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Detalhes Referência

Tipo
Capítulo em Livro


Título
Machine Learn Estimates of Downward Surface Long-wave Fluxes (DSLF) Based on Reanalysis and Satellite Observations.

Participantes na publicação
Lopes, F.M. (Author)
Dutra, E. (Author)
Trigo, I. (Author)

Resumo
A machine learning approach based on multivariate adaptive regression splines (MARS) is explored to integrate reanalysis data, satellite cloud information and ground observations of Downward Surface Long-wave Radiation Fluxes (DSLF), to estimate hourly DSLF for all-sky conditions. The MARS estimates are shown to have lower errors than other models when tested against 23 stations (BSRN/ARM), outperforming other DSLF estimates, including the current LSA-SAF operational roduct. In this work, the proposed methodology is shown to be consistent when new validation is performed, particularly with an independent network of 52 stations (FLUXNET2015). Further assessment of MARS estimates for the whole MSG disk is carried out, showing the potential of the model for operational purposes.

Data de Submissão/Pedido
2022-04-18
Data de Aceitação
2022-05-10
Data de Publicação
2022-06-27

Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Suporte
CITAB

Identificadores da Publicação
ISBN - 9789897045288

Edição
1

Identificadores do Documento
ISBN - 978-989-704-528-8

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Exportar referência

APA
Lopes, F.M., Dutra, E., Trigo, I., (2022). Machine Learn Estimates of Downward Surface Long-wave Fluxes (DSLF) Based on Reanalysis and Satellite Observations.. CITAB, -

IEEE
Lopes, F.M., Dutra, E., Trigo, I., "Machine Learn Estimates of Downward Surface Long-wave Fluxes (DSLF) Based on Reanalysis and Satellite Observations." in CITAB, 2022, pp. -

BIBTEX
@incollection{59459, author = {Lopes, F.M. and Dutra, E. and Trigo, I.}, title = {Machine Learn Estimates of Downward Surface Long-wave Fluxes (DSLF) Based on Reanalysis and Satellite Observations.}, booktitle = {CITAB}, year = 2022, pages = {-}, address = {}, publisher = {} }