Create a cross-section of geopotiential height for a specific region

Uses iris to open raw UM model data - predates automatic netCDF conversion in the AUS2200 suite. Useful for diagnosing model instabilities.

import xarray as xr
import datetime
import cartopy.crs as ccrs
from matplotlib import pyplot as plt, animation
from glob import glob
import iris

ts1=30
starttime=datetime.datetime(year=2022,month=2,day=21,hour=18)
td1=datetime.timedelta(seconds=ts1)

ts2=75
td2=datetime.timedelta(seconds=ts2)

tdg=datetime.timedelta(seconds=15)

ds=xr.open_dataset(f"/g/data/hh5/tmp/WACI-Hackathon-2023/AUS2200/data/surf/1hr/orog_1hr_{(starttime + datetime.timedelta(hours=7)).strftime('%Y%m%d')}_20220307.nc")

x_min=min(ds.longitude)
x_max=max(ds.longitude)
y_min=min(ds.latitude)
y_max=max(ds.latitude)
ex=(x_min,x_max,y_min,y_max)
x_minf=x_min.data.item()
x_maxf=x_max.data.item()
y_minf=y_min.data.item()
y_maxf=y_max.data.item()

ds

<xarray.Dataset>
Dimensions:    (latitude: 2120, longitude: 2600, time: 336)
Coordinates:
  * latitude   (latitude) float32 -48.79 -48.77 -48.75 ... -6.871 -6.852 -6.832
  * longitude  (longitude) float32 107.5 107.5 107.6 107.6 ... 158.9 159.0 159.0
  * time       (time) datetime64[ns] 2022-02-22T00:00:30 ... 2022-03-08
Data variables:
    orog       (time, latitude, longitude) float32 ...
Attributes: (12/25)
    Conventions:          CF-1.6, ACDD-1.3
    title:                AUS2200 UM amip simulation over Australian region f...
    summary:              A limited area model study of the entire Australian...
    source:               UM v12.2 ECMWF ERA5 forecast fields used as forcings
    license:              http://creativecommons.org/licenses/by/4.0/
    product_version:      0.1
    ...                   ...
    geospatial_lon_min:   107.5101
    geospatial_lon_max:   0.0198
    contributor_name:     Sam Green, Paola Petrelli
    contributor_email:    sam.green@unsw.edu.au, paola.petrelli@utas.edu.au
    contributor_url:      https://orcid.org/0000-0003-1129-4676, https://orci...
    contributor_role:     post-processing, post-processing

xarray.Dataset

• Dimensions:
  • latitude: 2120
  • longitude: 2600
  • time: 336
• Coordinates: (3)
  • latitude
    (latitude)
    float32
    -48.79 -48.77 ... -6.852 -6.832

    standard_name :

    latitude

    units :

    degrees

    array([-48.788   , -48.7682  , -48.748398, ...,  -6.871399,  -6.851597,
            -6.831799], dtype=float32)

  • longitude
    (longitude)
    float32
    107.5 107.5 107.6 ... 159.0 159.0

    standard_name :

    longitude

    units :

    degrees

    array([107.52    , 107.539795, 107.55959 , ..., 158.9406  , 158.96039 ,
           158.9802  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2022-02-22T00:00:30 ... 2022-03-08

    standard_name :

    time

    array(['2022-02-22T00:00:30.000000000', '2022-02-22T02:00:00.000000000',
           '2022-02-22T03:00:00.000000000', ..., '2022-03-07T22:00:00.000000000',
           '2022-03-07T23:00:00.000000000', '2022-03-08T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • orog
    (time, latitude, longitude)
    float32
    ...

    standard_name :

    surface_altitude

    units :

    m

    source :

    Data from Met Office Unified Model

    um_version :

    12.2

    STASH :

    [ 1 0 33]

    [1852032000 values with dtype=float32]

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Float64Index([ -48.78799819946289,   -48.7681999206543,  -48.74839782714844,
                  -48.728599548339844, -48.708797454833984,  -48.68899917602539,
                   -48.66919708251953,  -48.64939880371094,  -48.62959671020508,
                  -48.609798431396484,
                  ...
                   -7.009998321533203,  -6.990200042724609,   -6.97039794921875,
                   -6.950599670410156,  -6.930797576904297,  -6.910999298095703,
                   -6.891197204589844,   -6.87139892578125,  -6.851596832275391,
                   -6.831798553466797],
                 dtype='float64', name='latitude', length=2120))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([ 107.5199966430664,   107.539794921875,  107.5595932006836,
                  107.57939910888672, 107.59919738769531,  107.6189956665039,
                   107.6387939453125, 107.65859985351562, 107.67839813232422,
                  107.69819641113281,
                  ...
                    158.802001953125, 158.82179260253906,  158.8415985107422,
                  158.86138916015625, 158.88119506835938,  158.9010009765625,
                  158.92079162597656,  158.9405975341797, 158.96038818359375,
                  158.98019409179688],
                 dtype='float64', name='longitude', length=2600))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-02-22 00:00:30', '2022-02-22 02:00:00',
                   '2022-02-22 03:00:00', '2022-02-22 04:00:00',
                   '2022-02-22 05:00:00', '2022-02-22 06:00:00',
                   '2022-02-22 06:00:30', '2022-02-22 08:00:00',
                   '2022-02-22 09:00:00', '2022-02-22 10:00:00',
                   ...
                   '2022-03-07 15:00:00', '2022-03-07 16:00:00',
                   '2022-03-07 17:00:00', '2022-03-07 18:00:00',
                   '2022-03-07 18:00:30', '2022-03-07 20:00:00',
                   '2022-03-07 21:00:00', '2022-03-07 22:00:00',
                   '2022-03-07 23:00:00', '2022-03-08 00:00:00'],
                  dtype='datetime64[ns]', name='time', length=336, freq=None))

• Attributes: (25)

  Conventions :

  CF-1.6, ACDD-1.3

  title :

  AUS2200 UM amip simulation over Australian region for the 22 Feb to 07 Mar 2022

  summary :

  A limited area model study of the entire Australian continent at 2.2 km resolution, using the UM atmospheric model. ERA5 and ERA5 land reanalysis datasets were used to provide intial and boundary conditions. The study covers the time period of 2022-02-22 to 2022-03-7 including high rainfall events

  source :

  UM v12.2 ECMWF ERA5 forecast fields used as forcings

  license :

  http://creativecommons.org/licenses/by/4.0/

  product_version :

  0.1

  contact :

  dsroberts@unimelb.edu.au

  institution :

  Melbourne University

  organisation :

  ARC Centre of Excellence for Climate Extremes

  keywords :

  Cloud Physics, Meteorology, Atmospheric Dynamics

  date_created :

  2023-02-13

  creator_name :

  Dale Roberts

  creator_email :

  dsroberts@unimelb.edu.au

  creator_url :

  https://orcid.org/0000-0003-3952-6344

  creator_role :

  running model, post-processing

  time_coverage_start :

  2022-02-22

  time_coverage_end :

  2022-03-07

  geospatial_lat_min :

  -48.788

  geospatial_lat_max :

  -6.831799

  geospatial_lon_min :

  107.5101

  geospatial_lon_max :

  0.0198

  contributor_name :

  Sam Green, Paola Petrelli

  contributor_email :

  sam.green@unsw.edu.au, paola.petrelli@utas.edu.au

  contributor_url :

  https://orcid.org/0000-0003-1129-4676, https://orcid.org/0000-0002-0164-5105

  contributor_role :

  post-processing, post-processing

xr.where(ds.orog[0] < -10,True,False).sum()

<xarray.DataArray 'orog' ()>
array(2)
Coordinates:
    time     datetime64[ns] 2022-02-22T00:00:30

xarray.DataArray

'orog'

• 2

  array(2)

• Coordinates: (1)
  • time
    ()
    datetime64[ns]
    2022-02-22T00:00:30

    standard_name :

    time

    array('2022-02-22T00:00:30.000000000', dtype='datetime64[ns]')

• Indexes: (0)
• Attributes: (0)

fig, ax1 = plt.subplots()

ds.orog[0].sel(latitude=-6.851597,longitude=slice(143.1,143.5)).plot(ax=ax1)

[<matplotlib.lines.Line2D at 0x146b29188a60>]

cubes=iris.load("/scratch/w40/dr4292/aus2200-instability-analysis/30sec/20220222T0000Z/aus2200/d0198/RA3/um/umnsa_mdl_20220222T0000")

/g/data/hh5/public/apps/cms_conda/envs/analysis3-23.01/lib/python3.9/site-packages/iris/fileformats/rules.py:353: UserWarning: Unable to create instance of HybridHeightFactory. The source data contains no field(s) for 'orography'.
  warnings.warn(msg.format(factory=factory_name))

cubes

0: m01s03i468 / (unknown) (model_level_number: 45; latitude: 2120; longitude: 2600)

M01S03I468 (unknown)	model_level_number	latitude	longitude
Shape	45	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s03i468
source	'Data from Met Office Unified Model'
um_version	'12.2'

1: m01s03i473 / (unknown) (model_level_number: 45; latitude: 2120; longitude: 2600)

M01S03I473 (unknown)	model_level_number	latitude	longitude
Shape	45	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s03i473
source	'Data from Met Office Unified Model'
um_version	'12.2'

2: air_potential_temperature / (K) (model_level_number: 70; latitude: 2120; longitude: 2600)

Air Potential Temperature (K)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i004
source	'Data from Met Office Unified Model'
um_version	'12.2'

3: air_pressure / (Pa) (model_level_number: 71; latitude: 2120; longitude: 2600)

Air Pressure (Pa)	model_level_number	latitude	longitude
Shape	71	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i407
source	'Data from Met Office Unified Model'
um_version	'12.2'

4: air_pressure / (Pa) (model_level_number: 70; latitude: 2120; longitude: 2600)

Air Pressure (Pa)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i408
source	'Data from Met Office Unified Model'
um_version	'12.2'

5: air_temperature / (K) (model_level_number: 70; latitude: 2120; longitude: 2600)

Air Temperature (K)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s16i004
source	'Data from Met Office Unified Model'
um_version	'12.2'

6: height_above_reference_ellipsoid / (m) (model_level_number: 70; latitude: 2120; longitude: 2600)

Height Above Reference Ellipsoid (m)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	12.008333333360497 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 00:00:30
Attributes
STASH	m01s15i101
source	'Data from Met Office Unified Model'
um_version	'12.2'

7: height_above_reference_ellipsoid / (m) (model_level_number: 70; latitude: 2120; longitude: 2600)

Height Above Reference Ellipsoid (m)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	12.008333333360497 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 00:00:30
Attributes
STASH	m01s15i102
source	'Data from Met Office Unified Model'
um_version	'12.2'

8: specific_humidity / (kg kg-1) (model_level_number: 70; latitude: 2120; longitude: 2600)

Specific Humidity (kg kg-1)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i010
source	'Data from Met Office Unified Model'
um_version	'12.2'

9: upward_air_velocity / (m s-1) (model_level_number: 70; latitude: 2120; longitude: 2600)

Upward Air Velocity (m s-1)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i150
source	'Data from Met Office Unified Model'
um_version	'12.2'

10: x_wind / (m s-1) (model_level_number: 70; latitude: 2120; longitude: 2600)

X Wind (m s-1)	model_level_number	latitude	longitude
Shape	70	2120	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i002
source	'Data from Met Office Unified Model'
um_version	'12.2'

11: y_wind / (m s-1) (model_level_number: 70; latitude: 2121; longitude: 2600)

Y Wind (m s-1)	model_level_number	latitude	longitude
Shape	70	2121	2600
Dimension coordinates
model_level_number	x	-	-
latitude	-	x	-
longitude	-	-	x
Auxiliary coordinates
level_height	x	-	-
sigma	x	-	-
Scalar coordinates
forecast_period	13.0 hours
forecast_reference_time	2022-02-21 12:00:00
time	2022-02-22 01:00:00
Attributes
STASH	m01s00i003
source	'Data from Met Office Unified Model'
um_version	'12.2'

theta_da=xr.DataArray.from_iris(cubes[6]).chunk({"model_level_number":-1,"latitude":106,"longitude":130})

#small_da=theta_da.sel(latitude=-6.851597,longitude=slice(143.1,143.5))
small_da=theta_da.sel(latitude=-6.851597,method='nearest').sel(longitude=slice(143.09,143.5))

small_da

<xarray.DataArray 'height_above_reference_ellipsoid' (model_level_number: 70,
                                                      longitude: 21)>
dask.array<getitem, shape=(70, 21), dtype=float64, chunksize=(70, 21), chunktype=numpy.ndarray>
Coordinates:
  * model_level_number       (model_level_number) int64 1 2 3 4 ... 67 68 69 70
    latitude                 float32 -6.852
  * longitude                (longitude) float32 143.1 143.1 ... 143.5 143.5
    forecast_period          timedelta64[ns] ...
    forecast_reference_time  datetime64[ns] ...
    time                     datetime64[ns] ...
    level_height             (model_level_number) float64 dask.array<chunksize=(70,), meta=np.ndarray>
    sigma                    (model_level_number) float64 dask.array<chunksize=(70,), meta=np.ndarray>
Attributes:
    standard_name:  height_above_reference_ellipsoid
    units:          m
    source:         Data from Met Office Unified Model
    um_version:     12.2
    STASH:          m01s15i101

xarray.DataArray

'height_above_reference_ellipsoid'

• model_level_number: 70
• longitude: 21

• dask.array<chunksize=(70, 21), meta=np.ndarray>

  Array Chunk Bytes 11.48 kiB 11.48 kiB Shape (70, 21) (70, 21) Dask graph 1 chunks in 147 graph layers Data type float64 numpy.ndarray

• Coordinates: (8)
  • model_level_number
    (model_level_number)
    int64
    1 2 3 4 5 6 7 ... 65 66 67 68 69 70

    standard_name :

    model_level_number

    positive :

    up

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 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])

  • latitude
    ()
    float32
    -6.852

    standard_name :

    latitude

    units :

    degrees

    array(-6.851597, dtype=float32)

  • longitude
    (longitude)
    float32
    143.1 143.1 143.1 ... 143.5 143.5

    standard_name :

    longitude

    units :

    degrees

    array([143.1006 , 143.12039, 143.1402 , 143.16   , 143.1798 , 143.1996 ,
           143.21939, 143.2392 , 143.259  , 143.2788 , 143.2986 , 143.31839,
           143.3382 , 143.358  , 143.37779, 143.3976 , 143.41739, 143.4372 ,
           143.457  , 143.47679, 143.4966 ], dtype=float32)

  • forecast_period
    ()
    timedelta64[ns]
    ...

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • forecast_reference_time
    ()
    datetime64[ns]
    ...

    standard_name :

    forecast_reference_time

    [1 values with dtype=datetime64[ns]]

  • time
    ()
    datetime64[ns]
    ...

    standard_name :

    time

    [1 values with dtype=datetime64[ns]]

  • level_height
    (model_level_number)
    float64
    dask.array<chunksize=(70,), meta=np.ndarray>

    long_name :

    level_height

    units :

    m

    positive :

    up

    Array Chunk Bytes 560 B 560 B Shape (70,) (70,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sigma
    (model_level_number)
    float64
    dask.array<chunksize=(70,), meta=np.ndarray>

    long_name :

    sigma

    Array Chunk Bytes 560 B 560 B Shape (70,) (70,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (2)
  • model_level_number
    PandasIndex

    PandasIndex(Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 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],
               dtype='int64', name='model_level_number'))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([143.10060119628906, 143.12039184570312, 143.14019775390625,
                  143.16000366210938, 143.17979431152344, 143.19960021972656,
                  143.21939086914062, 143.23919677734375, 143.25900268554688,
                  143.27879333496094, 143.29859924316406, 143.31838989257812,
                  143.33819580078125, 143.35800170898438, 143.37779235839844,
                  143.39759826660156, 143.41738891601562, 143.43719482421875,
                  143.45700073242188, 143.47679138183594, 143.49659729003906],
                 dtype='float64', name='longitude'))

• Attributes: (5)

  standard_name :

  height_above_reference_ellipsoid

  units :

  m

  source :

  Data from Met Office Unified Model

  um_version :

  12.2

  STASH :

  m01s15i101

small_da[:50].plot.line(hue="model_level_number",ax=ax1)

[<matplotlib.lines.Line2D at 0x146b312281f0>,
 <matplotlib.lines.Line2D at 0x146b312c90d0>,
 <matplotlib.lines.Line2D at 0x146b2857cf40>,
 <matplotlib.lines.Line2D at 0x146b28ae0640>,
 <matplotlib.lines.Line2D at 0x146b28a948b0>,
 <matplotlib.lines.Line2D at 0x146b287dac10>,
 <matplotlib.lines.Line2D at 0x146b287da1f0>,
 <matplotlib.lines.Line2D at 0x146b287dadf0>,
 <matplotlib.lines.Line2D at 0x146b28777430>,
 <matplotlib.lines.Line2D at 0x146b28777190>,
 <matplotlib.lines.Line2D at 0x146b28777220>,
 <matplotlib.lines.Line2D at 0x146b28777340>,
 <matplotlib.lines.Line2D at 0x146b287774c0>,
 <matplotlib.lines.Line2D at 0x146b287776a0>,
 <matplotlib.lines.Line2D at 0x146b28777820>,
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ds.orog[0].sel(longitude=slice(143,143.5),latitude=slice(-7.33,-6.83)).plot.contour()

<matplotlib.contour.QuadContourSet at 0x1530d24c7250>

fig