mpcalc.isentropic_interpolation_as_dataset "vertical attribute is not available."
wenqi18 opened this issue · comments
I get an error "vertical attribute is not available." when interpolating bitwise vortices to isentropic surfaces using the mpcalc.isentropic_interpolation_as_dataset function.Here's the code and the specifics of the error reported：
f=xr.open_dataset(r'H:\data\dust\daily_atmosphere_data\daily_mean_era5_pv0.5_198604.nc')
f_1=xr.open_dataset(r'H:\data\dust\daily_atmosphere_data\daily_mean_era5_t0.5_198604.nc')
pv=f.pv.loc['1986-04-01',100000:20000,20:60,30:150] # （level，lat，lon）
t=f_1.t.loc['1986-04-01',100000:20000,20:60,30:150]# （level，lat，lon）
print(t,pv)
isentlevs = [345.] * units.kelvin
print(isen_lev)
isent_data = mpcalc.isentropic_interpolation_as_dataset(isentlevs, t, pv)C:\Users\Administrator\AppData\Local\Temp\ipykernel_8224\906077059.py:12: UserWarning: More than one vertical coordinate present for variable "t".
isent_data = mpcalc.isentropic_interpolation_as_dataset(isentlevs, t, pv)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [20], in <cell line: 12>()
10 print(isen_lev)
11 # 调用 isentropic_interpolation_as_dataset 函数
---> 12 isent_data = mpcalc.isentropic_interpolation_as_dataset(isentlevs, t, pv)
File G:\Anaconda\lib\site-packages\metpy\calc\thermo.py:2761, in isentropic_interpolation_as_dataset(levels, temperature, max_iters, eps, bottom_up_search, *args)
2756 all_args = xr.broadcast(temperature, *args)
2758 # Obtain result as list of Quantities
2759 ret = isentropic_interpolation(
2760 levels,
-> 2761 all_args[0].metpy.vertical,
2762 all_args[0].metpy.unit_array,
2763 *(arg.metpy.unit_array for arg in all_args[1:]),
2764 vertical_dim=all_args[0].metpy.find_axis_number('vertical'),
2765 temperature_out=True,
2766 max_iters=max_iters,
2767 eps=eps,
2768 bottom_up_search=bottom_up_search
2769 )
2771 # Reconstruct coordinates and dims (add isentropic levels, remove isobaric levels)
2772 vertical_dim = all_args[0].metpy.find_axis_name('vertical')
File G:\Anaconda\lib\site-packages\metpy\xarray.py:486, in MetPyDataArrayAccessor.vertical(self)
483 @property
484 def vertical(self):
485 """Return the vertical coordinate."""
--> 486 return self._axis('vertical')
File G:\Anaconda\lib\site-packages\metpy\xarray.py:418, in MetPyDataArrayAccessor._axis(self, axis)
416 coord_var = self._metpy_axis_search(axis)
417 if coord_var is None:
--> 418 raise AttributeError(axis + ' attribute is not available.')
419 else:
420 return coord_var
AttributeError: vertical attribute is not available.
If you look at the warning, it's saying that the t variable has more tan one coordinate detected as a potentially vertical coordinate; therefore when trying to use metpy's automatic determination of the vertical coordinate, the ambiguity causes it to fail.
Can you either point to/share the data, or at least share the output of print(t)?
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
[448983 values with dtype=float32]
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
<xarray.DataArray 'plev' (plev: 23)> Size: 184B
array([100000., 97500., 95000., 92500., 90000., 87500., 85000., 82500.,
80000., 77500., 75000., 70000., 65000., 60000., 55000., 50000.,
45000., 40000., 35000., 30000., 25000., 22500., 20000.])
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
Attributes:
long_name: pressure
units: Pa
positive: down
stored_direction: decreasing
standard_name: air_pressure
axis: Z
type: double
Can you share the output of:
for c in t.coords.values():
print(t)Thank you for your kind and patient reply, here are the output of my run of for c in t.coords.values(): print(t)
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
array([[[296.74127, 296.83005, ..., 297.4964 , 297.4444 ],
[296.36545, 296.44113, ..., 297.32965, 297.30157],
...,
[278.36902, 278.97278, ..., 263.3966 , 263.1721 ],
[277.52542, 278.12903, ..., 263.13138, 262.63293]],
[[294.85953, 295.24667, ..., 295.36108, 295.29068],
[294.16635, 294.7395 , ..., 295.28003, 295.1769 ],
...,
[276.9598 , 277.63232, ..., 261.61816, 261.77792],
[276.30508, 276.79745, ..., 261.86417, 260.95508]],
...,
[[225.48643, 225.51598, ..., 228.07014, 228.03014],
[225.46547, 225.49323, ..., 228.0713 , 228.02582],
...,
[224.0172 , 223.65071, ..., 215.03047, 214.88417],
[223.92244, 223.58626, ..., 215.68819, 215.57509]],
[[220.00923, 219.97023, ..., 221.78143, 221.78139],
[220.1403 , 220.13548, ..., 221.72295, 221.72882],
...,
[225.68274, 225.49408, ..., 216.64989, 216.5566 ],
[225.9568 , 225.77208, ..., 217.19447, 217.11772]]], dtype=float32)
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
array([[[296.74127, 296.83005, ..., 297.4964 , 297.4444 ],
[296.36545, 296.44113, ..., 297.32965, 297.30157],
...,
[278.36902, 278.97278, ..., 263.3966 , 263.1721 ],
[277.52542, 278.12903, ..., 263.13138, 262.63293]],
[[294.85953, 295.24667, ..., 295.36108, 295.29068],
[294.16635, 294.7395 , ..., 295.28003, 295.1769 ],
...,
[276.9598 , 277.63232, ..., 261.61816, 261.77792],
[276.30508, 276.79745, ..., 261.86417, 260.95508]],
...,
[[225.48643, 225.51598, ..., 228.07014, 228.03014],
[225.46547, 225.49323, ..., 228.0713 , 228.02582],
...,
[224.0172 , 223.65071, ..., 215.03047, 214.88417],
[223.92244, 223.58626, ..., 215.68819, 215.57509]],
[[220.00923, 219.97023, ..., 221.78143, 221.78139],
[220.1403 , 220.13548, ..., 221.72295, 221.72882],
...,
[225.68274, 225.49408, ..., 216.64989, 216.5566 ],
[225.9568 , 225.77208, ..., 217.19447, 217.11772]]], dtype=float32)
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
array([[[296.74127, 296.83005, ..., 297.4964 , 297.4444 ],
[296.36545, 296.44113, ..., 297.32965, 297.30157],
...,
[278.36902, 278.97278, ..., 263.3966 , 263.1721 ],
[277.52542, 278.12903, ..., 263.13138, 262.63293]],
[[294.85953, 295.24667, ..., 295.36108, 295.29068],
[294.16635, 294.7395 , ..., 295.28003, 295.1769 ],
...,
[276.9598 , 277.63232, ..., 261.61816, 261.77792],
[276.30508, 276.79745, ..., 261.86417, 260.95508]],
...,
[[225.48643, 225.51598, ..., 228.07014, 228.03014],
[225.46547, 225.49323, ..., 228.0713 , 228.02582],
...,
[224.0172 , 223.65071, ..., 215.03047, 214.88417],
[223.92244, 223.58626, ..., 215.68819, 215.57509]],
[[220.00923, 219.97023, ..., 221.78143, 221.78139],
[220.1403 , 220.13548, ..., 221.72295, 221.72882],
...,
[225.68274, 225.49408, ..., 216.64989, 216.5566 ],
[225.9568 , 225.77208, ..., 217.19447, 217.11772]]], dtype=float32)
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
array([[[296.74127, 296.83005, ..., 297.4964 , 297.4444 ],
[296.36545, 296.44113, ..., 297.32965, 297.30157],
...,
[278.36902, 278.97278, ..., 263.3966 , 263.1721 ],
[277.52542, 278.12903, ..., 263.13138, 262.63293]],
[[294.85953, 295.24667, ..., 295.36108, 295.29068],
[294.16635, 294.7395 , ..., 295.28003, 295.1769 ],
...,
[276.9598 , 277.63232, ..., 261.61816, 261.77792],
[276.30508, 276.79745, ..., 261.86417, 260.95508]],
...,
[[225.48643, 225.51598, ..., 228.07014, 228.03014],
[225.46547, 225.49323, ..., 228.0713 , 228.02582],
...,
[224.0172 , 223.65071, ..., 215.03047, 214.88417],
[223.92244, 223.58626, ..., 215.68819, 215.57509]],
[[220.00923, 219.97023, ..., 221.78143, 221.78139],
[220.1403 , 220.13548, ..., 221.72295, 221.72882],
...,
[225.68274, 225.49408, ..., 216.64989, 216.5566 ],
[225.9568 , 225.77208, ..., 217.19447, 217.11772]]], dtype=float32)
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
<xarray.DataArray 't' (plev: 23, lat: 81, lon: 241)> Size: 2MB
array([[[296.74127, 296.83005, ..., 297.4964 , 297.4444 ],
[296.36545, 296.44113, ..., 297.32965, 297.30157],
...,
[278.36902, 278.97278, ..., 263.3966 , 263.1721 ],
[277.52542, 278.12903, ..., 263.13138, 262.63293]],
[[294.85953, 295.24667, ..., 295.36108, 295.29068],
[294.16635, 294.7395 , ..., 295.28003, 295.1769 ],
...,
[276.9598 , 277.63232, ..., 261.61816, 261.77792],
[276.30508, 276.79745, ..., 261.86417, 260.95508]],
...,
[[225.48643, 225.51598, ..., 228.07014, 228.03014],
[225.46547, 225.49323, ..., 228.0713 , 228.02582],
...,
[224.0172 , 223.65071, ..., 215.03047, 214.88417],
[223.92244, 223.58626, ..., 215.68819, 215.57509]],
[[220.00923, 219.97023, ..., 221.78143, 221.78139],
[220.1403 , 220.13548, ..., 221.72295, 221.72882],
...,
[225.68274, 225.49408, ..., 216.64989, 216.5566 ],
[225.9568 , 225.77208, ..., 217.19447, 217.11772]]], dtype=float32)
Coordinates:
time datetime64[ns] 8B 1986-04-01
realization int64 8B ...
* plev (plev) float64 184B 1e+05 9.75e+04 9.5e+04 ... 2.25e+04 2e+04
* lat (lat) float64 648B 20.0 20.5 21.0 21.5 ... 58.5 59.0 59.5 60.0
* lon (lon) float64 2kB 30.0 30.5 31.0 31.5 ... 149.0 149.5 150.0
Attributes:
long_name: Temperature
units: K
standard_name: air_temperature
comment: Air temperature
type: real
1
My apologies, I had a typo in my line above. What I need is the output of:
for c in t.coords.values():
print(c)Note the c rather than t in the last line.
Here is the output of print(c), in addition, I found that I implemented isentropic surface interpolation using mpcalc.isentropic_interpolation instead of mpcalc.isentropic_interpolation_as_dataset.
<xarray.DataArray 'time' ()> Size: 8B
array('1995-05-17T00:00:00.000000000', dtype='datetime64[ns]')
Coordinates:
time datetime64[ns] 8B 1995-05-17
realization int64 8B ...
Attributes:
long_name: time
standard_name: time
axis: T
stored_direction: increasing
type: double
<xarray.DataArray 'realization' ()> Size: 8B
[1 values with dtype=int64]
Coordinates:
time datetime64[ns] 8B 1995-05-17
realization int64 8B ...
Attributes:
long_name: realization
units: 1
standard_name: realization
stored_direction: increasing
type: integer
<xarray.DataArray 'plev' (plev: 27)> Size: 216B
array([100000., 97500., 95000., 92500., 90000., 87500., 85000., 82500.,
80000., 77500., 75000., 70000., 65000., 60000., 55000., 50000.,
45000., 40000., 35000., 30000., 25000., 22500., 20000., 17500.,
15000., 12500., 10000.])
Coordinates:
time datetime64[ns] 8B 1995-05-17
realization int64 8B ...
* plev (plev) float64 216B 1e+05 9.75e+04 9.5e+04 ... 1.25e+04 1e+04
Attributes:
long_name: pressure
units: Pa
positive: down
stored_direction: decreasing
standard_name: air_pressure
axis: Z
type: double
<xarray.DataArray 'lat' (lat: 76)> Size: 608B
array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.,
24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37.,
38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65.,
66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
80., 81., 82., 83., 84., 85.])
Coordinates:
time datetime64[ns] 8B 1995-05-17
realization int64 8B ...
* lat (lat) float64 608B 10.0 11.0 12.0 13.0 ... 82.0 83.0 84.0 85.0
Attributes:
units: degrees_north
standard_name: latitude
long_name: latitude
stored_direction: decreasing
axis: Y
positive: up
type: double
valid_max: 90.0
valid_min: -90.0
<xarray.DataArray 'lon' (lon: 91)> Size: 728B
array([ 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,
42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53.,
54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65.,
66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77.,
78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 100., 101.,
102., 103., 104., 105., 106., 107., 108., 109., 110., 111., 112., 113.,
114., 115., 116., 117., 118., 119., 120.])
Coordinates:
time datetime64[ns] 8B 1995-05-17
realization int64 8B ...
* lon (lon) float64 728B 30.0 31.0 32.0 33.0 ... 118.0 119.0 120.0
Attributes:
units: degrees_east
standard_name: longitude
long_name: longitude
axis: X
positive: up
type: double
valid_max: 360.0
valid_min: -180.0
I've been failing to reproduce this with manually generated data following the metadata above, but was able to download some ERA5 data myself and reproduce. I'm digging in now to see what's going on.
Ok, so the problem is that the lat and lon coordinates have a 'positive' attribute, which in compliance with the CF metadata specification, causes them to be identified as vertical coordinates. I'm going to address this in MetPy to avoid this, as well as try to report this to ECMWF so that this potentially can be corrected.
As a workaround, can you try to add these lines to your script:
t.lon.attrs.pop('positive')
t.lat.attrs.pop('positive')
pv.lon.attrs.pop('positive')
pv.lat.attrs.pop('positive')