Essential diagnostics #190

dougiesquire · 2024-07-17T04:18:19Z

The set of diagnostics output by access-om3 configurations is still mostly just inherited from CESM. There are possibly many diagnostics that we want that aren't requested, and others that are requested that we don't want. It would be helpful to compile a set of "essential" diagnostics that should always be requested in configurations so that key metrics/analyses can be calculated.

I'm probably being naive in thinking that it's possible to come up with a single list of essential diagnsotics. In that case, we still need to know what diagnostics are needed to calculate particular metrics so that we can ensure everything we need is saved in upcoming test runs.

The text was updated successfully, but these errors were encountered:

aekiss · 2024-07-17T04:25:12Z

We should start with the CICE and MOM diagnostics used in ACCESS-OM2.

dougiesquire · 2024-07-17T04:27:19Z

Is there a single list of diagnostics for ACCESS-OM2?

aekiss · 2024-07-17T04:33:27Z

The default configs are pretty consistent

aekiss · 2024-07-17T06:29:31Z

It would be good if we could use make_diag_table to generate diag_table, as this makes it easier to create consistent filenames, e.g. when creating one file per variable.

I'm looking into this - see COSIMA/make_diag_table#5

dougiesquire · 2024-07-17T06:31:23Z

It would be good if we could use make_diag_table to generate diag_table, as this makes it easier to create consistent filenames, e.g. when creating one file per variable.

I'm looking into this - see COSIMA/make_diag_table#5

I was just doing the same thing. I'll leave it with you :)

minghangli-uni · 2024-07-18T00:09:12Z

Essential diagnostics are needed as discussed in TWG.

Zonal average temperature and salinity (i.e. depth/latitude maps) (Fig. 12 Kiss et al. 2020) [1993 - 2017]
Time series of global average temperature, salinity and sea surface temperature. (Fig. 3 in Kiss et al. 2020), and sea surface height.
Zonally integrated overturning in density / latitude space (Fig. 7 Kiss et al. 2020) [1993 - 2017]
Time series of Drake Passage zonal transport. (Fig. 4 in Kiss et al. 2020)

dougiesquire · 2024-07-18T06:07:08Z

@minghang is working through what MOM5 diagnostics output in the ACCESS-OM2 default configs are available in MOM6 and what they are called.

aekiss · 2024-07-19T00:52:29Z

I've confirmed that make_diag_table is compatible with MOM6 - see COSIMA/make_diag_table#5

minghangli-uni · 2024-07-19T00:56:33Z

Great to know! Thanks @aekiss

aekiss · 2024-07-19T00:57:32Z

To handle many output files I used these settings in input.nml:

 &diag_manager_nml
    debug_diag_manager = .true.
    issue_oor_warnings = .true.
    flush_nc_files = .true.
    max_axes          = 400
    max_files         = 200
    max_num_axis_sets = 200
 /

This also issues warnings in access-om3.err for any unavailable diagnostics, so a test run with an OM2 diag_table will reveal which diagnostics need to be renamed.

aekiss · 2024-07-19T00:59:24Z

We'll also need to consider which ones to vertically remap onto a non-native grid - see https://mom6.readthedocs.io/en/main/api/generated/pages/Diagnostics.html#native-diagnostics

minghangli-uni · 2024-07-19T01:06:51Z

Ref #178, we currently dont have a proper isopycnal coordinate at the moment.

However we dont have a proper 0.25deg density coordinate as was noted from ACCESS-NRI/access-om3-configs#40

minghangli-uni · 2024-07-22T01:26:27Z

Below diagnostics are the conversion between MOM5 and MOM6. The table is still being updated.

The diagnostics of MOM5, including description, unit, method and packing are generated by matching information from the MOM_diags.txt and diag_table. For diagnostics with missing description, unit, method and packing, these details are not present in the MOM_diags.txt or in mom5. I am not sure how these diagnostiics are traced. Any thoughts? @aekiss

Static 2D Grid Data

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
area_t	area_t	Tracer cell area	m²	none	real*4
area_u	area_u	Surface area of x-direction flow (U) cells	m²	none	real*4
area_v	N/A	Surface area of y-direction flow (V) cells	m²	none	real*4
drag_coeff	drag_coeff	Dimensionless bottom drag coefficient	dimensionless	none	real*4
dxt	dxt	Ocean dxt on t-cells	m	none	real*4
dxu	dxu	Ocean dxu on u-cells	m	none	real*4
dyt	dyt	Ocean dyt on t-cells	m	none	real*4
dyu	dyu	Ocean dyu on u-cells	m	none	real*4
N/A	geolat_c	Ucell latitude	degrees_N	none	real*4
N/A	geolat_t	Tracer latitude	degrees_N	none	real*4
N/A	geolon_c	UV longitude	degrees_E	none	real*4
N/A	geolon_t	Tracer longitude	degrees_E	none	real*4
geolat_c	N/A	Latitude of corner (Bu) points	degrees_north	none	real*4
geolat	N/A	Latitude of tracer (T) points	degrees_north	none	real*4
geolat_v	N/A	Latitude of meridional velocity (Cv) points	degrees_north	none	real*4
geolon_c	N/A	Longitude of corner (Bu) points	degrees_east	none	real*4
geolon	N/A	Longitude of tracer (T) points	degrees_east	none	real*4
geolon_v	N/A	Longitude of meridional velocity (Cv) points	degrees_east	none	real*4
depth_ocean	ht	Ocean depth on t-cells	m	none	real*4
depth_ocean	hu	Ocean depth on u-cells	m	none	real*4
	kmt	Number of depth levels on t-grid	dimensionless	none	real*4
	kmu	Number of depth levels on u-grid	dimensionless	none	real*4

Monthly 3D Fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
agessc	age_global	Sea water age since surface contact	yr	average	real*4
	buoyfreq2_wt	Squared buoyancy frequency at T-cell bottom	1/s²	average	real*4
difvho	diff_cbt_t	Total vertical diffusion of temperature (w/o neutral)	m²/s	average	real*4
	dzt	T-cell thickness	m	average	real*4
rhopot0	pot_rho_0	Potential density referenced to 0 dbar	kg/m³	average	real*4
rhopot2	pot_rho_2	Potential density referenced to 2000 dbar	kg/m³	average	real*4
thetao	pot_temp	Potential temperature	°C	average	real*4
so	salt	Practical Salinity	psu	average	real*4
	temp_xflux_adv	Temperature x flux (advection)
	temp_yflux_adv	Temperature y flux (advection)
	temp	Conservative temperature	°C	average	real*4
umo	tx_trans	T-cell i-mass transport	kg/s	average	real*4
	ty_trans_gm	T-cell mass j-transport from GM	kg/s	average	real*4
	ty_trans_nrho_submeso	T-cell j-mass transport from submesoscale param on neutral rho	kg/s	average	real*4
	ty_trans_rho_gm	T-cell j-mass transport from GM on potential density	kg/s	average	real*4
	ty_trans_rho	T-cell j-mass transport on potential density	kg/s	average	real*4
	ty_trans_submeso	T-cell mass j-transport from submesoscale param	kg/s	average	real*4
vmo	ty_trans	T-cell j-mass transport	kg/s	average	real*4
uo	u	i-current	m/sec	average	real*4
vo	v	j-current	m/sec	average	real*4
	vert_pv	Vertical piece of Ertel PV: (f+zeta)*N²	1/sec³	average	real*4
wd	wt	Dia-surface velocity T-points	m/sec	average	real*4

Monthly 3D Squared Fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
uo	u	i-current	m/sec	pow02	real*4
vo	v	j-current	m/sec	pow02	real*4

monthly 2d fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
KHTH_t	agm	GM diffusivity at surface	m^2/sec	average	real*4
KHTR_h	aredi	neutral diffusivity at k=1	m^2/sec	average	real*4
bmf_u	bmf_u	Bottom u-stress via bottom drag	N/m^2	average	real*4
bmf_v	bmf_v	Bottom v-stress via bottom drag	N/m^2	average	real*4
	ekman_we	Ekman vertical velocity averaged to wt-point	m/s	average	real*4
	eta_nonbouss	surface height including steric contribution	meter	average	real*4
latent_evap	evap_heat	latent heat flux into ocean (<0 cools ocean)	W/m^2	average	real*4
evap	evap	mass flux from evaporation/condensation (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4
heat_content_fprec	fprec_melt_heat	heat flux to melt frozen precip (<0 cools ocean)	W/m^2	average	real*4
fprec	fprec	snow falling onto ocean (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4
	frazil_3d_int_z	Vertical sum of ocn frazil heat flux over time step	W/m^2	average	real*4
lprec	lprec	liquid precip (including ice melt/form) into ocean (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4
LW	lw_heat	longwave flux into ocean (<0 cools ocean)	W/m^2	average	real*4
	melt	water flux transferred with sea ice form/melt (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4
seaice_melt_heat	mh_flux	heat into ocean due to melting ice (>0 heats ocean)	(W/m^2)	average	real*4
MLD_003	mld	mixed layer depth determined by density criteria	m	average	real*4
	net_sfc_heating	surface ocean heat flux coming through coupler and mass transfer	Watts/m^2	average	real*4
pbo	pbot_t	bottom pressure on T cells' // trim ( model_type )	dbar	average	real*4
	pme_net	precip-evap into ocean (total w/ restore + normalize)	(kg/m^3)*(m/sec)	average	real*4
	pme_river	mass flux of precip-evap+river via sbc (liquid	frozen	average	real*4
N/A	river	mass flux of river (runoff + calving) entering ocean	(kg/m^3)*(m/sec)	average	real*4
ficeberg	N/A	Frozen runoff (calving) and iceberg melt into ocean	(kg/m^3)*(m/sec)	average	real*4
friver	runoff	Liquid runoff (rivers) into ocean	(kg/m^3)*(m/sec)	average	real*4
N/A	sea_level_sq	square of effective sea level (eta_t + patm/(rho0*g)) on T cells	m^2	average	real*4
e_D	sea_level	effective sea level (eta_t + patm/(rho0*g)) on T cells	meter	average	real*4
hfsso	sens_heat	sensible heat into ocean (<0 cools ocean)	W/m^2	average	real*4
net_heat_coupler	sfc_hflux_coupler	surface heat flux coming through coupler	Watts/m^2	average	real*4
heat_content_lrunoff	sfc_hflux_from_runoff	heat flux (relative to 0C) from liquid river runoff	Watts/m^2	average	real*4
Heat_PmE	sfc_hflux_pme	heat flux (relative to 0C) from pme transfer of water across ocean surface	Watts/m^2	average	real*4
salt_flux_in	sfc_salt_flux_coupler	sfc_salt_flux_coupler: flux from the coupler	kg m-2 s-1	average	real*4
	sfc_salt_flux_ice		kg m-2 s-1	average	real*4
	sfc_salt_flux_restore		kg m-2 s-1	average	real*4
sfdsi	N/A	Net salt flux into ocean at surface (restoring + sea-ice)	kg m-2 s-1	average	real*4
tos	surface_pot_temp	Sea Surface Temperature	degC	average	real*4
sos	surface_salt	Sea Surface Salinity	psu	average	real*4
SW_pen	swflx	shortwave flux into ocean (>0 heats ocean)	W/m^2	average	real*4
taux	tau_x	i-directed wind stress forcing u-velocity	N/m^2	average	real*4
tauy	tau_y	j-directed wind stress forcing v-velocity	N/m^2	average	real*4
	temp_int_rhodz	vertical sum of Conservative temperature * rho_dzt	deg_C(kg/m^3)m	average	real*4
	temp_xflux_adv_int_z	z-integral of cprhodytutemp	W	average	real*4
	temp_xflux_gm_int_z	z-integral cpgm_xfluxdytrho_dzttemp	W	average	real*4
	temp_xflux_ndiffuse_int_z	z-integral cpndiffuse_xfluxdytrho_dzttemp	W	average	real*4
	temp_xflux_submeso_int_z	z-integral cpsubmeso_xfluxdytrho_dzttemp	W	average	real*4
	temp_yflux_adv_int_z	z-integral of cprhodxtvtemp	W	average	real*4
	temp_yflux_gm_int_z	z-integral cpgm_yfluxdytrho_dzttemp	W	average	real*4
	temp_yflux_ndiffuse_int_z	z-integral cpndiffuse_yfluxdxtrho_dzttemp	W	average	real*4
	temp_yflux_submeso_int_z	z-integral cpsubmeso_yfluxdxtrho_dzttemp	W	average	real*4
umo_2d	tx_trans_int_z	T-cell i-mass transport vertically summed	trim ( transport_dims )	average	real*4
vmo_2d	ty_trans_int_z	T-cell j-mass transport vertically summed	trim ( transport_dims )	average	real*4
seaice_melt	wfiform	water out of ocean due to ice form (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4
seaice_melt	wfimelt	water into ocean due to ice melt (>0 enters ocean)	(kg/m^3)*(m/sec)	average	real*4

monthly 2d fields with different reduction methods

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
MLD_003	mld	mixed layer depth determined by density criteria	m	max	real*4
tos	surface_pot_temp	Sea Surface Temperature	degC	max	real*4

daily 2d fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
	bottom_temp	Conservative temperature	degC	average	real*4
	frazil_3d_int_z	Vertical sum of ocn frazil heat flux over time step	W/m^2	average	real*4
MLD_003	mld	mixed layer depth determined by density criteria	m	average	real*4
	pme_river	mass flux of precip-evap+river via sbc (liquid	frozen	average	real*4
e_D	sea_level	effective sea level (eta_t + patm/(rho0*g)) on T cells	meter	average	real*4
net_heat_coupler	sfc_hflux_coupler	surface heat flux coming through coupler	Watts/m^2	average	real*4
heat_content_lrunoff	sfc_hflux_from_runoff	heat flux (relative to 0C) from liquid river runoff	Watts/m^2	average	real*4
Heat_PmE.	sfc_hflux_pme	heat flux (relative to 0C) from pme transfer of water across ocean surface	Watts/m^2	average	real*4
tos	surface_pot_temp
sos	surface_salt
SSU	usurf	i-surface current	m/sec	average	real*4
SSV	vsurf	j-surface current	m/sec	average	real*4

daily 2d maximum fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
	bottom_temp
e_D	sea_level	effective sea level (eta_t + patm/(rho0*g)) on T cells	meter	max	real*4
tos	surface_pot_temp	Sea Surface Temperature	deg_C	max	real*4

daily 2d minimum fields

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
tos	surface_pot_temp	Sea Surface Temperature	deg_C	min	real*4

daily scalar snapshots

Diagnostic (MOM6)	Diagnostic (MOM5)	Description	Unit	Method	Packing
	eta_global	global ave eta_t plus patm_t/(g*rho0)	meter	none	real*8
KE	ke_tot	Globally integrated ocean kinetic energy	10^15 Joules	none	real*8
	pe_tot	Globally integrated ocean potential energy	10^15 Joules	none	real*8
rhoinsitu	rhoave	global mean ocean in-situ density from ocean_density_mod	kg/m^3	none	real*8
soga	salt_global_ave	Global mean salt in liquid seawater	psu	none	real*8
sosga	salt_surface_ave	Global mass weighted mean surface salt in liquid seawater	psu	none	real*8
thetaoga	temp_global_ave	Global mean temp in liquid seawater	deg_C	none	real*8
tosga	temp_surface_ave	Global mass weighted mean surface temp in liquid seawater	deg_C	none	real*8
	total_net_sfc_heating	total ocean surface flux from coupler and mass transfer	Watts/1e15	none	real*8
	total_ocean_evap_heat	total latent heat flux into ocean (<0 cools ocean)	Watts/1e15	none	real*8
	total_ocean_evap	total evaporative ocean mass flux (>0 enters ocean)	(kg/sec)/1e15	none	real*8
	total_ocean_fprec_melt_heat	total heat flux to melt frozen precip (<0 cools ocean)	Watts/1e15	none	real*8
	total_ocean_fprec	total snow falling onto ocean (>0 enters ocean)	(kg/sec)/1e15	none	real*8
	total_ocean_heat	Total heat in the liquid ocean referenced to 0degC	Joule/1e25	none	real*8
total_net_heat_coupler	total_ocean_hflux_coupler	total surface heat flux passed through coupler	Watts/1e15	none	real*8
	total_ocean_hflux_evap	total ocean heat flux from evap transferring water across surface	Watts/1e15	none	real*8
	total_ocean_hflux_prec	total ocean heat flux from precip transferring water across surface	Watts/1e15	none	real*8
	total_ocean_lprec	total liquid precip into ocean (>0 enters ocean)	(kg/sec)/1e15	none	real*8
	total_ocean_lw_heat	total longwave flux into ocean (<0 cools ocean)	Watts/1e15	none	real*8
	total_ocean_melt	total liquid water melted from sea ice (>0 enters ocean)	(kg/sec)/1e15	none	real*8
	total_ocean_mh_flux	total heat flux into ocean from melting ice (>0 heats ocean)	Watts/1e15	none	real*8
	total_ocean_pme_river	total ocean precip-evap+river via sbc (liquid	frozen	none	real*8
	total_ocean_river_heat	total heat flux into ocean from liquid+solid runoff (<0 cools ocean)	Watts/1e15	none	real*8
	total_ocean_river	total liquid river water and calving ice entering ocean	kg/sec/1e15	none	real*8
	total_ocean_runoff_heat	total ocean heat flux from liquid river runoff	Watts/1e15	none	real*8
	total_ocean_runoff	total liquid river runoff (>0 water enters ocean)	(kg/sec)/1e15	none	real*8
	total_ocean_salt	total mass of salt in liquid seawater	kg/1e18	none	real*8
	total_ocean_sens_heat	total sensible heat into ocean (<0 cools ocean)	Watts/1e15	none	real*8
	total_ocean_sfc_salt_flux_coupler
	total_ocean_swflx_vis	total visible shortwave into ocean (>0 heats ocean)	Watts/1e15	none	real*8
	total_ocean_swflx	total shortwave flux into ocean (>0 heats ocean)	Watts/1e15	none	real*8

aekiss · 2024-07-22T02:56:53Z

Awesome, thanks @minghangli-uni this is super helpful!

aekiss · 2024-07-22T03:30:22Z

For diagnostics with missing description, unit, method and packing, these details are not present in the MOM_diags.txt or in mom5. I am not sure how these diagnostiics are traced. Any thoughts? @aekiss

Some diagnostic names are generated algorithmically, so they don't show up if you search for them in the code. The description and unit should be available from output .nc files, e.g. in /g/data/cj50/access-om2/raw-output/access-om2-01/01deg_jra55v140_iaf/output000/ocean, but it might be easier to use the cookbook tools (cc.querying.get_variables), since the cookbook database has indexed this metadata - see https://cosima-recipes.readthedocs.io/en/latest/Tutorials/Using_Explorer_tools.html#COSIMA-Cookbook-solution

minghangli-uni · 2024-07-22T05:45:03Z

Thank you @aekiss !

But I am still not sure about the exact process (algorithmically) of how these diagnostics were generated. While the descriptions and units can be tracked from the output nc files, these are essentially the final output files. Are we expected to have similar diagnostics in MOM6 as well? If so, we may end up tracking similar processes as MOM5.

minghangli-uni · 2024-07-26T04:12:52Z

Below is a sample of the diag_table generated by diag_table_source.yaml and make_diag_table.py with minor tweaks:

Static grids are included in a single netcdf file.
Scalar fields are included in a single netcdf file.
2D and 3D fields (daily or monthly) are saved in separate files.
We need to discuss which fields require remapped grids.

If every of us is happy with this format, I will generate the first draft.

ACCESS-OM3-025
1900 1 1 0 0 0

static 2d grid data

"access-om3.mom6.h.static", -1, "months", 1, "days", "time"
"ocean_model", "area_t", "area_t", "access-om3.mom6.h.static", "all", "none", "none", 2
"ocean_model", "area_u", "area_u", "access-om3.mom6.h.static", "all", "none", "none", 2

monthly 3d fields

"access-om3.mom6.h.3d.agessc.1.monthly.mean.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "agessc", "agessc", "access-om3.mom6.h.3d.agessc.1.monthly.mean.ym%4yr%2mo", "all", "mean", "none", 2

"access-om3.mom6.h.3d.difvho.1.monthly.mean.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "difvho", "difvho", "access-om3.mom6.h.3d.difvho.1.monthly.mean.ym%4yr%2mo", "all", "mean", "none", 2

monthly 3d squared fields

"access-om3.mom6.h.3d.uo.1.monthly.pow02.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "uo", "uo", "access-om3.mom6.h.3d.uo.1.monthly.pow02.ym%4yr%2mo", "all", "pow02", "none", 2

"access-om3.mom6.h.3d.vo.1.monthly.pow02.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "vo", "vo", "access-om3.mom6.h.3d.vo.1.monthly.pow02.ym%4yr%2mo", "all", "pow02", "none", 2

monthly 2d fields

"access-om3.mom6.h.2d.KHTH_t.1.monthly.mean.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "KHTH_t", "KHTH_t", "access-om3.mom6.h.2d.KHTH_t.1.monthly.mean.ym%4yr%2mo", "all", "mean", "none", 2

"access-om3.mom6.h.2d.KHTR_h.1.monthly.mean.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "KHTR_h", "KHTR_h", "access-om3.mom6.h.2d.KHTR_h.1.monthly.mean.ym%4yr%2mo", "all", "mean", "none", 2

monthly 2d fields with different reduction methods

"access-om3.mom6.h.2d.mlotst.1.monthly.max.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "mlotst", "mlotst", "access-om3.mom6.h.2d.mlotst.1.monthly.max.ym%4yr%2mo", "all", "max", "none", 2

"access-om3.mom6.h.2d.tos.1.monthly.min.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"
"ocean_model", "tos", "tos", "access-om3.mom6.h.2d.tos.1.monthly.min.ym%4yr%2mo", "all", "min", "none", 2

daily 2d fields

"access-om3.mom6.h.2d.tob.1.daily.mean.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "tob", "tob", "access-om3.mom6.h.2d.tob.1.daily.mean.ym%4yr%2mo", "all", "mean", "none", 2

"access-om3.mom6.h.2d.mlotst.1.daily.mean.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "mlotst", "mlotst", "access-om3.mom6.h.2d.mlotst.1.daily.mean.ym%4yr%2mo", "all", "mean", "none", 2

daily 2d maximum fields

"access-om3.mom6.h.2d.tob.1.daily.max.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "tob", "tob", "access-om3.mom6.h.2d.tob.1.daily.max.ym%4yr%2mo", "all", "max", "none", 2

"access-om3.mom6.h.2d.e_D.1.daily.max.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "e_D", "e_D", "access-om3.mom6.h.2d.e_D.1.daily.max.ym%4yr%2mo", "all", "max", "none", 2

daily 2d minimum fields

"access-om3.mom6.h.2d.tos.1.daily.min.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "tos", "tos", "access-om3.mom6.h.2d.tos.1.daily.min.ym%4yr%2mo", "all", "min", "none", 2

daily scalar snapshots

"access-om3.mom6.h.scalar.1.daily.ym%4yr%2mo", 1, "days", 1, "days", "time", 1, "years"
"ocean_model", "soga", "soga", "access-om3.mom6.h.scalar.1.daily.ym%4yr%2mo", "all", "none", "none", 1
"ocean_model", "sosga", "sosga", "access-om3.mom6.h.scalar.1.daily.ym%4yr%2mo", "all", "none", "none", 1

monthly 2d snapshots

monthly 3d snapshots

dougiesquire · 2024-07-26T04:27:59Z

A couple of comments questions using the following as an example:

"access-om3.mom6.h.3d.agessc.1.monthly.mean.ym%4yr%2mo", 1, "months", 1, "days", "time", 1, "years"

I think 1.monthly should be 1monthly (assuming that the 1 is the number of months)
Why do we need ym? Can this be dropped?
I think the date should reflect the time period of the data in the file. For example, this file includes 1 year of data so the time stamp should be %4yr only

Altogether, this would mean the above changes to:

"access-om3.mom6.h.3d.agessc.1monthly.mean.%4yr", 1, "months", 1, "days", "time", 1, "years"

or if we want to be more concise we could shorten the frequency to be consistent with CMIP6 vocab (though this only really saves a few characters)

"access-om3.mom6.h.3d.agessc.1mon.mean.%4yr", 1, "months", 1, "days", "time", 1, "years"

Thoughts @minghangli-uni, @aekiss?

minghangli-uni · 2024-07-26T04:47:15Z

@dougiesquire Thanks for the comments.

I am more inclined to this format, "access-om3.mom6.h.3d.agessc.1mon.mean.%4yr", 1, "months", 1, "days", "time", 1, "years"
It looks concise and doesn’t seem to lose any information.

aekiss · 2024-07-26T05:15:10Z

ym is just an aesthetic workaround because FMS prepends _ prior to the date, and it looks ugly having that preceded by another separator - see COSIMA/access-om2#185 (comment).

In our case
access-om3.mom6.h.3d.agessc.1mon.mean.%4yr
will produce filenames like
access-om3.mom6.h.3d.agessc.1mon.mean._1900.nc
and the ._ may offend those of a sensitive disposition.

aekiss · 2024-07-26T05:26:30Z

I'm not sure that .y_1900 looks a whole lot better than ._1900 though.

aekiss · 2024-07-26T05:27:22Z

Other than that, I'm happy with

"access-om3.mom6.h.3d.agessc.1mon.mean.%4yr", 1, "months", 1, "days", "time", 1, "years"

although it would be even better if the nuopc prefix was shortened somehow to omit h and maybe access- too (ditto for cice6 and ww3 outputs).

aekiss · 2024-07-26T05:28:14Z

@minghangli-uni I'm happy to look over your diag_table_source.yaml when you've finished.

minghangli-uni · 2024-07-26T05:32:44Z

I am thinking adding some scripts to make_diag_table.py to include descriptions for each of the diagnostics. This might make the table a bit messy, but it should clarify the diag_table. It’s not urgent, though.

aekiss · 2024-08-01T02:44:22Z

<n_dimension>.<descriptor> for multi-variable output all of the same dimensionality (e.g. 1d.scalar)

My suggestion here doesn't make sense - should be 0d.scalar if we're only considering spatial dimensionality.

ACCESS-OM2 just used scalar, but maybe we want 0d.scalar to keep a consistent number of components, e.g. in case we do filename.split('.') and want a list of a consistent length.

dougiesquire · 2024-08-01T03:45:54Z

I personally think it's good to have some flexibility in the naming scheme. I'm sort of viewing everything between the <component> and <frequency> fields as optional and unconstrained. For example, the CICE and WW3 output has nothing between these fields, e.g. access-om3.cice.1day.mean.1900-01.nc. So I think we could drop <n_dimension> in the case of multi-variable files.

As for the <time_cell_method> with multi-variable files, let's include this for consistency across the components. In many cases (e.g. access-om2 1deg_jra55_ryf) the cell method for all variables will be the same. But when it's not we can use mix.

I'll wait for any disagreements and then write this all up into a single comment.

aekiss · 2024-08-01T03:52:13Z

OK I'm happy with that

minghangli-uni · 2024-08-01T04:21:02Z

Thank you @dougiesquire .

I prefer to keep the <component> field for all model components (mom6, cice6, ww3). Something like this access-om3.cice6.cice.1mon.mean.1900-01.nc. It might be easier for users to directly recognise the diagnostics from the component they are working with.

For the scalar ncfiles, I agree to keep <time_cell_method> with multiple variable files when all variables share the same time method, otherwise mix.

dougiesquire · 2024-08-01T04:33:24Z

Something like this access-om3.cice6.cice.1mon.mean.1900-01.nc

@minghangli-uni what's the purpose of having both cice6 and cice?

minghangli-uni · 2024-08-01T04:54:09Z

I am following the format of this, cice6 is the component, cice is the variable_info, will that be more consistent?

access-om3.<component>.<variable_info>.<frequency>_<time_cell_method>_<date_stamp>.nc

aekiss · 2024-08-01T06:22:10Z

cice6.cice seems redundant. I'm happy to omit <n_dimension>.<variable_name/descriptor> for cice and ww3 even though it means the number of filename components differs between mom6 and cice6/ww3.

aekiss · 2024-08-01T09:58:26Z

Yet another wrinkle: MOM6 can produce diagnostic output that is remapped onto multiple vertical grids. These need to be distinguished in the filename, because the variable names are unchanged.
It's also possible to output spatially coarsened data.

dougiesquire · 2024-08-05T05:21:30Z

Yet another wrinkle: MOM6 can produce diagnostic output that is remapped onto multiple vertical grids. These need to be distinguished in the filename, because the variable names are unchanged.
It's also possible to output spatially coarsened data.

Good points @aekiss.

In both of these cases, the diagnostic is included in the diag_table using a modified version of the module_name entry. For example, one might specify diagnostics on a configured density coordinate using a "ocean_model_rho" module_name. SImilarly, down-sampled output can be specified by appending _d2 to the module_name (note only a down-sampling level of 2 is currently supported).

In both of these cases, we can capture the info in the filename by adding the text appended to the module name as optional additional fields:

access-om3.mom6.<n_dimension>.<variable_name/descriptor>.<optional:vertical_coordinate>.<optional:d2>.<frequency>.<time_cell_method><datestamp>.nc

Example:
access-om3.mom6.3d.agessc.rho.d2.1day.mean_1900_01.nc

Are people happy with this?

aekiss · 2024-08-05T05:54:51Z

Sounds good. My only concern is that having two optional fields will make it ambiguous to parse if only one of these is used. Should we require the vertical coordinate field, e.g. using nat for native coords instead of omitting it entirely?

dougiesquire · 2024-08-05T05:57:43Z

My only concern is that having two optional fields will make it ambiguous to parse if only one of these is used. Should we require the vertical coordinate field, e.g. using nat for native coords instead of omitting it entirely?

Yes whoops, good point.

I'll let this sit with people for a bit and then (re)write up everything above into a new post.

aekiss · 2024-08-05T06:02:29Z

alternatively, and a little more compactly, could make d2 (downscaled) or d1 (full resolution) mandatory

dougiesquire · 2024-08-06T04:39:35Z

alternatively, and a little more compactly, could make d2 (downscaled) or d1 (full resolution) mandatory

Unfortunately the vertical coordinate only makes sense for 3D output, and the downsampling only makes sense for min 2D output. So they might both have to be optional?

The d2 field is almost a flag: if it's there it's downsampled, if it's isn't it's not, so parsing shouldn't be too difficult (though hopefully we won't be relying on the filename for anything important!)

dougiesquire · 2024-08-07T00:42:07Z

Okay here's what I think we've arrived at:

The general scheme:
access-om3.<model>.<optional:model_specific_fields>.<frequency>.<time_cell_method>.<datestamp>.nc
is followed for all models. Where

<frequency> follows CMIP6 vocab
<datestamp> uses - separator
Multi-variable files with more than one <time_cell_method> use <time_cell_method> = mix

MOM6

Model-specific fields: <n_dimension>.<variable_name/descriptor>.<vertical_coordinate>.<d2>

<n_dimension> is the number of spatial dimensions, only included for files containing a single variable, e.g. 0d for scalars
<vertical_coordinate> is only included for non-native coordinates
<d2> is only included for down-sampled output
The annoying underscores in the FMS datestamp will be fixed up as a post-processing step

E.g.

3d variable on native vertical coords: access-om3.mom6.3d.agessc.1day.mean.1900-01.nc
3d variable on rho coords: access-om3.mom6.3d.agessc.rho.1day.mean.1900-01.nc
3d variable on native vertical coords and downsampled: access-om3.mom6.3d.agessc.d2.1day.mean.1900-01.nc
3d variable on rho coords and downsampled: access-om3.mom6.3d.agessc.rho.d2.1day.mean.1900-01.nc
Multi-variable with more than one <time_cell_method>: access-om3.mom6.scalar.1day.mix.1900-01.nc

It’s not ideal, but I think there are rules we can follow to reliably parse info.

CICE6

No model-specific fields (yet).

See Essential diagnostics #190 (comment)
Daily files will be concatenated into months as a post-processing step

E.g.

access-om3.cice.1mon.mean.1900-01.nc
access-om3.cice.1day.mean.1900-01.nc

WW3

No model-specific fields (yet).

Currently outputs access-om3.ww3.hi.YYYY-MM-DD-SSSSS.nc so will need to post-process or patch the NUOPC cap

E.g.

access-om3.ww3.1day.snap.1900-01.nc

Speak now or forever hold your peace

ezhilsabareesh8 · 2024-08-07T04:54:17Z

I don't see why these couldn't be post-processed to a format like
access-om3.ww3.1day.mean.YYYY-MM.nc
but perhaps @ezhilsabareesh8 or @anton-seaice can comment?

@dougiesquire WW3 cannot to do time averages in output history, refer here, the output access-om3.ww3.hi.YYYY-MM-DD-SSSSS.nc is a snapshot. The mean output can be post-processed to this format access-om3.ww3.1day.mean.YYYY-MM.nc, once we adopt these changes to the wave cap by NorESM.

Regarding changing the access-om3.ww3.hi.YYYY-MM-DD-SSSSS.nc, I think it is difficult to change the output format. The default output of WW3 is a binary file, however when history option is present in the wave_modelio of nuopc.runconfig, WW3 allows user to write a gridded netcdf output file using the w3iogoncd function at a user defined frequency. All these gridded output files are snapshots. However, the default format of WW3 time string isYYYY-MM-DD-SSSSS refer here. Once the history alarm triggers the w3iogoncd function, WW3 calls set_user_timestring function from w3timemd module (refer here), and converts the current time to YYYY-MM-DD-SSSSS format.

aidanheerdegen · 2024-09-24T09:48:21Z

I think I've come round to the idea of post-processing so that everything is consistent

Is this still the case? I really don't think this is a good idea if so.

Every post-processing step is another step in the provenance chain. If we can't capture it we're weakening the connections between model run and output data. If we do capture it, then it's just more complexity.

As @dougiesquire pointed out above, any post-processing step also has the potential for corrupting files. A simple mv isn't so bad, but if we're doing other post-processing actions that modify data or meta-data at all there is the potential for errors that can go undetected for a long period of time.

If it's as simple as some output filename formatting I'd definitely prefer to enable in code.

… (comment)

dougiesquire added help wanted Extra attention is needed ww3 Related to WW3 mom6 Related to MOM6 cice6 Related to CICE6 all_configurations labels Jul 17, 2024

dougiesquire added the priority:high label Jul 18, 2024

This was referenced Aug 14, 2024

Remove .h from CICE history output filenames #202

Merged

generalise support for cice filenames COSIMA/om3-scripts#22

Merged

aekiss mentioned this issue Aug 15, 2024

Analysis-ready chunking of diagnostic output files #203

Open

MartinDix mentioned this issue Aug 23, 2024

Single variable per file output ACCESS-NRI/um2nc-standalone#89

Open

This was referenced Aug 30, 2024

Postprocessing scripts to standardise file naming COSIMA/om3-scripts#32

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Postprocessing for standardised output naming #222

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minghangli-uni mentioned this issue Sep 9, 2024

Add standardise_mom6_filenames.sh script (#32) COSIMA/om3-scripts#35

Merged

minghangli-uni mentioned this issue Nov 22, 2024

[Parameter Testing] KPP vs ePBL vertical mixing #243

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anton-seaice added a commit to ACCESS-NRI/CICE that referenced this issue Dec 6, 2024

Format cice history filenames per COSIMA/access-om3#190 (comment)

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anton-seaice added a commit to ACCESS-NRI/CICE that referenced this issue Dec 20, 2024

Format cice history filenames per COSIMA/access-om3#190 (comment)

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anton-seaice mentioned this issue Jan 14, 2025

209 new build #250

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This was referenced Jan 17, 2025

Update diag_table ACCESS-NRI/access-om3-configs#156

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Update diag_table #257

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minghangli-uni added a commit to ACCESS-NRI/access-om3-configs that referenced this issue Jan 17, 2025

Update diag_table based on the agreed scheme in COSIMA/access-om3#190…

4a663e3

… (comment)

Essential diagnostics #190

Essential diagnostics #190

Comments

dougiesquire commented Jul 17, 2024

aekiss commented Jul 17, 2024

dougiesquire commented Jul 17, 2024

aekiss commented Jul 17, 2024

aekiss commented Jul 17, 2024

dougiesquire commented Jul 17, 2024

minghangli-uni commented Jul 18, 2024

dougiesquire commented Jul 18, 2024

aekiss commented Jul 19, 2024

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minghangli-uni commented Jul 19, 2024

minghangli-uni commented Jul 22, 2024 • edited Loading

Static 2D Grid Data

Monthly 3D Fields

Monthly 3D Squared Fields

monthly 2d fields

monthly 2d fields with different reduction methods

daily 2d fields

daily 2d maximum fields

daily 2d minimum fields

daily scalar snapshots

aekiss commented Jul 22, 2024

aekiss commented Jul 22, 2024

minghangli-uni commented Jul 22, 2024 • edited Loading

minghangli-uni commented Jul 26, 2024

static 2d grid data

monthly 3d fields

monthly 3d squared fields

monthly 2d fields

monthly 2d fields with different reduction methods

daily 2d fields

daily 2d maximum fields

daily 2d minimum fields

daily scalar snapshots

monthly 2d snapshots

monthly 3d snapshots

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MOM6

CICE6

WW3

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