Machine Learning-Based Retrieval of Total Ozone Column Amount and Cloud Optical Depth from Irradiance Measurements

Details

• Journal Title:
  Atmosphere
• Personal Author:
  Sztipanov, Milos ; Krizsán, Levente ; Li, Wei ; Stamnes, Jakob J. ; Svendby, Tove ; Stamnes, Knut
• NOAA Program & Office:
  NOAA General Documents
• Description:
  A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74° N, −74.03° E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014–2023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA’s AURA satellite. COD results are also provided.
• Source:
  Atmosphere, 15(9), 1103
• DOI:
  https://doi.org/10.3390/atmos15091103
• ISSN:
  2073-4433
• Format:
  pdf
• Publisher:
  MDPI AG
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:7ec3cbec069b442eafb7a759c53519b9dcb6418b36d443bb003ae34091a2b2281f137f2ce0d41982e3ec2f7fc05f63579a7fb76a219bd13bd1c70a903d8e57b6
• Download URL:
  https://repository.library.noaa.gov/view/noaa/69603/noaa_69603_DS1.pdf
• File Type:
  [PDF - 1.09 MB ]