S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts-Reference-Cited by-同舟云学术

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

Author:

Cohen Judah¹²,Coumou Dim³⁴,Hwang Jessica⁵,Mackey Lester⁶,Orenstein Paulo⁵,Totz Sonja⁴,Tziperman Eli⁷

Affiliation:

1. Atmospheric and Environmental Research, Inc. Lexington Massachusetts

2. Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge Massachusetts

3. Department of Water and Climate Risk, Institute for Environmental Studies VU University Amsterdam Amsterdam Netherlands

4. Potsdam Institute for Climate Impact Research Potsdam Germany

5. Stanford University Stanford California

6. Microsoft Research New England Cambridge Massachusetts

7. Department of Earth and Planetary Sciences, School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts

Funder

Directorate for Geosciences

Publisher

Wiley

Subject

Atmospheric Science,Geography, Planning and Development,Global and Planetary Change

Link

Reference58 articles.

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