Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples

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.

Quantifying development of shallow/warm marine cumulus clouds from geostationary observations

<p>In our study, we track shallow/warm marine cumulus clouds in the trade wind zone centred around the Canary Islands in August 2015. Tracking was performed in the CLAAS-2 data record (CM SAF CLoud property dAtAset using SEVIRI, [1]) which is based on time-resolved geostationary measurements with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation. The retrieval of cloud trajectories allows for the calculation of the cloud lifetime distribution, the horizontal cloud size distribution and to characterize temporal changes in cloud properties. Cloud physical properties are available in the daytime. Filtering for daytime and low-level clouds we found about 65 thousand trajectories. For the considered period and domain, the lifetime distribution follows a power law. Most frequent are clouds which live on a time scale of tens of minutes. In the horizontal cloud size distribution, we detected two intervals following an exponential law but with different scaling. The first interval includes cloud sizes smaller than 30 km<sup>2</sup> and the second interval includes cloud sizes equal to or larger than 30 km<sup>2</sup> but smaller than 1000 km<sup>2</sup>. Clouds having a mean horizontal cloud size of approximately 30 km<sup>2</sup> are most frequent. Furthermore, we present time series’ of cloud physical properties, as cloud droplet effective radius at cloud top r<sub>e</sub>, cloud optical thickness, cloud water path and cloud droplet number concentration. For comparison of the trajectories, we choose r<sub>e</sub> as a measure. If r<sub>e</sub> reaches a certain value the trajectories have been centred at this specific relative time.</p><p>References<br>[1] Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., Meirink, J. F.: The MSG-SEVIRI-based cloud property data<br>record CLAAS-2. Earth System Science Data 9(2), 415–434 (2017). DOI 10.5194/essd-9-415-2017</p>

Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples

The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard 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, where the HRV channel helps to better capture the large spatio-temporal variability induced by convective clouds. These results suggest that incorporating the HRV channel in the retrieval has potential for improving METEOSAT-based cloud products for several application domains.

Information Content of Ice Cloud Properties from Multi-Spectral, -Angle and -Polarization Observations

Ice clouds play an important role in the Earth’s radiation budget, while their microphysical and optical properties remain one of the major uncertainties in remote sensing and atmospheric studies. Many satellite-based multi-spectral, -angle and -polarization instruments have been launched in recent years, and it is unclear how these observations can be used to improve the understanding of ice cloud properties. This study discusses the impacts of multi-spectral, -angle and -polarization observations on ice cloud property retrievals by performing a theoretical information content (IC) analysis. Ice cloud properties, including the cloud optical thickness (COT), particle effective radius (Re) and particle habit (defined by the aspect ratio (AR) and the degree of surface roughness level (σ)), are considered. An accurate polarized radiative transfer model is used to simulate the top-of-atmosphere intensity and polarized observations at the cloud-detecting wavelengths of interest. The ice cloud property retrieval accuracy should be improved with the additional information from multi-spectral, -angle and -polarization observations, which is verified by the increased degrees of freedom for signal (DFS). Polarization observations at spectral wavelengths (i.e., 0.87 and 2.13 µm) are helpful in the improvement of ice cloud property retrievals, especially for small-sized particles. An optimal scheme to retrieve ice cloud properties is to comprise radiance intensity information at the 0.87, 1.24, 1.64 and 2.13 µm channels and polarization information (the degree of linear polarization, DOLP) at the 0.87 and 2.13 µm channels. As observations from multiple angles added, DFS clearly increases, while it becomes almost saturated when the number of angles reaches three. Besides, the retrieval of Re exhibits larger uncertainties, and the improvement in total DFS by adding multi-spectral, -angle and -polarization observations is mainly attributed to the improvement of Re retrieval. Our findings will benefit the future instrument design and the improvement in cloud property retrieval algorithms based on multi-spectral, -angle, and -polarization imagers.