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Add functionality to MALI ocean-data interpolation script #742

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Add functionality to MALI ocean-data interpolation script #742

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28 changes: 14 additions & 14 deletions landice/mesh_tools_li/interpolate_mpasOcean_to_mali.py
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Original file line number Diff line number Diff line change
Expand Up @@ -146,7 +146,7 @@ def get_data(self, year):

# open and concatenate diagnostic dataset
self.DS = xr.open_mfdataset(os.path.join(self.options.mpasoHistDir,
f'*.mpaso.hist.am.timeSeriesStatsMonthly.{year}-*-01.nc'), combine='nested', concat_dim='Time', decode_timedelta=False)
f'*.mpaso.hist.am.timeSeriesStatsMonthly.{year:04d}-*-01.nc'), combine='nested', concat_dim='Time', decode_timedelta=False)

# variables for time averaging
self.temperature = self.DS['timeMonthly_avg_activeTracers_temperature']
Expand All @@ -157,10 +157,8 @@ def get_data(self, year):
avg_layerThickness = self.DS['timeMonthly_avg_layerThickness']
self.layerThickness = avg_layerThickness.data

#xt = DS['xtime_startMonthly']

#xtime = np.array([xt],dtype=('S',64))
# << NOTE >>: may need to eventually use: "xtime.data.tobytes().decode()" but not sure yet
# monthly xtime strings (one per month), used when not time averaging
self.xtimeMonthly = self.DS['xtime_startMonthly'].values

@matthewhoffman matthewhoffman Jul 23, 2026

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Is xtime_startMonthly in all monthly average MPAS-O history files? I don't recognize the name, but maybe it is automatically generated by the time-averager in MPAS-O. Did you do any preprocessing of the MPAS-O history files or did your run of this script work successfully with the unmodified history files?

@stephenprice stephenprice Jul 23, 2026

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Good question.

char xtime_startMonthly(Time, StrLen) ;
char xtime_endMonthly(Time, StrLen) ;

... are in all timeSeriesStatsMonthly mpas-o output files (for the Max, Min files their names are altered accordingly).

But I did NO modification of these files prior to running this script. I appears to have worked on them as is.

Note that I have spot checked the outputs from this using ncdump and they look reasonable in the sense that there are a lot of zeroes and (fewer sets of) non-zeroes for the field ismip6shelfMelt_3dThermalForcing, which look like physically reasonable ocean temperature values. But we can let this PR sit until we test them to make sure. I just don't know enough about what these fields should look like or how to check them further


self.have_landIceFreshwaterFlux = False
try:
Expand Down Expand Up @@ -227,16 +225,18 @@ def time_average_output(self):
dates.append(xt_reformat)
#self.newXtime = dates
self.newXtime = xt_reformat
else : #do nothing if not time averaging
self.newTemp = self.temperature
self.newSal = self.salinity
self.newDens= self.density
self.newLThick = self.layerThickness
self.newMpasCCE = self.mpas_cellCenterElev
self.newAtmPr = self.atmPressure
else : #keep monthly resolution, but evaluate lazy arrays to NumPy
self.newTemp = self.temperature.values
self.newSal = self.salinity.values
self.newDens = self.density.values
self.newLThick = np.asarray(self.layerThickness)
self.newMpasCCE = np.asarray(self.mpas_cellCenterElev)
self.newAtmPr = self.atmPressure.values
if self.have_landIceFreshwaterFlux:
self.newLandIceFWFlux = self.landIceFreshwaterFlux
self.newXtime = np.nan #use as placeholder for now
self.newLandIceFWFlux = self.landIceFreshwaterFlux.values
# one xtime string per month, padded to 64 characters
self.newXtime = [b.tobytes().decode().strip().ljust(64)
for b in self.xtimeMonthly]

print("New xtime: {}".format(self.newXtime))

Expand Down