Some statistical characteristics of air temperature variations at four Mediterranean stations

Summary In this paper some statistical characteristics of air temperature variations over the Mediterranean are presented. The study is based on temperature data at stations in Marseille (1851–1985), Rome (1851–1985), Athens (1858–1985), and Jerusalem (1864–1985). First the homogeneity of temperature data for each station is examined by means of the short-cut Bartlett test. In another step, the existence of abrupt climatic changes towards warm or cold periods at all stations, with the exception of Athens is pointed out. Some statistical significant persistence of the annual and seasonal time series is also shown. Finally, the temperature fluctuations are examined and discussed in respect to possible causes where particularly the role of the Mediterranean Sea and the topography is considered.With 4 Figures     

Statistical analysis of lightning electric field measured under Malaysian condition

Lightning is an electrical discharge during thunderstorms that can be either within clouds (Inter-Cloud), or between clouds and ground (Cloud-Ground). The Lightning characteristics and their statistical information are the foundation for the design of lightning protection system as well as for the calculation of lightning radiated fields. Nowadays, there are various techniques to detect lightning signals and to determine various parameters produced by a lightning flash. Each technique provides its own claimed performances. In this paper, the characteristics of captured broadband electric fields generated by cloud-to-ground lightning discharges in South of Malaysia are analyzed. A total of 130 cloud-to-ground lightning flashes from 3 separate thunderstorm events (each event lasts for about 4–5 hours) were examined. Statistical analyses of the following signal parameters were presented: preliminary breakdown pulse train time duration, time interval between preliminary breakdowns and return stroke, multiplicity of stroke, and percentages of single stroke only. The BIL model is also introduced to characterize the lightning signature patterns. Observations on the statistical analyses show that about 79% of lightning signals fit well with the BIL model. The maximum and minimum of preliminary breakdown time duration of the observed lightning signals are 84 ms and 560 us, respectively. The findings of the statistical results show that 7.6% of the flashes were single stroke flashes, and the maximum number of strokes recorded was 14 multiple strokes per flash. A preliminary breakdown signature in more than 95% of the flashes can be identified.     

A baroclinic planetary boundary-layer model,and its application to the Wangara data

Nondimensional parameters characteristic of the outer part of the planetary boundary layer have been determined by fitting a simple, Ekman-type theory to a number of averaged, observed velocity distributions, using the Wangara data of Clarke et al. (1971). The theoretical model is based on constant eddy viscosity in the outer layer and a linear variation of the geostrophic wind with height. At the lower boundary of the outer layer, the condition is applied that stress and velocity are parallel. This yields an equation for the cross-isobar angle as a function of drag coefficient, depth coefficient and nondimensional thermal wind.The data could be sorted into three well-defined, distinct groups, each characterized by a more or less constant value of the depth coefficient. The group with the lowest value of this parameter contains most of the nighttime data, the middle group the remaining nighttime data and most of the daytime ones, and the group with the largest depth, daytime data with cold air advection. The difference between the lowest and highest depth coefficients found here is about a factor of three.Within each group separately, the theoretically derived cross-isobar angle agrees remarkably well with the observed one, as a function of thermal wind.     

Evaluating spatial scales of climate variability in sub-Saharan Africa

Summary Spatial scales of variability in seasonal rainfall over Africa are investigated by means of statistical and numerical techniques. In the statistical analysis spatial structure is studied using gridded 0.5° resolution monthly data in the period 1948–1998. The de-seasonalized time series are subjected to successive principal component (PC) analysis, allowing the number of modes to vary from 10 to 24, producing cells of varying dimension. Then the original rainfall data within each cell are cross-correlated (internal), then averaged and compared with the adjacent cells (external) for each PC solution. By considering the ratio of internal to external correlation, the spatial scales of rainfall variability are evaluated and an optimum solution is found whose cell dimensions are approximately 10⁶ km². The aspect of scale is further studied for southern Africa by consideration of numerical model ensemble simulations over the period 1985–1999 forced with observed sea surface temperatures (SSTs). The hindcast products are compared with observed January to March (JFM) rainfall, based on a station-satellite merged analysis of precipitation (CMAP) data at 2.5° resolution. Validations for different sized areas indicate that cumulative standardized errors are greatest at the scale of a single grid cell (10⁴ km²) and decrease 20–30% by averaging over successively larger areas (10⁶ km²).     

Assessing wavestorm hazard evolution in the NW Mediterranean with hindcast and buoy data

It has been suggested that climate change might modify the occurrence rate of large storms and their magnitude, due to a higher availability of energy in the atmosphere-ocean system. Forecasting physical models are commonly used to assess the effects. No one expects the physical model forecasts for one specific day to be accurate; we consider them to be good if they adequately describe the statistical characteristics of the climate. The Peak-Over-Threshold (POT) method is a common way to statistically treat the occurrence and magnitude of hazardous events: here, occurrence is modelled as a Poisson process and magnitude over a given threshold is assumed to follow a Generalized Pareto Distribution (GPD). We restrict our attention to Weibull-related GPDs, which exhibit an upper bound, to comply with the fact that any physical process has a finite upper limit. This contribution uses this framework to model time series of log-significant wave-height constructed joining quasi-collocated hindcast data and buoy measurements. Two of the POT model parameters (inhomogeneous Poisson rate and logarithm of the GPD shape parameter are considered to be a combination of a linear function of time and a series indicator function. The third parameter, logarithm of the GPD upper bound, is considered to have only a series indicator component. The resulting parameters are estimated using Bayesian methods. Using hincast and buoy series, the time span of the data set is extended, enhancing the precision of statistical results about potential linear changes. Simultaneously the statistical behaviour of hincast and buoy series are compared. At the same time, the step function allows to calibrate the statistical reproduction of storms by hindcasting.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

基于FY-2C卫星资料估算四川地面降水方法研究

应用2006年6月6日00时(北京时)至7日06时四川地区的自动站降水资料和FY-2C静止卫星红外亮温资料,共3854对样本,分析发现1小时内的最低红外亮温和红外亮温增量两因子能很好地表征地面1小时累计降水量,且能有效地进行四川地面1小时降水估算,并由此建立了估算降水方法.通过对2006年8月27日13-19时,2006年8月28日04-16时两个时段估算降水结果的初步检验来看,其均方根差都小于2.0mm,随着最低亮温的增加,其均方根差也越小.由此可见,基于FY-2C静止卫星1小时内最低红外亮温和亮温增量来估算四川地面1小时降水是可行的,且效果较好.     

中国气温的降尺度研究

Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.     

集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用

此文目的是讨论污染源反演问题的统计方法.基于Bayes估计理论,该文将资料同化中的集合平滑、集合卡尔曼平滑和集合卡尔曼滤波应用在污染源反演问题中.在详细给出污染源反演的集合平滑、集合卡尔曼平滑和集合卡尔曼滤波的严格数学表达后,用一个简单的模型演示了集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的可行性,并且通过对比理想试验结果比较了集合卡尔曼平滑和集合卡尔曼滤波方法在反演污染源排放的效果,讨论了观测误差和污染源先验误差估计对反演结果的影响.试验结果表明在观测间隔小和观测误差小的情况下,集合卡尔曼滤波和集合卡尔曼平滑都可以有效地反演出随时间变化的污染源排放.当观测误差增大时,集合卡尔曼滤波和集合卡尔曼平滑的反演效果都有一定降低,但是反演误差的增加少于观测误差的增加,同时集合卡尔曼平滑(Ensemble Kalman smoother,简称EnKS)对观测误差比集合卡尔曼滤波(Ensemble Kalman filter,简称EnKF)更为敏感.当观测时间间隔较大时,EnKF不能对没有观测时的污染源排放进行估计,仅能对有观测时的污染源排放进行较好的反演.而EnKS可以利用观测对观测时刻前的污染源排放进行反演,因此其效果明显好于EnKF,并且在观测时间间隔较大的情况下依然可以较好地反演出污染源排放.试验结果还显示污染源排放的先验误差估计对反演的结果有较大影响.     

江西省飑线的雷达回波特征分析

通过对天气雷达观测资料的统计分析，归纳出江西省1972－1999年4－7月飑线回波时空分布，产生源地和移动路径与地形的关系，基本结构和回波参数等特征，对飑线天气的短时分析预报有指导作用。     

深圳市霾日分布特征与城市经济指标关系

利用深圳市近30a来霾日UV法统计数据进行霾日分布特征分析,并结合1980～2007年深圳逐年国民经济指标统计数据,着重讨论了深圳市霾日与城市经济指标的相关性.研究表明,90、00年代的初期至中期,是深圳霾日增长趋势最明显的两个阶段,月均霾日高值在12月至次年1月,达8d/月以上;低值在6～7月,达2d/月以下,随年代...     

Fluctuation dissipation in a general circulation model

This paper considers the climate response to step function changes in the solar constant in two versions of a general circulation model with simplified geography. The NCAR CCM0 model is applied to an all-land planet with no topography (Terra Blanda). In one version there is moisture in the air (as well as self-generated clouds) as evaporated from an ideal surface at a fixed 80% of saturation. In the other version there is no moisture in the atmosphere. We examine the decay of natural anomalies in the large-scale temperature field in each model and compare the time dependence of the ensemble average with the average temporal behavior of the response to step function changes in the solar constant. The fluctuation-dissipation theorem of statistical mechanics makes specific predictions about the relationship between the two curves. We conduct the experiments for both versions of the model since the sensitivity is quite different for each. The theorem is found to hold reasonably well in each case.     

一个改进的混合型海气耦合模式：ENSO模拟

通过在中国科学院大气物理研究所热带太平洋环流模式与一个统计大气模式所建立的混合型海气耦合模式中引入次表层上卷海温非局地参数化方案, 对比分析了次表层上卷海温对耦合模式模拟结果的影响, 表明在引入次表层上卷海温非局地参数化方案前耦合模式模拟的SSTA最大变率中心位于日界线附近赤道南北狭窄范围内, 而在赤道东太平洋及南美沿岸一带变率过低, 周期呈准2年振荡。改进后, 耦合模式模拟结果的分布不论在东西方向亦或南北方向与观测更为相近, 振荡周期为4年左右, 而且还能模拟出观测中ENSO振荡的季节依赖性特征。进一步分析改进的耦合模式中海气耦合特征, 表明 “延迟振子” 理论、 “西太平洋振子” 理论、 “充电-放电振子” 理论及 “平流-反射” 理论所揭示的一些规律在该模式中都能被不同程度地描述出来, 这说明在实际的ENSO循环过程中, 可能有多种机制在同时起作用。     

Drought episodes over Greece as simulated by dynamical and statistical downscaling approaches

Drought over the Greek region is characterized by a strong seasonal cycle and large spatial variability. Dry spells longer than 10 consecutive days mainly characterize the duration and the intensity of Greek drought. Moreover, an increasing trend of the frequency of drought episodes has been observed, especially during the last 20 years of the 20th century. Moreover, the most recent regional circulation models (RCMs) present discrepancies compared to observed precipitation, while they are able to reproduce the main patterns of atmospheric circulation. In this study, both a statistical and a dynamical downscaling approach are used to quantify drought episodes over Greece by simulating the Standardized Precipitation Index (SPI) for different time steps (3, 6, and 12 months). A statistical downscaling technique based on artificial neural network is employed for the estimation of SPI over Greece, while this drought index is also estimated using the RCM precipitation for the time period of 1961–1990. Overall, it was found that the drought characteristics (intensity, duration, and spatial extent) were well reproduced by the regional climate models for long term drought indices (SPI12) while ANN simulations are better for the short-term drought indices (SPI3).

     

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961–2006. The statistical downscaling models are established through multiple stepwise regressions of predictor principal components (PCs). The predictors in this study include temperature at 850 hPa (T850), and the combination of geopotential height and temperature at 850 hPa (H850+T850). For the combined predictors, Empirical Orthogonal Function (EOF) analysis of the two combined fields is conducted. The modeling results from HadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models (GCMs). The results show that (1) the trend of temperature in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming; (2) although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the trend of main PCs of the GCM predictors; (3) when the two-field combination and the projected EOF analysis are used, temperature change scenarios have a similar seasonal variation to the observed one; and (4) compared with the results of the common EOF analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.     

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorol...     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

The method of the medium-range forecast of air temperature in the area of the “Baikonur” cosmodrome

The statistical model of the forecast (complex postprocessing) of surface air temperature with the lead time up to eight days is constructed using the results of the integration of hydrodynamic atmospheric models. The model is adapted to the area of the “Baikonur” using the method of central typing that allows increasing the accuracy of operational forecasts. The analysis of climate characteristics needed for constructing the proper statistical model for this area is given using both observational data for recent 25 years and the data of WMO (from the All-Russian Research Institute of Hydro-meteorological Information-World Data Center). Computed are the estimates of the accuracy of operational forecasts.     

The impact of atmospheric nonlinearities on the fastest growth of ENSO prediction error

In this paper we explore the impact of atmospheric nonlinearities on the optimal growth of initial condition error of El Niño and the Southern Oscillation (ENSO) prediction using singular vector (SV) analysis. This is performed by comparing and analyzing SVs of two hybrid coupled models (HCMs), one composed of an intermediate complexity dynamical ocean model coupled with a linear statistical atmospheric model, and the other one with the same ocean model coupled with a nonlinear statistical atmosphere. Tangent linear and adjoint models for both HCMs are developed. SVs are computed under the initial conditions of seasonal background and actual ENSO cycle simulated by the ocean model forced with the real wind data of 1980–1999. The optimization periods of 3, 6 and 9 months are individually considered. The results show that the first SVs in both HCMs are very similar to each other, characterized by a central east-west dipole pattern spanning over the entire tropical Pacific. The spatial patterns of the leading SV in both HCMs are not sensitive to optimization periods and initial time. However, the first singular value, indicating the optimal growth rate of prediction error, displays considerable differences between the two HCMs, indicating a significant impact of atmospheric nonlinearities on the optimal growth of ENSO prediction error. These differences are greater with increasing optimization time, suggesting that the impact of atmospheric nonlinearities on the optimal growth of prediction error becomes larger for a longer period of prediction.