Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification
This data set provides a global distribution of nine subgrid snow-depth-variability categories and a coefficient of variation applicable to each category, as the result of the Subgrid SNOW Distribution (SSNOWD) submodel that defines subgrid snow-depth and snow-cover variability. This data set provid...
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2016
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dataone:urn:uuid:ea08651f-2d69-4de1-85a7-e5764268d2a6 2024-06-03T18:46:41+00:00 Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification ENVELOPE(-180.0,180.0,90.0,-90.0) BEGINDATE: 1994-01-01T00:00:00Z ENDDATE: 2004-01-01T23:59:59Z 2016-01-19T00:00:00Z https://search.dataone.org/view/urn:uuid:ea08651f-2d69-4de1-85a7-e5764268d2a6 unknown Arctic Data Center Snow Depth Models/Analyses Arctic Dataset 2016 dataone:urn:node:ARCTIC 2024-06-03T18:07:04Z This data set provides a global distribution of nine subgrid snow-depth-variability categories and a coefficient of variation applicable to each category, as the result of the Subgrid SNOW Distribution (SSNOWD) submodel that defines subgrid snow-depth and snow-cover variability. This data set provides the distribution of those nine categories and their coefficient-of-variation values on a global, 2.5 arc-min latitude-longitude (approximately 5-km) grid. The time period covered is 1 January 1994 through 1 January 2004. The SSNOWD submodel was formulated to improve the depiction of autumn through spring land-atmosphere interactions and feedbacks within global weather, climate, and hydrologic models. From both atmospheric and hydrologic perspectives, the subgrid snow-depth distribution is an important quantity to account for within large-scale models. In the natural system, these subgrid snow-depth distributions are largely responsible for the mosaic of snow-covered and snow-free areas that develop as the snow melts, and the impacts of these fractional areas must be quantified in order to realistically simulate grid-averaged surface fluxes. SSNOWD's formulation incorporates observational studies showing that snow distributions can be described by a lognormal distribution and the snow-depth coefficient-of-variation (CV). Using an understanding of the physical processes that lead to these observed snow-depth variations, a global distribution of nine subgrid snow-depth-variability categories was developed, and coefficient-of-variation values were assigned to each category based on published measurements. Data are in binary format. Dataset Arctic Arctic Data Center (via DataONE) Arctic |
institution |
Open Polar |
collection |
Arctic Data Center (via DataONE) |
op_collection_id |
dataone:urn:node:ARCTIC |
language |
unknown |
topic |
Snow Depth Models/Analyses Arctic |
spellingShingle |
Snow Depth Models/Analyses Arctic Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
topic_facet |
Snow Depth Models/Analyses Arctic |
description |
This data set provides a global distribution of nine subgrid snow-depth-variability categories and a coefficient of variation applicable to each category, as the result of the Subgrid SNOW Distribution (SSNOWD) submodel that defines subgrid snow-depth and snow-cover variability. This data set provides the distribution of those nine categories and their coefficient-of-variation values on a global, 2.5 arc-min latitude-longitude (approximately 5-km) grid. The time period covered is 1 January 1994 through 1 January 2004. The SSNOWD submodel was formulated to improve the depiction of autumn through spring land-atmosphere interactions and feedbacks within global weather, climate, and hydrologic models. From both atmospheric and hydrologic perspectives, the subgrid snow-depth distribution is an important quantity to account for within large-scale models. In the natural system, these subgrid snow-depth distributions are largely responsible for the mosaic of snow-covered and snow-free areas that develop as the snow melts, and the impacts of these fractional areas must be quantified in order to realistically simulate grid-averaged surface fluxes. SSNOWD's formulation incorporates observational studies showing that snow distributions can be described by a lognormal distribution and the snow-depth coefficient-of-variation (CV). Using an understanding of the physical processes that lead to these observed snow-depth variations, a global distribution of nine subgrid snow-depth-variability categories was developed, and coefficient-of-variation values were assigned to each category based on published measurements. Data are in binary format. |
format |
Dataset |
title |
Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
title_short |
Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
title_full |
Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
title_fullStr |
Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
title_full_unstemmed |
Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification |
title_sort |
global snow-water-equivalent depth coefficient-of-variation classification |
publisher |
Arctic Data Center |
publishDate |
2016 |
url |
https://search.dataone.org/view/urn:uuid:ea08651f-2d69-4de1-85a7-e5764268d2a6 |
op_coverage |
ENVELOPE(-180.0,180.0,90.0,-90.0) BEGINDATE: 1994-01-01T00:00:00Z ENDDATE: 2004-01-01T23:59:59Z |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
_version_ |
1800869809798250496 |