Data headers for 'ClimAVA-SWE-MIE_s370'

Record

1

Generation date

2025-11-13

Method

ncdump -h

Header

 netcdf ClimAVA-SWE-MIE_s370_1_13084570864982690693 { 
 dimensions: 
 	longitude = 547 ; 
 	latitude = 426 ; 
 	time = 5475 ; 
 variables: 
 	double longitude(longitude) ; 
 		longitude:units = "degrees_east" ; 
 		longitude:long_name = "Longitude" ; 
 		longitude:standard_name = "longitude" ; 
 		longitude:axis = "X" ; 
 	double latitude(latitude) ; 
 		latitude:units = "degrees_north" ; 
 		latitude:long_name = "Latitude" ; 
 		latitude:standard_name = "latitude" ; 
 		latitude:axis = "Y" ; 
 	double time(time) ; 
 		time:units = "days since 20150101" ; 
 		time:long_name = "Time" ; 
 		time:standard_name = "time" ; 
 	double swe(time, latitude, longitude) ; 
 		swe:units = "kg m-2" ; 
 		swe:_FillValue = NaN ; 
 		swe:long_name = "Daily snow water equivalent" ; 
 		swe:standard_name = "surface_snow_amount" ; 
  
 // global attributes: 
 		:title = "Climate data for Adaptation and Vulnerability Assessments - Snow Water Equivalent (ClimAVA-SWE)" ; 
 		:Conventions = "CF-1.8" ; 
 		:version = "01" ; 
 		:creation_date = "2025-10-30 11:16:55.425844" ; 
 		:institution = "Utah State University, Department of Watershed Sciences" ; 
 		:source = "Downscaled model output using the Spatial Pattern Interaction Downscaling (SPID-SWE) method applied to CMIP6 GCM data." ; 
 		:address = "5210 Old Main Hill, NR 210, Logan, UT 84322" ; 
 		:creator = "Sajad Khoshnood Motlagh, Andre Geraldo de Lima Moraes, Kayla Smith" ; 
 		:contact = "
 sajad.khoshnoodmotlagh@nullusu.edu; 
 andre.moraes@nullusu.edu" ; 
 		:description = "The ClimAVA-SWE dataset provides high-resolution (4 km) bias-corrected, downscaled future climate projections based on 14 CMIP6 GCMs. The dataset includes the snow water equivalent (SWE) variable and three Shared Socioeconomic Pathways (SSP245, SSP370, SSP585) for the western United States. ClimAVA-SWE employs the Spatial Pattern Interaction Downscaling (SPID) method, which uses Random Forest models to capture relationships and interactions between coarse-resolution spatial patterns and fine-resolution pixel values. Two Random Forest models are trained per pixel—one for the accumulation period and one for the melting period—using high-resolution reference data as targets and nine neighboring pixels from spatially resampled (coarser) versions as predictors. These trained models are then applied to downscale the GCM data. For more details, refer to the publication describing the method and dataset." ; 
 		:lineage = "Reference SWE Data: We utilized the Daily Snow Water Equivalent, Version 1 dataset from the National Snow and Ice Data Center (NSIDC-0719) (https://nsidc.org/data/nsidc-0719/versions/1). This file contains data downscaled from model MIROC-ES2H, SSP ssp370, variant label r1i1p4f2, version x86_64-pc-linux-gnu." ; 
 		:license = "CC-BY-SA 4.0" ; 
 		:fees = "This dataset is free" ; 
 		:usage_notes = "This dataset is reliable for analyses focused on Annual Peak Snow Water Equivalent (Peak SWE)." ; 
 		:disclaimer = "While every effort has been made to ensure the accuracy and completeness of the data, no guarantee is given that the information provided is error-free or that the dataset will be suitable for any particular purpose. Users are advised to use this dataset with caution and to independently verify the data before making any decisions based on it. The creators of this dataset make no warranties, express or implied, regarding the dataset\'s accuracy, reliability, or fitness for a particular purpose. In no event shall the creators be liable for any damages, including but not limited to direct, indirect, incidental, special, or consequential damages, arising out of the use or inability to use the dataset. Users of this dataset are encouraged to properly cite the dataset in any publications or works that make use of the data. By using this dataset, you agree to these terms and conditions. If you do not agree with these terms, please do not use the dataset." ; 
 }