Modeling Of Mean Tropospheric Temperature From Convolutional Neural Networks

The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling's been traditionally performed using Multiple Linea...

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Bibliographic Details
Main Authors: Brum, Diego, Rofatto, Vinicius Francisco, Scalco, Leonardo, de Oliveira Pena, Rafaela, Gonzaga, Luiz, Zanotta, Daniel C., Fernando Sapucci, Luiz, Veronez, Mauricio Roberto
Format: Conference Proceeding
Language:English
Subjects:
Codes
Convolutional Neural Networks
Data models
Deep learning
Delays
Geoscience and remote sensing
Linear regression
Mean Tropospheric Temperature
Measurement
Precipitable Water Vapor
Radiosonde
Zenith Tropospheric Delay
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Summary:The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling's been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.
ISSN:2153-7003
DOI:10.1109/IGARSS52108.2023.10282413