基于机器学习从辐照度测量结果中检索臭氧柱总量和云光学深度

IF 2.5 4区地球科学 Q3 ENVIRONMENTAL SCIENCES

Atmosphere Pub Date : 2024-09-11 DOI:10.3390/atmos15091103

Milos Sztipanov, Levente Krizsán, Wei Li, Jakob J. Stamnes, Tove Svendby, Knut Stamnes

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引用次数: 0

摘要

机器学习算法与 NILU-UV 辐照度测量仪的测量结果相结合，可以确定臭氧柱总量（TOC）和云光学深度（COD）。在纽约地区，斯蒂文斯理工学院大楼（北纬 40.74°，东经 -74.03°）屋顶上的 NILU-UV 仪器已用于收集数据数年。受先前研究[Opt. Express 22, 19595 (2014)]的启发，本研究提出了一种基于神经网络的最新 TOC 和 COD 检索方法。该方法可在云量较多的条件下提供可靠的结果，并为同时检索 TOC 和 COD 值提供了一种便捷的算法。提供了 2014-2023 年的总有机碳值，并将两者与使用查找表（LUT）方法获得的结果以及美国宇航局 AURA 卫星上部署的臭氧监测仪器（OMI）的测量结果进行了比较。还提供了化学需氧量结果。

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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 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.

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来源期刊

Atmosphere METEOROLOGY & ATMOSPHERIC SCIENCES-

CiteScore

4.60

自引率

13.80%

发文量

1769

审稿时长

1 months

期刊介绍： Atmosphere (ISSN 2073-4433) is an international and cross-disciplinary scholarly journal of scientific studies related to the atmosphere. It publishes reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.