Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Northeast CASC > FY 2017 Projects > “Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes > Approved DataSets > Process-guided deep learning predictions of lake water temperature > Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) ( Show all descendants )

Location Folder Show Tree ROOT _ScienceBase Catalog __National and Regional Climate Adaptation Science Centers ___Northeast CASC ____FY 2017 Projects _____“Hyperscale” Modeling to Understand and Predict Temperature Changes in Midwest Lakes ______Approved DataSets _______Process-guided deep learning predictions of lake water temperature ________Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Filters Date Range Past year (0) Past month (0) Past week (0) Past day (0) Custom range... To   Search Contacts Jordan S. Read U.S. Geological Survey U.S. Geological Survey - ScienceBase Alison P. Appling Anuj Karpatne More... Categories Data (3) Tag Types Theme (3) Place (3) Science Themes (3) Organization (3) Water, Coasts and Ice (3) Wildlife and Plants (3) Tag Schemes none (3) ISO 19115 Topic Category (3) U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of the United States, and associated areas (Federal Information Processing Standard 5-2): Washington, D. C., NIST (3) Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions, Federal Information Processing Standard (FIPS) 10-4, Washington, D.C., National Institute of Standards and Technology (3) https://www.sciencebase.gov/vocab/category/NCCWSC/ScienceThemes (3) https://www.sciencebase.gov/vocab/category/NCCWSC/Project/OrganizationName (3) https://www.sciencebase.gov/vocab/category/NCCWSC/WaterCoastsandIce (3) More... View Results as: JSON ATOM CSV Browse Folder

Process-guided deep learning water temperature predictions: 3c All lakes historical inputs This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated 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... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags 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... Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs This dataset includes model inputs that describe local weather conditions for Lake Mendota, 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... Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags