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).
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This content will become publicly available on October 6, 2024
A seamless approach for evaluating climate models across spatial scales
In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.
more » « less- NSF-PAR ID:
- 10497881
- Publisher / Repository:
- frontiers
- Date Published:
- Journal Name:
- Frontiers in Earth Science
- Volume:
- 11
- ISSN:
- 2296-6463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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