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

A machine learning algorithm combined with measurements obtained by the Norwegian Institute for Air Research (NILU-UV) irradiance meter enables the determination of total ozone column (TOC) amount, radiation modification factor (RMF), and cloud optical depth (COD). Operating in the New York City area, a NILU-UV instrument on the rooftop of Stevens Institute of Technology (40.74ºN, -74.03ºE) 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 retrieval. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for simultaneous retrieval of TOC and COD. The TOC amounts are presented for 2014-2023, and compared with results obtained by the look-up table method (LUT) and from the Ozone Monitoring Instrument (OMI), deployed on NASA's AURA satellite. COD results are also provided.