Preprints
https://doi.org/10.5194/gi-2022-6
https://doi.org/10.5194/gi-2022-6
 
25 Apr 2022
25 Apr 2022
Status: this preprint is currently under review for the journal GI.

Upgrade of LSA-SAF Meteosat Second Generation daily surface albedo (MDAL) retrieval algorithm incorporating aerosol correction and other improvements

Daniel Juncu1, Xavier Ceamanos1, Isabel F. Trigo2, Sandra Gomes2, and Sandra C. Freitas2,3 Daniel Juncu et al.
  • 1CNRM, Météo-France, CNRS, Université de Toulouse, Toulouse, France
  • 2Instituto Português do Mar e da Atmosfera (IPMA), Lisboa, Portugal
  • 3Deimos Engenharia, Lisboa, Portugal

Abstract. MDAL is the operational Meteosat Second Generation (MSG) derived daily surface albedo product that is generated and disseminated in near real time by the EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA-SAF) since 2005. We propose and evaluate an update to the MDAL retrieval algorithm which introduces the accounting for aerosol effects, as well as other scientific developments: pre-processing recalibration of radiances acquired by the SEVIRI instrument aboard MSG and improved coefficients for atmospheric correction as well as for albedo conversion from narrow- to broad-band. We compare the performance of MDAL broad-band albedos pre- and post upgrade with respect to two types of reference data: EPS-Metop based 10-day albedo product ETAL (which was found to be comparable to MODIS-based albedo in terms of accuracy) and albedo derived from in-situ flux measurements acquired by ground stations. For the comparison to ETAL — based on differences over the whole coverage area of SEVIRI — we see a reduction of average white-sky albedo mean bias error (MBE) from -0.02 to negligible levels (< 0.001), and a reduction of average mean absolute error (MAE) from 0.034 to 0.026 (–24 %). Improvements can be seen for black-sky albedo as well, albeit less pronounced (14 % reduction in MAE). Further analysis distinguishing individual seasons, regions, and land covers show that performance changes have spatial and temporal dependence: for white-sky albedo we see improvements over almost all regions and seasons relative to ETAL, except for Eurasia in winter; resolved by land cover we see a similar effect with improvements for all types for all seasons except winter, where some types exhibit slightly worse results (crop-, grass- and shrublands). For black-sky albedo we similarly see improvements for all seasons when averaged over the full dataset, although sub-regions exhibit clear seasonal dependence: performance of the upgraded MDAL version is generally diminished in local winter but better in local summer. The comparison against in-situ observations is less conclusive due to the well known problem of spatial representativeness of near-ground observations with respect to satellite pixel footprint sizes. Considering all evidence presented in this study, the updated algorithm version is considered to be able to deliver a valuable improvement of the operational MDAL product.

Daniel Juncu et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2022-6', Anonymous Referee #1, 28 May 2022 reply
    • AC1: 'Reply on RC1', Daniel Juncu, 29 Jul 2022 reply

Daniel Juncu et al.

Data sets

MDAL v2: Experimental MSG daily albedo 01-11-2020 -- 31-10-2021 Juncu, Ceamanos, Trigo, Gomes, Freitas http://dx.doi.org/10.5281/zenodo.6414693

Daniel Juncu et al.

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Short summary
MDAL is a near real-time, satellite-based surface albedo product based on the geostationary Meteosat Second Generation mission. We propose an update to the processing algorithm that generates MDAL and evaluate the results of these changes through comparison against the pre-update, currently operational MDAL product, as well as another satellite based albedo product, ETAL, and data based on in-situ measurements. We find that the update provides a valuable improvement.