A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

  • Bley S
  • Deneke H
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Abstract

Abstract. A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

Figures

  • Fig. 1. Normalized spectral response functions of the Meteosat-9 SEVIRI radiometer for the 0.6 µm (red), 0.8 µm (green) and HRV (black) channels. The central wavelength of each channel is marked by a thick colored line, and the spectral region covered by the channel width has been shaded. The solar spectrum is added as dotted line.
  • Fig. 2. Day natural color (RGB) clear sky composite of Lensky and Rosenfeld (2008) based on clear sky reflectances of the 0.6, 0.8 and 1.6 µm channel. Median values of clear sky reflectance over a period of 16 days (1–16 June, 2011) and cloud-cleared with the EUMETSAT cloud mask are shown (at standard SEVIRI resolution). The following four regions used in our study are marked by boxes: (1) Atlantic, (2) the Alps, (3) Upper Rhine Valley and (4) Spain.
  • Fig. 3. Normalized frequency distribution of cloudy and clear sky HRV reflectances over Spain (yellow), Upper Rhine Valley (green), the Alps (red) and Atlantic (blue) observed by MSG SEVIRI. Separation between cloudy (dotted line) and clear sky (solid line) reflectances is based on EUMETSAT cloud mask. EUMETSAT cloud mask is upsampled to HRV resolution.
  • Fig. 4. Change in planetary albedo due to the occurrence of clouds with a COT of 0.2 (blue), 0.5 (green), 1 (red) and 2 (cyan) as a function of the clear-sky reflectance of the underlying surface. The δ (y axis) demonstrates the accuracy that is necessary to detect a cloud over a surface with a specific clear-sky reflectance. This simple model is based on Eq. (1) by Lacis and Hansen (1974).
  • Table 1. Contingency table with binary classification cloudy and clear.
  • Fig. 5. Normalized frequency distribution of clear sky HRV reflectances over Spain observed by MSG SEVIRI on 15 July 2011. The solid line is the distribution of clear sky reflectances identified by the EUMETSAT cloud mask. The dotted line is the histogram of the normalized clear sky reflectance after subtraction of the clear sky composite anomaly map. The green arrows symbolize the reduction of spatial variability.
  • Fig. 6. Flow chart of our HRV cloud mask algorithm, based on the HRV reflectance and the EUMETSAT cloud mask as i puts. The clear- ky composite i initiall calculated bas d on the HRV reflectance an th EUMETSAT cloud mask for 16-day p rio s. The panel showing the histograms represents the thresh l selection step based on maximizing the MCC. As a final step, the thin cloud restoral to consider thin clouds is carried out. An iterative approach including the HRV cloud mask is chosen for the calculation of the clear-sky composite for consistency.
  • Table 2. Results of the HRV cloud mask algorithm averaged over three 16-day periods starting 1 June, 1 July and 1 August 2011. The four regions considered are listed in column 1. Cc is the average cloud cover, and rcs is the spatially averaged temporal median HRV clear-sky reflectance including its standard deviation std(rcs). Columns 4 and 5 report the cloud detection thresholds above which a pixel is classified as cloudy. tabs lists the absolute threshold without use of the HRV clear-sky reflectance composite, while trel is the threshold relative to the composite. The percentage deviations between the HRV and the EUMETSAT cloud mask are given in columns 6–8. Here, Devabs and Devrel are the deviations found using tabs and trel, respectively. Column 8 lists the final deviation Devfi after applying the HRV clear-sky composite and thin cloud restoral.

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CITATION STYLE

APA

Bley, S., & Deneke, H. (2013). A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI. Atmospheric Measurement Techniques, 6(10), 2713–2723. https://doi.org/10.5194/amt-6-2713-2013

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