New mean tropospheric temperature models based on machine learning algorithms for Brazil-Reference-Cited by-同舟云学术

New mean tropospheric temperature models based on machine learning algorithms for Brazil

Published:2024-04-08 Issue:8 Volume:45 Page:2651-2673
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Brum Diego¹^ORCID,Rofatto Vinicius Francisco²³^ORCID,Scalco Leonardo¹^ORCID,Gonzaga Luiz¹^ORCID,de Oliveira Pena Rafaela²³,Schroeder Lucas¹^ORCID,Fernando Sapucci Luiz⁴^ORCID,Roberto Veronez Mauricio¹^ORCID

Affiliation:

1. VIZLAB—X-Reality and Geoinformatics Laboratory, Graduate Program in Applied Computing, Vale do Rio dos Sinos University, São Leopoldo, RS, Brazil

2. Institute of Geography, Federal University of Uberlandia – Monte Carmelo, Uberlandia, MG, Brazil

3. Graduate Program in Agriculture and Geospatial Information, Federal University of Uberlandia – Monte Carmelo, Uberlandia, MG, Brazil

4. Center for Weather Forecasting and Climate Studies, Brazilian Institute of Space Research (INPE) - Cachoeira Paulista, Cachoeira Paulista, SP, Brazil

Funder

Agência Nacional do Petróleo, Gás Natural e Biocombustíveis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2334197

Reference16 articles.

1. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system

2. Validation of precipitable water from ECMWF model analyses with GPS and radiosonde data during the MAP SOP

3. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

4. A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM

5. Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length