Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Abstract

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.

Real time automatic cloud detection using a low-cost sky camera

“…A similar observation was cited in [Román et al, 2017a, ]. In [Werkmeister et al, 2015, ] the observations took place in Germany and New Zealand, over time frames of 3 and 2 months, respectively. For a clear sky, the authors found detection probabilities of between 72 and 76%.…”

“…In addition, sky cameras have been used to record cloud detection from solar radiation data [Alonso et al, 2014a, ] and then to predict 45 the solar resource over the short term using digital levels and maximum cross-correlation [Alonso-Montesinos and Batlles, 2015; Alonso-Montesinos et al, 2015, ]. Other authors have compared cloud detection data using satellite imagery and sky cameras [Luiz et al, 2018;Werkmeister et al, 2015, ];in [Luiz et al, 2018, ], the comparison was made in south-eastern Brazil over a period of approximately three months. Good agreement was obtained for clear sky and overcast conditions, with detection probabilities of 92.8% and 80.7%, respectively.…”

Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras

“…However, the temporal resolution of satellite products is limited. From the Meteosat Second Generation (MSG) geostationary satellites, for instance, data on clouds are taken with a time resolution of 15 min (Werkmeister et al, 2015). Therefore, and for the validation of cloud products from satellites, ground-based observing systems such as all-sky cameras are necessary.…”

Final response to RC2

“…An alternative to detecting clouds from the ground by human observation is to detect them from space. With a temporal resolution of 5 to 15 min, Meteosat Second Generation (MSG) geostationary satellites are able to detect cloud coverage with a higher time resolution than is accomplished by human observers (Ricciardelli et al, 2010;Werkmeister et al, 2015). The geostationary satellite Himawari-8 (Da, 2015) even delivers cloud information with a temporal resolution of 2.5 to 10 min and a spatial resolution of 0.5 to 2 km.…”