AndreaBozzo/ceres-open-data-index huggingface dataset:
Drought and Moisture Surplus for the Conterminous United States, Annual Data 1-Year Windows (Image Service)

<div style='text-align:Left;'><div><div><p><span>The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361</span></p><p><span>Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.</span></p><p><span /></p></div></div></div>

Tags: 1900-2023, usfs_edw_moisture_difference_zscore_1yr
Organization: Department of Agriculture
Drought and Moisture Surplus for the Conterminous United States, Annual Data 3-Year Windows (Image Service)

<div style='text-align:Left;'><div><div><p><span>The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361</span></p><p><span>Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.</span></p><p><span /></p></div></div></div>

Tags: 1900-2023, usfs_edw_moisture_difference_zscore_3yr
Organization: Department of Agriculture
Drought and Moisture Surplus for the Conterminous United States, Annual Data 5-Year Windows (Image Service)

<div style='text-align:Left;'><div><div><p><span>The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361</span></p><p><span>Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.</span></p><p></div></div></div></p>

Tags: 1900-2023, usfs_edw_moisture_difference_zscore_5yr
Organization: Department of Agriculture
Climate Change Pressures Heat Zones Mean Days Over 30 Degrees Celsius RCP45 2010-2039

<p><span style='background-color:rgb(255,255,255); color:rgb(0,0,0); font-family:Verdana, Arial, Helvetica, sans-serif; font-size:14.4px;'><span style='display:inline !important; float:none; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature &gt;= 30 °C (86 °F). Daily maximum temperature values &gt;= 30 °C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.</span></span></p>

Tags: climate, conus, edw, enterprise-data-warehouse, heat, historical, iipp, interdepartmental-imagery-publication-platform, temperature, us-forest-service, usfs, zones
Organization: Department of Agriculture
Climate Change Pressures Heat Zones Mean Days Over 30 Degrees Celsius Historical 1980-2009 (Image Service)

<div style='text-align:Left;'><div><div><p><span>Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature &gt;= 30 °C (86 °F). Daily maximum temperature values &gt;= 30 °C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.</span></p></div></div></div>

Tags: climate, conus, edw, enterprise-data-warehouse, heat, historical, iipp, interdepartmental-imagery-publication-platform, temperature, us-forest-service, usfs, zones
Organization: Department of Agriculture
Climate Change Pressures Heat Zones Mean Days Over 30 Degrees Celsius RCP45 2070-2099 (Image Service)

<div style='text-align:Left;'><div><div><p><span>Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature &gt;= 30 °C (86 °F). Daily maximum temperature values &gt;= 30 °C during the period 2070 - 2099 were tallied annually and are reported as the average annual number of days during a 30-year period.</span></p></div></div></div>

Tags: climate, conus, edw, enterprise-data-warehouse, heat, historical, iipp, interdepartmental-imagery-publication-platform, temperature, us-forest-service, usfs, zones
Organization: Department of Agriculture
Climate Change Pressures Heat Zones Mean Days Over 30 Degrees Celsius RCP45 2040-2069

<div style='text-align:Left;'><div><div><p><span>Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature &gt;= 30 °C (86 °F). Daily maximum temperature values &gt;= 30 °C during the period 2040 - 2069 were tallied annually and are reported as the average annual number of days during a 30-year period.</span></p></div></div></div>

Tags: climate, conus, edw, enterprise-data-warehouse, heat, historical, iipp, interdepartmental-imagery-publication-platform, temperature, us-forest-service, usfs, zones
Organization: Department of Agriculture
Future Snow Residence Time (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The Snow Residence Time 2080  data contains modeled future snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months.</span></p></div></div></div>

Tags: mean-winter-average-temperature-and-precipitation, rocky-mountain-research-station, the-snow-residence-time
Organization: Department of Agriculture
Absolute Change in Snow Residence Time (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The Snow Residence Time; Absolute Changes contains modeled absolute changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: abschange, absolute-changes, snow-residence-time, snowresidencetime
Organization: Department of Agriculture
Historical Snow Residence Time (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>This raster contains historical snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005. Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, macav2, snow-residence-time-historical
Organization: Department of Agriculture
Percent Change in Snow Residence Time (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The SnowResidenceTime_PctChange contains modeled percent changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, macav2, snow-residence-time-historical
Organization: Department of Agriculture
Future April 1 Snow Water Equivalent (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The April Snow Water Equivalent data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, snow-water-equivalent, snowwaterequivapril1, usfs-rocky-mountain-research-station
Organization: Department of Agriculture
Absolute Change in April 1 Snow Water Equivalent (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The April Snow Water Equivalent data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, abschange, snow-water-equivalent, snowwaterequivapril1, usfs-rocky-mountain-research-station
Organization: Department of Agriculture
Historical April 1 Snow Water Equivalent (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The April 1 Snow Water Equivalent Historical data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, april-1-snow-water-equivalent-historical, snowwaterequivapril1_historical, usfs-rocky-mountain-research-station
Organization: Department of Agriculture
Percent Change in April 1 Snow Water Equivalent (CONUS) (Image Service)

<div style='text-align:Left;'><div><div><p><span>The The April 1 Snow Water Equivalent Percent Change data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.</span></p></div></div></div>

Tags: 1975-2005, april-1-snow-water-equivalent-percent-change, snowwaterequivapril1, snowwaterequivapril1_pctchange, usfs-rocky-mountain-research-station
Organization: Department of Agriculture