wdc-climate.de:5282855 eng; USA collection ClimAVA-NW Sajad Khoshnood Motlagh Utah State University https://www.usu.edu/ pointOfContact 2025-01-16T11:29:49 ISO 19115 ISO 19115-2:2009 Climate data for adaptation and vulnerability assessments — northwest 2025-02-05 publication 1 ClimAVA-NW doi:10.26050/WDCC/ClimAVA-NW Sajad Khoshnood Motlagh Utah State University https://www.usu.edu/ Email: sajad.khoshnoodmotlagh@usu.edu principalInvestigator Sajad Khoshnood Motlagh Utah State University https://www.usu.edu/ Email: sajad.khoshnoodmotlagh@usu.edu author Dr. Andre Geraldo de Lima Moraes Utah State University https://www.usu.edu/ Email: andre.moraes@usu.edu author WDCC publisher Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) 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-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. 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. Sajad Khoshnood Motlagh Utah State University https://www.usu.edu/ pointOfContact NetCDF CMIP6 Northwest United States downscale machine learning CC-BY-4.0: Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ eng; USA -124.7917 -104.0417 40.9583 49.0417 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 486609 https://www.wdc-climate.de/ui/entry?acronym=ClimAVA-NW information collection