Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Jordan S. Read", "@type": "vcard:Contact", "hasEmail": "mailto:jread@usgs.gov" } |
| description | This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "http://dx.doi.org/10.5066/P9AQPIVD", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d98e0dbe4b0c4f70d1186f3.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d98e0dbe4b0c4f70d1186f3 |
| keyword |
[ "US", "USGS:5d98e0dbe4b0c4f70d1186f3", "United States", "WI", "Wisconsin", "biota", "climate change", "deep learning", "environment", "hybrid modeling", "inlandWaters", "machine learning", "modeling", "reservoirs", "temperate lakes", "temperature", "thermal profiles", "water" ] |
| modified | 2020-08-20T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -89.7037723045351, 46.002272195262, -89.6957319045477, 46.0152963952417 |
| theme |
[ "geospatial" ] |
| title | Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs |
