Spatial Interpolation of Daily Precipitation in China: 1951--2005

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951--2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km×18 km grid system covering the whole country. Precipitation for each 0.5o×0.5o latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west (west of 100oE). Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.     

The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25?km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.     

Trends of precipitation extremes during 1960–2008 in Xinjiang, the Northwest China

The spatial–temporal variability of the precipitation extremes defined by eight precipitation indices based on daily precipitation dataset was analyzed using the linear regression method and the Mann–Kendall test. The results indicate that increasing trends in the precipitation amount, rainy days, and the intensity of the extreme precipitation were identified at above 70 % of the total rain stations considered in this study, with more than 30 % of them were significant, while most stations show notable decreasing trend in the annual maximum consecutive no-rain days. Significantly increasing trends of the precipitation extremes are observed mainly in the northern Xinjiang and the north of the southern Xinjiang. Most extreme precipitation indices show a potential regime shift starting from the middle of 1980s. The magnitude of the trends is compatible with their pattern of spatial stability. The generally increasing trends in precipitation extremes are found in this study.     

A Model to Interpolate Monthly Mean Climatological Data at Bavarian Forest Climate Stations

Summary  Six methods were used to interpolate the monthly mean climatological data from German climate stations to three Bavarian forest climate stations. The observed forest climatological data at the Bavarian forest climate stations were used as the reference data to which the interpolated data were compared. The results show that, for monthly mean daily maximum temperature at valley and plain forest climate stations, each of the six interpolation methods can give accurate estimates. For monthly mean daily maximum temperature, minimum temperature, air temperature and water vapor pressure at mountain forest climate stations, topographically aided interpolation can give the most accurate estimates. Barnes interpolation combined with empirical transfer functions can give accurate estimates forall climate variables at the plain and valley forest climate stations, and it can also give accurate estimates for monthly mean wind speed and monthly precipitation at the mountain forest climate station. The empirical transfer functions are very important for estimating the forest climatological data. These transfer functions will be used for reconstruction of long-term forest climatological data in Bavaria. Received September 9, 1998 Revised May 21, 1999     

Potential and limitations of using satellite data to evaluate the spatial detail in climatological air temperature maps

Abstract

Detailed patterns of spatial variability in surface temperature can be observed with the use of thermal infrared data from satellites. A method is developed to use clear‐sky thermal infrared satellite data to evaluate traditional monthly average maximum air temperature maps interpolated from observations at surface stations using a statistical thin plate smoothing spline method. Results of comparisons over Alberta, Manitoba and Saskatchewan from June to October, for the years 2001 to 2005, are presented. The satellite data allow identification of some limitations in the interpolation technique at high altitudes in mountain ranges and in data‐sparse areas due to low station density. In the data‐sparse areas, the highest discrepancies could be linked to the unrepresentativeness of the stations because of different land cover or the presence of water bodies. Conversely, the interpolated air temperature maps allow the identification of problems with using thermal infrared data to estimate near‐surface air temperatures in areas of significant moisture deficit and at the locations of water bodies.     

基于统计降尺度模型的江淮流域极端气候的模拟与预估

利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。     

用Kriging方法对中国历史气温数据插值可行性讨论

使用 Kriging 插值方法对已经过质量控制和均一化的1951年1月-2004年12月中国全部基本、基准站气温资料逐月进行空间插值.通过站点的实际序列与插值后格点序列进行比较,针对相关系数和线性趋势等多个量来检验 Kriging 方法对气候资料插值的效果.结果表明:插值前、后的气温空间分布、气温变化趋势都非常一致,从年际变化来看,插值序列与实际站点序列的相关性也非常高.对比分析还发现用距平序列的插值效果要明显优于原始气温序列插值,但不同的球面模型半径插值在站点稀疏地区的插值结果差别较大,需要先对气候要素进行空间代表性进行分析,以合适的球面半径进行插值.对于气候变化比较特殊的地区,如中国西南部分地区,插值序列很难反映更小尺度的气候变化规律.     

基于统计降尺度方法的夏季降水精细化预报

利用1999—2009年安徽省淮河以南地区60个县市站夏季逐日降水资料和安庆市探空站逐日资料,研究了中低层不同风向配置下局地降水与大尺度降水场之间的关系,以3种不同预报对象及相应的预报因子分别采用神经网络和线性回归方法设计6种预报模型对观测资料进行逼近和优化,从而实现空间降尺度.分析对比6种预报模型46站逐日降水量的拟合和预报效果,结果表明:采取相同的预报对象及预报因子的BP神经网络模型在拟合和预报效果上均好于线性回归模型,可见夏季降水场之间以非线性相关为主;神经网络模型预报结果同常用的Cressman插值预报相比,能很好地反映出降水的基本分布及局地特征;预报对象为单站降水序列的神经网络模型在以平原、河流为主要地形的区域预报效果较好,预报对象为REOF主成分的神经网络模型则在山地和丘陵地形区域预报效果较好.     

Variability and trends in daily precipitation extremes on the northern and southern slopes of the central Himalaya

This study focuses on the precipitation extremes recorded on the northern and southern slopes of the central Himalaya, especially those documented at higher altitudes. Daily precipitation data recorded over a 35-year period at nine meteorological stations in the region were studied. We used the precipitation extreme indices delineated by the Expert Team on Climate Change Detection and Indices (ETCCDI). The spatial and temporal variations in these precipitation extremes were calculated. When regional patterns were investigated to detect any anomalies, only 1 of the 10 precipitation extreme indices from the southern slopes of the central Himalaya showed a statistically significant trend; none from the northern slopes of the central Himalaya highlighted a statistically significant trend. On the southern slopes, all indices increased, apart from the maximum 1-day precipitation (RX1) and simple daily precipitation intensity (SDII) indices. Indices such as the consecutive dry days (CDDs) and RX1 indices exhibited similar increases on both the northern and southern slopes of the central Himalaya. These results suggest that increases in precipitation have been accompanied by an increasing frequency of extremes over the southern central Himalaya. Nonetheless, no relation could be established between the precipitation extreme indices and circulation indices for higher altitudes.

     

THE EFFECTS OF STATION NETWORK DENSITY ON STATISTICAL ANALYSES OF TROPICAL CYCLONE PRECIPITATION

In this paper, 1416 conventional ground-based meteorological observation stations on the mainland of China were subdivided into groups of differing spatial density. Data from each subgroup were then used to analyze variations in the tropical cyclone (TC) precipitation statistics derived from each subgroup across the mainland of China (excluding Taiwan, Hong Kong, and Macao), as well as in two regions (east China and south China) and three provinces (Guangdong, Hainan, and Jiangxi) between 1981 and 2010. The results showed that for the mainland of China, total precipitation, mean annual precipitation, mean daily precipitation, and its spatial distribution were the same regardless of the spatial density of the stations. However, some minor differences were evident with respect to precipitation extremes and their spatial distribution. Overall, there were no significant variations in the TC precipitation statistics calculated from different station density schemes for the mainland of China. The regional and provincial results showed no significant differences in mean daily precipitation, but this was not the case for the maximum daily precipitation and torrential rain frequency. The maximum daily precipitation calculated from the lower-density station data was slightly less than that based on the higher-density station schemes, and this effect should be taken into consideration when interpreting regional climate statistics. The impact of station density on TC precipitation characteristics was more obvious for Hainan than for Guangdong or Jiangxi provinces. In addition, the effects were greater for south China (including Guangxi Zhuang Autonomous region, Guangdong, and Hainan provinces) than east China (including Shandong, Jiangsu, Zhejiang, Shanghai, Fujian, Anhui, and Jiangxi provinces). Furthermore, the analysis proved that the statistical climatic characteristics began to change significantly when the station spacing was between 40 and 50 km, which are close to the mean spacing for all stations across the mainland of China. Moreover, TC areal precipitation parameters, including mean total areal precipitation and mean daily areal precipitation, also began to change significantly when the spacing was between 40 and 50 km, and were completely different when it was between 100 and 200 km.     

基于GIS的气温和降水推算方法研究

针对开展乡镇天气预报对高精度逐日气象要素输入值的需求,以辽宁地区为例,选用克立格法(Kriging)、距离权重反比法(IDw)、带高度梯度订正的距离权重反比法(GIDW)及样条函数法(spline)4种插值方法,进行有限气象站点1～12月逐日气象要素空间插值方法研究并对估值进行检验.结果表明:对温度而言,GIDW方法估值精度较高,插值结果分布趋势也较为接近实际站点的分布;对降水而言.IDW估值精度高于其他插值方法,更适合于日降水量的空间插值.     

Mapping near-surface air temperature,pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal reso- lution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km× 1 km. The approach comprises two steps： （1） fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; （2） apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Predic- tion （NCEP） Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.     

Changes in daily climate extremes in the arid area of northwestern China

There has been a paucity of information on trends in daily climate and climate extremes, especially for the arid region. We analyzed the changes in the indices of climate extremes, on the basis of daily maximum and minimum air temperature and precipitation at 59 meteorological stations in the arid region of northwest China over the period 1960–2003. Twelve indices of extreme temperature and six indices of extreme precipitation are examined. Temperature extremes show a warming trend with a large proportion of stations having statistically significant trends for all temperature indices. The regional occurrence of extreme cool days and nights has decreased by ?0.93 and ?2.36 days/decade, respectively. Over the same period, the occurrence of extreme warm days and nights has increased by 1.25 and 2.10 days/decade, respectively. The number of frost days and ice days shows a statistically significant decrease at the rate of ?3.24 and ?2.75 days/decade, respectively. The extreme temperature indices also show the increasing trend, with larger values for the index describing variations in the lowest minimum temperature. The trends of Min Tmin (Tmax) and Max Tmin (Tmax) are 0.85 (0.61) and 0.32 (0.17)?°C/decade. Most precipitation indices exhibit increasing trends across the region. On average, regional maximum 1-day precipitation, annual total wet-day precipitation, and number of heavy precipitation days and very wet days show insignificant increases. Insignificant decreasing trends are also found for consecutive dry days. The rank-sum statistic value of most temperature indices exhibits consistent or statistically significant trends across the region. The regional medians after 1986 of Min Tmin (Tmax), Max Tmin (Tmax), warm days (nights), and warm spell duration indicator show statistically more larger than medians before 1986, but the frost days, ice days, cool days (nights), and diurnal temperature range reversed. The medians of precipitation indices show insignificant change except for consecutive dry days before and after 1986.     

Future extreme events in European climate: an exploration of regional climate model projections

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.     

Statistical downscaling of summer temperature extremes in northern China

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011-35 relative to 1980-99.     

近38年中国气温和降水的1 km网格数据集

对中国38年的气温和降水进行了空间插值分析,选取最优模型去生成1km网格数据集,为中国大陆的植被分布、气候变化和环境生态等研究提供支持。基于国家气象中心839个气象站的逐日气温和降水数据,用经度、纬度和海拔作为ANUSPLIN软件插值的3个变量,对降水进行开平方预处理,采用3次样条的薄盘光滑样条法,得到了1980—2017年中国大陆月平均气温和月累计降水1km网格插值数据集。数据集的广义交叉验证均方根(RTGCV)和均方根误差(RMSE)具有年周期性和明显的季节变化特征;各站点的平均误差(MBE)的频率分布近似正态分布,绝对误差(MAE)的空间分布也符合中国大陆气候的变化特征。数据集在精准度和时间序列上较新,且提供公共下载服务,可为全国陆地生态系统的研究提供信息支持。     

Changes of precipitation and extremes and the possible effect of urbanization in the Beijing metropolitan region during 1960–2012 based on homogenized observations

Daily precipitation series at 15 stations in the Beijing metropolitan region (BMR) during 1960-2012 were homogenized using the multiple analysis of series for homogenization method, with additional adjustments based on analysis of empirical cumulative density function (ECDF) regarding climate extremes. The cumulative density functions of daily precipitation series, the trends of annual and seasonal precipitation, and summer extreme events during 1960-2012 in the original and final adjusted series at Beijing station were comparatively analyzed to show the necessity and efficiency of the new method. Results indicate that the ECDF adjustments can improve the homogeneity of high-order moments of daily series and the estimation of climate trends in extremes. The linear trends of the regional-mean annual and seasonal (spring, summer, autumn, and winter) precipitation series are -10.16, 4.97, -20.04, 5.02, and -0.11 mm (10 yr)-1, respectively. The trends over the BMR increase consistently for spring/autumn and decrease for the whole year/summer; however, the trends for winter decrease in southern parts and increase in northern parts. Urbanization affects local trends of precipitation amount, frequency, and intensity and their geographical patterns. For the urban-influenced sites, urbanization tends to slow down the magnitude of decrease in the precipitation and extreme amount series by approximately -10.4% and -6.0%, respectively; enhance the magnitude of decrease in precipitation frequency series by approximately 5.7%; reduce that of extremes by approximately -8.9%; and promote the decreasing trends in the summer intensity series of both precipitation and extremes by approximately 6.8% and 51.5%, respectively.     

A daily temperature dataset over China and its application in validating a RCM simulation

This paper describes the construction of a 0.5°×0.5°daily temperature dataset for the period of 1961- 2005 over mainland China for the purpose of climate model validation. The dataset is based on the in- terpolation from 751 observing stations in China and comprises 3 variables: daily mean,minimum,and maximum temperature.The"anomaly approach"is applied in the interpolation.The gridded climatology of 1971-2000 is first calculated and then a gridded daily anomaly for 1961-2005 is added to the climatologY to o...     

近30年江苏地区气候要素相关性变化分析

利用江苏省24个台站的1981-2012年的气温、湿度和降水的月平均观测资料,分别计算了每对台站之间的3个气候要素的结构关系和相关函数,用曲线回归分析了结构函数与台站距离的关系,用线性回归分析了相关函数与台站距离的关系,分析与评估江苏省气象台站网密度.通过分析结果发现,各个气象要素的结构函数和相关函数在江苏整个地区不满足各项同性和均匀性,江苏地区的气象台站网设计要根据气候要素进行分区设计.     

Trends in Extreme Precipitation Events in the Indus River Basin and Flooding in Pakistan

In the absence of a sufficiently dense network of climate stations covering all topographic regions of the Indus River basin and delivering high quality data over the last 30 years or more, daily precipitation data were obtained from the National Centers for Environmental Prediction-Department of the Enviornment (NCEP-DOE) Reanalysis 2 dataset for the period 1979 to 2011. The daily precipitation data were transformed into time series of frequency of extreme precipitation events of 1-day and 10-day durations defined in terms of 90th and 99th percentile threshold exceedances. The non-parametric Mann-Kendall trend test was applied to determine whether statistically significant changes in precipitation extremes occurred over time, in due consideration of autocorrelation in the data.

Extreme precipitation showed a high spatial variability, with the highest daily and 10-day precipitation totals, and thus highest 90th and 99th percentiles, in the southeastern lowlands at the foot of the Himalayas and the lowest in the Karakorum. Significantly decreasing trends in extreme precipitation were observed in the western part of the Indus River basin; significantly increasing trends were mainly detected in the very high mountainous regions in the east (Transhimalaya and Himalayas) and in the north (Hindu Kush and Karakorum) of the Indus basin. High precipitation rates are not common in the arid climate of these high mountainous regions. Future flood management plans need to consider the increasing trends in extreme precipitation events in these areas.