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 short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).
Highlights
The studies of local climate impact are critical for environmental management [1], including applications such as water resources, ecosystem services, and agricultural productivity [2,3]
Two images from different time slices are selected to show a brief view of the general performance of the bicubic method, DeepSD, RainFARM, and PreciPatch
A new precipitation downscaling method based on dynamic dictionary learning is proposed with detailed algorithms and procedures
Summary
The studies of local climate impact are critical for environmental management [1], including applications such as water resources, ecosystem services, and agricultural productivity [2,3]. Global Climate Models, known as general circulation models or GCMs [4], are our primary tools for simulating future climatological variables such as temperature, wind, and precipitation [5]. Downscaling the low-resolution climate variables is used as a primary method to obtain high-resolution information [9]. Regional Climate Models (RCMs) are nested in GCMs to produce higher resolution outputs in a dynamic downscaling fashion with limitations of computing-intensive and small study areas. Statistical downscaling methods are developed to enhance GCM outputs’ resolution directly to generate high-resolution results at low cost and with high efficiency
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
