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Open AccessArticle

PreciPatch: A Dictionary-based Precipitation Downscaling Method

1
NSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USA
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Department of Geography and GeoInformation Science, George Mason Univ., Fairfax, VA 22030, USA
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Department of Geography, Pennsylvania State University, State College, PA 16801, USA
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NASA Center for Climate Simulation, Greenbelt, MD 20771, USA
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NASA Headquarters, Washington, DC 20546, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1030; https://doi.org/10.3390/rs12061030 (registering DOI)
Received: 11 February 2020 / Revised: 17 March 2020 / Accepted: 17 March 2020 / Published: 23 March 2020
(This article belongs to the Section Remote Sensing of the Water Cycle)
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018). View Full-Text
Keywords: GeoAI; natural disaster; flooding; global water cycle; spatiotemporal data analytics GeoAI; natural disaster; flooding; global water cycle; spatiotemporal data analytics
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MDPI and ACS Style

Xu, M.; Liu, Q.; Sha, D.; Yu, M.; Duffy, D.Q.; Putman, W.M.; Carroll, M.; Lee, T.; Yang, C. PreciPatch: A Dictionary-based Precipitation Downscaling Method. Remote Sens. 2020, 12, 1030.

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