Convergence Space Experiment: Retrieving the Water-Vapor Profile of the Atmosphere by Means of Artificial Neural Networks
| Autor: | E. V. Pashinov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Artificial neural network Humidity Inverse problem Oceanography Atmospheric temperature 01 natural sciences Atmosphere 0103 physical sciences Convergence (routing) Environmental science 010303 astronomy & astrophysics Physics::Atmospheric and Oceanic Physics Water vapor Microwave 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Izvestiya, Atmospheric and Oceanic Physics. 56:898-908 |
| ISSN: | 1555-628X 0001-4338 |
| DOI: | 10.1134/s0001433820090194 |
| Popis: | In this work, the possibility of retrieving the profile of absolute humidity of the atmosphere by means of an artificial neural network is investigated based on modeling radiometric data of the MIRS passive microwave complex, which is a part of the scientific equipment of the Convergence space experiment. The process of modeling MIRS radiometric data is described. Optimum characteristics of the neural network are selected. The necessity of information about the atmospheric temperature profile for the best accuracy in solving the inverse problem is shown. The advantages of using “differential” channels in the 22-GHz absorption band for retrieving the humidity profile are demonstrated. The expected errors in retrieving the atmospheric humidity profile in the course of the Convergence space experiment at heights from 0 to 10 km are presented. |
| Databáze: | OpenAIRE |
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