利用机器学习方法分析毫米波长范围内的天体气候测量结果

IF 1.3 4区物理与天体物理 Q3 ASTRONOMY & ASTROPHYSICS

Astrophysical Bulletin Pub Date : 2024-07-31 DOI:10.1134/S1990341324700299

T. A. Khabarova, P. M. Zemlyanukha, E. M. Dombek, A. S. Marukhno, V. F. Vdovin

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引用次数: 0

摘要

摘要本文介绍了一种利用机器学习方法从辐射测量数据中估算可降水水汽的方法。本文介绍了对奇拉格（达吉斯坦）、特尔斯科尔峰（厄尔布鲁士地区）、巴达里观测站（布里亚特）和斯匹次卑尔根群岛地区降水水汽的研究结果。使用全球导航卫星系统、MERRA-2、水蒸气辐射计数据和基于 MIAP-2 微波辐射计数据的机器学习方法预测值，对 "巴达里 "地区的可降水水蒸气评估进行了比较分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Analysis of the Results of Astroclimate Measurements in the Millimeter Wavelength Range Using Machine Learning Methods

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Analysis of the Results of Astroclimate Measurements in the Millimeter Wavelength Range Using Machine Learning Methods

This paper presents a method for estimating precipitable water vapor from radiometric data using machine learning methods. The results of a study of precipitated water vapor for the territory of Chirag (Dagestan), Terskol peak (Elbrus region), Badary observatory (Buryatia) and the Spitsbergen archipelago are presented. A comparative analysis of the assessment of precipitable water vapor for the territory of ‘‘Badary’’ was carried out using GNSS, MERRA-2, water vapor radiometer data and predicting values using machine learning methods based on data from the MIAP-2 microwave radiometer.

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来源期刊

Astrophysical Bulletin 地学天文-天文与天体物理

CiteScore

2.00

自引率

33.30%

发文量

审稿时长

>12 weeks

期刊介绍： Astrophysical Bulletin is an international peer reviewed journal that publishes the results of original research in various areas of modern astronomy and astrophysics, including observational and theoretical astrophysics, physics of the Sun, radio astronomy, stellar astronomy, extragalactic astronomy, cosmology, and astronomy methods and instrumentation.