Work Description

Title: Rotary spectra of surface kinetic energy in drifters and high-resolution global ocean models Open Access Deposited

http://creativecommons.org/licenses/by/4.0/
Attribute Value
Methodology
  • The data are rotary spectra computed from the HYCOM and MITgcm (LLC4320) simulations subsampled to 1/4 degree. FOr each model, time series are generated based on the zonal (u) and meridional velocities (u) for a full year. These time series are split into 11 segments of 60 days each overlapping by 50%. 1D discrete Fourier transform of u + iv is computed. The Fourier coefficients are mutliplied by their complex conjugates and averaged over spegments to produce spectra. These spectra are integrated over defined frequency bands to produce kinetic energy values.
Description
  • For Drifters, HYCOM, MITgcm: Spectra and kinetic energy files. Please see readme.txt for a description of all data and code contained here.

  • - Compare kinetic energies (KE) of high-resolution global ocean models estimated from rotary spectra to KE in surface drifter observations. - Near-inertial KE is closer to drifter observations in models with frequently updated wind forcing - Internal tide KE is closer to drifter observations in models with topographic wave drag
Creator
Depositor
  • jbrasch@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
  • National Science Foundation (NSF)
  • Department of Defense (DOD)
ORSP grant number
  • This research was supported by funding from US National Science Foundation (NSF) grant OCE-1851164 as well as a Research Experience for Undergraduates Supplement to that grant. BKA also gratefully acknowledges funding from Office of Naval Research (ONR) grant N00014-18-1-2544 and NASA grants NNX16AH79G, NNX17AH55G, and 80NSSC20K1135.
Keyword
Date coverage
  • 2020-05-01 to 2020-08-31
Citations to related material
  • Elipot, S., Lumpkin, R., Perez, R. C., Lilly, J. M., Early, J. J., & Sykulski, A. M. (2016). A global surface drifter data set at hourly resolution. Journal of Geophysical Research: Oceans, 121(5), 2937–2966. https://doi.org/10.1002/2016JC011716
Resource type
Last modified
  • 08/26/2021
Published
  • 09/01/2020
Language
DOI
  • https://doi.org/10.7302/r8q1-g224
License
To Cite this Work:
Brasch, J., Elipot, S., Arbic, B. (2020). Rotary spectra of surface kinetic energy in drifters and high-resolution global ocean models [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/r8q1-g224

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Files (Count: 7; Size: 40.7 GB)

Date: 24 August, 2020

Dataset Title: Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models

Dataset Creators: Jonathan Brasch, Shane Elipot, Brian Arbic

Dataset Contacts: Jonathan Brasch jbrasch@umich.edu; Brian Arbic arbic@umich.edu

Funding: OCE-1851164 (NSF), N00014-18-1-2544 (ONR), NNX16AH79G (NASA), NNX17AH55G, (NASA), & 80NSSC20K1135 (NASA)

Key Points:
- We compare kinetic energies (KE) of high-resolution global ocean models estimated from rotary spectra to KE in surface drifter observations.
- Near-inertial KE is closer to drifter observations in models with frequently updated wind forcing
- Internal tide KE is closer to drifter observations in models with topographic wave drag

Methodology:
The data are rotary spectra computed from the HYCOM and MITgcm (LLC4320) simulations subsampled to 1/4 degree. FOr each model, time series are generated based on the zonal (u) and meridional velocities (u) for a full year. These time series are split into 11 segments of 60 days each overlapping by 50%. 1D discrete Fourier transform of u + iv is computed. The Fourier coefficients are mutliplied by their complex conjugates and averaged over spegments to produce spectra. These spectra are integrated over defined frequency bands to produce kinetic energy values.

Data-set information:
MITgcm model: https://data.nas.nasa.gov/ecco/data.php?dir=/eccodata/llc_4320
Drifter observations: https://www.aoml.noaa.gov/phod/gdp/hourly_data.php
Hycom model: HYCOM output can be accessed via the OSiRIS infrastructure, please contact for details on access.

Instrument and/or Software specifications: Python 3.7.6, Matlab

Files contained here:
The folders show divisions based on each simulation or observation analyzed. The folders and files are described below:

HYCOM: results from HYCOM subsampled to 1/4 degree

Rotary Spectra: spectra_hycom_0m.nc, spectra_hycom_15m.nc

X: horizontal grid spacing (1500x1)
Y: vertial grid spacing (1176x1)
ke_uv: spectra (1500x176x1440)
freq_time: spectral frequencies (1440x1)
freq_time_spacing: spacing between points in freq_time (1x1)
lat: model latitude in degrees north (1500x1176)
lon: model longitude in degrees east (1500x1176)

Rotary Spectra averaged to 1x1 degree bins: spectra_hycom_0m_bin.nc, spectra_hycom_15m_bin.nc

ke_uv_bin: spectra averaged to 1x1 degree bins (360x160x1440)
freq_time: spectral frequencies (1440x1)
lat: latitude range of bin-averaged data (360x1)
lon: longitide range of bin-averaged data (1x160)

Kinetic Energy Results: ke_hycom_15m.mat, ke_hycom_0m.mat

lat_grid: model latitude averaged to 1x1 degree bins (360x160)
lon_grid: model longitide averaged to 1x1 degree bins (360x160)
xmid: latitude at bin centers (160x1)
ymid: longitide at bin centers (360x1)

For each energy band:
[energy-band]: ke results on model grid (1500x1176)
[energy-band]_bin: ke results averaged to 1x1 degree bins (360x160)
[energy-band]_avg: ke results zonally averaged from 1x1 degree bins (160x1)

Grid Data: topo.mat

plat: latitude in degrees north (9000x7055)
plon: longitude in degrees east (9000x7055)
depth: ocean depth in meters (9000x7055)

MITgcm: results from MITgcm subsampled to 1/4 degree

Rotary Spectra: spectra_llc4320_0m.nc, spectra_llc4320_15m.nc

i: horizontal grid spacing (1440x1)
j: vertial grid spacing (1080x1)
ke_uv: spectra (1440x1080x1440x1)
freq_time: spectral frequencies (1440x1)
freq_time_spacing: spacing between points in freq_time (1x1)

Rotary Spectra averaged to 1x1 degree bins: spectra_llc4320_0m_bin.nc, spectra_llc4320_15m_bin.nc

ke_uv_bin: spectra averaged to 1x1 degree bins (360x162x1440)
freq_time: spectral frequencies (1440x1)
lat: latitude range of bin-averaged data (360x1)
lon: longitide range of bin-averaged data (1x162)

Kinetic Energy Results: ke_llc4320_0m.mat, ke_llc4320_15m.mat

lat_grid: model latitude averaged to 1x1 degree bins (360x162)
lon_grid: model longitide averaged to 1x1 degree bins (360x162)
xmid: latitude at bin centers (162x1)
ymid: longitide at bin centers (360x1)

For each energy band:
[energy-band]: ke results on model grid (1440x1080)
[energy-band]_bin: ke results averaged to 1x1 degree bins (360x162)
[energy-band]_avg: ke results zonally averaged from 1x1 degree bins (162x1)

Grid Data:

XC.nc
XC: longitude East of center of grid cell (1440x1080)

YC.nc
YC: latitude North of center of grid cell

Depth.nc
Depth: ocean depth in meters

(all grid files contain same i and j as MITgcm spectra files in addition to their grid variable)

Drifters: (more information on how this folder is organized is found at a readme within the folder)

Kinetic Energy Results (bin averaged): ke_drifters.mat

mz*: mean kinetic energy values on 1 degree by 1 degree bins
numz*: number of elements to form averages in each bin (168x360)
xmid*: longitide at bin centers (360x1)
ymid*: latitude at bin centers (168x1)

Kinetic Energy Results (zonally averaged): ke_drifters_avg.mat

ymid: latitude at bin centers (168x1)
xmid: longitide at bin centers (360x1)

For each energy band:
[energy-band]_[drouged|undrouged]: ke results averaged to 1x1 degree bins (168x360)
[energy-band]_[drouged|undrouged]_avg: ke results zonally averaged from 1x1 degree bins (168x1)

Rotary Spectra averaged to 1x1 degree bins, zonally averaged: spectra_drifters.mat

msnn: negative spectra for all drifters (120x721)
mspp: positive spectra for all drifters (120x721)
msnn_d: negative spectra for drogued drifters (120x721)
mspp_d: positive spectra for drogued drifters (120x721)
msnn_u: negative spectra for undrogued drifters (120x721)
mspp_u: positive spectra for undrogued drifters (120x721)
ymid: latitude at bin centers (120x1)
xmid: frequency at bin centers (721x1)

spectra: code to produce spectra

hycom:
hycom.py: Create zarrs, subsample model, compute rotary spectra & total KE
spectra_bin.m: average spectra to 1x1 degree bins

mitgcm:
subsample.ipynb: Subsample model
concat_rechunk.py: Concat and rechunk subsampled timeseries
spectra.ipynb: compute rotary spectra & total KE
spectra_bin.m: average spectra to 1x1 degree bins

ke: code to produce kinetic energy results from spectra

[hycom|mitgcm|drifter]_ke.m: organize integration, bin-average, shift grid
[hycom|mitgcm]_shift_grid.m: shift grid for easy mapping
ke_integrate.m: integrate frequency bands
ke_integrate_wide.m: integrate wide frequency bands

figures: code to produce figures

FIG_freq_lat.ipynb: visualize zonally averaged spectra
FIG_zonal_avgs.m: visualize zonally averaged kinetic energy for the frequency bands
FIG_maps.m: visualize kinetic energy for the frequency bands.

Related publication(s):
Brasch, J.M., et al. (2020). Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models. Forthcoming.

Use and Access:
This data set is made available under a Attribution 4.0 International (CC BY 4.0).

To Cite Data:
Brasch, J.M., et al. (2020). Factors Influencing Surface Kinetic Energy in Global High Resolution Ocean Models [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/r8q1-g224

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