Data for: Saturation of ocean surface wave slopes observed during hurricanes
Data files
Jun 13, 2023 version files 10.40 MB
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dataset_description.pdf
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fiona_spotter_coamps_data.json
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ian_spotter_coamps_data.json
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README.md
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tanh_fit_data.csv
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wave_spectra_binned_by_wave_age_data.json
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wave_spectra_binned_by_wind_speed_data.json
Jun 26, 2023 version files 10.39 MB
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binned_mss_data.csv
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dataset_description.pdf
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fiona_spotter_coamps_data.json
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ian_spotter_coamps_data.json
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README.md
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wave_spectra_binned_by_wave_age_data.json
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wave_spectra_binned_by_wind_speed_data.json
Jun 26, 2023 version files 10.39 MB
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binned_mss_data.csv
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dataset_description.pdf
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fiona_spotter_coamps_data.json
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ian_spotter_coamps_data.json
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README.md
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wave_spectra_binned_by_wave_age_data.json
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wave_spectra_binned_by_wind_speed_data.json
Abstract
README: Data to accompany the article "Saturation of ocean surface wave slopes observed during hurricanes"
Contains wave observations by free-drifting Spotter buoys from targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the free-drifting Sofar Ocean Spotter global network in Hurricane Fiona (2022). The observations are co-located with modeled surface wind speeds from the U.S. Naval Research Laboratorys Coupled Ocean-Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC). The data are used in the article to show the saturation of mean square slope at extreme wind speeds and the coincident transition from an equilibrium-dominated spectral tail to a saturation-dominated tail. This archive contains two main, table-like datasets of wind-wave data (from Ian and Fiona) and three derived datasets necessary to reproduce the results and figures in the article.
Description of the data and file structure
The dataset is organized into five files:
-
ian_spotter_coamps_data.json
- the main dataset from Hurricane Ian (2022) containing hourly records of surface wave statistics in the form of scalar energy spectra, directional moments, and derived products (including mean square slope) with co-located modeled surface wind speeds from COAMPS-TC. The data can be sorted by Spotter id (using thespotter_id
variable) to observe individual drift tracks as a function of time, or can be taken in aggregate to study bulk trends as a function of wind speed, etc. (as is done in the article with mean square slope versus wind speed). -
fiona_spotter_coamps_data.json
- same as above, but for Hurricane Fiona (2022). -
binned_mss_data.csv
- data to reproduce the binned mean square slopes and model wind speeds presented in the article. -
wave_spectra_binned_by_wind_speed_data.json
- mean energy density data, binned by wind speed, to reproduce the mean energy density versus wind speed plot in the article. This data is derived fromian_spotter_coamps_data.json
andfiona_spotter_coamps_data.json
by binning and averaging theenergy_density
in 10 m/s bins usingCOAMPS_10m_wind_speed
. -
wave_spectra_binned_by_wave_age_data.json
- mean energy density data, binned by mean period wave age, to reproduce the mean energy density versus wave age plot in the article. This data is derived fromian_spotter_coamps_data.json
andfiona_spotter_coamps_data.json
by binning and averaging theenergy_density
usingmean_wave_age
.
Variable descriptions are provided in dataset_description.pdf
.
Sharing/Access information
Data was derived from the following sources:
- Spotter buoys are a product of Sofar Ocean (San Francisco, CA). Data are accessible at https://spotter.sofarocean.com.
- COAMPS-TC is developed and operated by the U.S. Naval Research Laboratory, Monterey Marine Meteorology Division. More information can be found at https://www.nrlmry.navy.mil/coamps-web/web/home.
This is the only publicly accessible location of this dataset.
Code/Software
The datasets are stored as text-based JSON and CSV files which can be read without the use of special software. The JSON files are created using the Python Pandas package DataFrame.to_json
method with orient='records'
. Example usage in Python and MATLAB are provided in example.py
and example.m
.
Methods
This dataset contains wave measurements collected by free-drifting Spotter buoys (Sofar Ocean) which use GPS-derived motions to report hourly records of surface wave statistics in the form of scalar energy spectra and directional moments. The observational data are combined with modeled surface wind speeds from the U.S. Naval Research Laboratory’s Coupled Ocean-Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) which are interpolated onto the Spotter wave observations in time and space.
Usage notes
The datasets are stored as text-based JSON and CSV files which can be read without the use of special software. The JSON files are created using the Python Pandas package `DataFrame.to_json` method with orient='records'. Example code is provided in MATLAB and Python.