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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Sunspot cycle prediction using Warped Gaussian process regression

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Autor(es):
Goncalves, Italo G. [1] ; Echer, Ezequiel [2] ; Frigo, Everton [3, 1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Fed Pampa, Geophys Signals Anal Lab, Bage, RS - Brazil
[2] Inst Nacl Pesquisas Espaciais, Sao Jose Dos Campos - Brazil
[3] Univ Fed Rio Grande do Sul, Inst Geociencias, Porto Alegre, RS - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Advances in Space Research; v. 65, n. 1, p. 677-683, JAN 1 2020.
Citações Web of Science: 5
Resumo

Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0-35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 to 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 18/21657-1 - Estudo da variabilidade da atividade auroral em rádio da magnetosfera de Júpiter
Beneficiário:Ezequiel Echer
Modalidade de apoio: Auxílio à Pesquisa - Regular