Abstract
Abstract. The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1 km2 compared to the standard 3×3 km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6 µm, 0.8 µm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6 µm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6 µm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains.
Highlights
Clouds play an important role in Earth’s energy budget and hydrological cycle (e.g., Wild and Liepert, 2010)
Benefits of the increased spatial resolution of the resulting Spinning Enhanced Visible and Infrared Imager (SEVIRI) products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and highresolution visible (HRV) spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds
The HRV reflectance is first used in a threshold-based cloud mask, while the highfrequency component of the HRV reflectance is subsequently extracted with a high-pass filter and utilized as a physical constraint to resolve small-scale variability in cloud optical depth
Summary
Clouds play an important role in Earth’s energy budget and hydrological cycle (e.g., Wild and Liepert, 2010). Deneke et al.: Increasing the spatial resolution of Meteosat SEVIRI cloud property retrievals mate and weather forecast models remains limited due to a fundamental lack of understanding of the relevant cloud processes and the interaction of clouds with other components of the climate system (Bony et al, 2015) These shortcomings are widely recognized to be a dominant source of uncertainty in our understanding of the climate system and its response to anthropogenic forcings (Boucher et al, 2013). The underlying methods for inferring cloud properties from these instruments are, usually nonlinear and sensitive to assumptions and uncertainties in the applied forward models, which reflects the underconstrained nature of the underlying inversion problem (Stephens and Kummerow, 2007) This introduces sensitivities of the resulting products to sensor characteristics, such as spectral response and spatial resolution, as well as relatively minor differences in the implementation of retrieval algorithms (Roebeling et al, 2015).
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