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9 results for “Sea Surface Wind”

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zenodo40/100

CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL

<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise&nbsp;</li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied)&nbsp;</li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al:&nbsp;https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>

opencc-by-4.0Jul 2024View details →
zenodo40/100

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 &#39;SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt&#39; (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder &#39;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&deg;=North, 90&deg;=East, 180&deg;=South, 270&deg;=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 &lt;-&gt; &nbsp;SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass &nbsp;&lt;-&gt; &nbsp;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&#39;s formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0&deg; and 45&deg;]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-&alpha;) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45&deg;, 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 &#39;land pixels&#39;).</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> %&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % REFERENCES: &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;%<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [1] Rana, Fabio Michele (2016) &quot;Exploitation of Satellite &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean&quot;. Unpublished Ph.D thesis. Politecnico di Bari. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Some applications of the method are described in the following papers: &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, &quot;LG-Mod: A Modified Local Gradient (LG) Method to &nbsp; %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas,&quot; &nbsp; &nbsp; &nbsp;%<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. &nbsp; &nbsp; &nbsp; %<br> % doi:10.1155/2016/9565208. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [3] Rana, F. M., Adamo, M., &amp; Blanda, P. (2018, July). &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., &amp; Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical &nbsp; &nbsp;%<br> % weather prediction model data. Remote Sensing of Environment, 225, &nbsp; &nbsp; &nbsp;%<br> % 379-391. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Suggestions and comments are always welcome. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % Thanks in advance, &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % Fabio Michele Rana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % MOB: (+39) 3804114171 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % E-MAILS: fabiomichele.rana@gmail.com; &nbsp;fabiomichele.rana@iia.cnr.it &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % SKYPE: fabiomichelerana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % Author: Fabio M. Rana &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> &nbsp;</p>

opencc-by-4.0Apr 2019View details →
dryad36/100

Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts

<p class="MsoNormal"><span>Accurate gust forecasts can reduce potential threats to people's lives and properties, but we need more reliable forecasting methods and models. A recent development is the meteorologically stratified gust factor (MSGF) model, which is more accurate in forecasting gusts than the previous gust factor model. The regional terrain and underlying surface both have crucial effects on the gust factor. We therefore combined observations from the China Meteorological Administration over the ocean surface and along the coast with the MSGF model to explore the influence of topography and the underlying surface on wind and gust forecasts. The regional terrain and underlying surface affected the peak gust climatologies, the mean wind speed, the mean prevailing wind direction and the gust factors. The topography and the underlying surface had different impacts in different ranges of the mean wind speed. The strong turbulence that causes changes in the gust factor under light winds is not initiated over rough underlying surfaces. When the mean wind speed is &gt;2.5 m s<sup>−1</sup>, the underlying surface influences both the wind speed and the gust factor. A rough underlying surface stimulates stronger turbulence and increases the gust speed and gust factor, whereas a smooth underlying surface directly increases the mean wind speed and the gust speed by different magnitudes to reduce the difference between them, thus decreasing the gust factor. We evaluated the ability of the MSGF model to forecast gusts and verified a method combining the products of a numerical model and the MSGF model in gust forecasts.</span></p>

opencc-zeroDec 2022View details →
dryad36/100

Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts

Open the record for dataset details and reuse information.

publicApr 2023View details →
zenodo32/100

Dataset used in Sea surface wind structure observed by wave gliders during tropical cyclones

<p>Sea surface wind vector observed by three wave gliders deployed in the Western Pacific Ocean. The observation level is 1.2 meter.</p>

opencc-by-4.0Sep 2022View details →
nasa32/100

Aquarius CAP Level 2 Sea Surface Salinity, Wind Speed & Direction Data V5.0

The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

restrictednotspecifiedApr 2025View details →
nasa28/100

Aquarius Official Release Level 2 Sea Surface Salinity & Wind Speed Data V5.0

The version 5.0 Aquarius Level 2 product is the official third release of the orbital/swath data from AQUARIUS/SAC-D mission. The Aquarius Level 2 data set contains sea surface salinity (SSS) and wind speed data derived from 3 different radiometers and the onboard scatterometer. Included also in the Level 2 data are the horizontal and vertical brightness temperatures (TH and TV) for each radiometer, ancillary data, flags, converted telemetry and navigation data.Each data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. Enhancements to the version 5.0 Level 2 data relative to v4.0 include: improvement of the salinity retrieval geophysical model for SST bias, estimates of SSS uncertainties (systematic and random components), and inclusion of a new spiciness variable.

restrictednotspecifiedApr 2025View details →
zenodo8/100

Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter

<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper &quot;Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter&quot;.</p>

restrictedSep 2020View details →
zenodo8/100

Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter

<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper &quot;Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter&quot;.</p>

restrictedSep 2020View details →

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