S2S reboot: An argument for greater inclusion of machine learning in 
subseasonal to seasonal forecasts

Cohen, J.; Coumou, Dim; Hwang, J.; Mackey, L.; Orenstein, P.; Totz, S.; Tziperman, E.

doi:10.1002/wcc.567

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S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts

Authors

Cohen, J.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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Coumou, Dim
Potsdam Institute for Climate Impact Research;

Hwang, J.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Mackey, L.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Orenstein, P.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Totz, S.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Tziperman, E.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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Cohen, J., Coumou, D., Hwang, J., Mackey, L., Orenstein, P., Totz, S., Tziperman, E. (2019): S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts. - Wiley Interdisciplinary Reviews: Climate Change, 10, 2, Art. e00567.
https://doi.org/10.1002/wcc.567

Cite as: https://publications.pik-potsdam.de/pubman/item/item_23751

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