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270 results for “climatologies”
A derecho climatology over the United States from 2004 to 2021
<p><em>We develop the high-resolution (4 km and hourly) </em><em>observational derecho and derecho-producing mesoscale convective system (MCS) dataset over the United States east of the Rocky Mountains from </em><em>2004 to 2021 by using a </em><em>MCS </em><em>dataset generated by the Python Flexible Object Tracker </em><em>(PyFLEXTRKR) software, bow echoes detected by a semantic segmentation </em><em>convolutional neural network, gust speed</em><em>s from the Integrated Surface Database and the Storm Events Database, and physically based identification criteria.</em></p>
Water Temperatures and Climatology for Wachapreague, VA, 1982-2021
Daily average measured near-surface water temperature from 1982-2021 constructed for the site in the Virginia coastal bays where the NOAA Wachapreague station is located (Site CBW (Coastal Bay Wachapreague); 37.61N, 75.69W; water depth 1m) and for a site in the coastal ocean just outside the bays (Site COC (Coastal Ocean CHLV2); 36.91N, 75.71W; water depth 15m). Daily climatological mean temperature and marine heatwave threshold (90th percentile) calculated using 30 years of the record (1987-2016) are also provided.
Climatology of rainfall from Atlantic hurricanes in the USA from radar data
<p>Atlantic Tropical Cyclone Rainfall Climatology in the USA<br> Data sources (see references): NEXRAD level III data, hourly precipitation; IBtracs best track data; University of Colorado extended best track data<br> Available as NetCDF files and Matlab structure</p> <p>Classification as TC precipitation criteria: within radius of outermost closed isobar of a TC at a given time</p> <p>Scope: 100km radius around corresponding radar station</p> <p>Dealing with radar outages: up to 2h gap - interpolation of precipitation, larger gaps - rescaling of frequency with fraction of available data (see formulas)</p> <p>Available variables per radar station:</p> <ul> <li>Location: name [ ], coordinates [°N, °W]</li> <li>Grid: lat [°N], lon [°E]</li> <li>Frequency rescaling: re_freq [ ]</li> </ul> <p>Available variables per event:</p> <ul> <li>Storm identifiers: name [ ], year [a]</li> <li>Storm total precipitation <ul> <li>Area distribution: Ptot [kg/m<sup>2</sup>], gridded (0.1x0.1°)</li> <li>Area average: Ptot_av [kg/m<sup>2</sup>]</li> <li>Area maximum within 0.5x0.5°: Ptot_max [kg/m<sup>2</sup>]</li> </ul> </li> <li>Annual exceedance frequency: f(Ptot_max) [a^-1]</li> </ul> <p>Relevant formulas:</p> <p>re_freq = total duration of storm exposure / duration of viable measurements<br> f (Ptot_max) = (number of events exceeding Ptot_max / length of observation) * re_freq</p> <p>Matlab structure:</p> <ul> <li>Level 1: TCP_climatology</li> <li>Level 2: station variables -> station_event_data leads to event variables</li> <li>Level 3: event variables -> Ptot leads to spatially gridded precipitation</li> <li>Level 4: Ptot-grid</li> </ul>
Mesoscale Low-Level Jet Climatology for the North and Baltic Seas
<p><strong>Mesoscale Low-Level Jet Climatology for the North and Baltic Seas</strong></p> <p>This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas.</p> <p>The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below.</p> <p>Some examples of layers contained:</p> <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> <p>Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation. </p> <p>The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description.</p> <p>Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points. Contact btol@dtu.dk for feedback and requests for future versions. </p> <p><strong>Full list of variables</strong></p> <ul> <li><strong>ws100, ws150, ws200</strong>: wind speed at 100, 150, and 200 meters</li> <li><strong>wd100, wd150, wd200</strong>: wind direction at 100, 150, 200 meters</li> <li><strong>rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li><strong>cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine</li> <li><strong>llj_rate</strong>: LLJ detection rate </li> <li><strong>height_of_llj_max</strong>: height of LLJ peak in meters</li> <li><strong>llj_ws_max</strong>: wind speed of LLJ peak in meters per second</li> <li><strong>llj_wind_direction</strong>: wind direction of LLJ peak in degree</li> <li><strong>llj_duration</strong>: LLJ duration in hours</li> <li><strong>llj_most_prevalent_hour</strong>: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li><strong>llj_most_prevalent_hour_freq</strong>: relative frequency of most prevalent hour-of-day during LLJ events</li> <li><strong>llj_most_prevalent_season</strong>: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li><strong>llj_most_prevalent_season_freq</strong>: relative frequency of most prevalent month-of-year during LLJ events </li> <li><strong>llj_max_shear_below</strong>: maximum shear between the LLJ peak and the minimum below</li> <li><strong>llj_min_shear_above</strong>: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li><strong>height_of_max_shear_below_llj</strong>: height of maximum shear detected below the LLJ peak in meters</li> <li><strong>height_of_min_shear_above_llj</strong>: height of minimum shear detected above the LLJ peak in meters</li> <li><strong>llj_depth</strong>: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li><strong>llj_rews_iea15mw</strong>: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_cf_iea15mw</strong>: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li><strong>llj_abs_falloff_above</strong>: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_above</strong>: relative wind speed fall-off above the LLJ peak </li> <li><strong>llj_abs_falloff_below</strong>: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li><strong>llj_rel_falloff_below</strong>: relative wind speed fall-off below the LLJ peak </li> </ul> <p><strong>Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON)</strong></p> <p>This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.</p>
Daily climatological observations from Sapelo Island, Georgia, from June 1980 through June 2001
Daily summaries of air temperature and precipitation were collected from the National Weather Service weather station on Sapelo Island, Georgia, from June 1980 through June, 2001. The station (NWS Station 97808) is located at the University of Georgia Marine Institute (UGAMI), and has been in continuous operation since 1957. Observations are logged and transmitted to the NWS by UGAMI personnel, and archived by the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/). The monitoring station is described on the Georgia Coastal Ecosystems web site at http://gce-lter.marsci.uga.edu/lter/research/mon/ugami.htm.
Daily climatological observations from Sapelo Island, Georgia, from May 1957 through July 2001
Daily summaries of air temperature and precipitation were collected from the National Weather Service weather station on Sapelo Island, Georgia, from May 1957 through July 2001. The station (NWS Station 97808) is located at the University of Georgia Marine Institute (UGAMI), and has been in continuous operation since 1957. Observations are logged and transmitted to the NWS by UGAMI personnel, and archived by the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/). The monitoring station is described on the Georgia Coastal Ecosystems web site at http://gce-lter.marsci.uga.edu/lter/research/mon/ugami.htm.
Climatological Tidal Model of the Thermosphere - CTMT
<p>For detailed description, see <a href="http://dx.doi.org/10.1029/2011JA016784">Oberheide et al., 2011</a> and the <a href="http://globaldynamics.sites.clemson.edu/articles/ctmt.html">CTMT webpage</a>.</p> <p>Briefly, CTMT is based on tidal temperature and wind observations made in the MLT region by SABER and TIDI on TIMED that are extended into the thermosphere using Hough Mode Extension (HME) modeling. The latter can be thought of as constraining a tidal model with observations and produces self-consistent tidal fields in temperature, neutral density, and zonal, meridional and vertical winds from pol-to-pole and from 80-400 km. A monthly tidal climatology is compiled from averaged 2002-2008 TIMED observations. CTMT accounts for contributions from solar radiation absorption in the troposphere and stratosphere, tropospheric latent heat release, and non-linear wave-wave interactions occurring in the MLT or below. It is valid for a solar radio flux of F10.7 = 110 sfu and includes the 6 (8) most important migrating and nonmigrating diurnal (semidiurnal) tidal components. As such it is suitable for driving upper atmosphere models that require self-consistent tidal fields in the MLT region as a lower boundary condition or to study the effects of tidal density variations in the re-entry region, to name just a few examples. Thermospheric tidal forcing occurring above the MLT is not accounted for. CTMT, therefore, does not capture (i) migrating tides forced in-situ by the absorption of solar EUV radiation, and (ii) nonmigrating tides forced in the thermosphere.</p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></p>
Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations
<p>SD-WACCM data used in "<strong>Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations"</strong></p>
Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021
<p>This data set, described in detail in <a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al. (2022)</a>, contains Newport Hydrographic Line station data; gridded, cross-shelf hydrographic sections; and derived monthly climatologies for temperature, practical salinity, potential density, spiciness, and dissolved oxygen. It consists of CSV (Comma Separated Values) files (<em>newport_hydrographic_line_station_data</em><em>.</em><em>zip</em>) that contain CTD observations collected at the seven hydrographic stations located 1, 3, 5, 10, 15, 20 and 25 nautical miles west of Newport, Oregon between March 1997 and July 2021. Additionally, the data set contains three NetCDF files that follow CF (Climate and Forecast) metadata conventions: <em>newport_hydrographic_line_gridded_sections</em><em>.nc</em> contains observations gridded to a 0.01<sup>o</sup> x 1 dbar longitude - pressure grid to create cross-shelf hydrographic sections for each of the five variables for each cruise. <em>newport_hydrographic_line_gridded_section_climatologies</em><em>.nc</em> contains climatological hydrographic sections, calculated using harmonic analysis over the 24-year period March 1997 to February 2021 and reported here for the middle of each month, and <em>newport_hydrographic_line_gridded_section_coefficients.nc</em> contains the associated linear regression model coefficients for all five variables. From the regression coefficients, users can construct seasonal cycles at any location in the gridded section with a temporal resolution that best suits their specific needs. Finally, this data set includes example MATLAB and R scripts that show how to read the data files, plot cross-shelf hydrographic sections, and calculate daily and monthly climatologies using the regression coefficients.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (CMG)
<p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.</p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
Statistical blending of global-gridded climatological products: an approach to inverse hydrological model
<p>The growing use of global-scale environmental products in hydro-climatic modeling (with different assumptions, resolutions, and precisions) has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending (fusion) of these products to achieve optimal modeling while avoiding difficulties. The proposed statistical blending in this study includes five large-scale and satellite precipitation (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA5-Land of ECMWF (ERA), Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM), and Terra) and evapotranspiration (Global Land Evaporation Amsterdam Model (GLEAM), SSEBop, Moderate Resolution Imaging Spectroradiometer (MODIS), Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on <em>in situ</em> values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well<strong>.</strong></p>
Climatological maps of sea ice variability for the Antarctic marginal ice zone
<p>Monthly climatologies of a statistical indicator of sea ice variability computed from daily maps of sea ice concentration (SIC) from satellites (Vichi, 2022). The sigma_SIA indicator is based on the standard deviation of SIC daily anomalies computed over<br> the monthly time scales, using a monthly climatology over the period 1989-2019 as the baseline. The analysis starts from year 1989 since before that period data were every second day.</p> <p><strong>Please use the most updated version of this dataset and cite the main DOI unless required</strong></p> <p>The following files have been derived from NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 (<a href="https://doi.org/10.7265/efmz-2t65">https://doi.org/10.7265/efmz-2t65</a> and used in Vichi (2022). The code to produce them is available at the following repository <a href="https://github.com/mvichi/antarcticMIZ">https://github.com/mvichi/antarcticMIZ</a> (<a href="https://doi.org/10.25375/uct.21103363">DOI:10.25375/uct.21103363</a>) </p> <ol> <li><strong>NSIDC_cdr_clim_m_sigmaSIA.nc</strong>: Climatological monthly sigma_SIA (climatological 'pooled' monthly std of daily anomaly) from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration</li> <li><strong>NSIDC_cdr_clim_m_maskMIZ.nc</strong>: Climatological mask of the MIZ extent using a sigma_SIA>0.1 criterion</li> <li><strong>NSIDC_cdr_clim_m_sigmaSIA_exceedance.nc</strong>: Climatological probability maps of exceeding sigma_SIA>0.1 and sigma_SIA>0.2</li> <li><strong>NSIDC_cdr_m_sigmaSIA.nc</strong>: Monthly sigma_SIA from NOAA/NSIDC CDR V4 Southern Hemisphere Sea Ice Concentration. Please note that file 1 above is not the climatology obtained from this file.</li> </ol> <p>NetCDF files 1-3 contain 12 monthly arrays with the climatological sigma_SIA computed over the period 1989-2019. Please note that the indicator in these file is in % of SIC, while in the original paper it is presented in fractional units.</p> <p>File 4 has 396 records for all the months in the analyzed period. The units are fractional as in Vichi (2022).</p> <p><strong>Reference</strong></p> <p>Vichi, M.: An indicator of sea ice variability for the Antarctic marginal ice zone, The Cryosphere, 16, 4087–4106, <a href="https://doi.org/10.5194/tc-16-4087-2022">https://doi.org/10.5194/tc-16-4087-2022</a>, 2022.</p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
Annual summaries of daily climatological observations from the National Weather Service weather station at the UGA Marine Institute on Sapelo Island, Georgia for 1958 to 2004
Daily summaries of climatological observations from the National Weather Service weather station on Sapelo Island, Georgia, were obtained from the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/) covering the period 1958 through 2004. Records for incomplete years with more than one month of missing observations were deleted (i.e. 1964, 1969-1971), then missing values of daily minimum, maximum and mean temperature were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated for daily minimum, maximum and mean air temperature and total precipitation.
Annual summaries of daily climatological observations from the National Weather Service weather station at Brunswick, Georgia for 1915 to 2004
Daily summaries of climatological observations from the National Weather Service weather station in Brunswick, Georgia, were obtained from the NOAA National Climatic Data Center (http://www.ncdc.noaa.gov/) covering the period 1915 through 2004. Missing values of daily minimum, maximum and mean temperature were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated for daily minimum, maximum and mean air temperature and total precipitation.
Chlorophyll and phytoplankton composition climatological data on the Northwest Atlantic Shelf from 1978 to 2014: post-processed model data
This dataset includes 8-day composite of surface chlorophyll and bimonthly phytoplankton size composition climatological results on the Northwest Atlantic Shelf from the Gulf of Maine to the Mid-Atlantic Bight based on the physical-biological coupled model results from 1978 to 2014. Two size classes, small phytoplankton (SP) and large phytoplankton (LP), are provided. For more details please see: Zhengchen Zang, Rubao Ji, Zhixuan Feng, Changsheng Chen, Siqi Li, and Cabell S Davis (2021) Spatially varying phytoplankton seasonality on the Northwest Atlantic Shelf: a model-based assessment of patterns, drivers, and implications. ICES Journal of Marine Science, Volume 78, Issue 5, 1920-1934, https://doi.org/10.1093/icesjms/fsab102.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.