Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent

Abstract. Multi-layer aerosol optical depth (AOD) estimation with sufficient spatial and temporal resolution is crucial for effective aerosol monitoring, given the significant variations over time and space. While ground-based observations provide detailed vertical profiles, satellite data are essential for addressing the spatial and temporal gaps. This study utilizes profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate vertical AOD values at 1.5, 3, 5, and 10 km layers. These estimations are achieved with spatial and temporal resolutions of 3 km × 3 km and 15 min, respectively, over the European troposphere. We employed machine learning models – XGBoost (XGB) and random forest (RF) – trained on SEVIRI data from 2017 to 2018 for the estimations. Validation using CALIOP AOD retrievals in 2019 confirmed the reliability of our findings, emphasizing the importance of wind speed (Ws) and wind direction (Wd) in improving AOD estimation accuracy. A comparison between seasonal and annual models revealed slight variations in accuracy, leading to the selection of annual models as the preferred approach for estimating SEVIRI multi-layer AOD values. Among the annual models, the XGB model demonstrated superior performance over the RF model at all four layers, yielding more reliable AOD estimations with R2 values of 0.99, 0.97, 0.98, and 0.98 for the four layers from low- to high-altitude layers. Further validation using data from European Aerosol Research Lidar Network (EARLINET) stations across Europe in 2020 indicated that the XGB model still achieved better agreement with EARLINET AOD profiles, with R2 values of 0.86, 0.80, 0.75, and 0.59 and RMSE values of 0.022, 0.012, 0.015, and 0.005. We performed a qualitative validation of multi-layer AOD estimations by comparing spatial trends with CALIOP AOD retrievals for SEVIRI pixels on four dates in 2019, showing strong agreement across varying AOD levels. Additionally, the model successfully estimated AOD at 15 min intervals for two real events – a Saharan dust plume and the Mount Etna eruption – revealing consistent physical characteristics, including long-range transport in the upper layers and a gradual increase in AOD from lower to higher tropospheric layers during volcanic events. The results demonstrate that the proposed method facilitates comprehensive monitoring of AOD behavior throughout the four vertical layers of the troposphere, offering important insights into the dynamics of aerosol occurrence.

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Publication Details

Journal
Atmospheric measurement techniques
Published
2025-03-24
DOI
https://doi.org/10.5194/amt-18-1415-2025
Citations
3
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
3.32
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article

Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent

Mehran Satari, Maryam Pashayi, Mehdi Momeni Shahraki
3 citations
Atmospheric measurement techniques
Atmospheric aerosols and clouds
3.32
article

Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent

Mehran Satari, Maryam Pashayi, Mehdi Momeni Shahraki
article en
3 citations

Abstract

Abstract. Multi-layer aerosol optical depth (AOD) estimation with sufficient spatial and temporal resolution is crucial for effective aerosol monitoring, given the significant variations over time and space. While ground-based observations provide detailed vertical profiles, satellite data are essential for addressing the spatial and temporal gaps. This study utilizes profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate vertical AOD values at 1.5, 3, 5, and 10 km layers. These estimations are achieved with spatial and temporal resolutions of 3 km × 3 km and 15 min, respectively, over the European troposphere. We employed machine learning models – XGBoost (XGB) and random forest (RF) – trained on SEVIRI data from 2017 to 2018 for the estimations. Validation using CALIOP AOD retrievals in 2019 confirmed the reliability of our findings, emphasizing the importance of wind speed (Ws) and wind direction (Wd) in improving AOD estimation accuracy. A comparison between seasonal and annual models revealed slight variations in accuracy, leading to the selection of annual models as the preferred approach for estimating SEVIRI multi-layer AOD values. Among the annual models, the XGB model demonstrated superior performance over the RF model at all four layers, yielding more reliable AOD estimations with R2 values of 0.99, 0.97, 0.98, and 0.98 for the four layers from low- to high-altitude layers. Further validation using data from European Aerosol Research Lidar Network (EARLINET) stations across Europe in 2020 indicated that the XGB model still achieved better agreement with EARLINET AOD profiles, with R2 values of 0.86, 0.80, 0.75, and 0.59 and RMSE values of 0.022, 0.012, 0.015, and 0.005. We performed a qualitative validation of multi-layer AOD estimations by comparing spatial trends with CALIOP AOD retrievals for SEVIRI pixels on four dates in 2019, showing strong agreement across varying AOD levels. Additionally, the model successfully estimated AOD at 15 min intervals for two real events – a Saharan dust plume and the Mount Etna eruption – revealing consistent physical characteristics, including long-range transport in the upper layers and a gradual increase in AOD from lower to higher tropospheric layers during volcanic events. The results demonstrate that the proposed method facilitates comprehensive monitoring of AOD behavior throughout the four vertical layers of the troposphere, offering important insights into the dynamics of aerosol occurrence.

Atmospheric measurement techniquesVol. 18(6)
University of Isfahan (IR)
Openalex Percentile: Top 9%
Atmospheric aerosols and clouds
3.32
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