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DOI | 10.1007/s00382-018-4101-y |
Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia | |
Reinhardt, Katja1; Samimi, Cyrus1,2 | |
2018-11-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2018 |
卷号 | 51页码:3635-3652 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany |
英文摘要 | While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components (u) over right arrow and (v) over right arrow in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of (u) over right arrow and (v) over right arrow. In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results. |
英文关键词 | Spatial interpolation Wind Central Asia Complex topography |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000447366100026 |
WOS关键词 | SPATIAL INTERPOLATION ; AIR-TEMPERATURE ; SPEED ; MODEL ; PRECIPITATION ; PERFORMANCE ; SURFACES |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35560 |
专题 | 气候变化 |
作者单位 | 1.Univ Bayreuth, Dept Geog, Univ Str 30, D-95447 Bayreuth, Germany; 2.Univ Bayreuth, Bayreuth Ctr Ecol & Environm Res BAYCEER, D-95447 Bayreuth, Germany |
推荐引用方式 GB/T 7714 | Reinhardt, Katja,Samimi, Cyrus. Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia[J]. CLIMATE DYNAMICS,2018,51:3635-3652. |
APA | Reinhardt, Katja,&Samimi, Cyrus.(2018).Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia.CLIMATE DYNAMICS,51,3635-3652. |
MLA | Reinhardt, Katja,et al."Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia".CLIMATE DYNAMICS 51(2018):3635-3652. |
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