GS frontal location plots in python

[ShastriPaturi]

Purpose Adapt the Ferret code in python to re-plot the Gulf Stream frontal location.

Objective To use these scripts in the global-workflow as part of diagnostics.

[hyunchul386]

Following is a diag_table for z* coordinate with fluxes, and let me know any suggestions. This will be tested by RT run of ufs, if it is okay.

@[SYEAR]@[SMONTH]@[SDAY].@[SHOUR]Z.@[ATMRES].64bit.non-mono 2 @[SYEAR] @[SMONTH] @[SDAY] @[SHOUR] 0 0 3 4 "fv3_history", 0, "hours", 1, "hours", "time" 5 "fv3_history2d", 0, "hours", 1, "hours", "time" 6 ###################### 7 "ocn%4yr%2mo%2dy%2hr", 6, "hours", 1, "hours", "time", 6, "hours", "1901 1 1 0 0 0" 8 "SST%4yr%2mo%2dy", 1, "days", 1, "days", "time", 1, "days", "1901 1 1 0 0 0" 9 ############################################## 10 # static fields 11 "ocean_model", "geolon", "geolon", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 12 "ocean_model", "geolat", "geolat", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 13 #"ocean_model", "geolon_c", "geolon_c", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 14 #"ocean_model", "geolat_c", "geolat_c", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 15 #"ocean_model", "geolon_u", "geolon_u", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 16 #"ocean_model", "geolat_u", "geolat_u", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 17 #"ocean_model", "geolon_v", "geolon_v", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 18 #"ocean_model", "geolat_v", "geolat_v", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 19 #"ocean_model", "depth_ocean", "depth_ocean", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 20 #"ocean_model", "wet", "wet", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 21 "ocean_model", "wet_c", "wet_c", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 22 #"ocean_model", "wet_u", "wet_u", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 23 #"ocean_model", "wet_v", "wet_v", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 24 #"ocean_model", "sin_rot", "sin_rot", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 25 #"ocean_model", "cos_rot", "cos_rot", "ocn%4yr%2mo%2dy%2hr", "all", .false., "none", 2 26 27 # ocean output TSUV and others 28 "ocean_model", "SSH", "SSH", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 29 #"ocean_model", "SST", "SST", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 30 #"ocean_model", "SSS", "SSS", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 31 #"ocean_model", "speed", "speed", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 32 #"ocean_model", "SSU", "SSU", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 33 #"ocean_model", "SSV", "SSV", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 34 #"ocean_model", "frazil", "frazil", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 35 #"ocean_model", "ePBL_h_ML", "ePBL", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 36 #"ocean_model", "MLD_003", "MLD_003", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 37 "ocean_model", "MLD_0125", "MLD_0125", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 38 39 # save daily SST 40 "ocean_model", "geolon", "geolon", "SST%4yr%2mo%2dy", "all", .false., "none", 2 41 "ocean_model", "geolat", "geolat", "SST%4yr%2mo%2dy", "all", .false., "none", 2 42 "ocean_model", "SST", "sst", "SST%4yr%2mo%2dy", "all", .true., "none", 2 43 44 # Z-Space Fields Provided for CMIP6 (CMOR Names): 45 #=============================================== 46 "ocean_model_z","uo","uo" ,"ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 47 "ocean_model_z","vo","vo" ,"ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 48 "ocean_model_z","so","so" ,"ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 49 "ocean_model_z","temp","temp" ,"ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 50 51 # forcing 52 "ocean_model", "taux", "taux", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 53 "ocean_model", "tauy", "tauy", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 54 "ocean_model", "latent", "latent", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 55 "ocean_model", "sensible", "sensible", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 56 "ocean_model", "SW", "SW", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 57 "ocean_model", "LW", "LW", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 58 "ocean_model", "evap", "evap", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 59 "ocean_model", "lprec", "lprec", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 60 "ocean_model", "lrunoff", "lrunoff", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 61 # "ocean_model", "frunoff", "frunoff", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 62 "ocean_model", "fprec", "fprec", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 63 "ocean_model", "LwLatSens", "LwLatSens", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2 64 "ocean_model", "Heat_PmE", "Heat_PmE", "ocn%4yr%2mo%2dy%2hr","all",.true.,"none",2

[guillaumevernieres]

That won't work @hyunchul386 :

• We're doing 9hr forecast so you can't average over 24hours.
• A lot of that stuff will be saved when running the long forecast, can you reduce the diag_table to only what's needed for us and relevant to the 6 hour cycle?
• we also talked about using z* for the da ... I don't see this reflected in your diag_table.

As a reminder, this is the diag_table that is used for the 6 hour cycle (9hr forecat): https://github.com/guillaumevernieres/global-workflow/blob/develop/parm/parm_fv3diag/diag_table_da

[hyunchul386]

Following is a slimed version with 6 hour output frequency 6 ###################### 7 "ocn_da%4yr%2mo%2dy%2hr", 6, "hours", 1, "hours", "time", 6, "hours", "1901 1 1 0 0 0" 8 ############################################## 9 # static fields 10 "ocean_model", "geolon", "geolon", "ocn_da%4yr%2mo%2dy%2hr", "all", .false., "none", 2 11 "ocean_model", "geolat", "geolat", "ocn_da%4yr%2mo%2dy%2hr", "all", .false., "none", 2 12 13 # ocean output SSH and MLD 14 "ocean_model", "SSH", "SSH", "ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 15 "ocean_model", "MLD_0125", "MLD_0125", "ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 16 17 # Z*-Space Fields: 18 #=============================================== 19 "ocean_model_z","uo","uo" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 20 "ocean_model_z","vo","vo" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 21 "ocean_model_z","so","so" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 22 "ocean_model_z","temp","temp" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2

[hyunchul386]

Following is from the test diag_table in ufs of 6 ###################### 7 "ocn_da%4yr%2mo%2dy%2hr", 1, "hours", 1, "hours", "time", 1, "hours", "1901 1 1 0 0 0" 8 ############################################## 9 # static fields 10 "ocean_model", "geolon", "geolon", "ocn_da%4yr%2mo%2dy%2hr", "all", .false., "none", 2 11 "ocean_model", "geolat", "geolat", "ocn_da%4yr%2mo%2dy%2hr", "all", .false., "none", 2 12 13 # ocean output SSH and MLD 14 "ocean_model", "SSH", "SSH", "ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 15 "ocean_model", "MLD_0125", "MLD_0125", "ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 16 17 # Z*-Space: 18 #=============================================== 19 "ocean_model_z","uo","uo" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 20 "ocean_model_z","vo","vo" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 21 "ocean_model_z","ho","ho" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 22 "ocean_model_z","so","so" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2 23 "ocean_model_z","temp","temp" ,"ocn_da%4yr%2mo%2dy%2hr","all",.false.,"none",2

The depths of 33 levels are 2.5, 10, 20, 32.5, 51.25, 75, 100, 125, 156.25, 200, 250, 312.5, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1537.5, 1750, 2062.5, 2500, 3000, 3500, 4000, 4500, 5000, 5500

[hyunchul386]

The updated diag_table_da is 3 "ocn_da%4yr%2mo%2dy%2hr", 1, "hours", 1, "hours", "time", 1, "hours" 4 5 "ocean_model", "geolon", "geolon", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 6 "ocean_model", "geolat", "geolat", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 7 "ocean_model", "SSH", "ave_ssh", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 8 "ocean_model", "MLD_0125", "MLD", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 9 "ocean_model_z", "u", "u", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 10 "ocean_model_z", "v", "v", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 11 "ocean_model_z", "h", "h", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 12 "ocean_model_z", "salt", "Salt", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2 13 "ocean_model_z", "temp", "Temp", "ocn_da%4yr%2mo%2dy%2hr", "all", "false", "none", 2

[hyunchul386]

the change is in the branch of "feature/ocn_da_zstar" in https://github.com/hyunchul386/global-workflow.git

[hyunchul386]

Front locations in python from 5 degree z* ocean output of ufs-weather-model,

[guillaumevernieres]

Thanks @hyunchul386 . We would like to have your ocean front utility as part of the "verify" step of the global-workflow. Can you coordinate with @AndrewEichmann-NOAA as to how to do this?

[hyunchul386]

Sure, I will learn about how to put it into verify step in g-w to @AndrewEichmann-NOAA.

[hyunchul386]

The python code of the GS front plot was added into the ctest (#11) of GDASApp in G-W, Start 1: test_gdasapp_convert_bufr_temp_dbuoy 1/11 Test #1: test_gdasapp_convert_bufr_temp_dbuoy ....... Passed 8.83 sec Start 2: test_gdasapp_convert_bufr_salt_dbuoy 2/11 Test #2: test_gdasapp_convert_bufr_salt_dbuoy ....... Passed 0.22 sec Start 3: test_gdasapp_convert_bufr_temp_mbuoyb 3/11 Test #3: test_gdasapp_convert_bufr_temp_mbuoyb ...... Passed 0.21 sec Start 4: test_gdasapp_convert_bufr_salt_mbuoyb 4/11 Test #4: test_gdasapp_convert_bufr_salt_mbuoyb ...... Passed 0.21 sec Start 5: test_gdasapp_convert_bufr_tesacprof 5/11 Test #5: test_gdasapp_convert_bufr_tesacprof ........ Passed 0.24 sec Start 6: test_gdasapp_convert_bufr_trkobprof 6/11 Test #6: test_gdasapp_convert_bufr_trkobprof ........ Passed 0.22 sec Start 7: test_gdasapp_convert_bufr_sfcships 7/11 Test #7: test_gdasapp_convert_bufr_sfcships ......... Passed 0.22 sec Start 8: test_gdasapp_convert_bufr_sfcshipsu 8/11 Test #8: test_gdasapp_convert_bufr_sfcshipsu ........ Passed 0.21 sec Start 9: test_gdasapp_soca_obsdb 9/11 Test #9: test_gdasapp_soca_obsdb ....................Failed 0.29 sec Start 10: test_gdasapp_soca_nsst_increment_to_mom6 10/11 Test #10: test_gdasapp_soca_nsst_increment_to_mom6 ...Failed 15.72 sec Start 11: test_gdasapp_soca_front_plots_mom6 11/11 Test #11: test_gdasapp_soca_front_plots_mom6 ......... Passed 4.48 sec

ctest #9/10 were failed due to the EVA module, and were passed with GDAS modules. Following is ctest output of test_gdasapp_soca_front_plots_mom6

The updated GDASApp was pushed as the branch of feature/front_plot to https://github.com/hyunchul386/GDASApp.git

[hyunchul386]

Update the code : adding input yaml file for parameters which are dependent with the resolution, change the color range (min/max in the region), Following is the updated output of the ctest

Code was update with the commit of "add yaml" to the branch of feature/front_plot, https://github.com/hyunchul386/GDASApp.git

Update for adding tickmarks, and pushed repository as "add tickmark"

[hyunchul386]

Adding SST field with GS front

pushed with "add SST" to the repository

[guillaumevernieres]

@hyunchul386 :

• Are you testing for multiple regions?
• Can we have a global figure as well?
• This work is part of our "verification against obs or other reanalysis" effort ... Any progress on the sourcing of front location from other products?

[hyunchul386]

• checked with Kuroshio

[hyunchul386]

• on verification against the obs: considering NAVY data

[guillaumevernieres]

• checked with Kuroshio

Add more levels to the sst color map @hyunchul386 .

[AndrewEichmann-NOAA]

Are those cold spots real?

On Wed, May 31, 2023 at 2:38 PM Guillaume Vernieres < @.***> wrote:

• checked with Kuroshio [image: front_output_KS_SST] https://user-images.githubusercontent.com/14810515/242372196-4de9652c-6d26-4637-b311-3b925ac91cd9.png

Add more levels to the sst color map @hyunchul386 https://github.com/hyunchul386 .

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[hyunchul386]

[hyunchul386]

The spots seem to be due to islands

[hyunchul386]

First look for NAVO data in front plot. This is just a test for NAVO data on 2021-09-01 with the front plot data on 2021-03-22. Red is from NAVO, black is from 5 deg UWM.

[hyunchul386]

Same test but with SST,

[guillaumevernieres]

@hyunchul386 , your sst colormap resolution is too coarse.

[hyunchul386]

updated for SST

[hyunchul386]

NAVO data for Kuroshio convers only part of it,

[guillaumevernieres]

@hyunchul386 . Your choice of colormap and "color resolution" isn't working. You are not going to see anything interesting with 1/2 dozen sst contours. Increase the resolution to at least a dozen relevant contours.

[hyunchul386]

[hyunchul386]

[hyunchul386]

put the updated code for NAVO data into ctest of GDASApp:

Test project /scratch1/NCEPDEV/climate/Hyun-Chul.Lee/global-workflow_fplot/sorc/gdas.cd/build/test/soca Start 11: test_gdasapp_soca_front_plots_mom6 1/1 Test #11: test_gdasapp_soca_front_plots_mom6 ... Passed 4.92 sec

100% tests passed, 0 tests failed out of 1

[hyunchul386]

ctest results for the updated plot

[hyunchul386]

[hyunchul386]