Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study
- 1Institut für Meteorologie und Klimatologie, Leibniz Universität Hannover, Herrenhaeuser Str. 2, 30419 Hannover, Germany
- 2Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
- 3National Institute of Water and Atmospheric Research (NIWA), Lauder, Central Otago, New Zealand
- 4now at: Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, 3149 Miami, USA
Abstract. In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively.
Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14% for AVHRR and 8 ± 16% for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22%) than instantaneous SEVIRI cloud fraction estimates (8 ± 29%) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94% and false alarm rates between 21 and 29% for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76%. We find poor skill, however, whenever broken clouds occur (probability of detection is 32% for AVHRR and 12% for SEVIRI in Hannover, Germany).
In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0%) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each "cloud-contaminated" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.