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wdc-climate.de:5280925 eng; USA collection ClimAVA-SW Dr. Andre Geraldo de Lima Moraes Utah State University https://www.usu.edu/ pointOfContact 2024-07-17T11:24:11 ISO 19115 ISO 19115-2:2009 Climate data for adaptation and vulnerability assessments — southwest 2024-07-26 publication 1 ClimAVA-SW doi:10.26050/WDCC/ClimAVA-SW Dr. Andre Geraldo de Lima Moraes Utah State University https://www.usu.edu/ Email: andre.moraes@usu.edu principalInvestigator Dr. Andre Geraldo de Lima Moraes Utah State University https://www.usu.edu/ Email: andre.moraes@usu.edu author Sajad Khoshnood Motlagh Utah State University https://www.usu.edu/ Email: sajad.khoshnoodmotlagh@usu.edu author WDCC publisher Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events. Dr. Andre Geraldo de Lima Moraes Utah State University https://www.usu.edu/ pointOfContact NetCDF CMIP6 Southwest United States downscale machine learning CC-BY-4.0: Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ eng; USA -124.4583 -102.0833 31.25 43.29 365_day 1981-01-01 2100-12-31 info:eu-repo/grantAgreement/UAES//UTA01726/US//UAES grants air_temperature-atNearSurface (minimum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (minimum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: degC air_temperature-atNearSurface (maximum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (maximum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: degC air_temperature-atNearSurface (maximum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (maximum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: degC precipitation_flux In accordance with common usage in geophysical disciplines, "flux" implies per unit area, called "flux density" in physics. [CF-Standard Name]; unit: mm precipitation_flux In accordance with common usage in geophysical disciplines, "flux" implies per unit area, called "flux density" in physics. [CF-Standard Name]; unit: mm air_temperature-atNearSurface (minimum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (minimum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: degC NetCDF 605115 https://www.wdc-climate.de/ui/entry?acronym=ClimAVA-SW information collection n/a None n/a None