋旱的空间分布及时间变化特征,并初步讨论了各级别干旱形成的原因。结果表明：秋季干旱集中在川东、贵州中东部—华南,中旱、重旱、特旱主要出现在华南;近50 a来秋旱有显著增多的趋势,主要体现在轻旱的增多,而重旱和特旱趋势不明显。1960年代秋旱相对较多,1970年代初至1980年代后期秋旱较少,此后秋旱频繁,其中2002年以后秋旱突变性增多,干旱范围扩大的同时,其强度也在增强;秋旱频率具有显著的2.2 a周期,其中重旱有显著的12 a周期,特旱有显著的2.7 a周期;秋旱频率高的地方连旱频率也高,连旱高频区在川东—渝北、黔中—华南,连续5 a以上的秋旱较少,个别地方可达到6 a。700 h Pa上,西太平洋副热带高压、印缅槽、高原东部槽等是影响西南、华南地区秋季干湿的主要环流因子。     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

赤峰地区近50a气候变化诊断分析

利用线性回归、累积距平和多项式回归法,对赤峰地区1951—1990年12个气象台站的月、季、年平均气温、最高气温、最低气温序列进行连续性变化趋势分析,确定该区域的气候变化趋势。应用Mann-Kendall法和滑动t检验法检验气温序列变化的不连续性,确定突变时间。结果表明:赤峰地区12个月的平均气温均有升温趋势,增温幅度从0.56℃/10a到0.15℃/10a,其中2月份最强。季节增温最显著的是冬季,其次是秋季和春季,夏季最弱。年平均气温增温率是0.28℃/10a,1988年是变暖的第一年,突变时间在1993年;年平均最低气温增温率是0.29℃/10a,1988年是变暖的第一年,突变时间在1988年;年平均最高气温增温率是0.26℃/10a,1993年是变暖的第一年,突变时间在1993—1996年附近;平均最低气温和最高气温的变暖时间具有不对称性。     

华东沿海ASCAT反演风速的检验和订正

基于2010—2014年ASCAT反演风速、华东沿海14个浮标站和浙江沿海249个自动气象站资料,对华东沿海ASCAT反演风速进行检验和订正。研究表明:站点ASCAT风速误差不仅与离岸距离相关,而且与站点周围地形有关,误差较大的5个浮标站均位于舟山群岛附近海区,平均偏大4.79 m·s-1,其他海区浮标站的ASCAT反演风速平均偏差仅为0.46 m·s-1。ASCAT反演风速与浮标站风速的线性回归可有效减小反演风速误差,订正后误差大幅减小,误差越大的站点订正效果越好。相距160 km内的浮标站点间风速误差呈正相关,且站点间距越小,误差正相关越明显。考虑带影响半径的反距离权重,采用邻站方程订正法和邻站误差订正法分别对华东沿海ASCAT反演风速进行订正,均能明显减小平均偏差和均方根误差,两种方法订正效果接近,即两种方法均有较好的订正效果,可用于实际业务。     

华北地区地基GPS水汽反演中加权平均温度模型研究

运用几种不同的加权平均温度的算法,计算了华北地区张家口、北京和邢台的加权平均温度。结果表明,对于华北地区而言,常规的计算模型普遍存在一定的系统误差。在对加权平均温度与地面各气象要素的关系分析基础上,分别建立了华北地区基于地面气象要素的单因子和多因子回归方程,较好地满足了GPS遥感水汽总量实时性和高精度的要求。     

ECMWF模式地面气温预报的四种误差订正方法的比较研究

采用均方根误差对欧洲中期天气预报中心（ECWMF）确定性预报模式2007年1月至2010年12月的地面气温预报结果进行评估,并分别利用一元线性回归、多元线性回归、单时效消除偏差和多时效消除偏差平均的订正方法,对ECMWF模式地面气温预报结果进行订正。结果表明,4种订正方法都能有效地减小地面气温多个时效预报的误差,改进幅度约为1℃。在短期预报中仅考虑最新预报结果的一元线性回归订正方法要优于考虑多个预报结果的多元集成预报订正方法。在中期预报中考虑多个预报结果的多元集成预报订正方法更优,更稳定。在模式预报误差较大的情况下,多时效集成的订正方法能更稳定地减小误差。     

基于DEM的气温插值方法研究

以甘肃河东为研究区,利用河东及周边的82个气象站点1971~2004年的月平均气温数据,结合数字高程模型(DEM),在分析平均气温与经度、纬度、海拔高度、坡度、坡向地形要素相关关系基础上,提出了一种基于DEM的多元线性回归空间插值方法(MLR),并与传统的反距离平方法(IDS)、样条函数法(SPLINE)和普通克里金法(OK)进行了精度比较.精度验证结果显示:无论从误差大小还是从插值效果上,考虑了地形要素的MLR方法均优于传统的插值方法.最后,基于MLR插值方法生成84 m×84 m甘肃河东地区月平均气温栅格数据集.平均气温结果表明:河东各月平均气温大致呈现由东南向西北逐渐降低的空间格局,且平均气温的季节内波动差异较大.其中,夏季气温的波动幅度最小,波动幅度自西向东减弱;冬季次之,有自北向南减弱的趋势;春季和秋季较大,有自西南向东北降低的趋势.     

Mapping the mean monthly precipitation of a small island using kriging with external drifts

This study focuses on the spatial distribution of mean annual and monthly precipitation in a small island (1128 km²) named Martinique, located in the Lesser Antilles. Only 35 meteorological stations are available on the territory, which has a complex topography. With a digital elevation model (DEM), 17 covariates that are likely to explain precipitation were built. Several interpolation methods, such as regression-kriging (????????, ????????, and ????????) and external drift kriging (??????) were tested using a cross-validation procedure. For the regression methods, predictors were chosen by established techniques whereas a new approach is proposed to select external drifts in a kriging which is based on a stepwise model selection by the Akaike Information Criterion (AIC). The prediction accuracy was assessed at validation sites with three different skill scores. Results show that using methods with no predictors such as inverse distance weighting (??????) or universal kriging (????) is inappropriate in such a territory. ?????? appears to outperform regression methods for any criteria, and selecting predictors by our approach improves the prediction of mean annual precipitation compared to kriging with only elevation as drift. Finally, the predicting performance was also studied by varying the size of the training set leading to less conclusive results for ?????? and its performance. Nevertheless, the proposed method seems to be a good way to improve the mapping of climatic variables in a small island.     

Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901–2002 data,Assam, India

Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as practical point of view. Wavelet regression (WR) technique is proposed and developed to analyze and predict the rainfall forecast in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. This study uses rainfall data from 21 stations in Assam, India over 102 years from 1901 to 2002. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The root mean square errors (RMSE), N-S index, and correlation coefficient (R) statistics were used for evaluating the accuracy of the WR models. The accuracy of the WR models was then compared with those of the artificial neural networks (ANN) models. The results of monthly rainfall series modeling indicate that the performances of wavelet regression models are found to be more accurate than the ANN models.     

Comparison of four ensemble methods combining regional climate simulations over Asia

A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.     

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.