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

Terhikki Manninen,Emmihenna Jääskeläinen,Niilo Siljamo,Aku Riihelä,Karl-Göran Karlsson

doi:10.5194/amt-15-879-2022

Abstract

Abstract. This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 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 that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

Highlights

The surface albedo is a key indicator of climate change (GCOS, 2016) and is continuously and accurately measured across contrasting climatic zones by the Baseline Surface Radiation Network (BSRN), operated by the World Climate Research Programme (WCRP)
For relatively low surface albedo values, the clouds cause a tail at the high end of the albedo distribution; for high albedo values, a tail occurs at the low end
This study demonstrates the use of cloud probability information for surface albedo retrieval

Summary

Introduction

The surface albedo is a key indicator of climate change (GCOS, 2016) and is continuously and accurately measured across contrasting climatic zones by the Baseline Surface Radiation Network (BSRN), operated by the World Climate Research Programme (WCRP). Satellite remote sensing is required to augment these regional measurements with global estimates of surface albedo (König-Langlo et al, 2013; Driemel et al, 2018). Remote sensing is the only reasonable alternative for augmenting regional surface albedo estimates globally. EUMETSAT provides the climate community with satellite-based surface albedo products in the project CM SAF, which is part of the EUMETSAT Applications Ground Segment (Schulz et al, 2009). The CLARA data record contains cloud properties, surface albedo and surface radiation parameters derived from the AVHRR sensor onboard the polar-orbiting NOAA and METOP satellites. The CLARA-A2 (2nd ed.) covers the years 1982–2015 and the edition, A3, will cover the years 1979–2020

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Conclusion