Evaluation of gridding procedures for air temperature over Southern Africa

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

doi:10.5194/asr-14-163-2017

Kai-Uwe Eiselt, Frank Kaspar + Show 4 more

Open Access

https://doi.org/10.5194/asr-14-163-2017

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Abstract

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.

Highlights

Precise monitoring of climate variability and climate change are challenges for many regions in the world
Due to a lack of longterm records we focused on data ranging from September 2014 to August 2016
The Root Mean Square Error (RMSE) errors were highest for longitude (x) and elevation (z), lower for zonal mean temperature (b) while continentality index (K) and latitude (y) performed best (Table 1)

Summary

Introduction

Precise monitoring of climate variability and climate change are challenges for many regions in the world. For Africa it was noted that the lack of adequate data and observation systems seriously hinders the ability of scientists to assess the past and current state of climate (ACC, 2013). This applies to several developing regions in the world. SASSCAL is a multidisciplinary initiative which aims to improve knowledge in several different scientific areas such as agriculture, biology, and hydrology. In these disciplines there is a strong need for climate information at high spatial resolution. Such datasets are an established tool for climate monitoring in other regions (e.g. Kaspar et al, 2013)

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