江淮平原二类气象站网的设计

在进行天气预报,开展天气、气候研究以及制订有关工程计划时,我们往往需要利用气象台站的观测资料来内插非台站所在地的气象要素值。为了保证这些内插值具有一定的精度,气象台站必须达到相应的密度。1946年,O.A.德罗兹多夫曾用结构函数的方法对此作了研究,并得到气象要素的线性内插标准误差仅与该要素的结构函数有关的结论。此后,结构函数被广泛用于气象台站网的合理分布研究。     

空间结构函数在北京地区气象观测站网设计中的应用

通过统计1978—2000年北京东南低地形区有关台站在春季、夏季、秋季、冬季的逐日平均气温和水汽压的结构函数, 分季节分析了该地区这两个二类气象要素的线段及平面内插精度和台站间距的对应关系, 并根据内插标准误差不超过观测标准误差的原则, 对上述两要素在北京东南低地形区的合理布站方案及间距进行了估算, 可以为2008年北京奥运会气象服务系统建设中气象台站布网建设提供一定的依据。结果表明, 正三角形排列方案为北京东南地区二类气象台站的最佳布站方案, 且布站精度应小于等于16 km。     

四川盆地蒸发站网的合理布局探讨

本文采用四川盆地36个台站1980—1984年的逐日蒸发量资料，从统计各季代表月逐日蒸发量的相关函数着手，推算出相对内插标准误差与相关函数的统计关系，并根据内插标准误差不大于观测随机误差的原则，估算出该地区蒸发站网的合理间距。     

基于RS和GIS技术的新疆气温空间插值方法探讨

分析新疆90个气象站点2004年年均气温与经纬度、海拔高度的关系,之间具有较好的线形相关关系(r2=0.897)。通过"回归方程计算 空间残差"的方法对全疆2004年年均气温数据进行插值。验证结果为:插值计算值与实测值间相关系数r2=0.994,平均误差1.53%。插值结果表明:利用"回归方程计算 空间残差"的方法可以生成高精度、高空间分辨率新疆年平均网格温度结果。     

Delaunay三角剖分法在降水量插值中的应用

Delaunay三角剖分方法在空间分析中具有重要地位,文中简要介绍了Delaunay三角网特性和常用的3类算法,并对随机增长法实现过程进行了详细阐述.根据三角分片线性插值原理,求得插值系数,实现对任意点的三角分片线性插值.利用2008年中国2200个观测站的08时24 h降水量资料,对全中国范围及划分的8个区域内相应的0.28125°×0.28125°降水量格点场,使用交叉检验方法,对比分析了三角分片线性插值和反距离权重法的估值准确率.结果表明:在各区域,三角分片线性插值法的均方根误差偏小;在站点较密集的区域,均方根误差、平均绝对误差比较中,三角分片线性插值都有一定的优势;在平均误差对比中,三角分片线性插值优势明显,在全中国范围交叉检验中,三角分片线性插值法对应的年平均误差是0.005 mm,而反距离权重法为-0.107 mm,对其可能的原因进行了分析,证明了Delaunay三角剖分法的合理性.同时,从图形上展示了降水量的Delaunay三角网的三维结构图和三角分片线性插值后的格点场,在直观上,Delaunay三角剖分后得到降水分布和实况保持一致,并有较好的视觉效果;通过三角分片线性插值得到的格点场降水量分布图,克服了反距离权重法的固有缺陷,使获得的降水量格点场趋于合理,提高了插值精度.最后,探讨了Delaunay三角网在气象领域的应用前景.     

中国降水测量误差的研究

20世纪 ,国际上许多国家对降水测量进行了对比试验工作 ,以研究降水测量中的误差大小与分布。由于各个国家降水测量仪器的型式、尺寸以及安装高度不同 ,试验对比的降水测量误差的大小也就不同。为了弄清中国降水测量误差的大小 ,中国气象局选择了 30个基准基本站 ,建立标准雨量站网进行试验对比。本文介绍了中国标准雨量站网的设置以及对比资料的获取情况 ,对比分析了中国降水测量的随机误差、沾湿与蒸发误差、风场变形误差。经 30个标准雨量站 7a 2 90 0 0多次的 1台坑式雨量器、2台台站雨量器的对比观测 ,给出了中国降水量测量误差的大小、降水测量中的随机误差与系统误差的分布情况。经分析 ,对于收集口口径为 2 0cm ,安装高度为 70cm的台站普通雨量器 ,每次测量随机误差累计平均值为 0 .0mm ,沾湿误差为 0 .2mm ,蒸发误差为 0 .0mm ,降雨风场变形误差为 0 .19mm ,降雪为 0 .32mm。降雨测量的平均相对误差约为 4 .34%～ 15 .2 8% ,降雪测量的平均相对误差约为 6 .17%～ 39.99%。     

最佳内插法在气象站网设计中的应用

根据四川盆地1961—1985年7月、8月的降水资料,本文以最佳内插法计算了该区30个站点的内插标准误差值,并通过与线性内插结果的相互比较来讨论最佳内插法在气象站网设计中的应用阶值。     

基于Shepard和OI方法对雨量计逐时资料的分析

引入地形影响效应的日降水量气候分析场,分别运用Shepard和最优插值(OI)两种插值方法对广东和广西2007年5月20日—8月30日汛期小时雨量计降水量进行插值,得到0.125°×0.125°分辨率的规范化网格资料。结果表明:无论是直接插值还是用降水比率(地形影响效应的日降水量气候分析场)插值,两种方法均能很好地体现广东和广西雨量计站点观测降水的季内变化和日变化特征。虽然用直接插值方法比用降水比率插值方法得到的降水空间分布更为平滑,但估值精度没有用降水比率插值方法高。通过交叉检验进一步表明,总体上OI方法优于Shepard方法,而考虑地形影响效应的降水比率OI方法为最优,能有效提高相关性,并减少均方根误差和系统误差。     

近50a中国降水格点数据集的建立及质量评估

基于2012年6月更新的高质量2 400个台站降水资料,采用薄盘样条法,制定了采用3个自变量(经度、纬度、海拔高度)、降水量开平方预处理、3次样条的插值方案,并引入数字高程资料,以减弱中国独特地形条件下高程对降水空间插值精度的影响,并对1961—2010年中国区域地面降水站点资料进行了空间内插,得到了中国地面降水0.5°×0.5°格点数据集。经数据集的质量评估结果表明:分析值与站点观测值均方根误差平均为0.49 mm,相关系数平均达0.93(通过0.01的显著性检验),夏季插值误差高于冬季,东南地区误差普遍高于其他地区。冬、春、夏、秋季绝大多数台站绝对误差在±10 mm/月以内。冬、春、夏、秋季分别有60%、82%、54%、77%的台站相对误差在±10%之间。插值后的格点化降水资料能够比较细致、准确地描述中国大陆年平均降水场的东南多、西北少的主要空间特征,但也平滑掉了范围很小的降水极值中心。台站分布越密集的地方,插值效果越好,并且最近距离小于40 km的台站插值精度较高,大于40 km插值精度衰减较快。     

影响统计插值分析误差的若干因素分析

采用理想观测网,用数值方法计算统计插值分析误差。减小观测误差或初估值误差、增加观测数量、恰当减小观测空间间距、以及选取分布合理的观测资料,有利提高分析精度;如果计算使用的归一化观测误差方差、误差相关函数的特征和总体统计平均情况不一致,有可能造成分析误差的明显增加。最后,利用特征向量分析方法,对归一化观测误差方差、观测误差相关属性对于不同尺度统计插值分析误差的影响进行讨论。     

重庆山地区域气象要素空间插值方法对比

利用重庆地区1999年和2018年气象数据, 分别采用薄盘光滑样条、协同克里金、普通克里金、反距离加权4种方法, 从年和月两种尺度对气温、降水、太阳总辐射三个要素进行空间插值; 采取交叉验证方法, 用MAE、MRE、RMSE评估插值精度, 确定各要素最优插值方法。结果表明: 气温和太阳总辐射最优插值方法为薄盘光滑样条, 降水为反距离加权; 插值精度上气温、太阳总辐射高值月份优于低值月份, 降水则相反, 但三个要素均表现出年尺度优于月尺度。MRE检验表明, 插值精度为气温>太阳总辐射>降水, 1999年年尺度插值精度分别为1.86%、4.60%、6.87%, 月尺度插值精度分别为2.79%、5.82%、17.42%;2018年太阳总辐射年、月尺度插值精度分别为3.03%、4.88%, 区域站加密后气温、降水年尺度插值精度分别为2.03%、11.20%, 月尺度对应插值精度分别为3.20%、23.14%。     

台站温度质量控制中IDW算法改进及应用

在反距离加权插值算法的基础上,结合气象上温度随高程的变化,以及各测站垂直方向上温度场与气压场的关系,对国内地面气象观测站的温度数据进行插值算法改进。然后,从搜索半径与距离权重系数的关系,以及参与插值站点数量与距离权重系数的关系两个方面进行试验数据分析,试图找出相对最佳的算法参数和最小的插值误差。最后,将改进的算法应用到温度观测数据的质量控制中。结果表明:改进后的算法可明显改善因近地面逆温造成的插值误差,在台站分布较密集的我国中东部地区,温度质量检验的灵敏度可达到1至5摄氏度。     

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.     

中国区域高分辨率温度实况融合格点分析产品质量评估

采用两种插值方法将2018年2m气温实况融合格点分析产品插值到2380个国家级考核站,通过相关系数、平均值误差、平均绝对误差、均方根误差及准确率等指标对该产品进行评估。结果表明:利用邻近插值法得到的评估结果略优于双线性插值法,实况融合格点分析产品的评估结果具有一定的日变化和月变化。总体而言,逐小时实况融合格点产品与站点实况基本一致,具有较高的参考性,其相关系数达到0.99以上,均方根误差在1℃以下,2℃以内准确率达到98%以上,1℃以内准确率达到95%以上。分省和分海拔评估结果表明,评估结果随海拔高度的增加而变差,因此在海拔较高、地形较复杂区域、气象站较稀疏区域应用该产品时应谨慎;由小时实况融合格点产品获得的日最高、最低温度也有很高的指示性;该产品对高温过程也有较好的监测能力。      

Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

Spatial analysis of monthly precipitation in Turkey

Summary  The principal objectives of this paper are to develop and validate an optimum interpolation method for the spatial analysis of monthly precipitation in Turkey. A two-dimensional optimum interpolation objective analysis scheme has been developed for the spatial analysis of precipitation. The model is developed for generating statistically optimum interpolation based on the irregular distribution of meteorological stations. One question that affects the optimum interpolation method and, indeed, all such techniques, is how many observations should be allowed to influence a given grid point? The method developed in this paper addresses this question. For the implementation of the method, 52 stations are considered for Turkey, with 30 years of monthly data at each point. It is observed that each monthly average spatial correlation function shows a monotonically decreasing pattern based on 15 km interval averages. The method provides high estimation accuracy in dense station locations such as in northwestern Turkey. Precipitation contour maps obtained by the optimum interpolation method indicate two spatial trends over Turkey which have not been identified in any previous study. Received June 24, 1999/Revised April 26, 2000     

融合降水实况分析产品在四川地区的适用性评估

利用四川省地面自动站2018年6月—2019年5月的逐小时降水观测资料,在邻近插值和双线性插值对比分析的基础上,从晴雨准确率、降水时空特征、降水分量级检验等多个方面,对国家气象信息中心研制的融合降水实况分析产品在四川地区的适用性进行评估分析。评估结果表明:(1)邻近插值和双线性插值对评估结果影响小。(2)融合降水实况分析产品的完整性好,其平均晴雨准确率为92.6%,对探测降水有无存在较大可能。(3)融合降水实况分析产品的数据质量较高,能反映四川区域年内小时降水的时空变化特征,且随着降水量级的增大,误差相应增大,TS评分相应减小,说明在弱降水量级,融合降水实况分析产品与观测降水更接近。(4)非独立检验的效果好于独立检验,盆地的检验效果好于高原、山区等复杂地区,说明参与评估的站点分布、数据质量对评估结果存在一定影响。     

Trends in the annual extreme rainfall events of 1 to 3 days duration over India

Summary In this paper, the annual extreme rainfall series in the time scale of 1 to 3 days duration at 316 stations, well distributed over the Indian region, covering 80-years of rainfall data from 1901 to 1980 were analysed for trend and persistence using standard statistical tests. It has been found that the annual extreme rainfall records of most stations are free from trend and persistence. However, the extreme rainfall series at stations over the west coast north of 12°N and at some stations to the east of the Western Ghats over the central parts of the Peninsula showed a significant increasing trend at 95% level of confidence. Stations over the southern Peninsula and over the lower Ganga valley have been found to exhibit a decreasing trend at the same level of significance. The data series of the stations which showed trends were subjected to a 10-year moving average and the resulting smoothed series have been discussed. It may be said that this increasing or decreasing trend in the annual extreme rainfall events at a few places will have tremendous implications in the hydrologic studies and dam design projects.With 9 Figures