The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

The impact of boreal autumn SST anomalies over the South Pacific on boreal winter precipitation over East Asia

The possible mechanism behind the variability in the dipole pattern of boreal winter precipitation over East Asia is analyzed in this study. The results show that the SST anomalies(SSTAs) over the South Pacific Ocean(SPO) in boreal autumn are closely related to the variability in the dipole pattern of boreal winter precipitation over East Asia. The physical link between the boreal autumn SPO SSTAs and the boreal winter East Asian precipitation dipole pattern is shown to mainly be the seasonal persistence of the SPO SSTAs themselves. The seasonal persistence of the SPO SSTAs can memorize and transport the signal of the boreal autumn SSTAs to the following winter, and then stimulates a meridional teleconnection pattern from the SH to the NH, resulting in a meridional dipole pattern of atmospheric circulation over East Asia in boreal winter. As a major influencing factor, this dipole pattern of the atmospheric circulation can finally lead to the anomalous precipitation dipole pattern over East Asia in boreal winter. These observed physical processes are further confirmed in this study through numerical simulation. The evidence from this study, showing the impact of the SPO SSTAs in boreal autumn,not only deepens our understanding of the variability in East Asian boreal winter precipitation, but also provides a potentially useful predictor for precipitation in the region.     

Long-Term Variability of the Wind Field over the Indian Ocean Based on ERA-Interim Reanalysis

A detailed study of long-term variability of winds using 30 years of data from the European Centre for Medium-range Weather Forecasts global reanalysis (ERA-Interim) over the Indian Ocean has been carried out by partitioning the Indian Ocean into six zones based on local wind extrema. The trend of mean annual wind speed averaged over each zone shows a significant increase in the equatorial region, the Southern Ocean, and the southern part of the trade winds. This indicates that the Southern Ocean winds and the southeast trade winds are becoming stronger. However, the trend for the Bay of Bengal is negative, which might be caused by a weakening of the monsoon winds and northeast trade winds. Maximum interannual variability occurs in the Arabian Sea due to monsoon activity; a minimum is observed in the subtropical region because of the divergence of winds. Wind speed variations in all zones are weakly correlated with the Dipole Mode Index (DMI). However, the equatorial Indian Ocean, the southern part of the trade winds, and subtropical zones show a relatively strong positive correlation with the Southern Oscillation Index (SOI), indicating that the SOI has a zonal influence on wind speed in the Indian Ocean. Monsoon winds have a decreasing trend in the northern Indian Ocean, indicating monsoon weakening, and an increasing trend in the equatorial region because of enhancement of the westerlies. The negative trend observed during the non-monsoon period could be a result of weakening of the northeast trade winds over the past few decades. The mean flux of kinetic energy of wind (FKEW) reaches a minimum of about 100?W?m^?2 in the equatorial region and a maximum of about 1500?W?m^?2 in the Southern Ocean. The seasonal variability of FKEW is large, about 1600?W?m^?2, along the coast of Somalia in the northern Indian Ocean. The maximum monthly variability of the FKEW field averaged over each zone occurs during boreal summer. During the onset and withdrawal of monsoon, FKEW is as low as 50?W?m^?2. The Southern Ocean has a large variation of about 1280?W?m^?2 because of strong westerlies throughout the year.     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

多普勒雷达图像逆风区的自动监测识别

在多普勒雷达降水回波径向速度场中及时准确地发现逆风区,对灾害天气预报预警具有重要意义.根据逆风区在雷达径向速度图中的物理图像特征,采用数字图像处理和分析方法实现了逆风区自动监测识别.首先,以雷达图像色标为依据,采用阈值法分别获取正、负速度区域二值图像,再对2幅图像分别进行形态学运算,然后将上述4幅图像做交叉逻辑运算,得到逆风区监测识别结果和相关参数.通过在2005-2011年长沙雷达站47幅根据实况进行人工标注后的多普勒雷达径向速度图像上进行实验,表明该方法对逆风区可以进行快速准确识别,与人工标注结果比较准确率可达89%,满足实际应用需要.     

强台风海鸥登陆期间近地层风特性分析

利用位于海南文昌市的90 m测风塔观测的强台风海鸥多层测风数据,分析了台风海鸥登陆期间近地层风场时空特征、湍流强度、垂直风切变及阵风因子等风场特性,分析结果表明:台风海鸥登陆期间,近地层各高度风速呈现"M"型双峰特征,最大风速出现在台风后风圈;台风过境前后,风向旋转了180°;近地层风速随高度升高而增大,各高度风速垂直切变符合对数和指数规律;粗糙度长度、风廓线幂指数、湍流强度、阵风系数等风场特性与风速呈负相关关系,随着风速的增加而降低;从台风外围至台风眼,粗糙度长度随风速呈现"增大-减小-增大"特征;台风眼内部风速垂直切变剧烈,前后风圈的风速垂直切变较弱;强风区湍流强度较弱,弱风区湍流强度较强;台风风圈的湍流强度随高度增加而减小,台风眼内湍流强度随高度先减小再增加;台风影响各阶段阵风系数随高度升高而减小,各高度层阵风系数遵循指数定律;阵风系数随风速的增大而减小,当风速达到一定强度时,阵风系数随风速变化不明显。     

IEC的风机选型参数估算方法在中国的适用性研究

国际电工委员会编制的《风力发电机组设计要求》(IEC 61400-1)推荐了针对风电机组安全等级评估的极端风速和湍流强度特征值估算方法,因其简单便捷,在风电领域被广泛采用。利用全国风能资源专业观测网的193座测风塔观测数据,对IEC推荐的极端风速计算方法与我国规范推荐的基于极值I型概率分布方法进行比较,发现两种方法计算的193座塔70 m高度层50年一遇10 min平均最大风速,仅有7座测风塔较为一致,差异在±1%;IEC推荐方法的计算结果多数偏小,其中偏小10%以上的测风塔有121座,偏小30%以上的有44座测风塔,而偏大10%以上的只有9座测风塔;IEC方法计算的极值风速大幅度偏小的测风塔主要分布在台风影响的东南沿海地区,偏差较小的测风塔主要分布在西北和华北地形平缓区域,但同时偏大10%以上的测风塔也多分布在这一地区。以目前行业领域普遍采用的以15 m·s~(-1)风速的平均湍流强度作为风电机组选型指标,与严格按照规范,以15 m·s~(-1)风速段所有样本湍流强度的90%分位数处的值作为指标进行风电机组等级确定作对比,发现193座塔中有46座塔的选型是不安全的,甚至相差两个等级。     

新疆地区年平均风速数据非均一影响研究

利用惩罚最大F检验(The Penized Maximal F Test,PMFT),对新疆地区105个国家级气象站点建站至2014年逐年平均风速资料进行了均一性检验,并通过订正得出新疆地区年平均风速均一化数据集合;通过对订正后数据与原始数据进行对比评估,讨论数据非均一性对新疆风速的影响,研究结论得出:(1)在95%的显著性水平下,检验出所有待检站点均出现非均一间断点,共计151个;(2)元数据中记录的人为影响因素,超过1/3对年平均风速序列产生了非均一影响,其中仪器换型对年平均风速序列影响最为显著,其次为环境变化;(3)仪器换型和站址迁移对年平均风速的非均一影响与台站风力大小成正比,其它人为因素对年平均风速的影响与台站风力大小成反比;(4)订正后,数据时间序列的均一性得到改善,数据序列明显的趋势拐点趋于缓和,下降趋势普遍增强,数据可用性进一步改善。     

1998-2009年间台风不活跃期间热带云团活动分析

热带云团是台风生成的前兆,虽然一些研究将近20 a来台风不活跃与大尺度环境场相联系,但是还没有人分析台风不活跃期热带云团的活动特点。本文利用目前仅有的1989-2009年全球热带云团资料,分析了西北太平洋热带云团近20 a的变化特征。1998年以后西北太平洋台风生成减少主要发生在7-10月,集中在南海（13～23°N,110～120°E）和西北太平洋台风活动区域的东部（13～23°N,145～170°E）。热带云团除1月外各月都有增加的趋势,特别是与台风生成显著减少区域相联系的热带云团活动具有显著的增加趋势。通过对NCEP/NCAR再分析资料分析发现,1998年后热带云团活动增加与环境风垂直切变增加有关,而增强的垂直切变不利于台风生成。     

京津冀地区中α尺度飑线过程中大风特征分析及成因初探

应用常规天气观测资料、地面加密自动气象站资料、大风灾情报资料、京津冀地区7部多普勒天气雷达组网观测资料及VDRAS资料,从多个角度对2013年8月4日京津冀地区一次飑线过程产生的大范围大风天气过程进行了分析,结果显示:此次过程是在高空冷空气南下、低层暖湿气流北上、系统前倾及位势不稳定的有利层结条件下,由多单体风暴演变为中α尺度的强飑线所致。飑线形成于低层垂直切变加强、冷池合并之后;大风主要发生在飑线主体回波中,其次是主体回波前和中前,主体回波后很少发生。大风发生的位置取决于飑线结构中气流的性质,气流的性质与冷池前进的程度和对流的强度关系密切。大风大部分由下沉冷气流产生,少数为近地面上升暖气流导致。大风发生的范围和强度与低层风垂直切变的强度呈正比,大范围低层风垂直切变的加强增强了飑线入流和出流的强度,是大范围大风、局部强风形成的重要原因。大风发生站次与冷池的强度和范围密切相关,冷池的加强和范围的扩大加强了后侧冷入流和前侧暖入流的强度和范围,也是大范围大风形成的重要因素。