- xwiki:Main.WebHome
- Phase2
- Machine Learning
Changes for document Machine Learning
From version 58.1
edited by s2s_wiki
on 2020/04/09 18:59
on 2020/04/09 18:59
To version 59.1
edited by s2s_wiki
on 2020/04/09 19:00
on 2020/04/09 19:00
Change comment: There is no comment for this version
Content changes
| ... | ... | @@ -73,6 +73,4 @@ |
| 73 | 73 | (% style="background-color:#e9e9e9" %) |
| 74 | 74 | |=(% style="font-weight: normal;" %)//8. National Center of Scientific Research “Demokritos” (NCSRD) (From Dr. Athanasios Sfetsos. Updated on April 9, 2020)// |
| 75 | 75 | Generic Title: implementing a Deep Learning approach for spatial and time error correction of S2S simulation data over Greece |
| 76 | -The current work of National Center of Scientific Research “Demokritos” (NCSRD) with respect to Machine Learning (ML) and Seasonal to Subseasonal (S2S) is based on the temporal and spatial enhancement of S2S predictions with Deep Learning approaches. | |
| 77 | -More specifically, NCSRD locally produces a S2S prediction for Greece (at very high spatial resolution of 5x5 km2) downscaled from a European wide (at 20x20 km2 grid resolution) domain. The simulations are forced by the Climate Forecast System (CFS) model from the National Centers for Environmental Prediction (NCEP) in addition to existing datasets from the S2S database. | |
| 78 | -In order to effectively correct the error of the simulation result, a deep learning approach is tested based on a combination of Convolutional neural networks (CNN) and Recurrent Neural Network (RNN) architectures concerning the space and time domains respectively, focusing on Greece, thus enhancing the accuracy and predictability of longer S2S simulations. | |
| 76 | +The current work of National Center of Scientific Research “Demokritos” (NCSRD) with respect to Machine Learning (ML) and Seasonal to Subseasonal (S2S) is based on the temporal and spatial enhancement of S2S predictions with Deep Learning approaches. More specifically, NCSRD locally produces a S2S prediction for Greece (at very high spatial resolution of 5x5 km2) downscaled from a European wide (at 20x20 km2 grid resolution) domain. The simulations are forced by the Climate Forecast System (CFS) model from the National Centers for Environmental Prediction (NCEP) in addition to existing datasets from the S2S database. In order to effectively correct the error of the simulation result, a deep learning approach is tested based on a combination of Convolutional neural networks (CNN) and Recurrent Neural Network (RNN) architectures concerning the space and time domains respectively, focusing on Greece, thus enhancing the accuracy and predictability of longer S2S simulations. |