Advances in Science and Research
Advances in Science and Research
ASR
Articles | Volume 14
Articles | Volume 14
https://doi.org/10.5194/asr-14-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/asr-14-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 14
15 Jun 2017
 | 15 Jun 2017

Evaluation of gridding procedures for air temperature over Southern Africa

Kai-Uwe Eiselt, Frank Kaspar, Thomas Mölg, Stefan Krähenmann, Rafael Posada, and Jens O. Riede

Abstract. Africa is considered to be highly vulnerable to climate change, yet the availability of observational data and derived products is limited. As one element of the SASSCAL initiative (Southern African Science Service Centre for Climate Change and Adaptive Land Management), a cooperation of Angola, Botswana, Namibia, Zambia, South Africa and Germany, networks of automatic weather stations have been installed or improved (http://www.sasscalweathernet.org). The increased availability of meteorological observations improves the quality of gridded products for the region. Here we compare interpolation methods for monthly minimum and maximum temperatures which were calculated from hourly measurements. Due to a lack of longterm records we focused on data ranging from September 2014 to August 2016. The best interpolation results have been achieved combining multiple linear regression (elevation, a continentality index and latitude as predictors) with three dimensional inverse distance weighted interpolation.

How to cite. Eiselt, K.-U., Kaspar, F., Mölg, T., Krähenmann, S., Posada, R., and Riede, J. O.: Evaluation of gridding procedures for air temperature over Southern Africa, Adv. Sci. Res., 14, 163–173, https://doi.org/10.5194/asr-14-163-2017, 2017.
Received: 25 Jan 2017 – Revised: 24 Apr 2017 – Accepted: 07 May 2017 – Published: 15 Jun 2017
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Short summary
As one element of the SASSCAL initiative (a cooperation of Angola, Botswana, Namibia, Zambia, South Africa and Germany) networks of automatic weather stations have been installed or improved in Southern Africa. Here we compare interpolation methods for monthly minimum and maximum temperatures which were calculated from hourly measurements. The best interpolation results have been achieved combining multiple linear regression with three dimensional inverse distance weighted interpolation.
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