European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11269-021-03042-8.pdf
Reference32 articles.
1. Ababaei B (2020) Spatio-temporal variations of seven weather variables in iran: application of cru ts and gpcc data sets. Irrig Drain 69(1):164–185
2. Aksoy H, Dahamsheh A (2018) Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions. J Hydrol 562:758–779
3. Azimi S, Moghaddam MA (2020) Modeling short term rainfall forecast using neural networks, and gaussian process classification based on the spi drought index. Water Resour Manage 1–37
4. Barnston AG, Tippett MK (2017) Do statistical pattern corrections improve seasonal climate predictions in the north american multimodel ensemble models? J Clim 30(20):8335–8355
5. Campozano L, Tenelanda D, Sanchez E, Samaniego E, Feyen J (2016) Comparison of statistical downscaling methods for monthly total precipitation: case study for the paute river basin in southern ecuador. Advances in Meteorology 2016
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