Smooth Mean Daily Annual Cycle

Calculates a smooth mean daily annual cycle.

See also

https://www.ncl.ucar.edu/Document/Functions/Contributed/smthClmDayTLL.shtml

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

import xarray as xr
import easyclimate as ecl
import matplotlib.pyplot as plt

Preprocessed data

Tip

You can download following datasets here:

• Download olr-daily_v01r02_19800101_20231231.nc (3.34 GB)

• Download olr_daily_annual_cycle_mean.nc

lats, latn = -20, 20

olr_data = xr.open_dataset('olr-daily_v01r02_19800101_20231231.nc', chunks='auto').sel(lat=slice(lats,latn)).olr
olr_data_daily_annual_cycle_mean = ecl.calc_daily_annual_cycle_mean(olr_data).thin(lat = 2, lon = 5).compute()

olr_data_daily_annual_cycle_mean = ecl.utility.get_compress_xarraydata(olr_data_daily_annual_cycle_mean)
olr_data_daily_annual_cycle_mean.to_netcdf("olr_daily_annual_cycle_mean.nc")

Here, we directly load the data of mean daily annual cycle for outgoing longwave radiation (OLR).

olr_data_daily_annual_cycle_mean = xr.open_dataset("olr_daily_annual_cycle_mean.nc").olr
olr_data_daily_annual_cycle_mean

<xarray.DataArray 'olr' (dayofyear: 366, lat: 20, lon: 72)> Size: 2MB
[527040 values with dtype=float32]
Coordinates:
  * lon        (lon) float32 288B 0.5 5.5 10.5 15.5 ... 340.5 345.5 350.5 355.5
  * lat        (lat) float32 80B -19.5 -17.5 -15.5 -13.5 ... 12.5 14.5 16.5 18.5
  * dayofyear  (dayofyear) int64 3kB 1 2 3 4 5 6 7 ... 361 362 363 364 365 366
Attributes:
    standard_name:  toa_outgoing_longwave_flux
    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...
    units:          W m-2
    cell_methods:   time: mean area: mean

xarray.DataArray

'olr'

• dayofyear: 366
• lat: 20
• lon: 72

• ...

  [527040 values with dtype=float32]

• Coordinates: (3)
  • lon
    (lon)
    float32
    0.5 5.5 10.5 ... 345.5 350.5 355.5

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    bounds :

    lon_bnds

    array([  0.5,   5.5,  10.5,  15.5,  20.5,  25.5,  30.5,  35.5,  40.5,  45.5,
            50.5,  55.5,  60.5,  65.5,  70.5,  75.5,  80.5,  85.5,  90.5,  95.5,
           100.5, 105.5, 110.5, 115.5, 120.5, 125.5, 130.5, 135.5, 140.5, 145.5,
           150.5, 155.5, 160.5, 165.5, 170.5, 175.5, 180.5, 185.5, 190.5, 195.5,
           200.5, 205.5, 210.5, 215.5, 220.5, 225.5, 230.5, 235.5, 240.5, 245.5,
           250.5, 255.5, 260.5, 265.5, 270.5, 275.5, 280.5, 285.5, 290.5, 295.5,
           300.5, 305.5, 310.5, 315.5, 320.5, 325.5, 330.5, 335.5, 340.5, 345.5,
           350.5, 355.5], dtype=float32)

  • lat
    (lat)
    float32
    -19.5 -17.5 -15.5 ... 16.5 18.5

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    bounds :

    lat_bnds

    array([-19.5, -17.5, -15.5, -13.5, -11.5,  -9.5,  -7.5,  -5.5,  -3.5,  -1.5,
             0.5,   2.5,   4.5,   6.5,   8.5,  10.5,  12.5,  14.5,  16.5,  18.5],
          dtype=float32)

  • dayofyear
    (dayofyear)
    int64
    1 2 3 4 5 6 ... 362 363 364 365 366

    standard_name :

    time

    long_name :

    reference time

    bounds :

    time_bnds

    axis :

    T

    array([  1,   2,   3, ..., 364, 365, 366], shape=(366,))

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   5.5,  10.5,  15.5,  20.5,  25.5,  30.5,  35.5,  40.5,  45.5,
            50.5,  55.5,  60.5,  65.5,  70.5,  75.5,  80.5,  85.5,  90.5,  95.5,
           100.5, 105.5, 110.5, 115.5, 120.5, 125.5, 130.5, 135.5, 140.5, 145.5,
           150.5, 155.5, 160.5, 165.5, 170.5, 175.5, 180.5, 185.5, 190.5, 195.5,
           200.5, 205.5, 210.5, 215.5, 220.5, 225.5, 230.5, 235.5, 240.5, 245.5,
           250.5, 255.5, 260.5, 265.5, 270.5, 275.5, 280.5, 285.5, 290.5, 295.5,
           300.5, 305.5, 310.5, 315.5, 320.5, 325.5, 330.5, 335.5, 340.5, 345.5,
           350.5, 355.5],
          dtype='float32', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Index([-19.5, -17.5, -15.5, -13.5, -11.5,  -9.5,  -7.5,  -5.5,  -3.5,  -1.5,
             0.5,   2.5,   4.5,   6.5,   8.5,  10.5,  12.5,  14.5,  16.5,  18.5],
          dtype='float32', name='lat'))

  • dayofyear
    PandasIndex

    PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
           ...
           357, 358, 359, 360, 361, 362, 363, 364, 365, 366],
          dtype='int64', name='dayofyear', length=366))

• Attributes: (4)

  standard_name :

  toa_outgoing_longwave_flux

  long_name :

  NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere

  units :

  W m-2

  cell_methods :

  time: mean area: mean

Simply do a regional average for the tropics

olr_data_ave = olr_data_daily_annual_cycle_mean.mean(dim = ("lon", "lat"))
olr_data_ave.plot()

[<matplotlib.lines.Line2D object at 0x7fd6464a22c0>]

By removing excess noise in the mean daily annual cycle through the way of easyclimate.smooth_daily_annual_cycle, we can plot the following:

olr_data_ave_smoothed = ecl.smooth_daily_annual_cycle(olr_data_daily_annual_cycle_mean).mean(dim = ("lon", "lat"))

olr_data_ave.plot(label = "Daily annual cycle mean")
olr_data_ave_smoothed.plot(label = "Smoothed daily annual cycle mean")
plt.legend()

<matplotlib.legend.Legend object at 0x7fd6462f1390>