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Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

Title data

Reinhardt, Katja ; Samimi, Cyrus:
Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia.
In: Climate Dynamics. Vol. 51 (2018) Issue 9/10 . - pp. 3635-3652.
ISSN 1432-0894
DOI: https://doi.org/10.1007/s00382-018-4101-y

Official URL: Volltext

Project information

Project financing: Deutscher Akademischer Austauschdienst

Abstract in another language

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→ and v→ 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→ and v→. 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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Spatial interpolation; Wind; Central Asia; Complex topography
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professorship Climatology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professorship Climatology > Professorship Climatology - Univ.-Prof. Dr. Cyrus Samimi
Profile Fields > Advanced Fields > Ecology and the Environmental Sciences
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties
Profile Fields
Profile Fields > Advanced Fields
Research Institutions
Research Institutions > Research Centres
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 550 Earth sciences, geology
Date Deposited: 30 Jan 2018 07:37
Last Modified: 03 Apr 2019 12:41
URI: https://eref.uni-bayreuth.de/id/eprint/42168