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347 results for “western United States”
A 30-year high resolution simulation of the future climate over the Interior Western United States
<p>A high-resolution (4 km) convection-permitting regional climate simulation is conducted in the Interior Western United States (IWUS) using the Weather Research and Forecasting (WRF) model. The model integration is conducted over a 30-year period from October 1981 through September 2011, and over a 30-year future period centered at 2050 using the Pseudo–Global Warming (PGW) technique. The IWUS model output for the retrospective climate is available from https://doi.org/10.5281/zenodo.1157112.</p> <p>This repository contains a 30-year gridded dataset of precipitation, surface (2 m) temperature, surface pressure, water vapor mixing ratio at 2 m, and 10 m wind speed at a daily frequency from the IWUS simulation of future climate centered at 2050. Anyone interested in the full dataset of the IWUS simulation is encouraged to contact the lead author at yongganga.wang@gmail.com.</p>
Data for climate-resilient snowpack estimation in the Western United States
<p>Generated and preprocessed files for the resilient snowpack estimation project. All preprocessed data were originally produced by the WUS-D3 project (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6) or PRISM (https://www.prism.oregonstate.edu/).</p>
GHCN-daily: Western United States precipitation dataset
<p>This is a dataset of daily precipitation totals in the western United States (US) from January 1 1950 – February 29 2024. The dataset is based on daily precipitation measurements from 642 gauges across the western US, with periodic data gap-filling perfomed using estimates based on nearby gauges. From the daily records at the 642 gauge sites we produced a gridded record of daily precipitation totals at 0.25-degree resolution across the western US. The 642 gauges used as the basis for this dataset were selected for their uniquely long records and continuous coverage from the early 1950s through 2023. The purpose of producing this dataset was for evaluation of trends in sub-monthly cool-season (November–March) precipitation characteristics.</p>
DayCent-UV grassland productivity across the midwestern and western conterminous United States.
<p>This dataset provides information about daily and annual simulated aboveground plant productivity (ANPP) in grasslands grid cells across the midwestern and western conterminous United States from 1984 to 2015. Annual ANPP estimates from Rangeland Analysis Platform (RAP) are included for comparison.</p> <p><strong>Files Description:</strong></p> <ul> <li><strong>Grid_Cells_Shapefile.zip</strong></li> </ul> <p>Contains<strong> </strong>the spatial distribution for each simulated grassland grid cell. Each grid cell is identified by a <code>grid</code> number ranging from 0 to 2712, a <code>col</code> number increasing from west to east, and a <code>row</code> number increasing from south to north. </p> <ul> <li><strong>Daily_ANPP_DayCent-UV.zip:</strong></li> </ul> <p>Each NetCDF file contains the daily ANPP information for each grid cell during a specifc year. Daily ANPP for a grid cell can be acessed in the <code>cropANPP</code> variable, which depends on the grid cell identifier <code>row</code> and <code>col</code>, and <code>time</code> for the day of the year.</p> <p>For example, using the library <em>xarray</em> from Python, the daily ANPP value for a California grassland grid cell (grid : 1207, row: 78, col: 24) in day 150 in year 2000 can be obtained by using the following command lines:</p> <p>import xarray as xr</p> <p>dataset = xr.open_dataset( folder_directory + '/Daily_ANPP_DayCent-UV/daycent_cwrf_2000.nc')</p> <p>time = 150</p> <p>lon_index = 24</p> <p>lat_index = 78 </p> <p>value = dataset.cropANPP[time, lat_index, lon_index].values</p> <p>To calculate annual ANPP for each grid cell, sum the <code>cropANPP</code> values across all days within a file. </p> <ul> <li><strong>Annual_ANPP_DAYCENT-UV_1986-2015.csv</strong></li> </ul> <p>Contains the simulated annual ANPP in the <code>cum_ANPP</code> column (gC/m²), for each <code>year</code> and each grid cell identified by <code>grid</code>, <code>lon</code>, <code>lat</code> ,<code>row</code>, <code>col</code> (see shapefile description)</p> <ul> <li><strong>Annual_ANPP_RAP_1986-2015.csv</strong></li> </ul> <p>Provides RAP's annual ANPP estimates for each grid cell, identified by its <code>grid</code> number, by using two different equations: </p> <ul> <li> <ul> <li>Gerardhi & Sala equation: RAP-ANPP estimates are located in column <code>RAP_Sala_biomass</code> (g Biomass/m²), together with the annual fraction of carbon allocated to shoots in <code>fANPP_Sala</code></li> <li>Hiu equation: RAP-ANPP estimates are located in column <code>RAP_Hiu_biomass</code>, together with the annual fraction of carbon allocated to shoots in <code>fANPP_Hiu</code>.</li> </ul> </li> </ul> <p>We assumed a factor of 2.4 (gBiomass/m²)/(gC) to convert gC/m² to gBiomass/m².</p>
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Fig. 51. A, B in Late Campanian (Cretaceous) Heteromorph Ammonites From The Western Interior Of The United States
Fig. 51. A, B. Holotype of Ptychoceras crassum Whitfield, 1877 (from Whitfield, 1880: pl. 16, figs.
Fig. 30 in Late Campanian (Cretaceous) Heteromorph Ammonites From The Western Interior Of The United States
Fig. 30. Restoration of Didymoceras cheyennense (Meek and Hayden, 1856a). Stipple board drawing
Fig. 27. A, B in Late Campanian (Cretaceous) Heteromorph Ammonites From The Western Interior Of The United States
Fig. 27. A, B. Two views of the holotype of Ancyloceras? cheyenensis (sic) Meek and Hayden,
Fig. 121. Prionocyclites mite Kennedy, 1988. A–D. Holotype, USNM 420144, from locality 63 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 121. Prionocyclites mite Kennedy, 1988. A–D. Holotype, USNM 420144, from locality 63.
Fig. 118 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 118. Prionocyclus germari (Reuss, 1845). USNM 498490, a robust form from locality 57.
Fig. 117 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 117. Prionocyclus germari (Reuss, 1845). USNM 498456, a robust form from locality 26.
Fig. 116 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 116. Prionocyclus germari (Reuss, 1845). USNM 498489, a robust form from locality 60.
Fig. 94 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 94. Prionocyclus novimexicanus (Marcou, 1858). USNM 498422, a gracile form from locality
Fig. 112 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 112. Prionocyclus germari (Reuss, 1845). USNM 498477, a gracile form from locality 60.
Fig. 97 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 97. Prionocyclus novimexicanus (Mar
Fig. 74 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 74. External suture of Prionocyclus macombi Meek, 1876, from USNM 498854, from locality 36.
Fig. 83 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 83. Prionocyclus wyomingensis Meek,
Fig. 95 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 95. Prionocyclus novimexicanus (Marcou, 1858). USNM 498422; see also figure 94. Reduced
Fig. 39 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 39. Collignoniceras praecox (Haas, 1946), gracile form. A–C. USNM 498268; D–F. USNM
Fig. 25 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 25. Collignoniceras woollgari regulare (Haas, 1946). USNM 498248, robust form from locality
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.