Preprints
https://doi.org/10.5194/amt-2021-143
https://doi.org/10.5194/amt-2021-143

  31 May 2021

31 May 2021

Review status: this preprint is currently under review for the journal AMT.

Cloud probability-based estimation of black-sky surface albedo from AVHRR data

Terhikki Manninen1, Emmihenna Jääskeläinen1, Niilo Siljamo1, Aku Riihelä1, and Karl-Göran Karlsson2 Terhikki Manninen et al.
  • 1Meteorological research, Finnish Meteorological Institute, Helsinki, FI-00101, Finland
  • 2Atmospheric Remote Sensing Unit, Research Department, Swedish Meteorological and Hydrological Institute, Norrköping, SE-60176 Norrköping, Sweden

Abstract. Cloud cover constitutes a major challenge for the surface albedo estimation using Advanced Very High Resolution Radiometer AVHRR data for all possible conditions of cloud fraction and cloud type on any land cover type and solar zenith angle. Cloud masking has been the traditional way to estimate surface albedo from individual satellite images. Another approach to tackle cloudy conditions is presented in this study. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data of one month. A weighted mean approach based on the CP values was shown to produce very high accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and for the relative error it was 2.2 %. AVHRR based and in situ albedo distributions were in line with each other and also the monthly mean values were consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

Terhikki Manninen et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Terhikki Manninen et al.

Terhikki Manninen et al.

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Short summary
The manuscript tackles the problem of removing the effect of clouds from optical satellite images in order to derive the surface reflectance. Basic atmospheric radiation physics and statistical methods are used. The cloud tackling method presented can be applied in general instead of cloud masking. The results were better than when using binary cloud masking. The study was made for generating a surface albedo climate data record.