{"title":"罗马尼亚每日高分辨率雪深网格数据集(2005-2015)","authors":"A. Dumitrescu, M. Birsan, Ion-Andrei Nita","doi":"10.15233/GFZ.2017.34.14","DOIUrl":null,"url":null,"abstract":"This study presents the spatial interpolation procedure from snow depth measurements at weather stations implying the following stages: (1) Spatial interpolation at 1 km × 1 km resolution of the mean multiannual values (20052015) corresponding to each month, computed from the data extracted from the climatological database; (2) Computation of the daily deviations against the multiannual monthly mean for every day and year over 2005–2015 and their spatial interpolation; (3) Spatio-temporal datasets were obtained through merging the two surfaces obtained in stages 1 and 2. The anomalies were considered to be the ratio between the daily snow depth values and the climatology. The spatial variability of the data used in the first stage was accounted for through the use of a series of predictors derived from the digital elevation model (DEM). To plot the maps with the climatological normals (multiannual means), the Regression-Kriging (RK) spatial interpolation method was used. In order to choose the optimum method applied in spatializing deviations, four interpolation methods were tested using a cross-validation procedure: Multiquadratic, Ordinary Kriging (separated and pooled variograms) and 3d Kriging.","PeriodicalId":50419,"journal":{"name":"Geofizika","volume":"34 1","pages":"275-295"},"PeriodicalIF":0.9000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Romanian daily high-resolution gridded dataset of snow depth (2005-2015)\",\"authors\":\"A. Dumitrescu, M. Birsan, Ion-Andrei Nita\",\"doi\":\"10.15233/GFZ.2017.34.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents the spatial interpolation procedure from snow depth measurements at weather stations implying the following stages: (1) Spatial interpolation at 1 km × 1 km resolution of the mean multiannual values (20052015) corresponding to each month, computed from the data extracted from the climatological database; (2) Computation of the daily deviations against the multiannual monthly mean for every day and year over 2005–2015 and their spatial interpolation; (3) Spatio-temporal datasets were obtained through merging the two surfaces obtained in stages 1 and 2. The anomalies were considered to be the ratio between the daily snow depth values and the climatology. The spatial variability of the data used in the first stage was accounted for through the use of a series of predictors derived from the digital elevation model (DEM). To plot the maps with the climatological normals (multiannual means), the Regression-Kriging (RK) spatial interpolation method was used. In order to choose the optimum method applied in spatializing deviations, four interpolation methods were tested using a cross-validation procedure: Multiquadratic, Ordinary Kriging (separated and pooled variograms) and 3d Kriging.\",\"PeriodicalId\":50419,\"journal\":{\"name\":\"Geofizika\",\"volume\":\"34 1\",\"pages\":\"275-295\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geofizika\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.15233/GFZ.2017.34.14\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geofizika","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.15233/GFZ.2017.34.14","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 6
摘要
本文提出了气象站雪深测量数据的空间插值过程,包括以下几个阶段:(1)从气候学数据库提取的数据中计算出每个月对应的多年均值(2005 - 2015)的1 km × 1 km分辨率的空间插值;(2) 2005-2015年逐日、逐年相对多年月均值的日偏差计算及其空间插值;(3)将阶段1和阶段2的两个曲面合并得到时空数据集。这些异常被认为是日雪深值与气候的比值。通过使用一系列来自数字高程模型(DEM)的预测因子来解释第一阶段使用的数据的空间变异性。采用回归-克里格(RK)空间插值方法绘制气候正态线(多年平均值)图。为了选择最适合偏差空间化的插值方法,采用交叉验证方法对4种插值方法进行了测试:多重二次插值法、普通克里格插值法(分离变量和混合变量)和三维克里格插值法。
A Romanian daily high-resolution gridded dataset of snow depth (2005-2015)
This study presents the spatial interpolation procedure from snow depth measurements at weather stations implying the following stages: (1) Spatial interpolation at 1 km × 1 km resolution of the mean multiannual values (20052015) corresponding to each month, computed from the data extracted from the climatological database; (2) Computation of the daily deviations against the multiannual monthly mean for every day and year over 2005–2015 and their spatial interpolation; (3) Spatio-temporal datasets were obtained through merging the two surfaces obtained in stages 1 and 2. The anomalies were considered to be the ratio between the daily snow depth values and the climatology. The spatial variability of the data used in the first stage was accounted for through the use of a series of predictors derived from the digital elevation model (DEM). To plot the maps with the climatological normals (multiannual means), the Regression-Kriging (RK) spatial interpolation method was used. In order to choose the optimum method applied in spatializing deviations, four interpolation methods were tested using a cross-validation procedure: Multiquadratic, Ordinary Kriging (separated and pooled variograms) and 3d Kriging.
期刊介绍:
The Geofizika journal succeeds the Papers series (Radovi), which has been published since 1923 at the Geophysical Institute in Zagreb (current the Department of Geophysics, Faculty of Science, University of Zagreb).
Geofizika publishes contributions dealing with physics of the atmosphere, the sea and the Earth''s interior.