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22 results for “Coastal winds”
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
Local fruit availability and en route wind conditions are poor predictors of bird abundance and composition during fall migration in coastal Yucatán Peninsula
<p>In migratory stopover habitats, bird abundance and composition change on a near daily basis. On any given day, the local bird community should reflect local environmental conditions but also the environments that birds encountered previously along their migratory route. For example, during fall migration, the coast of the Yucatán Peninsula in Mexico receives birds that have just crossed the Gulf of Mexico and their abundance and composition may be associated with regional factors such as wind conditions experienced on previous dates but also local factors such as fruit availability. Thus, we used three data sets to quantify the influence of wind and fruit on near daily variation in bird abundance and composition. Our bird data consists of the number of individuals per species captured using mist nets for two coastal national parks in the Yucatán Peninsula during fall migration in 2016 and 2017. The data is provided daily, as are the "net-hours," i.e., the sum of the hours each net was open summed across nets. Thus, we analyzed bird captures standardized by net-hours. Our fruit data consists of the number of unripe and ripe fruits per species counted on each side of each mist net lane at various points during fall migration. Our wind data consists of the wind costs birds arriving at our sites would have experienced when departing the north coast of the Gulf of Mexico two days prior to their arrival. Wind cost reflects wind speed and direction and it was calculated using the wind.dl_2 function in the rWind package. The wind costs are averaged across the entire US Gulf coastline. We used Moran eigenvector maps to quantify the temporal structure of the bird, wind, and fruit data and we partitioned the variance in the bird data into the components explainable by wind or fruit, the temporal structure of wind or fruit, and temporal structure independent of wind or fruit. After running the analysis, we did not find a strong association between daily changes in bird abundance or community composition with wind conditions and ripe fruit availability. Thus, despite wind and fruit being known to be important to individual birds (influencing stopover duration and departure decisions), their effects might not scale up to be drivers of population and community-level variation.</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
Local fruit availability and en route wind conditions are poor predictors of bird abundance and composition during fall migration in coastal Yucatán Peninsula
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Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
An Analysis of the Coastal Evolution of the South Coast of County Wexford and the Impact of Wind
<p>An assessment of coastal change along the south coast of County Wexford in conjunction with the GSI using orthophoto sets from 3 different years spanning between 2014 and 2019 (2014, 2016, and 2019) using the ArcGIS add-in tool DSAS to calculate the changes. The study area was the coastline between Carnsore Point and Hook Head.</p> <p>The key findings of the study were that between 2014 and 2019, just under 59% of the coastline was eroding and the average rate of erosion of these areas was 46 cm per year. </p> <p>Wind speed and direction data for County Wexford provided by the GSI was used to observe the correlation between wind speed, mean height of waves (SWH) and coastal erosion. There was a strong correlation between wind speed and mean wave height in both periods 2014-2016 and 2016-2019. Average wind speed and mean wave height were both lower during 2014-2016 than during 2016-2019. The average rate of erosion between 2016-2019 was higher than during 2014-2016 which points to a connection between wind and coastal erosion as average wind speed and wave height were higher during the 2016-2019 period. Irish wind data available on the Met Éireann (Ireland's National Meteorological Service) website was also used to highlight the stilling phenomenon of wind observable in Ireland during the last century.</p> <p> </p>
Climatology of Sundowner Winds in Coastal Santa Barbara, California, Based on 30 yr High Resolution WRF Downscaling
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Data from: Wind and rain compound with tides to cause frequent and unexpected coastal floods
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Global coastal wind hazard maps from the CHAZ tropical cyclone model
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Florida Coastal Everglades site, station Long Key C-MAN weather station, study of wind speed (mean) in units of metersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Florida Coastal Everglades (FCE) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Georgia Coastal Ecosystems site, station Hudson Creek, study of wind speed (mean) in units of metersPerSecond on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a monthly timescale.
Georgia Coastal Ecosystems site, station Hudson Creek, study of wind speed (mean) in units of metersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains wind speed (mean) measurements in metersPerSecond units and were aggregated to a yearly timescale.
Datatset from "Coastal flooding in the Maldives induced by mean sea-level rise and wind-waves: from global to local coastal modelling"
<p>Resulting downscaled wave fields from the WaveWatch III simulations for the four main wave directions identified and six return periods (10, 20, 50, 100, 500, and 1000 years).</p>
On the pathways of wind-driven coastal upwelling: nonlinear momentum flux and baroclinic instability
<p>The data that support the findings in "On the pathways of wind-driven coastal upwelling: nonlinear momentum flux and baroclinic instability"</p>
MetOp-A ASCAT Level 2 12.5-km Ocean Surface Wind Vector Climate Data Record Optimized for Coastal Ocean
This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the vector cell wind using the same CMOD7 geophysical model function as in the operational OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds are computed as close to ~15 km from the coast. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 12.5km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-Coastal. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PAPAGAYO, COSTA RICA V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Papagayo, Costa Rica dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Papagayo, Costa Rica. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
MetOp-A ASCAT Level 2 Ocean Surface Wind Vectors Optimized for Coastal Ocean
This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.
MetOp-B ASCAT Level 2 Ocean Surface Wind Vectors Optimized for Coastal Ocean
This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD5.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF TEHUANTEPEC, MEXICO V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Tehuantepec, Mexico dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Tehuantepec, Mexico. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PANAMA, PANAMA V1
The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Panama, Panama dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Panama, Panama. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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