APEC前后北京几次静稳天气边界层特征对比分析

对北京地区2014年11月3-11日APEC会议期间的大气扩散条件与2014年10月的4次雾-霾过程进行对比分析,表明APEC会议期间静稳天气指数略低于10月4次过程,具备一定的污染物积累潜势。数值模拟结果表明,对北京及周边地区污染排放的控制是APEC期间北京保持低水平气溶胶浓度的重要原因。周边地区污染物传输以及特殊地形条件下山谷风环流的日变化对北京地区污染物浓度的区域分布及演变有重要影响,夜间地形北风对近地面污染物有明显清除作用。相较于重污染天气,APEC期间气溶胶浓度低,温度效应小,山谷风环流明显,同时白天混合层高度上升幅度大,有利于污染物扩散。减排使气溶胶浓度减小,而低浓度气溶胶又使污染扩散条件进一步好转的相互反馈作用,是"APEC蓝"出现的主要原因。     

上海城市热岛的变化特征分析

利用最近安装于上海城市和近郊的Davis自动气象观测每半小时一次详细的记录，对上海市热岛的变化特征进行初步分析。结果表明：上海市城市热岛全年出现概率为87．8％，月平均热岛强度值大于0．8℃；热岛强度日变化明显，存在24小时的主要周期和12小时的次周期，一般是夜间热岛强于白天；其季节变化亦较显著 ，平均热岛强度秋冬季节较强，夏季较弱；热岛中心存在位置漂移现象。     

Storms Generator Method That Preserves Their Historical Statistical Characteristics. Application To Mexico City Basin Daily Rainfall Fields

A first procedure to generate synthetic storms, which preserves the statistical characteristics of the historical daily precipitation events registered in 49 stations located within the basin of Mexico City is presented. This procedure (a variation of the Svanidze method) implicitly supposes that the correlation between the maximum rainfall and its spatial distribution is meaningless. However, the obtained results did not validate that hypothesis. For this reason a second procedure was developed, which allows to consider the correlation between maximum rainfall and spatial distribution. This second procedure allowed reproducing the statistical characteristics of the daily rainfall for each station and also its spatial distribution, as shown in this work.     

A strategy for merging objective estimates of global daily precipitation from gauge observations,satellite estimates,and numerical predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates(SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps.First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions(MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation(OI)–based merging scheme(referred as the HL-OI scheme)is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d(Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period(2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality.Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD(GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.     

Evaluation of Precipitation Indices over North America from Various Configurations of Regional Climate Models

This study presents the evaluation of simulations from two new Canadian regional climate models (RCMs), CanRCM4 and CRCM5, with a focus on the models’ skill in simulating daily precipitation indices and the Standardized Precipitation Index (SPI). The evaluation was carried out over the past two decades using several sets of gridded observations that partially cover North America. The new Canadian RCMs were also compared with four reanalysis products and six other RCMs. The different configurations of the Canadian RCM simulations also permit evaluation of the impact of different spatial resolutions, atmospheric drivers, and nudging conditions. The results from the new Canadian models show some improvement in precipitation characteristics over the previous Canadian RCM (CRCM4), but these differ with the seasons. For winter, CanRCM4 and CRCM5 have better skill than most other models over all of North America. For the summer, CRCM5 0.44° performs best over the United States, while CRCM4 has the best skill over Canada. Good skill is exhibited by CanRCM4 and CRCM4 in simulating the 6-month SPI over the Prairies and the western US Corn Belt. In general, differences are small between runs with or without large-scale spectral nudging; differences are small when different boundary conditions are used.     

The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the observations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R²) between the observed and estimated daily series were >0.98, and root mean square errors (RMSEs) of the estimated daily mean (T_mean), maximum (T_max), and minimum (T_min) of air temperature were <3℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.     

ArcGIS离线编辑在日常地籍变更中的应用

针对日常地籍变更频繁、零散、时限要求较高的特点,利用ArcGIS平台的版本机制,采用离线编辑的方式对地籍数据库的矢量图形数据更新进行设计,开发了适用于区、县级地籍数据日常变更的功能模块,提高了变更效率,很好地保障了地籍数据的现势性。     

基于空气污染指数的太阳日总辐射计算方法

通过对2001—2012年全国23个站实测资料的分析,利用非线性回归法建立了以气温日较差、天文日照百分率和空气污染指数为主导因子的太阳日总辐射模型,这里简称为DSRM-Y模型 (Daily Solar Radiation Model-Y),检验其效果并与已有的DSRM-C模型 (Daily Solar Radiation Model-C) 进行效果比对。结果表明:太阳日总辐射与空气污染指数呈显著负相关,DSRM-Y模型的太阳日总辐射估算值与实测值的散点图以及平均偏差、均方根误差、误差分析均表现出较好的拟合效果。将模型应用于西宁、上海、昆明3个代表站,空气污染指数上升后,3个站太阳日总辐射均呈减少趋势;23个站DSRM-Y模型的均方根误差均小于DSRM-C模型,即DSRM-Y模型的拟合效果好于DSRM-C模型。     

Factors influencing long range dependence in streamflow of European rivers

Investigating long range dependence of river flows, especially in connection with various climate and storage related factors, is important in order to improve stochastic models for long range dependence and in order to understand deterministic and stochastic variability in long‐term behaviour of streamflow. Long range dependence expressed by the Hurst coefficient H is estimated for 39 (deseasonalized) mean daily runoff time series in Europe of at least 59 years using five estimators (rescaled range, regression on periodogram, Whittle, aggregated variances, and least squares based on variance). All methods yield estimates of H > 0.5 for all data sets. The results from the different estimators are significantly positively correlated for all pairs of methods indicating consistency of the methods used. Correlations between H and various catchment attributes are also analysed. H is strongly positively correlated with catchment area. Apparently, increasing storage with catchment area translates into increasing long range dependence. H is also positively correlated with mean discharge and air temperature and negatively correlated with the mean specific discharge and the seasonality index (maximum Pardé coefficient). No significant correlation is found between the Hurst coefficient and the length of the analyzed time series. The correlations are interpreted in terms of snow processes and catchment wetness. Copyright © 2012 John Wiley & Sons, Ltd.     

Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet–artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet–multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component.