{
  "id": "1910.06789",
  "version": "v1",
  "published": "2019-10-14T17:35:08.000Z",
  "updated": "2019-10-14T17:35:08.000Z",
  "title": "Deep learning for Aerosol Forecasting",
  "authors": [
    "Caleb Hoyne",
    "S. Karthik Mukkavilli",
    "David Meger"
  ],
  "comment": "Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada",
  "categories": [
    "cs.LG",
    "cs.CV",
    "physics.ao-ph",
    "physics.data-an",
    "stat.ML"
  ],
  "abstract": "Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-10-14T17:35:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "aerosol forecasting",
      "aeronet ground truth",
      "convolutional neural network",
      "aod prediction model"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}