TY - JOUR
T1 - Consecutive one-week model predictions of land surface temperature stay on track for a decade with chaotic behavior tracking
AU - Ren, Jinfu
AU - Liu, Yang
AU - Liu, Jiming
N1 - This work was supported in part by the National Science and Technology Major Project under Grant No. 2021ZD0112500, in part by the General Research Fund from the Research Grant Council of Hong Kong SAR under Projects RGC/HKBU12201619, RGC/HKBU12202220, and RGC/HKBU12203122, and in part by the RGC Research Matching Grant Scheme (RMGS) for project "Artificial Intelligence and Big Data Analytics for Social Good".
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/10/25
Y1 - 2024/10/25
N2 - Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.
AB - Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85207825719&partnerID=8YFLogxK
UR - https://www.nature.com/articles/s43247-024-01801-0#Ack1
U2 - 10.1038/s43247-024-01801-0
DO - 10.1038/s43247-024-01801-0
M3 - Journal article
AN - SCOPUS:85207825719
SN - 2662-4435
VL - 5
JO - Communications Earth and Environment
JF - Communications Earth and Environment
IS - 1
M1 - 627
ER -