Published June 19, 2024 | Version v1
Dataset Open

Addressing Observational Gaps in Aerosol Parameters using Machine Learning: Implications to Aerosol Radiative Forcing

Authors/Creators

  • 1. ROR icon Ahmedabad University
  • 2. ROR icon Physical Research Laboratory
  • 3. ROR icon Indian Space Research Organisation

Description

This dataset represents Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Absorption Parameter (AP) data over Kanpur, India, sourced from AERONET with initial data gaps of approximately 37%, 62%, and 58% respectively. To reduce these gaps, XGBoost, a machine learning model trained with reanalysis and satellite datasets, was employed with optimized hyperparameter tuning. Using AERONET data for training, XGBoost effectively addressed gaps, improving AOD by 10%, SSA by 23%, and AP by 21%.

Files

Gap_filled_dataset.csv

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