ArticleScientific data2023
Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- INDRA-CMIP6: Indian subcontinent high resolution Downscaled pRecipitation and Air temperature data from CMIP6.Scientific data · 2026Article
- Attention-Enhanced CNN-LSTM with Spatial Downscaling for Day-Ahead Photovoltaic Power Forecasting.Sensors (Basel, Switzerland) · 2026Article
- Spatially explicit and regionalized quantification of blue and green crop water footprints in a water-stressed river basin.Water science and technology : a journal of the International Association on Water Pollution Research · 2026Article
- Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature dataset (1950-2100).Scientific data · 2025Article
- CMIP6-driven 10 km super-resolution daily climate projections with PET estimates in China.Scientific data · 2025Article
- Predicting Atlantic and Benguela Niño events with deep learning.Science advances · 2025Article
- Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia.Scientific data · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
U-Net, a deep-learning convolutional neural network, is used to downscale coarse meteorological data. Based on 19 models from the Coupled Model Intercomparison Project Phase 6 and the Multi-Source Weather (MSWX) dataset, bias correction and UNet downscaling approaches are used to develop high resolution dataset over the East Asian region, referred to as Climate Change for East Asia with Bias corrected UNet Dataset (CLIMEA-BCUD). CLIMEA-BCUD provides nine meteorological variables including 2-m air temperature, 2-m daily maximum air temperature, 2-m daily minimum air temperature, precipitation, 10-m wind speed, 2-m relative humidity, 2-m specific humidity, downward shortwave radiation and downward longwave radiation with 0.1° horizontal resolution at daily intervals over the historical period of 1950-2014 and three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of 2015-2100. Validation against MSWX indicates that CLIMEA-BCUD shows reasonable performance in terms of climatology, and it is capable of simulating seasonal cycles and future changes well. It is suggested that CLIMEA-BCUD can promote the application of deep learning in climate research in the areas of climate change, hydrology, etc.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.