塔克拉玛干沙漠腹地1961-1998年逐月平均气温序列的重建

 利用塔里木盆地周边27个气象站1961-2006年逐月平均气温和塔中气象站1999-2006年逐月平均气温资料,同时选取1961-2006年NCEP/NCAR 2.5°×2.5°经纬度距地表2 m的月平均气温再分析格点资料,分别用逐步回归分析、EOF分解和NCEP资料3种方法对塔中气象站1961-1998年历年逐月平均气温序列进行了恢复与重建,分析了误差,并与周边气象站的变化特征进行对比。结果表明,逐步回归和EOF法都能够作为重建塔中逐月平均气温的方法,但相对而言,逐步回归法重建的序列误差更小,平均拟合绝对误差为0.3℃,最大绝对误差为1.9℃。而NCEP/NCAR资料由于冬季存在明显的系统性误差,数值显著偏高,不能用于塔中气温序列的重建。     

Monthly prediction of rainfall over India and its homogeneous zones during monsoon season: a supervised principal component regression approach on general circulation model products

A supervised principal component regression (SPCR) technique has been employed on general circulation model (GCM) products for developing a monthly scale deterministic forecast of summer monsoon rainfall (June–July–August–September) for different homogeneous zones and India as a whole. The time series of the monthly observed rainfall as the predictand variable has been used from India Meteorological Department gridded (1°?×?1°) rainfall data. Lead 0 (forecast initialized in the same month) monthly products from GCMs are used as predictors. The sources of these GCMs are International Research Institute for Climate and Society, Columbia University, National Center for Environmental Prediction, and Japan Agency for Marine Earth Science and Technology. The performance of SPCR technique is judged against simple ensemble mean of GCMs (EM) and it is found that over almost all the zones the SPCR model gives better skill than EM in June, August, and September months of monsoon. The SPCR technique is able to capture the year to year observed rainfall variability in terms of sign as well as the magnitude. The independent forecasts of 2007 and 2008 are also analyzed for different monsoon months (Jun–Sep) in homogeneous zones and country. Here, 1982–2006 have been considered as development year or training period. Results of the study suggest that the SPCR model is able to catch the observational rainfall over India as a whole in June, August, and September in 2007 and June, July, and August in 2008.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.     

气温的天气和气候记忆性特征分析:以济南和青岛为例

气温的天气和气候记忆性特征分析对于提高气候预测水平具有积极意义。利用济南和青岛1961—2020 年逐日、月和年平均气温资料,运用自相关性函数和标准化频率分布分析了上述时间序列的气温记忆性特征和概率分布特征,并利用结构函数法建立了月、年平均气温距平与日平均气温距平之间的分数阶导数关系。结果表明:(1)济南和青岛的月、年平均气温距平呈现不同程度的记忆性特征,其中年平均气温距平相比于月平均气温距平具有更好的记忆性。(2)济南和青岛的月、年平均气温距平与日平均气温距平之间存在分数阶导数关系,济南和青岛相应的月、年尺度阶数分别为0. 529、0. 665 和0. 553、0. 791,两地的月尺度阶数相近,但青岛略大,青岛的年尺度阶数大于济南,即青岛月和年平均气温距平的记忆性大于济南。(3)济南和青岛的月和年平均气温距平相比于日平均气温距平有不同程度的长尾特征,长尾特征反映了极值温度发生的概率。     

Application of random forest time series,support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros,Iran)

We have conducted a case study to investigate the performance of support vector machine, multivariate adaptive regression splines, and random forest time series methods in snowfall modeling. These models were applied to a data set of monthly snowfall collected during six cold months at Hamadan Airport sample station located in the Zagros Mountain Range in Iran. We considered monthly data of snowfall from 1981 to 2008 during the period from October/November to April/May as the training set and the data from 2009 to 2015 as the testing set. The root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R ²), coefficient of efficiency (E%), and intra-class correlation coefficient (ICC) statistics were used as evaluation criteria. Our results indicated that the random forest time series model outperformed the support vector machine and multivariate adaptive regression splines models in predicting monthly snowfall in terms of several criteria. The RMSE, MAE, R ², E, and ICC for the testing set were 7.84, 5.52, 0.92, 0.89, and 0.93, respectively. The overall results indicated that the random forest time series model could be successfully used to estimate monthly snowfall values. Moreover, the support vector machine model showed substantial performance as well, suggesting it may also be applied to forecast snowfall in this area.     

GRAPES-GFS模式2 m温度预报的最优时窗滑动订正方法

利用2017—2018年GRAPES-GFS模式预报资料和广西区域自动站逐时气温观测资料,分析模式预报偏差特征,发现GRAPES-GFS模式对广西区域2m温度的预报系统性偏低,随着预报时效增加,预报偏差增大,系统性偏差主要出现在桂北山区、左右江河谷及沿海;春夏秋三季的午后气温预报偏差有明显的系统性,冬季午后气温和四季凌晨气温预报偏差的随机性较大。为了确定滑动订正的最优时窗,通过活动时窗长度的方法,设计不同的滑动订正方案,制定最优时窗滑动订正方案,并进一步利用2020年最优时窗滑动订正业务试验产品,对比验证了该方案的订正效果。结果表明:分别采用固定时窗、季节最优时窗、月份最优时窗等滑动平均订正方案进行订正,春夏秋3季的订正效果明显好于冬季、午后订正技巧高于夜间,其中固定时窗滑动平均方案中的长时窗(15～60d)订正、季节最优时窗滑动订正以及月份最优时窗滑动订正这几种方式订正效果最优;所制定的最优时窗滑动平均订正方案,可以在不同滑动方案的基础上稳定地提高预报准确率,达到最优时窗滑动的目的。     

On the Spatio-Temporal Variations of the Tropopause Height over India and Indian Summer Monsoon Activity

The spatio-temporal variation of the tropopause height (TH) over the Indian region (5°N-35°N, 70°E-95°E) has been studied using monthly mean TH data, for 22-year period, 1965 to 1986. The study revealed that the stations south of 20° showed maximum TH in April / May and minimum in September. This variation in TH has been attributed to the corresponding variation of average sea surface temperature (SST) over ± 20° latitudinal belt over Indian Ocean, Arabian Sea and Bay of Bengal. Further the stations north of 20°N showed maximum in June and minimum in October/ November. This maximum in TH has primarily been attributed to the increased insolation and convection. Furthermore it is noticed that the anomaly of TH moved northwards during the period April to July.The interannual variability of the Indian Summer Monsoon Activity (ISMA) has been studied in relation to all India mean TH (at 12 GMT) for six months April through September. The composites of mean TH for good and bad monsoon years showed that     

纬向平均环流预报的系统性误差及其改进

大量的月预报实例分析表明,纬向平均环流(本指高度场纬向平均分量)存在明显的系统性预报误差,且在总误差中占有可观的份额。国内外其它模式也存在类似的现象。为克服这一困难,本尝试了“结合”(hybrid)的途径。应用重构相空间理论和非线性时空序列预测方法,在大量历史资料的基础上,构造了月尺度逐侯纬向平均高度场(零波分量)距平场的非线性预报模型。然后,将非线性预报和谱模式动力预报结合起来,即将非线性预报结果转化为模式需要的颅报量,再在模式积分过程中的每一步取代其相应部分,实施过程订正。初步试验结果表明,这种途样合效地减少了模式纬向环流的预报误差;特别是通过非线性波流相互作用,还改善了部分波动分量的预报。     

中国825个基准、基本站地面气压系统误差的检验与订正

使用RHtest均一化方法结合元数据信息对中国825个基准、基本站的地面气压月值数据进行均一性检验与订正,结果发现有400个站的气压数据均一,425个站存在系统误差。对于后者,使用静力模式订正可消除255个站的系统误差,另外170个站采用均值订正,均值订正的断点共245个。对气候趋势和个例的分析表明,上述方法对气压数据均一化订正效果明显。均一化之后站点气压长期趋势的空间一致性更好,中国东南沿海和西北部的新疆地区气压表现出下降趋势,中部地区主要表现出上升趋势。     

信息替换的均生函数主分量多步预测

提出随着时间的推移，用新信息取代旧信息的“限定记忆”的时间序列数据处理方法。且仅对均生函数外延矩阵的前Ｌ阶方阵作主分量分析，用数目较少又包含主要信息量的主分量因子对时间序列建模。该方案用于四川省２８个县市的年平均气温的多步预测值与实况值相对误差均在±４％以内，表明该方案用于气温多步预测是有效的。     

基于改进EMD算法和BP神经网络的SST预测研究

海洋表面温度(Sea Surface Temperature,SST)具有非平稳、非线性的特征,直接将处理平稳数据序列的方法应用到非平稳非线性特征明显的序列上显然是不合适的,预测的误差将会很大。为了提高预测精度,更好地解决非平稳非线性序列预测的问题,本文以东北部太平洋(40°N~50°N、150°W~135°W)区域的月平均海洋表面距平温度为例,首先分别应用集合经验模态分解(EEMD)和互补集合经验模态分解(CEEMD)方法将SST分解为不同尺度的一系列模态分量(IMF),再运用BP(Back Propagation)神经网络模型对每一个模态分量进行分析预测,最后将各IMF预测结果进行重构得到SST的预测值。数值试验的结果表明,CEEMD分解精度比EEMD分解精度高,CEEMD提高了基于BP神经网络的预测精度。系列试验统计分析说明应用这种方法对SST的1年预测是有效的。     

依据月平均资料作月预报——利用自然相似的探讨

本文利用1956年1月—1972年12月的月平均1000,500,100 hPa位势高度和太平洋、大西洋、印度洋的海表温度资料中存在的自然相似,对依据月平均资料作的海洋—大气变量月平均值的预报可能达到的水平进行了分析研究。结果表明在一个海—气耦合系统中,大气变量的预报是比海温预报更为困难的一环。根据月平均海表温度决定相应的月平均位势高度场的准确度较低,但在考虑了过去的海温和高度场资料后能有所改进。     

Simulation of maximum surface air temperature over India using the UK Met Office Global Seasonal (GloSea) Model

Summary A comparative study was performed to evaluate the performance of the UK Met Office’s Global Seasonal (GloSea) prediction General Circulation Model (GCM) for the forecast of maximum surface air temperature (T_max) over the Indian region using the model generated hindcast of 15-members ensemble for 16 years (1987–2002). Each hindcast starts from 1^st January and extends for a period of six months in each year. The model hindcast T_max is compared with T_max obtained from verification analysis during the hot weather season on monthly and seasonal scales from March to June. The monthly and seasonal model hindcast climatology of T_max from 240 members during March to June and the corresponding observed climatology show highly significant (above 99.9% level) correlation coefficients (CC) although the hindcast T_max is over-estimated (warm bias) over most parts of the Indian region. At the station level over New Delhi, although the forecast error (forecast-observed) at the monthly scale gradually increases from March to June, the forecast error at the seasonal scale during March to May (MAM) is found to be just 1.67 °C. The GloSea model also simulates well T_max anomalies on monthly and seasonal scales during March to June with the lower Root Mean Square Error (RMSE) of bias corrected forecast (less than 1.2 °C), which is much less than the corresponding RMSE of climatology (reference) forecast. The anomaly CCs (ACCs) over the station in New Delhi are also highly significant (above 95% level) on monthly to seasonal time scales from March to June, except for April. The skill of the GloSea model for the seasonal forecast of T_max as measured from the ACC map and the bias corrected RMSE map is reasonably good during MAM and April to June (AMJ) with higher ACC (significant at 95% level) and lower RMSE (less than 1.5 °C) found over many parts of the Indian regions. Authors’ addresses: D. R. Pattanaik, H. R. Hatwar, G. Srinivasan, Y. V. Ramarao, India Meteorological Department (IMD), New Delhi, India; U. C. Mohanty, P. Sinha, Centre for Atmospheric Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India; Anca Brookshaw, UK Met Office, UK.     

中国近50a气候变化复杂性分析

分析了我国气温和降水量变化的兰帕尔－齐夫复杂度空间分布特征，结果表明，平均而言，我国平均气温和降水量变化的复杂度约为10－11，小于随机序列的复杂度13，年平衡气温变化序列的复杂度最小，秋季平均气温变化序列的复杂度最大，季节和年平均气温序列的复杂度小于月平均气温变化序列的复杂度，月总降水量变化序列的复杂度为西部，北部大于南部和东部，我国东南沿海地区气候要素变化的复杂度最大。     

基于TIGGE资料的预报跳跃性特征

利用2011年3月—2013年2月TIGGE资料中ECMWF,NCEP及CMA 3个中心的500 hPa位势高度场、850 hPa温度场和海平面气压场的集合预报资料,采用Jumpiness指数同时结合单点跳跃、异号两点跳跃等预报跳跃相关概念,研究了集合控制预报和集合平均预报的预报跳跃特征问题,并进行对比分析。结果表明:平均而言,短时效预报之间的跳跃性低于长时效预报之间的跳跃性。集合平均预报的结果之间较其相应的控制预报具有更好的一致性。在预报跳跃的频率统计方面,集合平均预报结果总体上明显低于集合控制预报,两者在长时效的预报跳跃情况差别较大。该文研究了预报跳跃对不同区域、时间和变量的敏感性,结果表明:时间平均的预报跳跃性对区域和变量很敏感。不同预报跳跃类型出现的频率及集合控制预报和集合平均预报在预报跳跃性方面的差异对区域、时间和变量的敏感性有限。     

由最高最低气温求算的平均气温对我国年平均气温序列影响

针对研究全国近百年平均气温长期变化的实际需要，利用603个测站1961—2002年气温观测资料，比较分析了最高最低平均气温距平序列和4次观测记录平均气温距平序列的差异，讨论了最高、最低气温变化趋势。结果表明:两种统计方法得到的平均气温距平序列及增温速率的差异均不明显，在一定条件下两者可以互相替换。此外，最高、最低气温变化普遍存在不对称现象，且可分为4种类型，这种不对称性对平均气温变化速率并没有明确一致的影响。     

T63模式月动力延伸预报高度场的改进实验

为克服T63模式月动力延伸预报中纬向平均环流的系统性误差较大的情形,文章利用NCEP/NCAR逐候再分析500 hPa高度场资料和非线性时空序列预测理论的局域近似法进行逐候纬向平均高度距平场预报.近30组个例的预报效果分析表明,就1～3旬总体而言,非线性时空序列预测方法对纬向平均高度距平场的预报优于持续性预报和模式动力延伸预报,体现了改善纬向平均高度场的能力.尤其是第3旬的预报,当持续性预报偏差与实况偏差明显增大、动力预报技巧相对于第1旬和第2旬降低时,相空间重构结果仍然保持一定的优势.     

Analysis of recent trends in mean maximum and minimum temperatures in a region of the NW of Spain (Castilla y León)

Summary The present paper is an analysis of mean maximum and minimum temperatures carried out on monthly, seasonal and annual time-scales examining the data collected at 171 meteorological stations over a region in the North West of Spain (Castilla y León) for the period 1961–1997. Various statistical tools were used to detect and describe significant trends in these data. The magnitude of the trends was derived from the slopes of the regression lines using the least squares method, and the statistical significance was determined by means of the non-parametric Mann-Kendall test. The pattern obtained is quite similar for mean maximum and minimum temperatures with increases in all months of the year, and in the annual series. The seasonal series corresponding to winter and summer also followed this same pattern. Spring and autumn were found to be more irregular. Because maximum temperature increased at a higher rate than minimum temperature in this period, an increase in the annual diurnal temperature range (DTR) was observed. The correlation between the North Atlantic Oscillation (NAO) and the regional maximum and minimum temperatures and DTR series for the period 1961–1997 have also be studied in this paper.     

Partition of Forecast Error into Positional and Structural Components

Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters(cf., e.g.,Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components:(a) large scale positional,(b) large scale structural, and(c) small scale error variance.The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored.     

我国月地面温度场的自回归滑动平均模型

对1952—1980年我国连续的月地面气温用时间序列ARMA（p、q）模型进行随机建模。月温度由60个站组成，用经验正交函数加以展开，取不同的样本长度即348,336和300月，以便考察经验正交展开的稳定性。前四个主成分，即z1,z2,z3,z4取为多维时间序列的变数，因为它们的总方差贡献达99.26%。在这四个主成分序列中的决定性周期用周期图和最大熵方法加以揭露。对一维变量zi,(i=1,2,3,4)的ARMA（p,q）的模型识别用Pandit-Wu方法进行，这样就可求得实验模型。用zi模型的外推值来预报月温度场。距平预报的命中率评分为78.3%，高于目前的业务长期天气预报。