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

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

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;

/persons/resource/coumou

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;

URL

There are no locators available

フルテキスト (公開)

8864oa.pdf
(全文テキスト（全般）), 5MB

付随資料 (公開)

There is no public supplementary material available

引用

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). doi:10.1002/wcc.567.

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

要旨

要旨はありません