Bruno Dürr

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.

A cloud mask, cloud fractional coverage (CFC) and cloud top pressure (CTP) retrieval scheme called HelioFTH is presented. The algorithm relies on infrared (IR) window channel observations only. 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 high cloud coverage (HCC) from low level clouds. 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. Also, the HelioFTH HCC was inter-compared to CM SAF and ISCCP-DX over different regions. The probability of false detection of cloud-free HCC pixels is 15% lower for HelioFTH than for ISCCP-DX compared to the CM SAF HCC product over the full-disk area. HelioFTH could be used for generating a climate data record of cloud physical properties once its consistency and homogeneity is validated for the full Meteosat time series.

Verification results from different satellite-based surface shortwave irradiance retrievals and sensitivity runs for key input parameters are presented for the Alpine region. Overall the uncertainty of the hourly retrievals at the Federal Office of Meteorology and Climatology (MeteoSwiss) validated with high-quality surface measurements is comparable with results from the standard Heliosat-3 model, but clearly improved for situations with snow-cover. The sensitivity study reveals that it is recommended to precisely georeference the High Resolution Visible (HRV) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) channels of the Meteosat Second Generation (MSG) satellites to obtain accurate surface shortwave irradiance estimates. They also confirm the benefit of terrain corrections for sites located in deep Alpine valleys. Monthly mean shortwave irradiance retrievals provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM-SAF) are verified with the MeteoSwiss products. For the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)-based product about 60% of all satellite 15 km × 15 km grid cells match the MeteoSwiss product, if locally dependent 90% confidence intervals are applied. This percentage decreases to 52%, if the standard CM-SAF 90% confidence interval of 20 Wm⁻² is used. Taking the local spatial variability of the shortwave irradiance field into account therefore allows obtaining more realistic verification results over heterogeneous terrain such as the Alpine region.

In the framework of the Satellite Application Facility for Climate Monitoring (CM-SAF) an upgraded formulation of the HELIOSAT surface global irradiance retrieval scheme is proposed, which is suitable for real-time application to METEOSAT Second Generation (MSG) satellite data. The new scheme includes image georeferencing, pixel-wise snow-cover detection, special treatment of clouds above snow and correction of terrain effects over the Alpine region. Results show that the mean bias difference between the revised irradiance estimates from satellite and from ground measurements can be substantially reduced by correctly distinguishing between clouds and snow, and by applying the new cloud-index scheme for clouds blue above snow. The increase of the root mean square difference with increasing altitude can be mainly attributed to the increase of the variability of the natural irradiance field. We strongly recommend the use of MSG-based irradiance estimates for locations at more than 4 km distance to the next measurement site.