HelioFTH: combining cloud index principles and aggregated rating for cloud masking using infrared observations from geostationary satellites

Abstract. In this paper a cloud mask and cloud fractional coverage (CFC) retrieval scheme called HelioFTH is presented. The algorithm is self-calibrating and relies on infrared (IR) window-channel observations only. It needs no input from numerical weather prediction (NWP) or radiative transfer models, nor from other satellite platforms. The scheme is applicable to the full temporal and spatial resolution of the Meteosat Visible and InfraRed Imager (MVIRI) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensors. The main focus is laid on the separation of middle- and high-level cloud coverage (HCC) from low-level clouds based on an internal cloud-top pressure (CTP) product.

CFC retrieval employs a IR-only cloud mask based on an aggregated rating scheme. CTP retrieval is based on a Heliosat-like cloud index for the MVIRI IR channel. CFC from HelioFTH, the International Satellite Cloud Climatology Project (ISCCP) DX and the Satellite Application Facility on Climate Monitoring (CM SAF) were validated with CFC from the Baseline Surface Radiation Network (BSRN) and the Alpine Surface Radiation Budget (ASRB) network. HelioFTH CFC differs by not more than 5–10% from CM SAF CFC but it is higher than ISCCP-DX CFC. In particular the conditional probability to detect cloud-free pixels with HelioFTH is raised by about 35% compared to ISCCP-DX. The HelioFTH CFC is able to reproduce the day-to-day variability observed at the surface.

Also, the HelioFTH HCC was inter-compared to CM SAF and ISCCP-DX over different regions and stations. The probability of false detection of cloud-free HCC pixels is in the same order as ISCCP-DX compared to the CM SAF HCC product over the full-disk area. HelioFTH could be used for generating an independent climate data record of cloud physical properties once its consistency and homogeneity is validated for the full Meteosat time series.