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1,604 results for “Wintering”

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zenodo44/100

Level 2 Winter data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions

WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020. These data are Level 2 of the nested configuration with a horizontal resolution of 270 m. Also included in this repository are the Level 2 and Level 3 grid files. Data are in Netcdf format including the metadata. The two simulation periods are December 2006 and Spring 2007. List of files: L2_Dec2006.nc --         December control solution only forced by winds L2_cew_Dec2006.nc --   December solution including forcing by both winds and currents. L2_grid.nc  --   Level 2 grid L3_grid.nc -- Level 3 grid

opencc-by-4.0Oct 2022View details →

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Winter cumulated water presence of "Marais Breton nord" calculated with the WIW

The WIW was calculated for all SENTINEL 2 cloudless images available within the period from January to April from 2017 to 2024. All WIW rasters were then added to provide the cumulated water presence ranging from 1 to 40 corresponding to the number of dates related to available cloudless images at that period. The table provides the dates of the images used. EPSG 3857

opencc-by-4.0May 2024View details →

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Wim Winters (w3364)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Wim Winters musiXplora-ID: w3364 musiXplora-URI: <a href="https://musixplora.de/mxp/w3364">https://musixplora.de/mxp/w3364</a> Gender: m Date of Birth: 06 March 1972 Place of Birth: Lommel First Mentioned: 1984 Sectors: Alte Musik, Medien, Orgelbau Professions (Historical): Clavichordist, Hammerflügelspieler Professions (Musical): Musikforscher, Organist, Orgelbauer Other Places of Activity: Amsterdam Institutionen: <table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Konservatorium Amsterdam</td><td><a href="https://musixplora.de/mxp/3010260">3010260</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Johann Hinrich Winter (w3249)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Johann Hinrich Winter musiXplora-ID: w3249 musiXplora-URI: <a href="https://musixplora.de/mxp/w3249">https://musixplora.de/mxp/w3249</a> Gender: m Date of Birth: 07 February 1773 Place of Birth: Lübeck Date of Death: 26 April 1801 Place of Death: Lübeck First Mentioned: 1797 Sectors: Klavierbau Professions (Musical): Klavierbauer Main Place of Activity: Lübeck Verwandtschaft: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Onkel und Tanten</td><td>Neffe</td><td>Leonard Conrad Winter</td><td><a href="https://musixplora.de/mxp/w3251">w3251</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Leonard Conrad Winter (w3251)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Leonard Conrad Winter musiXplora-ID: w3251 musiXplora-URI: <a href="https://musixplora.de/mxp/w3251">https://musixplora.de/mxp/w3251</a> Gender: m Date of Birth: 10 January 1728 Place of Birth: Lübeck Date of Death: 01 July 1777 Place of Death: Undefined First Mentioned: 1753 Sectors: Klavierbau Professions (Musical): Klavierbauer Other Places of Activity: Lübeck Herkunftsfamilie: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Sohn</td><td>Johann Conrad Winter</td><td><a href="https://musixplora.de/mxp/w3244">w3244</a></td></tr></tbody></table> Verwandtschaft: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Onkel und Tanten</td><td>Onkel</td><td>Johann Hinrich Winter</td><td><a href="https://musixplora.de/mxp/w3249">w3249</a></td></tr></tbody></table> Ausbildung: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>LehrerInnen und AusbilderInnen</td><td>Schüler</td><td>Johann Conrad Winter</td><td><a href="https://musixplora.de/mxp/w3244">w3244</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Stanislaus Winter (w2997)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Stanislaus Winter musiXplora-ID: w2997 musiXplora-URI: <a href="https://musixplora.de/mxp/w2997">https://musixplora.de/mxp/w2997</a> Gender: m First Mentioned: 1826 Professions (Musical): Geigenbauer Other Places of Activity: Schönbach Institutionen: <table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Innung der Schönbacher Geigenmacher</td><td><a href="https://musixplora.de/mxp/3070037">3070037</a></td></tr></tbody></table> Titel/Medien: <table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Lütgendorff 1922</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände. Lüt2</td><td><a href="https://musixplora.de/mxp/5002059">5002059</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Veit Georg Winter (w1376)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Veit Georg Winter musiXplora-ID: w1376 musiXplora-URI: <a href="https://musixplora.de/mxp/w1376">https://musixplora.de/mxp/w1376</a> Gender: m Confessions: evangelisch-lutherisch Date of Birth: 04 March 1672 Place of Birth: Nürnberg Date of Death: 29 July 1697 Place of Death: Nürnberg First Mentioned: 1697 Sectors: Instrumentenbau Professions (Musical): Instrumentenbauer Other Places of Activity: Nürnberg Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Johann Winter (w1374)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Johann Winter musiXplora-ID: w1374 musiXplora-URI: <a href="https://musixplora.de/mxp/w1374">https://musixplora.de/mxp/w1374</a> Gender: m Confessions: evangelisch-lutherisch Date of Birth: 07 December 1668 Place of Birth: Kirchensittenbach Date of Death: 16 May 1737 Place of Death: Nürnberg First Mentioned: 1681 Last Mentioned: 1722 Sectors: Instrumentenbau Professions (Musical): Flötenbauer, Instrumentenbauer Other Places of Activity: Nürnberg Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Georg Winter (w1371)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Georg Winter musiXplora-ID: w1371 musiXplora-URI: <a href="https://musixplora.de/mxp/w1371">https://musixplora.de/mxp/w1371</a> Gender: m Date of Birth: 1647 Place of Birth: Kirchensittenbach Date of Death: 24 October 1704 Place of Death: Nürnberg First Mentioned: 1681 Sectors: Instrumentenbau Professions (Musical): Instrumentenbauer Other Places of Activity: Nürnberg Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Jun 2024View details →

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Comprehensive Mini-Database of the Northern Hemisphere's Winter Sky: 100 Raw Images from Ensenada, Mexico

We carried out several test sessions for data collection to adjust the settings of our optical system. From October 2022 to June 2023, we executed numerous sessions to assemble our primary catalog, capturing an extensive array of sky views. A total of 100 sky observations were recorded from various directions without restrictions. These sessions were held at the peak of a hill where CICESE, our research institute, is situated at coordinates 31°52′21.5′′ N 116°40′11.8′′ W in Ensenada, Baja California, Mexico. This location was chosen because it is relatively free from urban light pollution and noise, despite its proximity to the city outskirts. This position minimizes city light interference on one side, slightly reducing light pollution, although image quality was occasionally compromised by the light pollution and facility lighting. Using the ASI Studio software, we captured high-resolution images of 5496 × 3672 pixels without employing pixel binning to achieve the highest possible resolution. The camera's settings were adjusted to an exposure time of 0.5 seconds and standard gain, with the lens focused at infinity and an aperture set at f/4. This setup enabled us to detect significant background noise and numerous areas that could potentially contain stars. More information about the article is in the: <a href="https://doi.org/10.3390/aerospace10090748">https://doi.org/10.3390/aerospace10090748</a>

opencc-by-4.0Jul 2024View details →

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Multiple DJI drone flights with thermal camera over Lithuanian forests in winter for wild boar detection

5 Flight over multiple days in the evening time for better thermal conditions for boar detection. Flight were conducted with DJI thermal cameras filmed at the speed of about 5m/s.  Flights 1, 2, 4 and 5 were filmed at from 90m height with camera pointing straight down. Flight 3 was filmed at 120m height.   Link for Dataset download: <a title="Thermal imaging dataset over Lithuanian forests in winter" href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip" target="_blank" rel="noopener">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip</a>

opencc-by-4.0Jul 2024View details →

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Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets

<h1>[Description]</h1> This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024.  <blockquote> Shi, H., Tonboe, R., Lee, M., Dybkjær, G., Sohn, J., Singha, S., & Baordo, F. (2024). A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval. Earth and Space Science, 11(10), e2024EA003715. https://doi.org/10.1029/2024EA003715 </blockquote> Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:     1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)     2) Update of the coefficients for the radar freeboard correction equations     3) Extension of the retrieval period for the CS2IS2 method (April is now included)     4) Removal of OIB data points used for the regression from the validation datasets     5) Inclusion of the Fram Strait mooring dataset in the validation dataset It consists of three directories, each described below. <h2>01_retrieval_results</h2> This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months of the 2011-2022 period (January-March for alpha method and January-April for CS2IS2 method). Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods. Retrieval methods <ul> <li>alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data</li> <li>CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data</li> </ul> Radar freeboard correction methods <ul> <li>Wave speed correction method: Mallet et al. (2020)</li> <li>Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor</li> <li>Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction</li> </ul> The datasets used for generating this dataset are as follows: <ul> <li>CryoSat-2  - AWI CryoSat-2 sea ice thickness v2p6 (doi: <a href="https://doi.org/10.5281/zenodo.10044554" target="_blank" rel="noopener">10.5281/zenodo.10044554</a>)</li> <li>ICESat-2 - NSIDC ATL20 dataset (doi: <a href="https://doi.org/10.5067/ATLAS/ATL20.004" target="_blank" rel="noopener">10.5067/ATLAS/ATL20.004</a>)</li> <li>AVHRR - Copernicus Marine Service's surface temperature datasets (doi: <a href="https://doi.org/10.48670/MOI-00130" target="_blank" rel="noopener">10.48670/MOI-00130</a>, doi: <a href="https://doi.org/10.48670/MOI-00123" target="_blank" rel="noopener">10.48670/MOI-00123</a>)</li> <li>AMSR - JAXA AMSR-E 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73ayng11rpwk7n54aynyj1" target="_blank" rel="noopener">10.57746/EO.01gs73ayng11rpwk7n54aynyj1</a>) - JAXA AMSR2 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j" target="_blank" rel="noopener">10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j</a>)</li> <li>Auxiliary data - Sea ice concentration: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0013" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0013</a>, doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0014" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0014</a>) - Sea ice type: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_NRT_2006</a>)</li> </ul> The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command: <code>import numpy as np</code> <code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code> <code>hi = data[1,:,:]</code> The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: <a href="https://doi.org/10.5067/N6INPBT8Y104" target="_blank" rel="noopener">10.5067/N6INPBT8Y104</a>). <h2>02_valdiation data </h2> This directory includes reference data used for quality assessment of retrievals. There are three sub-directories: 'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/), Fram Strait (doi: <a href="https://doi.org/10.21334/npolar.2022.b94cb848" target="_blank" rel="noopener">10.21334/npolar.2022.b94cb848</a>), and the Laptev Sea (doi: <a href="https://doi.org/10.1594/PANGAEA.912927" target="_blank" rel="noopener">10.1594/PANGAEA.912927</a>, doi: <a href="https://doi.org/10.1594/PANGAEA.899275" target="_blank" rel="noopener">10.1594/PANGAEA.899275</a>). 'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBridge campaign (doi: <a href="https://doi.org/10.5067/G519SHCKWQV6" target="_blank" rel="noopener">10.5067/G519SHCKWQV6</a>, doi: <a href="https://doi.org/10.5067/GRIXZ91DE0L9" target="_blank" rel="noopener">10.5067/GRIXZ91DE0L9</a>). Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The OIB data points used for the regression were excluded when processing the monthly gridded data. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014. <code>import numpy as np</code> <code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code> <h2>03_CS2_OIB_matchup</h2> This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened. <h1>[Abbreviations]</h1> AMSR: Advanced Microwave Scanning Radiometer AVHRR: Advanced Very High Resolution Radiometer AWI: Alfred Wegener Institute CS2: CryoSat-2 JAXA: Japan Aerospace Exploration Agency NASA: National Aeronautics and Space Administration NSIDC: National Snow and Ice Data Center OIB: Operation IceBridge OSI SAF: Ocean and Sea Ice Satellite Application Facility

opencc-by-4.0Apr 2024View details →

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Assessment of the condition of winter crops before winter dormancy on the basis of Planet data; season 2018

NDVI determined on the basis of images of Planets from the dates 07.09.2018 and 14.10.2018, were used to study the assessment of the winter crop before winter dormancy. Available data from the September and October dates were used to assess the degree of development and density of plants.

opencc-by-4.0Mar 2019View details →

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Assessment of the condition of winter crops on the basis of Planet data; season 2017/2018

NDVI determined on the basis of images of Planets from the dates 17.10.2017 and 13.04.2018, were used to study the assessment of wintering of crops.  Acquisition of data before and after winter rest allows to assess the condition of winter crops. Available data come from the research area of the Kujawsko-Pomorskie voivodeship.

opencc-by-4.0Mar 2019View details →

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Assessment of the condition of winter crops before winter dormancy on the basis of Sentinel-2 data; season 2018

NDVI determined on the basis of images of Sentinel-2 from the dates 15 and 18.10.2018, were used to study the assessment of the winter crop before winter dormancy. Data were used to assess the degree of development and density of plants. The data was used to study the correlation with Planet.

opencc-by-4.0Mar 2019View details →

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Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.

NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters: <code>[mesh]</code> <code>filename=small_arctic_10km.msh</code> <code>[simul]</code> <code>duration=150</code> <code>time_init=2006-11-15</code> <code>timestep=900</code> <code>[dynamics]</code> <code>compression_factor=13800</code> <code>C_lab=2675000</code> <code>nu0=0.301</code> <code>tan_phi=0.624</code> <code>substeps=90</code> <code>time_relaxation_damage=15</code> <code>use_temperature_dependent_healing=true</code> <code>[output]</code> <code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code> <code>output_per_day=4</code> <code>variables=M_VT</code> <code>variables=Concentration</code> <code>variables=Thickness</code> <code>[setup]</code> <code>atmosphere-type=era5</code> <code>ice-type=topaz_osisaf_icesat</code> <code>ocean-type=topaz</code> <code>bathymetry-type=etopo</code> <code>dynamics-type=bbm</code> <code>[thermo]</code> <code>diffusivity_sss=0</code> <code>diffusivity_sst=0</code> <code>h_young_max=0.3</code> <code>newice_type=1</code> <code>hnull=0.5</code> <code>[debugging]</code> <code>check_fields_fast=false</code> The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook: https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb

opencc-by-4.0Aug 2024View details →

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Two-wave Post-Disaster Survey on Climate Change Attitudes: Texas after Hurricane Harvey and the 2021 North American Winter Storms

Overview This repository contains data needed to reproduce the analysis results from Chen et al. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas," Global Environmental Change. It is a study about climate change attitudes and experience with climate disasters across U.S. partisan groups. For details about the data, please see the published paper. Results reproduction code is available at <a href="https://github.com/tedhchen/floodStorm" target="_blank" rel="noopener">https://github.com/tedhchen/floodStorm</a>.   Data Set Details `texas_climate_attitudes.csv` contains data from two waves of surveys of Democrats and Republicans living in Texas, with the following groups of variables. <ul> <li>climate change attitudes</li> <li>self-reported exposure to climate disasters</li> <li>scientific information treatment condition and checks</li> <li>political leaning</li> <li>sociodemographics and residential location</li> <li>survey administration details</li> </ul> `outage2021_data.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021. `outage2021_data_multithreshold.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021, aggregated to the county level based on different thresholds of uncertainty about which cities people live in. `gtrends_archive.RData` contains Google Trends data for "hurricane", "astros", and "power", in Texas between 2017 and 2021.   References Please reference the original study when using this data set. Ted Hsuan Yun Chen, Christopher J. Fariss, Hwayong Shin, Xu Xu. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas." Global Environmental Change. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102918" target="_blank" rel="noopener">doi:10.1016/j.gloenvcha.2024.102918</a>.

opencc-by-4.0Aug 2024View details →

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Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023

The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023. Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis.  Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.

opencc-by-4.0Sep 2024View details →

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Data on the sex and age composition of Bramblings Fringilla montifringilla during autumn migration and winter in Europe

Abstract Bramblings Fringilla montifringilla are known to vary in winter distribution according to sex and age (differential migration). This pattern is complicated by the large yearly fluctuation in their preferred winter food, beech seeds. In beechmast areas, large concentrations of Bramblings occur, and their sex and age composition differs from that of winters without a beechmast. Here we present data on the sex and age composition of Bramblings during autumn migration and winters with and without beechmast, mainly from Switzerland, supplemented with data from northern and southern Europe.   Literature cited in the Excel-file Arizaga J, Zuberogoitia I, Zabala J, Crespo A, Iraeta A, Belamendia G (2012) Seasonal patterns of age and sex ratios, morphology and body mass of Bramblings Fringilla montifringilla at a large winter roost in southern Europe. Ring. Migr. 27:1–6. https://doi.org/10.1080/03078698.2012.686707 Browne SJ, Mead CJ (2003) Age and sex composition, biometrics, site fidelity and origin of Brambling Fringilla montifringilla wintering in Norfolk, England. Ring. Migr. 21:145–153. https://doi.org/10.1080/03078698.2003.9674283 Khil L, Samwald O, Tiefenbach A, Tiefenbach M, Pacher H (2011) Der Massenschlafplatz von Bergfinken Fringilla montifringilla in Österreich im Winter 2008/2009. Limicola 25:81–100 Kjellén N, Lindström Å (1993) Bergfinkens övervintringsstrategier samt några iakttagelser från en skånsk sovplats i januari-februari 1993. Anser 32:187–199 Robson D (1996) Influencia de la temperatura en la masa corporal del Pinzon Real. Ardeola 43:139–144 Schierer A (1957) Geschlechts- und Altersverhältnis der Nordfinken. Vögel Heimat 27:68 Widemo U (1977) Bergfink Fringilla montifringilla Jan. - Apr. 1977. Sex and age distribution, winglength and change of weight. Fåglar i Sörmland 10:76–81

opencc-by-4.0Jul 2021View details →

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Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020

Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being: <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track. The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows: \(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\) For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.

opencc-by-4.0Aug 2021View details →

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