Changes for document Machine Learning

From version 16.1
edited by S2S_mchnlearn
on 2020/04/02 15:27
To version 17.1
edited by S2S_mchnlearn
on 2020/04/02 22:55
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1 -Machine Learning / Artificial Intelligence for S2S prediction
1 += Machine Learning / Artificial Intelligence for S2S prediction =
2 2
3 3 There is currently a lot excitement in the weather and climate communities to explore the potential of data driven approaches based on Artificial Intelligence/Machine Learning/Deep learning for S2S prediction through, for instance, improved parameterization, improved calibration and multi-model calibration, extreme event attribution, verification... The publicly available S2S database which contains a considerable amount of data (re-forecasts and real-time forecasts from 11 operational centres) represents an ideal testbed for these data-driven methods. The SubX database provides another such opportunity ([[http:~~/~~/cola.gmu.edu/subx/index.html>>url:http://cola.gmu.edu/subx/index.html]]).
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5 -
6 6 Potential applications :
7 7 ~1. Improved data assimilation (e.g. better quality control of observations)
8 8 2. Improved parameterization (e.g. radiative schemes)
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11 11 5. S2S event attribution (e.g. origins of extreme events)
12 12 6. Empirical forecasts
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14 -http:~/~/enterprise.xwiki.org/xwiki/bin/view/UserGuide/
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14 +----
16 16
17 -===== **Researches on AI/ML for S2S prediction** =====
16 +===== **Ongoing Community Research on Machine Learning / Artificial Intelligence for S2S prediction** =====
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19 19 (% style="background-color:#f9f9f9" %)
20 20 |=(% style="font-weight: normal;" %)//1. Scripps Institute of Oceanography (From Dr. Peter Gibson. Updated April 1 2020)//
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27 27
28 28 (% style="font-weight: normal;" %)We are at the early stages of work developing a ML-based vegetation model which uses outputs of the Bureau's seasonal prediction ACCESS-S.The purpose of this work is twofold:
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30 - * (% style="font-weight: normal;" %)Investigate the possibility of making predictions of vegetation ​characteristics in the coming weeks and seasons using model outputs as predictors. Forecasts of vegetation could have potential use for a number of sectors including fire agencies and agriculture
29 +* (% style="font-weight: normal;" %)Investigate the possibility of making predictions of vegetation ​characteristics in the coming weeks and seasons using model outputs as predictors. Forecasts of vegetation could have potential use for a number of sectors including fire agencies and agriculture
31 31
32 - * (% style="font-weight: normal;" %)Attempt to use the vegetation model we develop to periodically update the vegetation ancillary file used in model runs. Currently ACCESS-S1 uses a static vegetation file. We plan to investigate what possible skill gains could be got from a more dynamic representation of vegetation, and then to try updating the vegetation ancillary of the model every N timesteps by passing it through our vegetation model, along with the latest model parameters.
31 +* (% style="font-weight: normal;" %)Attempt to use the vegetation model we develop to periodically update the vegetation ancillary file used in model runs. Currently ACCESS-S1 uses a static vegetation file. We plan to investigate what possible skill gains could be got from a more dynamic representation of vegetation, and then to try updating the vegetation ancillary of the model every N timesteps by passing it through our vegetation model, along with the latest model parameters.
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34 34 (% style="font-weight: normal;" %)We intend to start by trying an LSTM Neural Network for the vegetation model, potentially also including some convolutional layers. We will however be investigating what is most effective as we go. Initially we will be training using the ACCESS-S1 hindcast, however if a larger training set is needed we may investigate using a larger set to train, followed by transfer learning techniques to update the model to ACCESS-S.
35 35 )))

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