On the use of convolutional Gaussian processes to improve the seasonal forecasting of precipitation and temperature

Author:

Wang ChaoORCID,Zhang WeiORCID,Villarini GabrieleORCID

Funder

U.S. Army Corps of Engineers

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference46 articles.

1. Special issue: NMME;Archambault;Clim. Dyn.,2019

2. Predictability and forecast skill in NMME;Becker;J. Clim.,2014

3. Boyle, P., Frean, M., 2005. Dependent Gaussian Processes. 217--224.

4. Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia;Cash;Clim. Dyn.,2019

5. S2S reboot: an argument for greater inclusion of machine learning in subseasonal to seasonal forecasts;Cohen;Wiley Interdisciplinary Reviews-Climate Change,2019

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