Hourly gridded air temperatures of South Africa derived from MSG SEVIRI

Meyer, H; Schmidt, J; Detsch, F; Nauss, T

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

Monitoring of climate variables such as air temperature is gaining increasing importance under climate change. This study aimed at developing an hourly gridded 3.5 × 3.5 km air temperature (Tair) data set for entire South Africa. In a Random Forest approach, MSG SEVIRI data from 2010 to 2014 were used and related to Tair measured by 78 weather stations. An external validation on new climate stations and years that were not used for model training indicated the ability of the model to predict Tair with a RMSE of 2.61 °C and a R2 of 0.89. The resulting model can be applied to the entire MSG SEVIRI time series since 2004. It hence allows for spatio-temporal pattern analysis as well as for the detection of trends which is relevant in the context of climate change.

Details zur Publikation

FachzeitschriftInternational Journal of Applied Earth Observation and Geoinformation (Int J Appl Earth Obs Geoinf)
Jahrgang / Bandnr. / Volume78
Seitenbereich261-267
StatusVeröffentlicht
Veröffentlichungsjahr2019 (01.06.2019)
Sprache, in der die Publikation verfasst istEnglisch
DOI10.1016/j.jag.2019.02.006
Link zum Volltexthttp://www.sciencedirect.com/science/article/pii/S0303243419300315
StichwörterAir temperature; Climate; Machine learning; Meteosat; Random Forest; South Africa

Autor*innen der Universität Münster

Meyer, Hanna
Juniorprofessur für Remote Sensing und Image Processing (Prof. Meyer)
Professur für Remote Sensing und Spatial Modelling (Prof. Meyer)