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Changes for document Machine Learning
From version 56.1
edited by s2s_wiki
on 2020/04/06 19:14
on 2020/04/06 19:14
To version 57.1
edited by s2s_wiki
on 2020/04/06 19:15
on 2020/04/06 19:15
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Content changes
| ... | ... | @@ -65,7 +65,7 @@ |
| 65 | 65 | Yun Fan, Vladimir Krasnopolsky, Huug van den Dool, Chung-Yu Wu and Jon Gottschalck; 2020: Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and 2 Meter Air Temperature Forecasts. |
| 66 | 66 | |
| 67 | 67 | (% style="background-color:#f9f9f9" %) |
| 68 | -|=(% style="font-weight: normal;" %)//7. Royal Dutch Meteorological Institute (KNMI) and the Institute for Environmental Studies at the Vrije Universiteit Amsterdam (IVM) (From Chiem van Straaten Updated on April 6, 2020)// | |
| 68 | +|=(% style="font-weight: normal;" %)//7. Royal Dutch Meteorological Institute (KNMI) and the Institute for Environmental Studies at the Vrije Universiteit Amsterdam (IVM) (From Chiem van Straaten. Updated on April 6, 2020)// | |
| 69 | 69 | At the Royal Dutch Meteorological Institute (KNMI) and the Institute for Environmental Studies at the Vrije Universiteit Amsterdam (IVM) we run a research project called ‘Improvement of sub-seasonal probabilistic forecasts of European high-impact weather events using machine learning techniques’. The project uses ML for post-processing and diagnostics (mainly dimension reduction and learning connections). |
| 70 | 70 | We evaluate whether probabilistic forecasts at the sub-seasonal timescale contain skill for surface variables in Europe (e.g. 2-meter temperature), and how this depends on scale, location and extremity. Then, for events in which some predictability is found (for hot extremes predictability is expected), we try to find their physical precursors in other variables. Ridge regression and unsupervised clustering are used for dimension reduction in SST’s, geopotential height and more. |
| 71 | 71 | Lastly, we combine the information on observed driving factors with information on shortcomings of the ensemble prediction systems (e.g. propagation of waves from the tropics to the mid-latitudes) to post-process the forecasts. We have experience with RF’s and CNN’s for post-processing at shorter timescales. Regarding data: forecast evaluation was done on ECMWF cycle 45r1, precursors are currently searched in ERA5, and we might apply our post-processing to the EPS’s in the S2S database. |