Artificial Neural Networks for Determining the Empirical Relationship between Meteorological Parameters and High-Level Cloud Characteristics

Author:

Kuchinskaia Olesia1,Penzin Maxim1,Bordulev Iurii1ORCID,Kostyukhin Vadim1,Bryukhanov Ilia123,Ni Evgeny12,Doroshkevich Anton12ORCID,Zhivotenyuk Ivan12,Volkov Sergei3,Samokhvalov Ignatii12

Affiliation:

1. Laboratory for Analysis of High Energy Physics Data, Faculty of Physics, National Research Tomsk State University, 634050 Tomsk, Russia

2. Department of Optoelectronic Systems and Remote Sensing, Faculty of Radiophysics, National Research Tomsk State University, 634050 Tomsk, Russia

3. Center of Laser Atmosphere Sensing, V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences, 634055 Tomsk, Russia

Abstract

The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do not take into account a number of atmospheric effects, in particular, the orientation of crystalline ice particles due to the simplified physical description of the medium, or within the framework of these models, accounting for such dependencies becomes a highly nontrivial task. Neural networks are able to take into account the complex interaction of meteorological parameters with each other, as well as reconstruct almost any dependence of the HLC characteristics on these parameters. In the process of prototyping the software product, the greatest difficulty was in determining the network architecture, the loss function, and the method of supplying the input parameters (attributes). Each of these problems affected the most important issue of neural networks—the overtraining problem, which occurs when the neural network stops summarizing data and starts to tune to them. Dependence on meteorological parameters was revealed for the following quantities: the altitude of the cloud center; elements m22 and m44 of the backscattering phase matrix (BSPM); and the m33 element of BSPM requires further investigation and expansion of the analyzed dataset. Significantly, the result is not affected by the compression method chosen to reduce the data dimensionality. In almost all cases, the random forest method gave a better result than a simple multilayer perceptron.

Funder

Russian Science Foundation

Publisher

MDPI AG

Reference23 articles.

1. Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios;EyvazOghli;Complexity,2022

2. Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model;Donnelly;Water Res.,2022

3. Deep learning-based uncertainty quantification of groundwater level predictions. Stoch. Environ;Nourani;Res. Risk Assess,2022

4. Exploring machine learning potential for climate change risk assessment;Zennaro;Earth-Sci. Rev.,2021

5. Prediction of groundwater level in Ardebil plain using support vector regression and M5 tree model;Sattari;Groundwater,2018

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3