Onset and Retreat of Monsoon

Note

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Onset and Retreat of Monsoon#

Before proceeding with all the steps, first import some necessary libraries and packages

import xarray as xr
import cartopy.crs as ccrs
import easyclimate as ecl

Open precipitable water example data (must be daily frequency data)

pw_data = ecl.open_tutorial_dataset("pr_wtr_eatm_2022").pr_wtr
pw_data

<xarray.DataArray 'pr_wtr' (time: 365, lat: 73, lon: 144)> Size: 15MB
[3836880 values with dtype=float32]
Coordinates:
  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * time     (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31
Attributes:
    long_name:     mean Daily Precipitable Water for entire atmosphere
    units:         kg/m^2
    precision:     2
    GRIB_id:       54
    GRIB_name:     PWAT
    var_desc:      Precipitable Water Content
    dataset:       NCEP Reanalysis Daily Averages
    level_desc:    Entire Atmosphere Considered As a Single Layer
    statistic:     Mean
    parent_stat:   Individual Obs
    valid_range:   [-50. 150.]
    actual_range:  [-4.8      81.075005]


Calculate the NPWI index using easyclimate.field.monsoon.calc_index_NPWI .. seealso:

- Zeng, X., and E. Lu, 2004: Globally Unified Monsoon Onset and Retreat Indexes. J. Climate, 17, 2241–2248, https://journals.ametsoc.org/view/journals/clim/17/11/1520-0442_2004_017_2241_gumoar_2.0.co_2.xml.
- Tang Xu, Chen Baode, Liang Ping, Qian Weihong. Definition and features of the north edge of Asian summer monsoon. Acta Meteorologica Sinica (Chinese), 2009, (1): 83-89. doi: http://dx.doi.org/10.11676/qxxb2009.009

NPWI_index = ecl.field.monsoon.calc_index_NPWI(pw_data)
NPWI_index

<xarray.DataArray 'pr_wtr' (time: 365, lat: 73, lon: 144)> Size: 15MB
array([[[0.11188813, 0.11188813, 0.11188813, ..., 0.11188813,
         0.11188813, 0.11188813],
        [0.09598216, 0.0935412 , 0.09242763, ..., 0.10045148,
         0.09988777, 0.09955256],
        [0.09099708, 0.0909971 , 0.09108527, ..., 0.08562992,
         0.08699901, 0.08949416],
        ...,
        [0.484375  , 0.4631579 , 0.45161286, ..., 0.52763814,
         0.5177665 , 0.49230772],
        [0.5232558 , 0.51445085, 0.5058139 , ..., 0.5257143 ,
         0.52298856, 0.52298856],
        [0.57812494, 0.57812494, 0.57812494, ..., 0.57812494,
         0.57812494, 0.57812494]],

       [[0.13519815, 0.13519815, 0.13519815, ..., 0.13519815,
         0.13519815, 0.13519815],
        [0.10491072, 0.10022272, 0.09799556, ..., 0.10948082,
         0.10886644, 0.10738254],
        [0.10648596, 0.10745402, 0.10852714, ..., 0.09744094,
         0.10068426, 0.1040856 ],
...
        [0.42708334, 0.42105263, 0.4139785 , ..., 0.45226127,
         0.44162434, 0.43589744],
        [0.4127907 , 0.40462428, 0.40697673, ..., 0.3885714 ,
         0.39080462, 0.40229887],
        [0.40624997, 0.40624997, 0.40624997, ..., 0.40624997,
         0.40624997, 0.40624997]],

       [[0.1864802 , 0.1864802 , 0.1864802 , ..., 0.1864802 ,
         0.1864802 , 0.1864802 ],
        [0.1964286 , 0.19265033, 0.18708242, ..., 0.20541763,
         0.20314254, 0.19910516],
        [0.1577928 , 0.1568248 , 0.15600777, ..., 0.16043305,
         0.15933527, 0.15758753],
        ...,
        [0.41666666, 0.41578948, 0.40860215, ..., 0.40703517,
         0.41116747, 0.4051282 ],
        [0.37209302, 0.3699422 , 0.3837209 , ..., 0.34285712,
         0.3448276 , 0.36206898],
        [0.3359375 , 0.3359375 , 0.3359375 , ..., 0.3359375 ,
         0.3359375 , 0.3359375 ]]], shape=(365, 73, 144), dtype=float32)
Coordinates:
  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * time     (time) datetime64[ns] 3kB 2022-01-01 2022-01-02 ... 2022-12-31
Attributes:
    long_name:     mean Daily Precipitable Water for entire atmosphere
    units:         kg/m^2
    precision:     2
    GRIB_id:       54
    GRIB_name:     PWAT
    var_desc:      Precipitable Water Content
    dataset:       NCEP Reanalysis Daily Averages
    level_desc:    Entire Atmosphere Considered As a Single Layer
    statistic:     Mean
    parent_stat:   Individual Obs
    valid_range:   [-50. 150.]
    actual_range:  [-4.8      81.075005]


Separation of monsoon affected areas with easyclimate.field.monsoon.find_PW_monsoon_region

PW_monsoon_region = ecl.field.monsoon.find_PW_monsoon_region(pw_data)
PW_monsoon_region

<xarray.DataArray 'pr_wtr' (lat: 73, lon: 144)> Size: 11kB
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]], shape=(73, 144))
Coordinates:
  * lat      (lat) float32 292B 90.0 87.5 85.0 82.5 ... -82.5 -85.0 -87.5 -90.0
  * lon      (lon) float32 576B 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
Attributes:
    long_name:     mean Daily Precipitable Water for entire atmosphere
    units:         kg/m^2
    precision:     2
    GRIB_id:       54
    GRIB_name:     PWAT
    var_desc:      Precipitable Water Content
    dataset:       NCEP Reanalysis Daily Averages
    level_desc:    Entire Atmosphere Considered As a Single Layer
    statistic:     Mean
    parent_stat:   Individual Obs
    valid_range:   [-50. 150.]
    actual_range:  [-4.8      81.075005]


Schematization of monsoon impact areas

fig, ax = ecl.plot.quick_draw_spatial_basemap(central_longitude = 200)
PW_monsoon_region.plot(
    ax = ax,
    cbar_kwargs = {'location': 'bottom'},
    transform = ccrs.PlateCarree(),
    levels = [0, 1],
    colors = ["grey"]
)
plot monsoon npwi
<cartopy.mpl.geocollection.GeoQuadMesh object at 0x7f5f76cb4f40>

Calculation of monsoon onset with easyclimate.field.monsoon.calc_NPWI_monsoon_onset

monsoon_onset_date = ecl.field.monsoon.calc_NPWI_monsoon_onset(NPWI_index)
monsoon_onset_date

<xarray.DataArray (lat: 71, lon: 142)> Size: 81kB
array([[199., 199., 199., ..., 198., 198., 198.],
       [199., 199., 199., ..., 199., 199., 199.],
       [199., 199., 199., ..., 199., 199., 199.],
       ...,
       [ nan,  nan,  22., ...,  nan,  nan,  nan],
       [ nan,  nan,  nan, ...,  nan,  nan,  nan],
       [ nan,  nan,  nan, ...,  nan,  nan,  nan]], shape=(71, 142))
Coordinates:
  * lat      (lat) float32 284B 87.5 85.0 82.5 80.0 ... -80.0 -82.5 -85.0 -87.5
  * lon      (lon) float32 568B 2.5 5.0 7.5 10.0 ... 347.5 350.0 352.5 355.0


Analyzing and mapping the monsoon onset time in the monsoon impact area

fig, ax = ecl.plot.quick_draw_spatial_basemap(central_longitude = 180)
monsoon_onset_date.where(PW_monsoon_region).plot(
    ax = ax,
    cbar_kwargs = {'location': 'bottom'},
    transform = ccrs.PlateCarree(),
    cmap = 'Reds'
)
plot monsoon npwi
<cartopy.mpl.geocollection.GeoQuadMesh object at 0x7f5f76a85270>

Calculation of monsoon retreat time with easyclimate.field.monsoon.calc_NPWI_monsoon_retreat

monsoon_retreat_date = ecl.field.monsoon.calc_NPWI_monsoon_retreat(NPWI_index, monsoon_onset_date)
monsoon_retreat_date

<xarray.DataArray (lat: 71, lon: 142)> Size: 81kB
array([[218., 218., 218., ..., 218., 218., 218.],
       [217., 222., 222., ..., 215., 215., 215.],
       [218., 222., 222., ..., 203., 216., 217.],
       ...,
       [ nan,  nan,  28., ...,  nan,  nan,  nan],
       [ nan,  nan,  nan, ...,  nan,  nan,  nan],
       [ nan,  nan,  nan, ...,  nan,  nan,  nan]], shape=(71, 142))
Coordinates:
  * lat      (lat) float32 284B 87.5 85.0 82.5 80.0 ... -80.0 -82.5 -85.0 -87.5
  * lon      (lon) float32 568B 2.5 5.0 7.5 10.0 ... 347.5 350.0 352.5 355.0


Analyzing and mapping the monsoon retreat time in the monsoon impact area

fig, ax = ecl.plot.quick_draw_spatial_basemap(central_longitude = 180)
monsoon_retreat_date.where(PW_monsoon_region).plot(
    ax = ax,
    cbar_kwargs = {'location': 'bottom'},
    transform = ccrs.PlateCarree(),
    cmap = 'Reds'
)
plot monsoon npwi
<cartopy.mpl.geocollection.GeoQuadMesh object at 0x7f5f76953910>

Total running time of the script: (0 minutes 9.639 seconds)