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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #200 on: June 14, 2020, 04:54:04 PM »
t58 since oct2, no traumatic events, just thickening. These aren't the melting profiles I was expecting. Maybe jdallen is watching.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #201 on: June 14, 2020, 10:48:41 PM »
Yeah I didn't notice the numbers. 80cm of top melt in 4 days is impossible. But once the ice reaches 0C I think melt progress should be rapid nonetheless.
I still wonder how 0C can propagate so deeply if the ice is solid, especially considering it's supposed to be somewhat salty. Above my pay grade unfortunately.
It could be 'flushing'
https://www.cambridge.org/core/journals/annals-of-glaciology/article/on-the-thermodynamics-of-melting-sea-ice-versus-melting-freshwater-ice/B88EBD53A0209295EA0662825C48EBAE/core-reader
Quote
The simulations show clearly that after the onset of surface warming, flushing sets in, which transports cold and salty brine from higher up in the ice to the region close to the ice-ocean interface.
Still frozen, fresher ice.     or something else completely.
Quote
One final notable feature related to the evolution of internal temperature in our experiments is the signature of a localized flushing event (ellipse in Fig. 5a): Around 25 hours after we raised the air temperature, the ice temperature increased strongly and subsequently decreased again in the upper two-thirds of the ice within a few hours. The measured temperature reaches 0°C and even higher. The sudden onset and short duration of this event point towards a localized flushing event, where penetrating comparably fresh meltwater from the surface desalinates a small region of ice quickly. The rapid desalination leads to a short release of latent heat, as a fraction of the percolated water freezes in order to reach thermal equilibrium. Once the desalination ends, much of the percolated freshwater refreezes in the interior of the ice due to heat diffusion from the colder surrounding ice which was not desalinated by the localized flushing event.

 In our set-up, it is likely that the thermistor chain sticking in the ice supported or even induced this localized flushing event due to additional heat conduction that let the ice melt around the chain. After the flushing event the measured temperatures stay above 0°C in the upper 0.02-0.03 m of the ice, which indicates that the thermistors are no longer in contact with the ice, but instead with comparably warm meltwater. Note, however, that a similar flushing event was observed by Pringle and others (2007) in a set-up with only horizontal thermistor strings.
« Last Edit: June 14, 2020, 10:58:06 PM by uniquorn »

oren

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Re: Maximising asif skills using near real time Mosaic data
« Reply #202 on: June 14, 2020, 11:08:54 PM »
Very interesting.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #203 on: June 15, 2020, 11:12:06 AM »
All the mosaic temp_procs I could find back to back, jun1-15.
Much quicker for me this way.

SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #204 on: June 15, 2020, 11:27:03 AM »
Very interesting, had no idea about "flushing"
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #205 on: June 15, 2020, 02:52:39 PM »
https://www.essoar.org/pdfjs/10.1002/essoar.10503129.1

platelets and supercooling. A bit late for the freezing season but a good read.

SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #206 on: June 15, 2020, 06:39:19 PM »
https://www.essoar.org/pdfjs/10.1002/essoar.10503129.1

platelets and supercooling. A bit late for the freezing season but a good read.

Normally get a brain-drain reading arctic papers because of the physics, but that was a good read
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #207 on: July 03, 2020, 01:51:06 PM »
Hate to say this, so decided to wait a few days before I did.
I really cant see a way of getting th(ocean) anymore. Uniquorn I believe if you run the code on your end youll see what i mean. The temps at the ocean:ice interface are just too variable now.
That being said, if anyone has any data they would like to see graphed, let me know
I had another look at the heat120 data. It's possible that as the ocean warms we might still be able to retrieve thickness data from some of the buoys. Perhaps the last few weeks has been a tricky transition period. Of course, they may not last much longer.

T67 floating in open water?

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #208 on: September 10, 2020, 10:11:10 PM »
continuing from mosaic thread
squared up the grid and added some labels. Looking at adding some distances but tracking them is difficult as the delaunay lines move around. It is in the code somewhere though.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #209 on: September 14, 2020, 12:34:11 PM »
t78 and t81 heat120 temps
t78 has some odd features

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #210 on: September 15, 2020, 04:32:35 PM »
t78 and t81 temp proc

oren

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Re: Maximising asif skills using near real time Mosaic data
« Reply #211 on: September 15, 2020, 06:52:28 PM »
Thanks for these updates uniquorn.
It appears that at least for T78 bottom melt has been halted at last, and the ice core temp has equalized and soon hopefully to be inverted.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #212 on: September 15, 2020, 08:15:32 PM »
They were probably just spam on the mosaic thread.
@SimonF92 is it possible to take surface temperature into account when using the heat files in the algo?So that it amplifies the stdev in some way.
« Last Edit: September 15, 2020, 08:20:48 PM by uniquorn »

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #213 on: September 16, 2020, 12:07:02 PM »
latest t78 and t81. T78 is an algo nightmare

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #214 on: September 16, 2020, 09:42:30 PM »
example of ice/ocean interface

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #215 on: September 19, 2020, 10:42:08 PM »
tbuoy update
« Last Edit: September 19, 2020, 10:51:45 PM by uniquorn »

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #216 on: September 23, 2020, 09:43:09 PM »
tbuoy update. The quick 'refreeze' on T78 might be the porous layer at the bottom cooling.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #217 on: December 05, 2020, 04:35:46 PM »
Very interesting, had no idea about "flushing"
Could be that many of our assumptions about ice melting and refreeze need confirming.
Quote
For the time being, instead of having a set value of -1.8 I just set ice bottom as the thermistor below which the temp is 0.2degC less than whatever the ocean temp is at that measurement time.
''
ice_thermistors=ice_or_water_thermistors.where(ice_or_water_thermistors<mean_ocean_heat-0.2)
''
Looks good if my ice temperature doubts turn out to be wrong.

17 older tbuoys deployment report update
« Last Edit: December 05, 2020, 09:01:53 PM by uniquorn »

SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #218 on: December 06, 2020, 12:59:43 AM »
Very interesting, had no idea about "flushing"
Could be that many of our assumptions about ice melting and refreeze need confirming.
Quote
For the time being, instead of having a set value of -1.8 I just set ice bottom as the thermistor below which the temp is 0.2degC less than whatever the ocean temp is at that measurement time.
''
ice_thermistors=ice_or_water_thermistors.where(ice_or_water_thermistors<mean_ocean_heat-0.2)
''
Looks good if my ice temperature doubts turn out to be wrong.

17 older tbuoys deployment report update

I have been spending most of my time working on the HEAT data as I agree there are probably issues in the TEMP data. The former is in some ways more powerful for estimating thickness,
and i suspect when they eventually publish their own method it will centre on HEAT not TEMP, or maybe a combination.

In my opinion its more powerful because you will never see a flat profile through the chain in HEAT, but you do sometimes see it in TEMP.

Its good they are providing their top ice thermistor now, that make things easier.

 If i get anything solid ill send you a message with the code and you can take a look.
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #219 on: February 20, 2021, 12:07:30 AM »
Playing with stat functions on T81

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #220 on: February 21, 2021, 11:18:15 PM »
Larger version of t78 temp and heat120 overlay, no dots. Analysis here

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #221 on: March 14, 2021, 07:46:37 PM »
I ran the python script on T78 yesterday out of interest and it didnt bug entirely

I dont think its accurate but I was surprised it worked!

PS, taking from this graph, 1.2m thickening is what we estimate with our code too- I just dont think the 30cm starting thickness is correct
There might still be a way to improve the script

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #222 on: March 14, 2021, 08:26:25 PM »
Buoy, Deployment thickness,snow,comment
2019T58,1.57,0.18
2019T59,0.8,0.1
2019T63,1.06,0.1
2019T64,1.74,0.14
2019T65,1.31,0.15
2019T67,1.44,0.13
2019T68,1.8,0.15
2019T69,0.7,0.1
2019T70,0.45,0.06
2019T71,0.7,0.1
2019T72,0.97,0.13
2020T60,6.1,0.1, 4cm therms 10mchain
2020T61,7.05,0.065
2020T73,1.77,0.03
2020T74,1.67,0.03
2020T76,1.51,0.09
2020T77,1.65,0.05
2020T78,1.52,0
2020T81,1.14,0

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #223 on: May 05, 2021, 12:20:44 AM »
https://www.cryosphereinnovation.com/articles/simb3-python

Quote
A quick introduction

One of the best parts about working with SIMB3 data is that the results are highly visual. With the surface and bottom rangefinder data alone we can see how the ice (and snow!) change thickness through the season. By incorporating data from the vertical temperature string, we can examine the vertical conductive heat flux. If we combine all three measurements, we create a comprehensive picture of the ice state through time that we refer to as a plot a mass balance plot.


The mass balance plot

A mass balance plot shows ice and snow growth, melt, and internal temperature on a single, easily understandable figure (Figure 1). On the x-axis is time and on the y-axis is thickness or depth. As the ice grows and melts, the distances recorded by the SIMB3 surface and bottom rangefinders lengthen and shorten. With a little knowledge of the SIMB3 dimensions and the deployment conditions, we can turn these distances into ice and snow thickness values. Over time, changes in these values represent ice and snow growth and melt.



441910 looks like the image below after following the tutorial and a few small edits.
Tech notes:
Made using Spyder 3.7, downloaded the files and copy pasted their code.
Edited upper and lower bound, the plotfromcsv name and the snow height
Also had to remove the first 5 rows of csv data as they were all the same ID no.
Don't know how to make the dates fit yet kludged


The Blue at the bottom is ocean, not -30C. For some reason thickness is ~1m instead of ~2.4m zero is wrong place
« Last Edit: May 05, 2021, 12:45:19 AM by uniquorn »

SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #224 on: May 07, 2021, 04:38:45 PM »
I ran the python script on T78 yesterday out of interest and it didnt bug entirely

I dont think its accurate but I was surprised it worked!

PS, taking from this graph, 1.2m thickening is what we estimate with our code too- I just dont think the 30cm starting thickness is correct
There might still be a way to improve the script

uniquorn did you draw that mask on or did you compute it? Looks perfect

edit: wait did you overlay the plot? Lol did i just accidentally compliment myself? Going to take a look at this over the weekend and also the SIMB3 platform, it looks like they precompute ice thickness- I wonder how

I sent them an email asking if I could make python tutorial for their hub, the more of these things available the better
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SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #225 on: May 07, 2021, 04:43:57 PM »
Quote
The Blue at the bottom is ocean, not -30C. For some reason thickness is ~1m instead of ~2.4m zero is wrong place

I think the colour of their 'ocean' infill is just very similar to their 'jet' colourbar colour for -30. Its possible using the 'plasma', 'viridis' or 'inferno' colourmaps for their temps could be better, but I think the red contrasts well with the blue ocean. You could try changing this line in spyder:



im = ax.imshow(dtc, cmap='jet', vmin=-30, vmax=0, extent=[timestamp[0], timestamp[-1], temp_string_bottom, temp_string_top],
                   aspect='auto', zorder=0) 


to



im = ax.imshow(dtc, cmap='plasma', vmin=-30, vmax=0, extent=[timestamp[0], timestamp[-1], temp_string_bottom, temp_string_top],
                   aspect='auto', zorder=0) 




Or 'gnuplot', i dunno
https://matplotlib.org/stable/tutorials/colors/colormaps.html

« Last Edit: May 07, 2021, 04:50:03 PM by SimonF92 »
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cjp

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Re: Maximising asif skills using near real time Mosaic data
« Reply #226 on: May 07, 2021, 05:44:52 PM »
Hey everyone,

This is Cameron Planck, the developer of SIMB3 and the co-founder/owner of Cryosphere Innovation.

It's really cool to see you using our data/website! Regarding the plots, yes the lower "blue" section is the ocean and is not meant to be referenced to the colorbar (it is not -30 C). I should find a better color for the ocean, but it's hard to find a "water-like" color that works with the jet colormap that's common for these plots. I suppose I could color them bright pink or something?  :o. In the past I've just labeled this section as "ocean" which helps with the confusion.

Regarding ice thickness, we compute it directly using the acoustic rangefinder data (this is the primary advantage to SIMB3). We don't use the temperature profile in this calculation at all.

Best,
Cameron

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Re: Maximising asif skills using near real time Mosaic data
« Reply #227 on: May 07, 2021, 05:51:02 PM »
Cameron Planck of Cryosphere Innovation got back to me with an explanation of how they precompute:

"
RE: your recent question about ice thickness on the forum. Yes, we do precompute it. SIMB3 uses acoustic rangefinders to directly measure ice surface/bottom position, so it's pretty straightforward to get the time-series of ice thickness and growth/melt. This is the primary advantage of SIMB3 vs temperature-string based platforms -- we don't need a temperature profile to calculate growth and melt.
"

Interesting stuff, seems much more accurate as overcomes potential supercooling beneath the ice
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #228 on: May 07, 2021, 07:04:42 PM »
I ran the python script on T78 yesterday out of interest and it didnt bug entirely

I dont think its accurate but I was surprised it worked!

PS, taking from this graph, 1.2m thickening is what we estimate with our code too- I just dont think the 30cm starting thickness is correct
There might still be a way to improve the script

uniquorn did you draw that mask on or did you compute it? Looks perfect

edit: wait did you overlay the plot? Lol did i just accidentally compliment myself? Going to take a look at this over the weekend and also the SIMB3 platform, it looks like they precompute ice thickness- I wonder how
yep. overlaid your plot with some scaling. I think if we are given the starting thickness and assume that no thickening occurs until temps drop within a small distance (tbd) from starting  thickness we may have a usable method.

Need some new Tbuoys now.

Here are some old ones.
« Last Edit: May 07, 2021, 09:00:38 PM by uniquorn »

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Re: Maximising asif skills using near real time Mosaic data
« Reply #229 on: May 07, 2021, 11:44:47 PM »
Hey everyone,

This is Cameron Planck, the developer of SIMB3 and the co-founder/owner of Cryosphere Innovation.
Welcome Cameron. Thanks for stopping by!
Þetta minnismerki er til vitnis um að við vitum hvað er að gerast og hvað þarf að gera. Aðeins þú veist hvort við gerðum eitthvað.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #230 on: May 08, 2021, 01:06:54 AM »
Hey everyone,

This is Cameron Planck, the developer of SIMB3 and the co-founder/owner of Cryosphere Innovation.

It's really cool to see you using our data/website! Regarding the plots, yes the lower "blue" section is the ocean and is not meant to be referenced to the colorbar (it is not -30 C). I should find a better color for the ocean, but it's hard to find a "water-like" color that works with the jet colormap that's common for these plots. I suppose I could color them bright pink or something?  :o. In the past I've just labeled this section as "ocean" which helps with the confusion.

Regarding ice thickness, we compute it directly using the acoustic rangefinder data (this is the primary advantage to SIMB3). We don't use the temperature profile in this calculation at all.

Best,
Cameron

Thanks Cameron. The SIMB3's are a great product. Very useful to have a thickness measurement and an ice temperature profile at the same time, providing some helpful pointers during our attempts to estimate thickness from the mosaic Tbuoy temperatures alone.

I'm still playing with your py code though I'm more familiar with R so the charting is very basic. Hopefully I can copy a better layout from some of SimonF92's work.
Here is SIDEx 2021#2 with magma which avoids any confusion with the ocean colour, though I think jet shows temp difference well.

The ice is clearly warming up in that location

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Re: Maximising asif skills using near real time Mosaic data
« Reply #231 on: May 08, 2021, 01:18:39 AM »
The SIMB3 is a great product indeed. Finally, sea ice buoys that one can easily understand and provide actual snow and ice thickness. The thermistor method of the Tbuoys has been highly confusing to derive thicknesses from, despite heroic efforts by uniquorn and Simon.

Cameron - thanks for joining and posting.

Is there also a measurement of the air temperature above the snow, and of the temperature profile inside the snow layer? This would help in the comparisons to Tbuoy data and to ice thickening formulas.

uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #232 on: May 08, 2021, 01:40:04 AM »
Is there also a measurement of the air temperature above the snow, and of the temperature profile inside the snow layer? This would help in the comparisons to Tbuoy data and to ice thickening formulas.
I tried alpha=0.5 (transparency) on the grey, complete guess for now :)
Adding surface temp probably beyond me at the moment.

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Re: Maximising asif skills using near real time Mosaic data
« Reply #233 on: May 08, 2021, 11:01:27 AM »
Is there also a measurement of the air temperature above the snow, and of the temperature profile inside the snow layer? This would help in the comparisons to Tbuoy data and to ice thickening formulas.
I tried alpha=0.5 (transparency) on the grey, complete guess for now :)
Adding surface temp probably beyond me at the moment.

Great idea regarding alpha. Some of the contrast is lost admittedly without jet. I have a method of creating my own colourmaps- will take a shot at it with a custom map
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #234 on: May 08, 2021, 01:22:36 PM »
alpha'd the white as well (both are 0.2) and reduced the upper bound to keep it closer to the data. Could add another white layer maybe. Two thin lines might be better than alpha but it's a colorbar and I don't know how to do that...

second image shows all data points, maybe some temp inversions there

and a quick look at ocean temps. dtc needs calibration or at limit of accuracy?


ax.fill_between(timestamp, snow_height, 0, color="grey", zorder=1, alpha=0.2) #plot snow height on top of ice
    ax.fill_between(timestamp, temp_string_top, snow_height, color='white', zorder=2, alpha=0.2)
 #set plot bounds
    upper_bound = 1.4
    lower_bound = -2.7
ax.fill_between(timestamp, ice_thickness, temp_string_bottom, color='b', zorder=3, alpha=0.2)
« Last Edit: May 08, 2021, 01:56:01 PM by uniquorn »

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Re: Maximising asif skills using near real time Mosaic data
« Reply #235 on: May 08, 2021, 03:48:04 PM »
In theory this allows maximum contrast without allowing blue into the colourmap

''''''''

from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap

colors=['darkmagenta','mediumorchid','forestgreen','green','yellow','goldenrod','orangered','r','darkred']

cmap = LinearSegmentedColormap.from_list("mycmap", colors)

''''''''



then replace cmap='magma' with cmap=cmap when you create the plot

I actually quite like where you are going with it, but if you want to go fully custom you can make a list of any of these colours and alpha as you see fit
https://matplotlib.org/2.0.2/_images/named_colors.png
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Re: Maximising asif skills using near real time Mosaic data
« Reply #236 on: May 08, 2021, 03:51:33 PM »
colors = ['indigo','darkmagenta','magenta','mediumorchid','darkslategrey','forestgreen','green','yellow','goldenrod','orangered','r','darkred']



pops even more due to the weighting of temps above -20





PS looking at the raw data of ocean temps i believe this is probably because the termistors sampling resolution is 0.125 degC so the temps in oceans will only really be either -1.875 or -2- explaining the binary appearance
« Last Edit: May 08, 2021, 04:02:55 PM by SimonF92 »
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #237 on: May 08, 2021, 05:02:27 PM »
PS looking at the raw data of ocean temps i believe this is probably because the termistors sampling resolution is 0.125 degC so the temps in oceans will only really be either -1.875 or -2- explaining the binary appearance
yep, should have checked that. Well the colours won't attract any interior decorating customers but they are very good at highlighting temp difference.
Explains why there is very little thickening recently

worked out how to save lol
« Last Edit: May 08, 2021, 05:16:12 PM by uniquorn »

SimonF92

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Re: Maximising asif skills using near real time Mosaic data
« Reply #238 on: May 08, 2021, 06:08:54 PM »
Ha, its surprisingly difficult to save. Also you can edit dimensions by changing this line

fig, ax = plt.subplots(figsize=(15,7))

15 is width, 7 is height, change to anything you want


Also, I think I will get motion sickness staring at that too long
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #239 on: May 09, 2021, 04:52:33 PM »
441910 again. The snow layer doesn't make sense with the air temps.

Made some adjustments to layers

    temp_string_offset = 0.99 #the axial distance between the surface rangefinder and the first thermister on the DTC
    #set plot bounds
    upper_bound = 1.4
    lower_bound = -2.9

ax.fill_between(timestamp, snow_height-.85, 0, color="grey", zorder=1, alpha=0.5) plot snow height on top of ice
ax.fill_between(timestamp, temp_string_top, snow_height, color='white', zorder=2, alpha=0.2)#create whitespace above snow
ax.fill_between(timestamp, ice_thickness-0.85, temp_string_bottom, color='b', zorder=3)#plot lower boundary of ice


with these adjustments it looks better, no idea if they are meaningful. It may become clearer over time
« Last Edit: May 09, 2021, 05:56:43 PM by uniquorn »

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Re: Maximising asif skills using near real time Mosaic data
« Reply #240 on: May 09, 2021, 09:21:45 PM »
Thats a good point, I cannot think of any reason- but I guess part of the problem is im not familiar with this accoustic technique. Now that Cameron Planck has an account here he may visit from time to time so it would be good to see his thoughts.

They are allowing me to write a python tutorial for their website based on tracking buoys over the season, what do you think about this?

I put snowdepth on the colourmap too as it might be informative for you



EDIT same chunk of thick snow appearing on the same buoy in my figure too
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Re: Maximising asif skills using near real time Mosaic data
« Reply #241 on: May 09, 2021, 09:31:48 PM »
You can see a stinking big low in October/November 2020 if you colour them on air pressure. Which when viewed side by side with the temp data, raised temperature significantly
« Last Edit: May 09, 2021, 09:39:00 PM by SimonF92 »
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #242 on: May 09, 2021, 09:40:43 PM »
A great start.
Unless you animate it the timings are all going to be different for snow, temp and pressure aren't they?
I'm assuming the metres in the box is thickness.

Played a bit more with your colours on 052460. Ice warms faster than I expected.

Still working it out but the sharp temp change has to be the top of the snow line. Checking why it is thinner than that shown on their site
« Last Edit: May 09, 2021, 10:42:23 PM by uniquorn »

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Re: Maximising asif skills using near real time Mosaic data
« Reply #243 on: May 09, 2021, 10:39:30 PM »
Thanks for the comments. Im reluctant to animate as its meant to be for beginners, but ill look into it.

Colours look really nice on the ice there, going to rummage on the website for more info so i can be more useful. Could it be that something fell over in the wind? I doubt its very likely
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #244 on: May 09, 2021, 10:43:39 PM »
Fell over and stood back up again even more unlikely ;)

I can't easily match their ice thickness and snow thickness with temps but maybe I'm misunderstanding snow insulation.

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Re: Maximising asif skills using near real time Mosaic data
« Reply #245 on: May 09, 2021, 11:05:15 PM »
Lol point taken. I can see from your attachment that when it gets extremely cold in the polar night, they largely overestimate snow thickness.

Thats interesting because i thought thickness measurements are detatched from temperature sensing. Could this actually be due to the changes of "sound" wave propagation in extremely cold temperatures?

 I can actually 'see' the snow layer later in the season, you can make out the insulative property but also see that it isn't 'ice' either
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Re: Maximising asif skills using near real time Mosaic data
« Reply #246 on: May 10, 2021, 02:26:43 PM »
Nice visualisations.

For those different on map projections it would be cool if there was some webpage with a tool where you could just click through the days at your leisure (or set it to loop over intervals over several speeds). Would be lovely for the usual drift speed too.
And if you had that you could probably add overlays like OSASIF vs the data types but i will stop daydreaming and let you get one with the thickness.  ;)
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Re: Maximising asif skills using near real time Mosaic data
« Reply #247 on: May 10, 2021, 03:34:00 PM »
Thanks for the feedback Kassy, I agree that would be really nice- the guys at cryosphere innovation were saying at one point they had the functionality to overlay NSIDC concentration over their data- would have loved to have seen that
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uniquorn

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Re: Maximising asif skills using near real time Mosaic data
« Reply #248 on: May 10, 2021, 09:27:34 PM »
Lol point taken. I can see from your attachment that when it gets extremely cold in the polar night, they largely overestimate snow thickness.

Thats interesting because i thought thickness measurements are detatched from temperature sensing. Could this actually be due to the changes of "sound" wave propagation in extremely cold temperatures?

 I can actually 'see' the snow layer later in the season, you can make out the insulative property but also see that it isn't 'ice' either

Looking at snow depth on 441910. The digital temperature chain is on the other side of the buoy to the sounders. I think there was a sudden build up of snow on the sounder side that slid down the buoy during the first 'warm' day.

The dates don't quite match up but there is the conversion from timestamp to date
#then subtract 1 to account for the famous 1900 excel leap year timestamp bug
and nullschool is a model
« Last Edit: May 10, 2021, 09:37:30 PM by uniquorn »

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Re: Maximising asif skills using near real time Mosaic data
« Reply #249 on: May 10, 2021, 10:00:02 PM »
In theory this allows maximum contrast without allowing blue into the colourmap

''''''''

from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap

colors=['darkmagenta','mediumorchid','forestgreen','green','yellow','goldenrod','orangered','r','darkred']

cmap = LinearSegmentedColormap.from_list("mycmap", colors)

''''''''



then replace cmap='magma' with cmap=cmap when you create the plot

I actually quite like where you are going with it, but if you want to go fully custom you can make a list of any of these colours and alpha as you see fit
https://matplotlib.org/2.0.2/_images/named_colors.png

This is a nice modification. If I ever roll temp-string plotting into our real-time plots, I think I'll use this colormap!

Regarding snow thickness, I wouldn't try too hard to get your temperature-string derived snow thicknesses to perfectly match the sounder-derived plots from our website. Unless I have the initial snow thickness from deployment (which we ask our deployment teams to collect), the snow-thickness magnitude that you see on the website is an estimate because the floatation height of the buoy varies during freeze-in (i.e., the "datum" for the real-time plots is not necessarily the same across deployments). Often I don't receive the deployment values of snow/ice thickness and freeboard until many months after deployment. Let me know if this doesn't make sense, I can try to explain it better.

We also occasionally see periods of erroneous values from our snow sounders in winter (likely due to icing and/or snow accumulation around the hull). The good news is that this almost always goes away in early spring.  :)