PreciPatch: A dictionary-based precipitation downscaling method

Mengchao Xu, Qian Liu, Dexuan Sha, Manzhu Yu, Daniel Q. Duffy, William M. Putman, Mark Carroll, Tsengdar Lee, Chaowei Yang

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Abstract

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 shortduration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).

Original languageEnglish (US)
Article number1030
JournalRemote Sensing
Volume12
Issue number6
DOIs
StatePublished - Mar 1 2020

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All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

Cite this

Xu, M., Liu, Q., Sha, D., Yu, M., Duffy, D. Q., Putman, W. M., Carroll, M., Lee, T., & Yang, C. (2020). PreciPatch: A dictionary-based precipitation downscaling method. Remote Sensing, 12(6), [1030]. https://doi.org/10.3390/rs12061030