Effect of extreme value loss on long-term correlated time series

Effects of extreme value loss on long-term correlated time series are analyzed by means of detrended fluctuation analysis (DFA) and power spectral density analysis. Weaker memory can be detected after removing of extreme values for the artificial long-term correlated data, indicating the emergence of extreme events may be closely related to long-term memory. For observational temperature records, similar results are obtained, but not in all stations. For example, in some stations, only extending of scaling range to smaller time scales occurs, which may be due to the asymmetric distribution of values in the record. By comparing our findings with previous works, clustered positions of the extreme events are recognized as an important property in long-term correlated records. Through a simple numerical test, close relations between extreme events and long-term memory are discovered, which is helpful for our understanding of the effects of extreme value loss on long-term correlated records.     

Scaling behaviors of precipitation over China

Scaling behaviors in the precipitation time series derived from 1951 to 2009 over China are investigated by detrended fluctuation analysis (DFA) method. The results show that there exists long-term memory for the precipitation time series in some stations, where the values of the scaling exponent α are less than 0.62, implying weak persistence characteristics. The values of scaling exponent in other stations indicate random behaviors. In addition, the scaling exponent α in precipitation records varies from station to station over China. A numerical test is made to verify the significance in DFA exponents by shuffling the data records many times. We think it is significant when the values of scaling exponent before shuffled precipitation records are larger than the interval threshold for 95 % confidence level after shuffling precipitation records many times. By comparison, the daily precipitation records exhibit weak positively long-range correlation in a power law fashion mainly at the stations taking on zonal distributions in south China, upper and middle reaches of the Yellow River, northern part of northeast China. This may be related to the subtropical high. Furthermore, the values of scaling exponent which cannot pass the significance test do not show a clear distribution pattern. It seems that the stations are mainly distributed in coastal areas, southwest China, and southern part of north China. In fact, many complicated factors may affect the scaling behaviors of precipitation such as the system of the east and south Asian monsoon, the interaction between sea and land, and the big landform of the Tibetan Plateau. These results may provide a better prerequisite to long-term predictor of precipitation time series for different regions over China.     

基于测风塔数据的最大风速与极大风速 关系研究

利用辽宁省风能资源专业观测网2009年6月至2010年5月26座测风塔10—70 m(部分塔为100 m)高度的逐10 min梯度风观测数据,采用线性相关分析的方法,研究了近地层最大风速和极大风速的关系。结果表明:年内最大风速与极大风速多数出现在同一大风天气过程中,但极大风速并非主要发生在出现最大风速的20 min内;最大风速和极大风速易出现在午后和傍晚;极大风速与最大风速普遍具有较好的线性相关关系,日时距、10 min时距和大风条件下,日时距的相关性最好,平均相关系数达到0.970;不同时距和大风条件下,极大风速与最大风速的比值系数相差不大,但从相关性看可以考虑优先使用日时距样本的最大风速推算极大风速;随着高度的增加,极大风速与最大风速的比值系数减小,10 m高度日极大风速是日最大风速的1.42倍左右,70 m高度则是1.24倍左右,利用最大风速推算不同高度极大风速时不宜采用统一的比值系数。     

1981—2020年安徽省不同重现期最大风速和极大风速时空变化特征

分别从质量控制级别、有效数据完整率、是否均一等方面考虑,选取安徽省51个气象站1981—2020年逐日10 min最大风速和2006—2020年逐日极大风速资料,基于最大风速资料应用阵风系数法构建1981—2005年极大风速,得到1981—2020年极大风速的长时间序列数据;对风速资料进行拟合适度检验,估算了安徽省不同重现期最大风速和极大风速的时间变化以及空间分布,并对极大风速序列延长前后重现期估算情况进行了对比。结果表明:(1) 利用阵风系数法构建的极大风速数据可信,可为因缺少长时间序列的极大风速观测而无法进行50年或者更长重现期估算提供参考;(2) 1981—2020年安徽省历年最大风速强度为12.38 m/s,极大风速强度为20.55 m/s,均为皖南低矮山区的风速值较低,沿江西部及江淮之间中部处于相对大值区;(3) 30年重现期最大风速为12.09~27.23 m/s,50年为12.64~29.01 m/s,均是石台站最小,桐城站最大;30年重现期的极大风速为23.51~39.56 m/s,50年为24.58~41.93 m/s,均为池州站最小,桐城站最大;(4) 短期的观测资料会降低重现期估算结果的可靠性。      

大气边界层风速脉动的分形模拟

应用曲线分数维计算方法计算大气边界层实际风速观测资料的分数维值,并根据分形理论构造出一个理论模型来模拟真实的大气边界层风速时间序列。将模拟和真实数据的一些重要的统计特征,例如方差、风速概率分布、谱密度函数和自相关函数等,进行了比较,结果表明二者具有很好的一致性。     

中国极端温度的群发性研究

从极端事件再现时间的角度研究了中国极端高、低温事件再现时间序列的长程相关性、群发性以及二者的关系。发现极端温度事件再现时间序列具有长程相关性,表征长程相关性的标度指数分布存在着明显的区域性特征,并与大气环流有关,当中国大陆在盛夏或隆冬稳定的西风环流控制下,极端温度的长程相关性较好,标度指数较大。通过定义极端事件的群发指数,对极端高、低温进行研究,发现具有长程相关性再现时间序列的极端温度呈现群发现象,且极端温度再现时间序列的标度指数和群发指数二者在空间分布上有较好的对应,极端温度再现时间序列的长程相关性可能是导致极端温度群发性的原因。从年际变化的角度看,极端高温Ⅰ级群发区域的群发指数增长趋于平缓,而极端低温Ⅰ级群发区域的群发指数有下降趋势,这与近几十年来全球变暖一致。在年代际尺度上,群发指数分布的演变特征明显,极端高温Ⅰ级群发区域总体面积变化不大,而极端低温Ⅰ级群发区域面积明显减少。因此,极端低温事件群发性减弱很可能是年代际气候变暖的主要原因。     

基于DFA的雷暴日时间序列分形特征分析

运用最小二乘法、非趋势波动分析(DFA)与小波变换三种方法对比分析许昌市1961—2012年52 a雷暴日时间序列的变化特性,揭示雷暴日的长程相关性及其内在规律。研究结果表明:许昌市52 a月雷暴日时间序列属于分形时间序列,存在内在的长程相关性,其雷暴日数每10 a减少1.6689天,雷暴日时间序列长程幂律相关的标度指数为0.8940,该幂律关系至少可持续17个月,并将2011年和2012年观测数据作为验证数据加以验证,结果与DFA分析结果一致;雷暴日时间序列具有2个显著的标度不变区域,存在一个突变点,反映了雷暴日系统具有复杂的物理作用机制;三种分析方法均得出许昌市雷暴日呈减少趋势的结论,最小二乘法虽然能定量计算出雷暴日每10 a的减少量,但年度尺度较大,精确度变低,而DFA和小波分析法的分析结果更加细致;但在定量描述雷暴日变化趋势上DFA法优于小波分析结果,而在分析雷暴日时序的细节分量和周期特性时小波分析更加清晰;DFA法可作为预测未来雷暴日发展趋势时长的有效方法之一。     

On estimating the return values for time series of wind speed and wind wave height

In order to estimate the return values ofthe time series ofwind speed and wind wave height, it is proposed to use the extrapolation of polynomial approximation constructed for the small part of the tail of probability function of the series considered. The method is based on the optimization of the accuracy of polynomial approximation implemented by varying the length and shift of the used part of probability function and the polynomial exponent. Optimization includes control over quality criteria for approximation that are aimed at increasing approximation accuracy. On the example of wind reanalysis data and the results of numerical simulation of wind waves in the Indian Ocean for the period of 1980-2010, the proposed method is applied to obtain the return value estimates of the mentioned parameters at several fixed points in the ocean.     

基于模糊时间序列的华南台风登陆时最大风速极值预测模型

利用1995—2017年登陆华南地区的台风登陆时最大风速极值数据,构建基于模糊时间序列的台风登陆时最大风速极值预测模型,并将该模型与传统时间序列ARIMA模型作对比。其预测结果表明,模糊时间序列的平均绝对误差、平均相对误差和均方根误差分别为2.621 m·s-1、0.066和2.727 m·s-1,预测的精确度明显高于传统时间序列ARIMA模型,同时也表明将模糊时间序列应用于登陆时最大风速极值的预测能够获得较理想的预测结果。     

Is there any long-term memory effect in the tropical cyclones?

The investigation of the intrinsic properties of the annual tropical cyclone count over Atlantic, during 1870–2006, is herewith attempted. The motivation behind this exploration is to contribute to the current understanding about the dynamics of these disastrous events, as tropical cyclones create destructive impacts for people living around tropical areas. The analytical tool used is the detrended fluctuation analysis, and the exponent obtained reveals that the time series of the annual tropical cyclone count over Atlantic obeys the classical random walk (white noise). In other words, the number of tropical cyclones seems to exhibit neither persistent nor antipersistent behavior. The reliability of the lack of scaling dependence in the time series of the annual tropical cyclone count is confirmed, by applying error bounds statistics and studying the decay of the autocorrelation function (i.e., not rejected exponential decay) and the variability of local slopes (i.e., lack of constancy in a sufficient range). In addition, the fact that the series used is fractional Gaussian noise depicts that the results obtained are reliable, despite the fact that the available data set is still limited. The indication of a nearly white noise signal in the tropical cyclone count fluctuations does not suggest that the climate change phenomenon does not exist.     

Fluctuation regimes of soil moisture in ERA-40 re-analysis data

Soil moisture variability is analysed in the re-analysis data ERA-40 of the European Centre for Medium-Range Weather Forecasts (ECMWF) which includes four layers within 189 cm depth. Short-term correlations are characterised by an e-folding time scale assuming an exponential decay, whilst long-term memory is described by power law decays with exponents determined by detrended fluctuation analysis. On a global scale, the short-term variability varies congruently with long-term memory in the surface layer. Key climatic regions (Europe, Amazon and Sahara) reveal that soil moisture time series are non-stationary in arid regions and in deep layers within the time horizon of ERA-40. The physical processes leading to soil moisture variability are linear according to an analysis of volatility (the absolute differences), which is substantiated by surrogate data analysis preserving the long-term memory.     

乌鲁木齐市近地层风切变指数特征

利用乌鲁木齐市5座100 m气象铁塔2012年6月—2014年4月10层风速观测资料,应用统计学方法详细分析了乌鲁木齐市城区和郊区近地层风切变指数特征,得出以下结果:乌鲁木齐市风切变指数分布范围在-1.5~1.5,基本呈正态分布。风切变指数与风速大小关系密切,当风速1 m/s时,切变指数变化较大;当风速2 m/s时,切变指数变化较小。城区和郊区最大切变指数出现高度差异较大,南郊燕南立交切变指数最大在36~46 m,城区水塔山在60~77m,城区鲤鱼山在13~22 m,近北郊红光山在46~60 m,北郊米东在28~36 m。各层切变指数白天变化幅度大,夜间变化幅度小。切变指数日变化不规律与城市边界层变化的复杂性密切相关。降温幅度大的秋季-冬季时段,易出现切变指数小于0的情况。     

用短期大风资料推算极值风速的一种方法

根据复合极值分布理论，试用二项—对数正态复合极值分布，利用海上短期实测大风资料求算海面多年一遇极值风速，并以此作为基础值，以沿岸站长年代大风经验公式计算风速为订正值，基础值与订正值的叠加作为海面多年一遇工程设计风速。该方法计算结果与皮尔逊Ⅲ型、泊松—龚贝尔复合极值分布计算结果相近，较单纯由二项—对数正态分布计算稳定度增大。     

Nonstationary analysis of the extreme temperatures in Turkey

Record-breaking extreme temperatures have been measured in the last two decades all over Turkey, with recent studies detecting positive trends in extreme temperature time series. In this study, nonstationary extreme value analysis was performed on extreme temperature time series obtained from fifty stations scattered over the seven geographical regions of Turkey. Basic characterization of the data set was defined through outlier detection, homogeneity, trend detection, and stationarity tests. Trend-including non-stationary extreme temperature time series were analyzed with non-stationary Generalized Extreme Value distribution. Three main physical drivers were considered as the leading causes that trigger the observed trends in extreme temperatures over Turkey: time, teleconnection patterns of the Arctic Oscillations, and those of the North Atlantic Oscillations. The results showed that most of the absolute annual minimum and maximum temperature time series are inhomogeneous while the possible breakpoints date back to the1970s and 1990s, respectively. More than half of the absolute annual maximum time series (26/50 and many of the absolute annual minimum time series (21/50) showed a positive trend. No negative trend was detected in the extreme temperature time series. Based on the frequency analysis of the 21 annual maximum time series, the non-stationary estimations of 50-year return levels were detected to be higher than in the stationary model (between 0.44 °C and 3.73 °C). The return levels in 15 of the 20 minimum temperature time series increased from 0.11 °C up to 12.28 °C. Elevation increases the nonstationarity impact on absolute minimum temperatures and decreases it on absolute maximums. The findings in this study indicate that the consideration of non-stationarity in extreme temperature time series is a necessity during return level estimations over the study area.     

On momentum flux and velocity spectra over waves

Data from a research tower in Lake Ontario are used to study the validity of Monin--Obukhov scaling in the marine atmospheric boundary layer under various wave conditions. It is found that over pure wind seas, the velocity spectra and cospectra follow established universal scaling laws. However, in the presence of swells outrunning weak winds, velocity spectra and cospectra no longer satisfy universal spectral shapes. Here, Monin–Obukhov similarity theory, and the classical logarithmic boundary layers, are no longer valid. It is further shown that, in the presence of such swells, the momentum flux can be significantly modified in comparison to pure wind sea values. The implications of these findings for bulk flux estimations and on the inertial dissipation method for calculating fluxes are discussed.     

集合预报在渤海极大风预报中的应用

利用2015年2月至2018年2月地面气象常规观测中逐小时极大风及欧洲中期天气预报中心集合预报中6 h极大风预报数据,选取渤海海域代表站点,对集合预报极大风产品进行预报误差特征分析。分析表明:集合预报极大风产品的离散度明显偏小于均方根误差,各个预报成员的预报结果集中与否并不能反映出预报可信度。受模式预报能力所限,无法简单通过集合预报选取出最为可信的预报结果。集合平均、第75%分位值、最大值在极大风预报中各有优劣,因此基于以上三个统计量及不同量级风速发生的频率建立了渤海极大风预报客观订正方法,试验对比分析表明,该订正方法可以使极大风预报准确率有效的提高,为大风天气过程预报提供重要参考。     

Important meteorological variables for statistical long-term air quality prediction in eastern China

Weather is an important factor for air quality. While there have been increasing attentions to long-term (monthly and seasonal) air pollution such as regional hazes from land-clearing fires during El Niño, the weather-air quality relationships are much less understood at long-term than short-term (daily and weekly) scales. This study is aimed to fill this gap through analyzing correlations between meteorological variables and air quality at various timescales. A regional correlation scale was defined to measure the longest time with significant correlations at a substantial large number of sites. The air quality index (API) and five meteorological variables during 2001–2012 at 40 eastern China sites were used. The results indicate that the API is correlated to precipitation negatively and air temperature positively across eastern China, and to wind, relative humidity and air pressure with spatially varied signs. The major areas with significant correlations vary with meteorological variables. The correlations are significant not only at short-term but also at long-term scales, and the important variables are different between the two types of scales. The concurrent regional correlation scales reach seasonal at p < 0.05 and monthly at p < 0.001 for wind speed and monthly at p < 0.01 for air temperature and relative humidity. Precipitation, which was found to be the most important variable for short-term air quality conditions, and air pressure are not important for long-term air quality. The lagged correlations are much smaller in magnitude than the concurrent correlations and their regional correction scales are at long term only for wind speed and relative humidity. It is concluded that wind speed should be considered as a primary predictor for statistical prediction of long-term air quality in a large region over eastern China. Relative humidity and temperature are also useful predictors but at less significant levels.     

First Rocketsonde Launched from an Unmanned Semi-submersible Vehicle

The unmanned semi-submersible vehicle(USSV) developed by the unmanned surface vehicle team of the Institute of Atmospheric Physics is an unmanned, rugged, and high-endurance autonomous navigation vessel designed for the collection of long-term, continuous and real-time marine meteorological measurements, including atmospheric sounding in the lower troposphere. A series of river and sea trials were conducted from May 2016 to November 2017, and the first rocketsonde was launched from the USSV. Real-time meteorological parameters in the marine atmospheric boundary layer(MABL) were obtained, including sea surface temperature, and vertical profiles of the pressure, temperature, relative humidity, wind speed,and wind direction. These data are extremely useful and important for research on air–sea interactions, sea surface heat and latent heat flux estimations, MABL modeling, and marine satellite product validation.     

黄河流域蒸散发与气温和降水以及风速的相关性分析

黄河流经我国干旱半干旱地区,其流域蒸散发变化对当地的生态安全和经济发展尤其重要。本文利用欧洲中期天气预报中心第五代再分析产品（ERA5）定量分析了1979-2020年黄河流域蒸散发的时空变化特征,并结合气温、降水和风速数据,对黄河流域蒸散发与3种气候因子进行了相关性分析。结果表明：黄河流域蒸散发在1979-2020年呈波动下降趋势,空间分布差异明显,源区附近蒸散发上升,上游的干旱区附近蒸散发基本不变,而中游和下游地区主要呈现下降趋势。1979-2020年黄河流域气温持续上升,降水呈波动下降趋势,风速呈上升趋势。对黄河流域蒸散发与气候因子的相关性分析表明,蒸散发与气候因子的相关性空间差异较为明显,蒸散发与气温、风速呈负相关,与降水呈正相关的区域占流域的较大部分;而在复相关性方面,黄河流域大部分地区蒸散发与气候因子的相关性较强,其中以流域上游的干旱区附近复相关性最强。研究黄河流域不同地区蒸散发与气候因子的相关性可为黄河流域水资源的开发管理和区域气候调节提供科学参考。