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Deep Learning for Daily Precipitation and Temperature Downscaling

Published:2021-04 Issue:4 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Res

Author:

Wang Fang¹,Tian Di¹^ORCID,Lowe Lisa²,Kalin Latif³,Lehrter John⁴

Affiliation:

1. Department of Crop, Soil, and Environmental Sciences Auburn University Auburn AL USA

2. Office of Information Technology North Carolina State University Raleigh NC USA

3. School of Forestry and Wildlife Sciences Auburn University Auburn AL USA

4. Department of Marine Sciences Dauphin Island Sea Lab University of South Alabama Mobile AL USA

Funder

National Oceanic and Atmospheric Administration

National Institute of Food and Agriculture

Alabama Agricultural Experiment Station

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029308

Reference58 articles.

1. Development of gridded surface meteorological data for ecological applications and modelling

2. A comparison of statistical downscaling methods suited for wildfire applications

3. Downscaling Extremes—An Intercomparison of Multiple Statistical Methods for Present Climate

4. Configuration and intercomparison of deep learning neural models for statistical downscaling

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2. Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain;Atmosphere;2024-09-07

3. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning;Climate Dynamics;2024-09-04

4. Deep neural network based on dynamic attention and layer attention for meteorological data downscaling;ISPRS Journal of Photogrammetry and Remote Sensing;2024-09

5. Quantifying spatially explicit uncertainty in empirically downscaled climate data;International Journal of Climatology;2024-08-22

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