Deep Learning for Daily 10-m Wind Downscaling-Reference-Cited by-同舟云学术

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Deep Learning for Daily 10-m Wind Downscaling

Published:2024-06-12 Issue: Volume: Page:146-150
ISSN:
Container-title:2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII)
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Author:

Fu Kaixiang¹,Ding Shuyan²,Zhang Linbo¹,Pan Mengting²,Zhi Xiefei²,Zhang Jie¹

Affiliation:

1. Yunnan Electric Dispatching and Control Center,Kunming,China

2. Nanjing University of Information Science and Technology,Key Laboratory of Meteorology Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD),Nanjing,China

Funder

China Southern Power Grid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10659854/10659821/10660071.pdf?arnumber=10660071

Reference11 articles.

1. Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part I: Daily Maximum and Minimum 2-m Temperature

2. Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

3. Downscaling general circulation model output: a review of methods and limitations

4. An attention mechanism based convolutional network for satellite precipitation downscaling over China

5. Deep Learning for Daily Precipitation and Temperature Downscaling

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