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

被引：0

作者：

Sztipanov, Milos ^{[1
,2
]}

Krizsan, Levente ^{[2
]}

Li, Wei ^{[2
]}

Stamnes, Jakob J. ^{[3
]}

Svendby, Tove ^{[4
]}

Stamnes, Knut ^{[2
]}

机构：

[1] NOAA, Lynker, 5830 Univ Res Ct, College Pk, MD 20740 USA

[2] Stevens Inst Technol, Dept Phys, 1 Castle Point Terrace, Hoboken, NJ 07030 USA

[3] Univ Bergen, Dept Phys & Technol, N-5007 Bergen, Norway

[4] NILU, Inst Veien 18, N-2007 Kjeller, Norway

来源：

ATMOSPHERE | 2024年 / 15卷 / 09期

基金：

美国海洋和大气管理局;

关键词：

neural network; machine learning; ozone; cloud optical depth; composition; retrieval; measurement; modeling; radiative transfer; irradiance; BANDWIDTH FILTER INSTRUMENT; MULTICHANNEL;

D O I：

10.3390/atmos15091103

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 degrees N, -74.03 degrees 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.

引用

页数：16

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