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Changes for document Machine Learning
From version 52.1
edited by S2S_mchnlearn
on 2020/04/04 08:45
on 2020/04/04 08:45
To version 53.1
edited by S2S_mchnlearn
on 2020/04/04 08:46
on 2020/04/04 08:46
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Content changes
| ... | ... | @@ -59,6 +59,7 @@ |
| 59 | 59 | |
| 60 | 60 | (% style="background-color:#e9e9e9" %) |
| 61 | 61 | |=(% style="font-weight: normal;" %)//6. Climate Prediction Center, NOAA/NWS/NCEP (From Dr. Yun Fan with Dr. Jon Gottschalck. Updated on April 4, 2020)// |
| 62 | -Benefiting from great advances in the machine learning techniques in recent years, such as more flexible and capable machine learning algorithms and availability of big dataset, we designed a more beneficial neural network setups which enable us not only to explore nonlinear impacts from big data, but also extract //more sophisticated pattern and co-variabilities relationships hidden behind the multiple dimensional predictors and predictands.// Then these learned more complicate relationships and high level statistical information are used to correct the original bias corrected NOAA NCEP Climate Forecast System(CFSv2) Week 34 precipitation and 2 meter temperature forecasts. The results show that to some extent neural network techniques can clearly improve the Week 34 forecast accuracy and greatly increase the efficiency over the traditional pointwise multiple linear regression methods. The following link has our NN short paper (on page 59-63: [[https:~~/~~/www.nws.noaa.gov/ost/climate/STIP/43CDPW/43CDPW_Digest.pdf>>url:https://www.nws.noaa.gov/ost/climate/STIP/43CDPW/43CDPW_Digest.pdf]]). The dataset currently used is the NOAA NCEP CFSv2. In the near future, we will work on the NCEP GEFS, ECMWF, CMC etc real-time data sets available here in the NOAA CPC. | |
| 62 | +Benefiting from great advances in the machine learning techniques in recent years, such as more flexible and capable machine learning algorithms and availability of big dataset, we designed a more beneficial neural network setups which enable us not only to explore nonlinear impacts from big data, but also extract //more sophisticated pattern and co-variabilities relationships hidden behind the multiple dimensional predictors and predictands.// Then these learned more complicate relationships and high level statistical information are used to correct the original bias corrected NOAA NCEP Climate Forecast System(CFSv2) Week 34 precipitation and 2 meter temperature forecasts. The results show that to some extent neural network techniques can clearly improve the Week 34 forecast accuracy and greatly increase the efficiency over the traditional pointwise multiple linear regression methods. The dataset currently used is the NOAA NCEP CFSv2. In the near future, we will work on the NCEP GEFS, ECMWF, CMC etc real-time data sets available here in the NOAA CPC. | |
| 63 | +The following link has our NN short paper (on page 59-63: [[https:~~/~~/www.nws.noaa.gov/ost/climate/STIP/43CDPW/43CDPW_Digest.pdf>>url:https://www.nws.noaa.gov/ost/climate/STIP/43CDPW/43CDPW_Digest.pdf]]). | |
| 63 | 63 | A paper submitted to the AMS Journal: WAF (under revision): |
| 64 | 64 | 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. |