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270 results for “Climatology”
Multisensor Advanced Climatology (MAC) Daily Ocean-Only Surface Wind Speed
<p>Multisensor Advanced Climatology (MAC) of Over-Ocean Surface Wind produced using the MAC-LWP algorithm (Elsaesser et al., 2017, J. Climate), but with Remote Sensing Systems surface wind products swapped in for liquid water path products, and with the product being provided at daily resolution instead of at monthly resolution. Available for 1 deg grid boxes, from 1998 - 2016. Data in grid-boxes over land are set to missing. Uncertainty estimates also provided, following Elsaesser et al. 2017. </p> <p>Reference:</p> <p>Elsaesser, G.S., C.W. O'Dell, M.D. Lebsock, R. Bennartz, and T.J. Greenwald, 2017: The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP). J. Climate, <strong>30</strong>, no. 24, 10193-10210, doi:10.1175/JCLI-D-16-0902.1.</p>
Data for the paper "Climatologies of Various OH Lines From About 90,000 X-shooter spectra"
<p>The provided ASCII tables are related to the paper "Climatologies of Various OH Lines From About 90,000 X-shooter spectra", which has been published in Journal of Geophysical Research: Atmospheres, Vol. 128, 2023 (DOI: 10.1029/2022JD038275).</p> <p>v1.0.0: initial submission</p> <p>v1.1.0: ohclp_linepar.dat with additional column for line wavelengths</p>
A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era, submitted to: The Cryosphere. 2022</p> <p>Code can be found at:</p> <pre>https://doi.org/10.5281/zenodo.7987926</pre>
Data for "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear"
<p>This dataset contains a .csv file published alongside the article entitled "Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear" for consideration in <em>Geophysical Research Letters</em>.</p>
Bias corrected era5 skin temperature over the Arctic sea ice – 1981 to 2018 monthly means and climatology
<p>This dataset is generated in the context of the peer-reviewed study of Zampieri et al., 2023. The users can find a detailed description of the bias correction strategy and information on the scientific value of the dataset in the paper. Please, do not hesitate to contact me to obtain further information and suggestions on how to employ this dataset for your specific purpose. </p> <p><strong>References:</strong></p> <p>Zampieri, L.,<strong> </strong>Arduini, G., Holland, M., Keeley, S., Mogensen, K., Shupe, M., Tietsche, S. (2023) A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses. <em>Monthly Weather Review</em>. DOI:<a href="https://doi-org.cuucar.idm.oclc.org/10.1175/MWR-D-22-0130.1">10.1175/MWR-D-22-0130.1</a></p> <p><strong>Acknowledgments:</strong></p> <p>As part of the Virtual Earth System Research Institute (VESRI), funding for the Multiscale Machine Learning In coupled Earth System Modeling (M2LInES) project was provided to Lorenzo Zampieri by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. </p>
CCClim - A machine-learning powered cloud class climatology
<p>CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud\_cci dataset, adding relative occurrences of eight major cloud types as defined by the World Meteorological Organization (WMO) at 1° resolution. </p><p>The cloud types are predicted using a two stage machine learning framework, in which a 1 km pixel-level classifier is followed up with a grid-box scale Random Forest regression model.</p><p>CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological time scale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models.</p><p>The compressed tarball contains 35 netCDF files, each covering one calendar year. Each file provides daily averages of nine cloud-related variables and the nine classes (eight cloud types+undetermined) as per 1° grid box fractional amounts.</p><p>Cloud-related variables:</p><ul><li>cloud water path</li><li>ice water path</li><li>liquid water path</li><li>cloud optical depth</li><li>effective liquid droplet radius at cloud top</li><li>effective ice particle radius at cloud top</li><li>cloud top pressure</li><li>surface temperature</li><li>cloud area fraction</li></ul><p>Cloud types:</p><ul><li>Ci: Cirrus/Cirrostratus</li><li>As: Altostratus</li><li>Ac: Altocumulus</li><li>St: Stratus</li><li>Sc: Stratocumulus</li><li>Cu: Cumulus</li><li>Ns: Nimbostratus</li><li>Dc: Deep convective</li></ul>
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
Open the record for dataset details and reuse information.
Hourly and Daily Climatologies for VCR LTER Weather Stations 1989-2021
This dataset contains daily and hourly climatologies for VCR LTER Weather Stations. Each measurement is summarized with the 5th percentile, 10 percent trimmed mean, and 95th percentile values. The datasets contain climatologies for each station, as well as for all data combined. The dataset also contains R scripts used to obtain these climatologies.
Extended reference scenarios (ERS) file (including only the temperature and trace species climatology)
<p>Extended reference scenarios are store in a single NetCDF file providing the atmospheric state (pressure, temperature and composition) expected to be seen by CAIRT for different conditions (altitude, latitude, season, time, solar activity and volcanic activity). </p>
Data for the publication "Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model" - part 2
<p>The data is split into two datasets, for each to be smaller than 50 GB.</p>
Proxy-based hail-prone-day climatology and trends for Australia 1979-2021
<p><strong>Climatology of and trends on hail-prone days over Australia from 1979-2021</strong></p> <p>Hail-prone day climatology is calculated using the proxy of Raupach et al., 2023a (DOI: 10.1175/MWR-D-22-0127.1) applied to ERA5 pressure-level reanalysis data (DOI: 10.24381/cds.bd0915c6) at gridded 0.25 degree resolution for 1979-2021 (daily) and March 1979-May 2022 (seasonally). A day was considered hail-prone if a hail-prone atmosphere was detected at 03, 06, or 09 UTC. Trends in hail-prone days are calculated as in Raupach et al., 2023b (DOI: 10.1038/s41612-023-00454-8).</p> <p>The climatology shows the mean annual hail-prone days per grid point over 1979-2021, while seasonal climatologies are over March 1979-May 2022. The trends information shows changes in annual hail-prone days over the same period.</p> <p>NB: The seasonal climatology values differ slightly from those in Supp. Figure 3 in Raupach et al 2023, owing to an improved calculation of the climatology. The differences for each season are less than hail-prone 0.5 days. For details of the change see https://github.com/traupach/era5_hail_climatology/blob/main/analysis/climatology_for_zenodo.ipynb.</p> <p>These data are licensed with a Creative Commons Attribution 4.0 International license (CC-BY-4.0, https://creativecommons.org/licenses/by/4.0/legalcode). These results contain modified Copernicus Climate Change Service information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>If using these data, please inform T. Raupach at t.raupach@unsw.edu.au, and please cite the following paper for which these data were calculated: </p> <p>Raupach, T.H., Soderholm, J.S., Warren, R.A. <em>et al.</em> Changes in hail hazard across Australia: 1979–2021. <em>npj Clim Atmos Sci</em> <strong>6</strong>, 143 (2023). https://doi.org/10.1038/s41612-023-00454-8</p> <p>Acknowledgements: This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.</p>
Climatologies of agriculture related solar products over Cyprus
<p>This dataset presents a detailed 20-year climatology of solar radiation quantities related to agriculture across Cyprus, covering the period from 2004 to 2023. It includes Photosynthetically Active Radiation, plant damage and plant growth, offering a comprehensive view of solar radiation impacts at agricultural products. The data boasts a high temporal resolution of 15 minutes and a fine spatial resolution of 0.05°x0.05°.</p> <p>The creation of this climatology involved integrates re-analysis data and satellite observations with radiative transfer modeling. This approach allows for a detailed and accurate representation of solar radiation patterns over the island, catering to the needs of researchers and professionals in fields such as climatology, environmental science and agriculture.</p> <p>This dataset is associated with the upcoming publication: K. Fragkos et al., (2024). "Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling," submitted to the journal Remote Sensing. The study describes the methodology in detail and discusses the implications of these climatologies.</p>
Climatologies of effective UV doses over Cyprus
<p>This dataset presents a detailed 20-year climatology of solar ultraviolet (UV) radiation across Cyprus, covering the period from 2004 to 2023. It includes effective UV doses relevant for erythemal (sunburn) effects, DNA damage, vitamin D synthesis, offering a comprehensive view of solar radiation impacts. The data boasts a high temporal resolution of 15 minutes and a fine spatial resolution of 0.05°x0.05°.</p> <p>The creation of this climatology involved integrates re-analysis data and satellite observations with radiative transfer modeling. This approach allows for a detailed and accurate representation of solar radiation patterns over the island, catering to the needs of researchers and professionals in fields such as climatology, environmental science, public health, and agriculture.</p> <p>This dataset is associated with the upcoming publication: K. Fragkos et al., (2024). "Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling," submitted to the journal Remote Sensing. The study describes the methodology in detail and discusses the implications of these climatologies.</p>
Global climatology of sea-surface methanethiol (MeSH) concentration fields
<p>This dataset contains a global climatology of sea-surface MeSH concentration and sea-to-air flux, as described in this article (see README file):</p> <p>Wohl, C., Villamayor, J., Galí, M., Mahajan, A. S., Fernández, R. P., Cuevas, C. A., Bossolasco, A., Li, Q., Kettle, A. J., Williams, T., Sarda-Esteve, R., Gros, V., Simó, R., and Alfonso Saiz-Lopez, A. (2024). Marine emissions of methanethiol increase aerosol cooling in the Southern Ocean. Science Advances, 10(48), eadq2465. DOI: 10.1126/sciadv.adq2465</p> <p>Given the several choices and uncertainties associated with sea-air gas flux calculations, we refrain from providing flux estimates, and encourage data users to calculate their own emission fields in a way that maximizes consistency with their chosen geophysical fields or model setup.</p>
Interpolated Climatology -- CBIOMES-global (alpha version) -- surface variables
<pre>**CBIOMES-global (alpha version):** CBIOMES-global (alpha version) is a global ocean state estimate that covers the period from 1992 to 2011. It is based on Forget et al 2015 for ocean physics [MIT general circulation model](https://mitgcm.readthedocs.io/en/latest/index.html) and on Dutkiewicz et al 2015 for marine biogeochemistry and ecosystems [Darwin Project model](https://darwin3.readthedocs.io/en/latest/phys_pkgs/darwin.html). **References:** Forget, G., J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch, 2015: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscientific Model Development, 8, 3071-3104, <http://dx.doi.org/10.5194/gmd-8-3071-2015> or [this URL](http://www.geosci-model-dev.net/8/3071/2015/) Dutkiewicz, S., A.E. Hickman, O. Jahn, W.W. Gregg, C.B. Mouw, and M.J. Follows, 2015: Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model. Biogeoscience, 12, 4447-4481, <http://dx.doi.org/10.5194/bg-12-4447-2015> or [this URL](https://www.biogeosciences.net/12/4447/2015/)</pre>
Monthly and seasonal climatologies of the mixed layer potential temperature and salinity budgets in the Red Sea
<p>Monthly and seasonal climatologies of the mixed layer temperature and salinity budgets in the Red Sea (RS), analyzed using the outputs of a high-resolution (1/100) ocean general circulation model (MITgcm) covering the period 2001 to 2015 (Krokos et al., 2021).</p> <p>The data support the analysis presented in the paper "Processes governing the seasonal evolution of mixed layers in the Red Sea", accepted for publication in the Journal of Geophysical Research - Oceans.</p> <p>The model diagnostics’ terms include the mixed layer depth (MLD), the surface heat (Qnet) and freshwater fluxes (FW), the mixed layer pot. temperature (MLT) and salinity (MLS), and the surface velocities over the RS.</p> <p>The analyzed budget terms include the tendency of pot. temperature θ and salinity S (TT, ST), the surface fluxes of θ and S (TF, SF), the divergence of the advective fluxes of θ and S (TA, SA), the diffusive flux of θ and S (TD, SD), and processes at the ML base (Tbase, Sbase) that comprise en(de)trainment and the advective and diffusive fluxes acting on the en(de)trained volume.</p> <p>Krokos, G., Cerovečki, I., Zhan, P., Hendershott, M. C., & Hoteit, I. (2021). Seasonal Evolution of Mixed Layers in the Red Sea and the Relative Contribution of Atmospheric Buoyancy and Momentum Forcing, 1–44. Retrieved from http://arxiv.org/abs/2112.08762</p>
A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline)
<p>Incorporation satellite-driven data (TRMM3B43 and MODIS LST) and more 2000 weather stations in optimal TPS interpolation, ChinaClim_baseline is a brand-new and high-quality baseline climatology surface for China at spatial resolution of 1km. The data includes average monthly precipitation and temperatures (average, maximum and minimum temperature) over the most recent three full decades (1981-2010). The scale factor of precipitation and temperatures are 0.01 and 0.1, respectively.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m)
<p>Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/.</p> <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 follows the basic MODIS product format and employed the sinusoidal projection. It includes 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. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.</p>
CESM1-SOM Climatologies used for "Climate Sensitivity is Sensitive to Changes in Ocean Heat Transport" (published in Journal of Climate, Mar 2022)
<p>CESM1-SOM climatologies.</p> <p>Pre-industrial control run = SOM_Control.cam5.0030-0059.ann.nc</p> <p>CO2-doubling experiments:</p> <ul> <li>OHT + 30% = SOM_OHFC_P30_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT + 15% = SOM_OHFC_P15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>Control OHT = SOM_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 15% = SOM_OHFC_M15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 30% = SOM_OHFC_M30_2XCO2_032019.cam5.0030-0059.ann.nc</li> </ul>
Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables -- data set
<p>This file constitutes the data set containing the snow course survey, North American Regional Reanalysis (NARR)-derived degree-day indices, and climatological variables data used to conduct the analysis, and generate the figures and tables in the manuscript titled "Modelling snowpack bulk density using snow depth, cumulative degree-days and climatological predictor variables" by Andras J. Szeitz and R. Dan Moore. The manuscript was submitted for publication in the journal 'Hydrological Processes'.</p> <p>Due to the size of the NARR data files used to derive the air temperature time series for each snow course location, we recommend acquiring them from the National Oceanic and Atmospheric Administration's data portal directly (<a href="https://psl.noaa.gov/data/gridded/data.narr.html">https://psl.noaa.gov/data/gridded/data.narr.html</a>).</p> <p>Likewise, the ClimateNA software application used to extract the climatological variables for each snow course location can be obtained from the Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, UBC, directly (<a href="https://climatena.ca/">https://climatena.ca/</a>).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.