Antti Arola

About

Antti Arola is a distinguished atmospheric scientist whose research centers on aerosol remote sensing, satellite-based aerosol retrieval, and the radiative effects of atmospheric particulates. Working extensively with the AERONET sun-photometer network and satellite platforms such as MODIS and Sentinel-3, Arola has made foundational contributions to our understanding of how aerosols are measured, characterized, and monitored across the globe. Among his most impactful contributions is his work on merging multi-satellite aerosol optical depth (AOD) records to create consistent long-term climate datasets (213 citations), addressing the critical challenge of limited individual satellite lifetimes. His pioneering research on distinguishing absorbing aerosol species—including soot carbon, brown carbon, and iron-containing minerals—using AERONET retrievals (155 citations) has significantly advanced aerosol composition science. Arola has also illuminated the complex interactions between aerosols and clouds or fog, demonstrating how these phenomena modify aerosol optical properties (154 citations). His methodological innovations span Bayesian retrieval algorithms, neural network approaches, and deep learning post-processing techniques, reflecting a commitment to advancing retrieval accuracy. His work estimating brown carbon's direct radiative effect over the Indo-Gangetic Plain further highlights his contributions to understanding regional climate forcing. Collectively, his publications have garnered over 800 citations, establishing him as a leading voice in aerosol remote sensing science.

Research Focus

Key Achievements

Top Papers

1. 1
   Merging regional and global aerosol optical depth records from major available satellite products

   213 citations · 2020
2. 2
   Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species

   155 citations · 2016
3. 3
   Fog‐ and cloud‐induced aerosol modification observed by the Aerosol Robotic Network (AERONET)

   154 citations · 2012
4. 4
   Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method

   101 citations · 2020
5. 5
   Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing

   86 citations · 2018
6. 6
   Observations of rapid aerosol optical depth enhancements in the vicinity of polluted cumulus clouds

   78 citations · 2014
7. 7
   Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

   43 citations · 2018
8. 8
   Retrieval of aerosol optical depth from surface solar radiation measurementsusing machine learning algorithms, non-linear regression and a radiativetransfer-based look-up table

   34 citations · 2016
9. 9
   Direct radiative effect by brown carbon over the Indo-Gangetic Plain

   34 citations · 2015
10. 10
    Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product

    30 citations · 2022

Key Collaborators

Contact & Links