Changes for document Machine Learning

From version 46.1
edited by S2S_mchnlearn
on 2020/04/03 20:34
To version 47.1
edited by S2S_mchnlearn
on 2020/04/03 20:36
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Content changes

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16 16 ===== **Ongoing Community Research on Machine Learning / Artificial Intelligence for S2S prediction** =====
17 17
18 18 (% style="background-color:#f9f9f9" %)
19 -|=(% style="font-weight: normal;" %)//1. Scripps Institute of Oceanography (From Dr. Peter Gibson. Updated April 1, 2020)//
19 +|=(% style="font-weight: normal;" %)//1. Scripps Institute of Oceanography (From Dr. Peter Gibson. Updated on April 1, 2020)//
20 20 The project explores the potential for modern machine learning tools to improve seasonal prediction skill of precipitation over the Western US. Modern machine learning approaches are 'data hungry' while observations are data limited (relatively short in length for the purposes of seasonal forecasting). To circumvent this issue, we train a variety of machine learning tools on perturbed initial condition climate model ensembles that span several thousands of years, then use these 'learnt' teleconnections to make seasonal predictions. We are testing a hierarchy of machine learning approaches from simple to complex: simple logistic regression, LASSO, Random Forests, Gradient Boosted decision trees, and convolutional neural networks. This project is a collaboration between researchers at Scripps CW3E and JPL, and funded by the California Department of Water Resources.
21 21
22 22 |=(% style="background-color:#e9e9e9" %)(((
23 -(% style="font-weight: normal;" %)//2. Australian Bureau of Meteorology (From Catherine de Burgh-Day​ with Oscar Alves and Debbie Hudson. Updated April 2, 2020))//
23 +(% style="font-weight: normal;" %)//2. Australian Bureau of Meteorology (From Catherine de Burgh-Day​ with Oscar Alves and Debbie Hudson. Updated on April 2, 2020))//
24 24
25 25 (% 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:
26 26
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32 32 )))
33 33
34 34 |=(% style="background-color:#f9f9f9" %)
35 -(% style="font-weight: normal;" %)//3. APEC Climate Center (From Dr. Hyung Jin Kim with Dr. Uran Chung and Dr. Kyungwon Park. Updated April 3, 2020)//
35 +(% style="font-weight: normal;" %)//3. APEC Climate Center (From Dr. Hyung Jin Kim with Dr. Uran Chung and Dr. Kyungwon Park. Updated on April 3, 2020)//
36 36 Our project is to develop a deep learning ensemble technique to improve subseasonal forecast over the Korean Peninsula. Deep learning is now recognized as a technique to improve climate forecasting, especially subseasonal climate prediction; however there is a limit to the application of deep learning due to insufficiency in size of subseasonal forecast data to train and test for deep learning models. Therefore, we are testing ensemble techniques for constructing sufficient subseasonal prediction data of the Korean Peninsula from climate models, and developing the application of machine learning and various deep learning algorithms (e.g. SVM, RF, RNN, LSTM, and Convolution LSTM) to the multi-model-ensemble based-subseasonal prediction data, to improve the daily maximum and minimum temperatures, and precipitation of the Korean Peninsula.
37 37
38 38 (% style="background-color:#e9e9e9" %)
39 39 (((
40 -|=(% style="font-weight: normal;" %)//4. NOAA (ESRL/PSD) (From Dr. Michael Scheuerer. Updated April 3, 2020)//
40 +|=(% style="font-weight: normal;" %)//4. NOAA (ESRL/PSD) (From Dr. Michael Scheuerer. Updated on April 3, 2020)//
41 41 'Using artificial neural networks for generating probabilistic subseasonal precipitation forecasts over California'
42 42 Data:
43 43 We have NOT obtained our data from the publicly available S2S database mentioned in the email below. For this study, we have used
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54 54 )))
55 55
56 56 (% style="background-color:#f9f9f9" %)
57 -|=(% style="font-weight: normal;" %)//5. Colorado State University (From Professor Elizabeth A. Barnes. Updated April 3, 2020)//
57 +|=(% style="font-weight: normal;" %)//5. Colorado State University (From Professor Elizabeth A. Barnes. Updated on April 3, 2020)//
58 58 I have multiple members of my group using ML for S2S prediction. Specifically, we are focused on interpretable neural networks - so the goal is to not only make better empirical predictions, but to also understand where the predictability is coming from. We are also working on using ML to leverage climate model information to improve observational predictions.

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