kurtisanstey / internal_waves_barkley_canyon Goto Github PK

In most of the Barkley Canyon papers I've read, they use the same regional overview from ONC, or an old contour plot with little detail. So I grabbed some public data from GEBCO and made my own, showing more bathymetric contours and the locations of my primary ADCP.

If you're interested in checking it out, @jklymak , see here:
https://github.com/kurtisanstey/project/blob/master/archive/bathymetry/bathymetry.pdf

Check process, using Euler, to confirm 30 degrees positive rotation to align v with along-slope (NNW) and u with cross-slope (ENE).

Checking raw .nc files for each instrument.

if you add a readme, it could very briefly say what is in each of your subdirectories.

• Create documents folder.
• Schedule document.
• Analysis document.
• Current Proposal draft.

• Just leave the dates as datetime64 and don't mix pandas. np.datetime64('2018-01-01') is just as easy (or easier) than datetime(2018,1,1)

I don't think you need to go from pandas and back just to linearly interpolate... np.interp should work .

• we already discussed the output of the co-ords in the netcdf files.

• not clear why you have an if/else for the saving of the files....

• I think its probably fine to just save your "segments" as separate files. But those time series should concatenate if possible. Another approach would've been to assign a segment number as a variable. Then you could have done

ds = xr.load_dataset('boo.nc')
d = ds.isel(segment=i)

and d would just be the data that had the segment you are interested in. The advantage of this, is that you have the data all in one place.

File 2

I think its OK to deal with lists of data sets like this, but consider the approach suggested above as well...

Building lists of arrays is a bit inelegant. I'd instead do:

um_PSD = np.zeros((nsegs, ndepths, nfs )) 
um_PSD[i, j, :] = sig.welch()....

But its not terrible to do what you are doing...

OK, you can just save N2 as a variable with just depth dimension, and multiply when needed during plotting. You don't need to divide by "int_scale" and save that. Its just a multiplicative factor. Regardless, you definitely want to save that factor.

Again, not your only dims here are f_PSD and depth

MAJOR I'm actually confused here. I thought the plan was for you to make matrices of raw spectral estimates, and then average them as need be. i.e. don't average, and hence have many many segments, and save as 3-D matrices, with dimensions depth, time, f, where time is the beginning or middle of each time slice, and each time slice represents 1024 15-minute averages (or whatever NFFT is). i.e. for a year it would be a matrix of 220, 30, 1024. spectra that are gaps, are just left as NaN. Then when you want to plot the average, you need only select the time interval to average (ignoring NaN). Or if you are plotting a spectrogram, you simply pcolor the spectrogram at the depth of interesting.

Or is that coming later? It really should be here.

• Investigate near N spectral humps, this resolution data is pretty unprecedented over such a long period.
• More research into spectral shoulder (more recent than D'Asaro?). Can't find much else!
• Pinkel (1975) refers to the prominent near-N peak as internal wave 'ringing', and ascribes it much of the observed high-frequency energy.
  • Observed to widen with depth as N(z) decreases.
• Leder (2002) builds on the work of Sabinin (1966), Pinkel (1975), Kase and Clarke (1978), Kase and Siedler (1980), and Levine et al. (1983), and determines that the near N spectral 'shoulder' is a result of an enhanced cascade of energy due to resonant wave-wave interaction (in their study mostly due to NI input by wind events) into the internal wave continuum occurring above the 'resonant' buoyancy frequency - essentially the depth-mean value of N below the buoyancy variable thermocline.
  • Leder's depth-mean N below the thermocline was 3 cph, and they observe a prominent spectral shoulder at this frequency.
  • Another (unlikely, according to Leder) theory is solitons, with periods of about 30+ minutes that can occur over shelf-break topography, forming in the troughs of semidiurnal internal tides during spring-tide generation periods.
    -They attribute the departure from the GM progressive wave model to high-frequency motions (near N and higher) being better modelled as first mode standing waves, due to high-frequency waves within the thermocline being highly correlated and in phase (and backed up in this by Pinkel (1975)).
  • Kase and Clarke (1978) used GATE data to model this response, and their 'resonant' N was 2 cph (similar to Barkley Canyon), at which the spectral shoulder would form.
• D'Asaro et al. (2007) observe a broad near-N peak, common in deep ocean observations, with weak correlation to the continuum or tidal range, and containing about 0.5 of the super-tidal energy, and analysis suggests little association with the occasionally observed solibores.
• Pinkel (2014) briefly speculates the high-wavenumber 'shoulder' could also be a product of sub-mesoscale motions.
• Alford (2016) associates this hump with NI IW due to enhanced high-frequency shear associated with wind-forced motions.
• Check for M2 solitons near bottom? Need temperature data for M2 tidal amplitudes, or identify M2 velocity spikes upward of 1 m/s.
• There are no velocity events upward of 1 m/s (or even close to that) that would indicate solitons, at either site.

• Get 1-minute data for investigation.
• Depth-band plots for depth and seasonal trends.
• There is evidence of the spectral shoulder at both Slope and Axis (75 kHz instruments), always peaking near N.
• Also appears to widen with depth (just slightly, the effect noted by Pinkel possibly weakened by the WKB-scaling).
• The continuum amplitude also increases with depth, effectively masking the shoulder in the lowest ~100 m.
• Shoulder depth-band power (1e-4 to 1e-3 Hz) shows some seasonality and depth dependence.
• Pulses at Slope seem to recur each spring and fall.
• At Axis there is a lack of obvious seasonality in the two observable years (Axis 55 noise floor limits), though there may be additional activity in the fall.
• However, Axis data is poor, and there may processing artifacts at either site.
• There appears to be an obvious correlation in shoulder and continuum depth-band power.

Anstey_CMOS_3.pptx

Old & new data are the same pre-May 2013. Half-day March 2013:

Old & new data are the same pre-May 2013. Week in March 2013:

Old & new data are the same pre-May 2013. Everything before fix (July 2012- May 2013):

New data, half-day March 2014 (after fix date):

Old data, half-day March 2014:

New data, week in March 2014 (after fix date):

Old data, week in March 2014:

• To confirm vertical scales, full depth range of ADCP was checked for depth-dependent plots (beyond what is considered a reliable range). Vertical scales are still as before.

Semidiurnal example:

• Furthermore, non-WKB plots were generated to confirm the expected surface intensification due to heightened stratification (higher stratification supports higher velocities):

Continuum velocity snapshot - (bandpass > 1.2e-5 Hz, entire hump) and (bandpass 1.2e-5 to 1.8e-5, first spike)):

The 'spike' contains much of the bad data, but not all. Obviously the 'hump' is contains bad data, in general. Bad ping in 15-min averaging?

Main points of continuum write-up:

Results

• Continuum energy is elevated near topography, up to 1.5 and 2x, 150 m and 250 m AB, at Slope and Axis, respectively.
  • Depth-band power spectra.
• Forcing is inter-annually consistent, and site dependent. There are regular spring and fall pulses at Slope, and fall pulses at Axis.
  • Depth-band power spectra.

Discussion

• Compared to GM spectra, near-topography energy is elevated (up to 7x) while the slope is similar (around omega^-2), leading to enhanced dissipation (exceeding 10^-8 Wkg^-1) and diffusivity (exceeding 10^-4 m^2s^-1).
  • c/c_{GM} (terminology to be corrected) comparisons.
  • Dissipation and diffusivity estimates.
• Semidiurnal energy cascades to high-frequency (continuum) dissipative processes near topography, with positive correlations (> 0.5) and continuum/semidiurnal energy ratios of up to 10%.
  • Pearson correlations of depth-mean depth-band power time series.

• Non-isotropic at slope and canyon due to the topographically guided nature of forcing (Kunze et al., 2002; Nash et al., 2007; Kunze et al., 2012; Waterhouse et al., 2017).
• Tendencies of other bands that contribute to the cascade of energy into the continuum? Research!
• For continental slopes mean currents and semidiurnal influence have been noted to contribute to increased dissipation rates near-bottom (Nash et al., 2007; Kunze et al., 2012).
• For canyons the semidiurnal constituent has been noted as the primary contributor to seasonal, enhanced near-bottom dissipation rates (Kunze et al., 2002; Waterhouse et al., 2017).
• Look into multi-component / multi-factor analysis for quantifying how much each other band also contributes to continuum/dissipation (scipy.optimise for power law fits of each???).
• Must determine relative weights for each contributing constituent. This is difficult due to the inconsistent nature of the correlations with different constituents. Even M2 at Slope has poor correlation in some seasons.
• Epsilon vs M2 power plots instead/in addition to correlations.
• Difficult to quantify turbulence, so M2 connection is useful for modelling. Try to quantify relative energy and/or slope of continuum with M2 parameters.
• scipy.optimize.curve_fit for power law parameters ax^b for all years.

Ways to average:

• example year (2013)
• years averaged to get an average season cycle

frequency bands:

• sub-diurnal
• diurnal
• near-inertial
• Semidiurnal
• continuum

energy sources:

• wind
• tides
• CTWs

Code should be reformatted to save NetCDF structures using xarray at each major step. This will allow for individual sections of code to work independently without rerunning the entire process, each time.

Hi, Kurtis:

I have read through your thesis proposal and have a number of edits and comments that I will save for our conference call. I think this is a challenging and worthwhile undertaking and you are off to a very good start. I am delighted that you are making good use of the ONC records and I quite like the spectral plots on pages 12 and 13. I am looking forward to seeing what you find with respect to the properties and origins of: (1) baroclinic diurnal shelf waves; (2) near inertial motions (are these blue-shifted internal waves of inertial period?); (3) semidiurnal (M2) tides; and (4) fM2 internal waves. A comparison of the spatial and temporal variability of the various forms of internal motions over the shelf and in the canyon should be especially interesting. I am also interested to learn if there are internal solibores at the base of the canyon. As you can see by the attached reprint link below (the paper is online but too big to send via DFO webmail), these features can be highly dynamic.

I was hoping you could make use of all the 1-hour current meter data we (DFO) collected in Barkley Canyon in the late 1990s in a collaborative project with UBC, and used/discussed by Allen et al. (2001) in some detail. I have the data (current velocity, temperature and salinity) but suspect that the data would add too much to your processing load, although they would provide good information on the seasonal background flows for your MSc.

I suspect that your proposed schedule is optimistic but Jody would know better if it is reasonable, especially during a pandemic. I wish you the best of luck with your course work.

Kindest regards

Rick

Thomson, R. E., & Spear, D. J. (2020). Gravity currents facilitate formation of high‐frequency internal solitons and bores at the base of the Fraser Delta in the southern Strait of Georgia. Journal of Geophysical Research: Oceans, 125, e2020JC016589. https://doi.org/10.1029/2020JC016589; Received 7 JUL 2020; Accepted 29 SEP 2020; Accepted article online 4

Hi Kurtis,

I also read through it and will pass on edits (review) and comments in a word.doc.
And I also think this is a really good start! It is hard time to be a student.

Since Rick has given such a nice overview, I will pursue some details.

• You've listed it as optional but I think it may be a very valuable component to do an analysis of critical slope. I think we have all the data in hand at ONC to provide a physical slope map based on some sort of smoothed resolution and a range of incoming wave directions. You can then pick a seasonal buoyancy frequency and probably more importantly a direction of incoming wave - so yes this could get messy very quick. But I think knowing the possible source areas would add a lot of insight into the single deployment site.

• I think you should look at the rotary spectrograms with a little more frequency resolution. It will make it easier to confirm that the analysis is correct, and also provide more information on currents that are likely from internal waves. Also I think you are correct, the flattening of the spectra at high frequency is indicating noise, and then you are whitening the white, which makes it hard to see what is going on in the tidal and inertial frequencies.

I think, and this could be discussed at an upcoming meeting, that you may need to focus on certain aspects of your proposal. The best way to pick those aspects, is let the data lead you - follow what shows up the most interestingly and clearly in the data. Often a spectrogram can show you that. For example, is there inertial energy? Is it modulated seasonally? As Rick asks, how much of a blue shift is there, is there a variety of blue shifts and there is episodicity in these blue shifts, or is it just a barrage of near-inertial energy.

Looking forward to having a meeting and discussing further with your committee, and particularly with your supervisor on how best I can guide you to achieve your goals. You've done really well in getting a handle on some of these techniques.

Talks soon, Steve

• Critical slope angles (beta) for both Upper Slope and Axis were determined from 3 km E-W and N-S cross-sections of bathymetry, respectively, centred about the ADCP, from rise-over-run estimations.
• Incident internal wave and tide angles determined for major constituents as alpha = sqrt[ (omega^2 - f^2) / (N^2 - omega^2) ], making use of N_0 from WKB scaling.
• Upper Slope is supercritical (beta > alpha) to the semidiurnal tide (alpha_M2 ~ 0.037), at beta ~ 0.060, so there should be reflection (M2 is CW/downward in the slope intensification layer, so this could be).
• Axis is subcritical (beta < alpha) to all observable free internal waves and tides as beta ~ 0.019 (alpha_f ~ 0.023).
• This is about critical for near-inertial waves, so there could be canyon floor amplification of the near-inertial band (there appears to be).
• Both sites are in the 'near-critical' zone where beta / alpha ~ 1, where despite some reflection or scattering there should also be significant near-topography intensification (as is observed).
• Created new topography map for each site to show relative location to local features (slope and canyon).
• Updated bathymetry to show 3 km cross-section lines (red dashed).

• Reworked all major plots to show inter-annual comparisons. Some examples, below. All are available in https://github.com/kurtisanstey/project/tree/master/plots/interannual_plots
• These go directly into the thesis replacing the 2013 plots at the appropriate Results sections, and inter-annual seasonality (or lack thereof) will be discussed as they appear.
• A summary of any notable variability (see below) will likely be in the Discussion / Conclusions.

• In most cases, there is little inter-annual variability; mean currents, spectra, frequency depth dependence, and individual frequency band seasonality are mostly similar during each analysis year.
• However, sub-diurnal inter-annual seasonality is variably intermittent at Slope (but not at Axis). What is forcing the sub-diurnal band?
• Near-inertial inter-annual seasonality is also variably intermittent at Slope, with only some events showing up at Axis. As expected if forcing is mostly due to sporadic regional weather events, still investigating!

• Long-term evidence is difficult to find.
• Drakopoulos and Marsden (1993) noted an increase in baroclinic internal tide energy on the VICS from the beginning of June that died off by September, due to expected seasonal changes in stratification.
• Cummins et al. (2000) suggest that diurnal currents over the VICS is seasonally responsive to the large-scale spring shift from downwelling to upwelling mean currents, and fairly regular inter-annually (1979-82, 1984, 1996-2000).
• Xu and Noble (2009) found that K1 currents around Monterey Canyon peaked in April-June and October-December, relative to the annual cycle of largest spring/neap tide amplitudes. M2 currents were a complex mix of barotropic and baroclinic forcing, and so are somewhat more consistent throughout the year. Both showed inter-annual consistency.

Examples:

Velocities (40h low-pass - Slope):

Power spectra (Rotary - Axis, will be side by side with PSD):

Depth-frequency spectra (PSD - Axis):

Depth-band spectra (PSD - Sub-diurnal - Slope):

Depth-band spectra (Rotary - Near-inertial - Slope):

Characterise the features of K1

#33 Subdiurnal summary
#21 Diurnal summary
#27 Inertial summary
#31 Semidiurnal summary
#32 Continuum summary

• Outline this 'chapter'.
• Critical slope analysis for Upper Slope.
• Check mid. and min/max depths to confirm effect vertical scales.
• Check non-WKB data for upper layer effects.

For notifying Jody of writing updates as the thesis progresses.

Access the rough thesis document from the readme, documents folder, or using the following link:
https://github.com/kurtisanstey/project/blob/master/documents/Anstey_Thesis_Rough.pdf

• Estimate dissipation rates, using c/GM amplitude ratios calculated in #40.

  • What method? There are many, but in this case I'm assuming it's the last one (internal wave interaction theory).
    X eps = K<N^2>/y where y=0.2. But can't find K (diffusivity) from continuum. (Osborn, 1980; Klymak et al., 2013; Kunze, 2017)
    X Thorpe scale method. Overturning, would need CTD? (Thorpe, 1977)
    X Turbulent dissipation rate. Heat-momentum dissipation equivalence; but still need temperature. (Zhang and Moum, 2010)
    X D = sum(F_n), where F_n is the rate that energy is predicted to be put into each mode. I don't know enough about this one to say. (Klymak et al., 2013)

• Internal wave interaction theory (Gregg, 1989; Polzin et al., 1995; Sun and Kunze, 1999; Althaus et al., 2003)
  

  • Need shear variance from shear spectral level over the continuum vs for GM (is this ratio similar to the ratio of continuum amplitude vs GM that I found?).
  • Also need shear/strain ratio from
    
    

For discussing vertical structure. Issue created as a record. Vertical structure of tides and near-inertial waves seems more uniform in the canyon.

Example:

• Notable effects: Apparent blue-shift. Strongest shelf and canyon-axis intensification effects. Seasonality (summer lull).

Semidiurnal outline

Upper Slope - Near-shelf intensification

Describe (what do I see):

• Depth-averaged / multi-annual time-averaged PSD comparison to K1 and M2 (K1 < f < M2, shape of peak, etc.).
• Identify critical frequency for slope below the Upper Slope ADCP.
• Some apparent blue-shift in spectral peak.
• Fairly equally distributed between cross- and along-slope, with along-slope slightly stronger above -250 m.
• Nearly entirely CW, as expected for Coriolis affected motions in the northern hemisphere.
• Intensification of about 2x orders of magnitude in both cross- and along-slope, and CW components, below -250 m.
• Lesser CCW intensification below -250 m (as at K1, about an order of magnitude).
• Vertical scale of effect about 150 m AB.
• Seasonality is mostly evident in the late-spring, with lesser pulses in the winter (as at K1, compare to wind data), and a reduction in the summer months, mostly evident in the bottom-intensified layer below -250 m.
• There is spring-neap modulation, similar to K1 (depth-mean vs surface tides align).
• Be sure these are all quantified and related in clear figures.

Compare (what did others see):

• Drakoupolos & Marsden, 1993.
• Foreman & Thomson, 1997.
• Polzin et al., 1997.
• Jane & St. Laurent, 2001.
• Nash et al., 2004.
• Kelly et al., 2010.
• Klymak et al., 2012.
• Kunze et al., 2012.
• Klymak et al., 2013.
• Terker et al., 2014.
• Johnston & Rudnick, 2014.
• Gemmrich & Klymak, 2015.
• Quantify (compare) where possible.
• Find additional sources.

Explain (what may be causing this):

• Discuss strengths and weaknesses of each theory.

Axis - Canyon-axis intensification

Describe (what do I see):

• Depth-averaged / multi-annual time-averaged PSD comparison to f and M2 (f < K1 < M2, shape of peak, etc.).
• Identify critical frequency for canyon-axis slope (and perhaps the walls).
• Some apparent blue-shift in spectral peak.
• Mostly along-canyon, rectilinear, as expected for canyon-guided flow.
• Below -750 m intensification in along-canyon, CW and CCW signals, of about 2x orders of magnitude (as seen in K1, etc.).
• Vertical scale of effect approximately 250 m AB.
• Seasonality is mostly evident in the late-fall/winter, with an apparent reduction through the summer, mostly evident in the bottom-intensified layer below -750 m.
• There is spring-neap modulation, similar to K1 (depth-mean vs surface tides align).
• Be sure these are all quantified and related in clear figures.

Compare (what did others see):

• Hotchkiss & Wunsch, 1982.
• Petruncio et al., 1998.
• Kunze et al., 2002.
• Carter & Gregg, 2002.
• Xu & Noble, 2009.
• Wain et al., 2013.
• Alberty et al., 2017.
• Kampf, 2018.
• Hamann & Alford, 2020.
• Quantify (compare) where possible.
• Find additional sources.

Explain (what may be causing this):

• Discuss strengths and weaknesses of each theory.

Same process as for the diurnal band. Check for trends (or lack thereof) in general plots, create band-specific plots that demonstrate these trends, research for comparisons and evidence, then write.

@jklymak

Steve mentioned that a student may have been using an instrument in Barkley Canyon for their MSc, when we first met with him. I had checked around, and found nothing, and it never came up in any of my Barkley Canyon research through Google Scholar or the UVic library. Until yesterday.

https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=8604&context=etd_theses

It looks as if they only examine 2013, but they do use both Axis and Upper Slope, as I do. However, and this could be a controversial personal opinion, it doesn't seem very 'polished'. What do you think? Is my project a bust, or do I need to shift focus? This is very worrying to me.

#33 Subdiurnal summary
#21 Diurnal summary
#27 Inertial summary
#31 Semidiurnal summary
#32 Continuum summary
#30 Barotropic comparison

• Recheck barotropic comparisons for each frequency band to estimate locality of spring-neap forcing.
• Finish multi-annual averaging for each analysis type.
• Band-filtered velocity plots.
• CW wind spectrum for estimated input.
• Alford (2001) slab model - find f from wind, get filters. Depends on change in mag. and direction.
• Check mid and min/max depths for effect extent.
• Check non-WKB data for upper layer effects.

Fix scaling issue with GM spectrum, and determine whether there is a rotary version.

Rotational spectra need to have Welch's method applied to them. Get them to work for time-domain

• check versus toy model and hard-wired code.

• 2-D rotational spectra. Get to work in t-z domain...

New and worth sharing:

• A lack of inter-annual variability is noted over four years of overlapping data coverage.
• IW energy is enhanced near topography, up to 1.5x and 2x, 150 m and 250 m AB, at slope and canyon sites, respectively.
• There is unique annual seasonality for each band.
  • Tidally, forcing for the diurnal band is primarly local barotropic (spring-neap phase lag less than a day), while forcing for the semidiurnal band is a mix of local barotropic and remote baroclinic (spring-neap phase lag up to four days).
  • Wind forcing of the NI band is intermittent, with seasonal variation in ML energy propagation time-scales (up to two weeks) and deep canyon response (typically in fall).
  • The semidiurnal band is the biggest contributor to a cascade of energy to high-frequency (continuum) dissipative processes, where observed continuum energy is up to 10% of observed semidiurnal.

These could be summarised as 'the Barkley Canyon internal wave field is highly dependent on depth and frequency', but that seems far too vague to be the 'main point'.

To do:

• Update outline(s), based on new analysis.
• Refocus writing, based on above.
• Focus on my observations in the Results, unless analysis was directly inspired as a comparison with other studies. Why am I showing these Results? Further analysis (like slab model) is still Results. Most comparisons and speculation are Discussion.
• New version, but save old versions.
• Send new plots to Jody before next draft (e.g. epsilon vs M2 power).
• Cut some fluff, including plots or details that don't emphasise the main stories.
• p.18/19 discuss spectra in logical order and expand discussion, mention different lines are different years.
• Jody revisions in email(s).
• Pitch stories in Intro/Theory, then emphasise telling those stories throughout (including plots).
• Rework intro so that each main topic is introduced appropriately, e.g. more about mixing, etc.
• What are the questions that go along with each story? > Discussion.
• Secondary topics can be shorter paragraphs in Discussion. Comparisons are good!
• Cut wordiness (e.g. just say 'correlations').
• Cut any figure that doesn't say something new and relevant. e.g. many of the Axis rotary depth-band plots are pointless, as most things are rectilinear. Appendix figures should be relevant, too. e.g. Axis rotary plots don't need to be in the Appendix.
• Cross-check reference list!
• UVic thesis template.
• JGR style guide for publishing.

I used information from a few papers (primarily https://www.osti.gov/servlets/purl/5688766) to determine the instrument noise floor for a Welch PSD, which should be 2*(standard deviation of noise signal)^2.

RDI Workhorse Long Ranger 75 kHz ADCP (all of Upper Slope data, and early Axis data):

1. I found the 'single ping uncertainty' for the RDI instruments in their supplied datasheet, as 7.6 cm/s for 8 m depth bins and wide bandwidth (as set by ONC). I believe this is equivalent to the standard deviation of the noise signal, as noted in the datasheet footnotes: http://www.teledynemarine.com/Lists/Downloads/long_ranger_datasheet_lr.pdf
2. These ADCP are set to a single ping per ensemble (2 seconds), so I believe I can use the single ping uncertainty value directly in 2*(standard deviation of noise signal)^2.
3. Using this information, I plotted a sample of the noise floor for both the Upper Slope 75 kHz data (this noise floor should be the same for the Axis 75 kHz data, as they're the same instrument and have the same calibration parameters during their deployments).

Nortek Signature55 55 kHz ADCP:

1. I cannot find any record of a 'single ping uncertainty' or something similar. However, as in the RDI datasheet, Nortek supplies a 'velocity accuracy' of 1% +/- 0.5 cm/s. Is there any way to use this, or are any of the other listed specs relevant? https://www.nortekgroup.com/products/signature55/pdf

Is this how you would go about this? I did not find much on 'noise floor' in terms of using a predefined value (i.e. single ping uncertainty/accuracy), and some papers talk about using the RMS to estimate it from the data, directly.

• a bit more on internal wave continuum...

Using appropriate averaging and weighting. Trying to automate it so I don't have to do it manually every time I want these years or seasons.

• Outline this summary, for reference.

• Wind data was converted to u and v velocity vectors.
• CW rotary spectrograms were taken for ease of integrating over desired time periods, to determine input 'power' from storms.
• (m/s)^2 isn't actually power, though it's proportional (sort of). How would I convert this watts, which requires an additional kg/s? I still need to look this up, as it doesn't make sense (to me) that the wind input is a direct function.

• Bandwidth >M4 (~5e-5 Hz) to base buoyancy frequency, N_0 (~4e-4 Hz).
• Notable effects: Greater than GM. Moderate shelf and canyon-axis intensification. Very specific recurring seasonality. Not rectilinear in canyon.

Continuum outline

Upper Slope - Near-shelf intensification

Describe (what do I see):

• fM2 and M4 peaks.
• Depth-averaged / multi-annual time-averaged PSD comparison to GM (in both Cartesian and rotary).
• Identify critical frequency for slope below the Upper Slope ADCP.
• Mostly cross-slope, with along-slope becoming about equal below -250 m.
• Mostly CW, with CCW becoming about equal below -250 m.
• Intensification of about 1x orders of magnitude, mostly evident in the cross-slope and CW components, below -250 m.
• Vertical scale of effect about 150 m AB.
• Seasonality is mostly evident in a recurring pulse each April/May and October/November, which shows up mostly in the bottom-intensified layer below -250 m.
• There is little to no evidence of spring-neap modulation, similar to the inertial signal.
• Be sure these are all quantified and related in clear figures.

Compare (what did others see):

• Cairns & Williams, 1976.
• Muller et al., 1978.
• Garrett & Munk, 1979.
• Munk & Garrett, 1979.
• Gargett et al., 1981.
• Levine et al., 1986.
• Gregg & Kunze, 1991.
• Levine et al., 1997.
• Polzin et al., 1997.
• Levine, 2002.
• Polzin, 2004.
• Kunze & Llewellyn, 2004.
• Rainville & Pinkel, 2006.
• Alford et al., 2007.
• Polzin & Lvov, 2011.
• Kunze, 2017.
• Alford et al., 2017.
• Chen et al., 2019.
• Nelson et al., 2020.
• Quantify (compare) where possible.
• Find additional sources.

Explain (what may be causing this):

• Discuss strengths and weaknesses of each theory.

Axis - Canyon-axis intensification

Describe (what do I see):

• fM2 and M4 peaks.
• Depth-averaged / multi-annual time-averaged PSD comparison to GM (in both Cartesian and rotary).
• Identify critical frequency for canyon-axis slope (and perhaps the walls).
• Equally distributed between cross- and along-canyon components.
• Oddly, it's mostly CW, with CCW becoming about equal below -850 m.
• Intensification of about 1x orders of magnitude, mostly evident in the cross- and along-slope, CW components, below -750 m.
• Vertical scale of effect about 250 m AB.
• Seasonality is mostly evident in a recurring pulse each April/May and October/November, which shows up mostly in the bottom-intensified layer below -750 m.
• There may be weak spring-neap modulation (compare depth-mean to surface tides).
• Be sure these are all quantified and related in clear figures.

Compare (what did others see):

• Hotchkiss & Wunsch, 1982.
• Kunze et al., 2002.
• Carter & Gregg, 2002.
• Kunze & Llewellyn, 2004.
• Xu & Noble, 2009.
• Klymak et al., 2012.
• Gemmrich & Klymak, 2015.
• Kampf, 2018.
• Chen et al., 2019.
• Thomson & Spear, 2020.
• Quantify (compare) where possible.
• Find additional sources.

Explain (what may be causing this):

• Discuss strengths and weaknesses of each theory.

• Power law fits were manually plotted for PSD spectra as Ax^(-k).
• The first range of frequencies, the 'continuum', is also the new range for the depth-band integrated power analysis, from 7e-5 Hz to 1.6e-4 Hz, which is greater than M4 to about 1/2*N. This frequency range characterises adherence to the GM -2 slope.
• The second range of frequencies, the 'roll-off', characterises the spectral roll-off, emphasising energy transfer from low- to high-frequency processes. It ranges from 1.7e-4 Hz to 4.0e-4 Hz, approximately 1/2*N to N.
• Samples are below (the rest are in the PSD plots folder, for reference).
• Upper Slope continuum is close to the expected GM slope, ranging between -1.8 and -2, with the along-slope component often the shallower of the two.
• Upper Slope roll-off is consistent at -1.5, except for -1.4 in 2018.
• Axis75 continuum is much shallower, at -1.7, likely heightened due to canyon effects.
• Axis75 roll-off varies between -0.8 and -0.7, again likely heightened due to canyon effects.
• Axis55 continuum is shallower again, ranging between -1.2 and -1.4.
• Axis55 roll-off varies between -0.3 and -0.4.
• I doubt that comparing the 55 kHz instrument with the two 75 kHz instruments is OK, as Axis55 has a much higher noise floor, and this seems to begin affecting the slope as early as the 'continuum' bandwidth.

These are useful to get propagation direction....

Main points of NI write-up:

Results

• NI energy is attenuated (up to 1.5x, 150 m AB) with depth at Slope, yet is elevated (up to 2x, 250 m AB) in the deep canyon at Axis.
  • Depth-band power spectra.
• Forcing is intermittent, though there is inter-annually consistent activity in the fall.
  • Depth-band power spectra.
• NI energy propagates from the ML base to about -100 m over a few days to a few weeks, before there is a deep response.
  • Depth-band power spectra.

Discussion

• Intermittent seasonality is linked to wind events, with consistently positive correlations (> 0.5) in the fall.
  • Slab model.
  • Correlations.
• When lower modes (1, 2) contribute to a greater portion of NI energy (~75%), there is a longer propagation period from the ML to about -100 m, before any deep response.
  • Seasonal vertical mode decomposition.
• When higher modes (>2) contribute to a greater portion of NI energy (~50%), a deep canyon response is more likely (typically early fall).
  • Seasonal vertical mode decomposition.

• Check out the NI delay time-scales and potentially localised blue shifts, for each year.

• Histogram of wind event peak vs depth-response time (quantify lag for each event).
• 16 events visually quantified for delay, +/- 2 days.
• Not all events had an easily quantifiable delay.
• Fall 6 events, short delays (7-8 days).
• Winter 4 events, medium to long delays (10-18 days).
• Spring 4 events, short to medium delays (8-15 days)
• Summer 2 events, short to medium delays (8-12 days).
• Bins aren't clear!

• Show a plot of a single event with clear ray tracing of NI vertical propagation, to demonstrate quantification process. Draw lines? Show number of days? Show on NI power or NI velocity plot, or both side by side?

Presentation slides with:

• List of progress
• Previous MSc outline
• Thesis outline

I get the following parameters from the device Additional Attributes https://data.oceannetworks.ca/DeviceListing?DeviceId=22925 and the NetCDF metadata:

AverageInterval = 108
MAveragingInterval = 108
NumberPings = 6
adcp_setup_cell_size_meters = 20 (from metadata and agrees with depth intervals in data; differs from Additional Attributes which says 10 but with no record of any changes, this could be the problem - see end of comment)
SoundVelocity = 0
Salinity = 34
PowerLevel = -2

I use these values in the Nortek Signature 55 Deployment software (https://www.nortekgroup.com/software) to get the ping uncertainty (called horizontal precision). The noise floor should be somewhere around 4e-1, and I'm sure the PSD amplitudes are correct as they are similar for the other instruments, which have a confirmed noise floor.

I start by assuming 1 ping every 18 s since that is the raw time interval, for which software gives an uncertainty of 3.82 cm/s. This is too low.

Then I try the ONC given parameters of 6 pings per 108 sec averaging interval, which still suggests 18s for a single ping. This is the same noise floor as the first case, and I don't understand how the time steps could be 18 s when all the intervals are listed as 108 s. And as the Nyquist frequency gets smaller for the 108 s interval it's even more off. This isn't right, either.

So that's all wrong. However, the software says the lowest the pings can go is 6 s intervals, so maybe that's the case and ONC hasn't listed it properly. This gives a larger uncertainty from the software, at 6.62 cm/s, but it turns out it's just the same noise floor. The 18 s uncertainty must just be averaged from the 6 s value. This doesn't help, for either 18s or 108s averaging intervals.

The noise floor is the same in every case - and too low.

In attempting a depth analysis it is important to consider the effects of stratification on the observed spectra.

Currently

• Using CTD data from Line P Station P4 (~26 km WNW from Barkley Canyon).
• Assuming fairly uniform stratification below -200 m.
• Sample summer plot: https://github.com/kurtisanstey/project/blob/master/plots/N2_plots/N2_lower_Summer_2018.pdf
• Sample winter plot: https://github.com/kurtisanstey/project/blob/master/plots/N2_plots/N2_lower_Winter_2018.pdf
• Between -200 m and -900 m, for 2009 - 2019, finding a depth average N^2 of 1.11e-5 (rad/s)^2.

To do

• Get a DFO Water Properties account. Access has been granted.
• Get closest La Perouse CTD data, rather than Line P. Retrieved data for 2013, 2014, 2017, and 2018 at station LB14, the closest to Barkley Canyon with sufficient depth (-1180 m) at ~21 km to the SE. This is closer than the previous Line P station, and is situated in a similar position on the slope as the section of Barkley Canyon being investigated.
• Smooth N^2 data and average a few profiles, to get a better picture of depth dependence.
• Re-evaluate stratification and consider WKB scaling to mitigate its effects, if necessary (ask Jody about this).

• Plots that show any variability in this flow.
• Questions that arise.
• What have others seen in canyons? (Kunze, Allen, etc.)

• Plots show along-canyon low-pass velocities through depth (top) and in the lower canyon (centre), for each year.
• Annual mean velocities and 2-week rolling mean velocities (bottom) are taken within the dashed lines (~700 - 900 m depth).
• Annual mean velocities in this depth range fluctuate within 0.007 - 0.010 m/s, positive up-canyon.
• 2-week rolling mean velocities (bottom) also indicate that flow in this depth-range is consistently up-canyon (positive), with no obvious seasonality or inter-annual variability.
• Question: Why is this up-canyon flow restricted to a very specific depth range (between ~ 700 - 900 m depth)?
• Question: Why does the near-bottom flow appear to be opposite (down-canyon)?

• Cabrera et al. (2018) found seasonally consistent up-canyon flow in a similar depth range (70 - 300 m AB) and of similar magnitude (~ 0.010 m/s) during a biological study at Barkley Canyon Axis in 2013-2014. They also found consistent down-canyon flow near the bottom (using a 2 MHz ADCP), suggesting a circulation cell within the canyon.
• Chauvet et al. (2018) also found seasonally consistent down-canyon BBL currents of about 0.010 m/s, possibly attributed to turbidity currents that are typically within 50-60 m AB, in a biological study in Barkley Canyon from 2012-2015 .
• Xu and Noble (2009) found that Monterey Canyon typically sees up-canyon mean-flow at depths greater than a few hundred metres. They also attribute near-bottom down-canyon flow to turbidity currents or river-flood-induced underflows. They found that mean-flow in canyons is largely depth and topographically dependent; one site at similar depth and scale to Barkley Canyon Axis showed similar up-canyon mean-flow above a ~60 m down-canyon bottom layer. These up/down-canyon mean flows are fairly consistent as they are typically driven by long-term cross-shore pressure gradients due to along-shelf currents; the long-term nature of the forcing tends to mean that monthly and annual averages of current layers generally maintain the same sign/direction.
• Question: Could the Juan de Fuca Strait produce something like a river-flood-induced underflow down Barkley Canyon, or is turbidity a more likely explanation for the down-canyon near-bottom flow?

@jklymak

I believe these are what you suggested, and show the results we were discussing for slide 7. I tightened up the frequency axis limits to better scale colorbars to show depth trends in the tidal range. Any comments?

Upper Slope - PSD - 2013

Upper Slope - Rotary - 2013

Axis - PSD - 2013

Axis - Rotary - 2013

To check for threshold depths using correlation and intensity

http://cproof.uvic.ca/gliderdata/bathy/british_columbia_3_msl_2013.nc

Small enough area that can just scale axes aspect ratio by 1/cos(lat)

Obtained data from DFO Neah Bay (station 46206) for relevant years.