Daily statistics from hourly ERA5 data

[1]:

# If first time running, uncomment the line below to install any additional dependencies
# !bash requirements-for-notebooks.sh

[2]:

import matplotlib.pyplot as plt
from earthkit.data.testing import earthkit_remote_test_data_file

from earthkit import data as ekd
from earthkit.transforms import aggregate as ekt

ekd.settings.set("cache-policy", "user")

Calculate a rolling mean with a 50 timestep window

To calculate a rolling mean along the time dimension you can use the rolling_reduce function.

NOTE: An improved API to the rolling_reduce method is an ongoing task

[6]:

era5_rolling = ekt.temporal.rolling_reduce(
    era5_xr,
    50,
    how_reduce="mean",
    center=True,
)
era5_rolling

/tmp/ipykernel_1103/3975254819.py:1: DeprecationWarning: The function 'rolling_reduce' from the legacy aggregate module is deprecated and will be removed in version 2.X of earthkit.transforms. Use 'earthkit.transforms.temporal.rolling_reduce' instead.
  era5_rolling = ekt.temporal.rolling_reduce(

[6]:

<xarray.Dataset> Size: 660MB
Dimensions:     (valid_time: 1460, latitude: 201, longitude: 281)
Coordinates:
  * valid_time  (valid_time) datetime64[us] 12kB 2015-01-01 ... 2015-12-31T18...
  * latitude    (latitude) float64 2kB 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0
  * longitude   (longitude) float64 2kB -10.0 -9.75 -9.5 ... 59.5 59.75 60.0
Data variables:
    t2m         (valid_time, latitude, longitude) float64 660MB dask.array<chunksize=(1459, 26, 36), meta=np.ndarray>
Attributes: (12/13)
    param:        2t
    paramId:      167
    class:        ea
    stream:       oper
    levtype:      sfc
    type:         an
    ...           ...
    date:         20150101
    time:         0
    domain:       g
    number:       0
    Conventions:  CF-1.8
    institution:  ECMWF

Plot a random point location to see the different aggregation methods

[8]:

isel_kwargs = {"latitude": 100, "longitude": 100}

fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 5))

era5_xr.t2m.isel(**isel_kwargs).plot(label="Raw data", ax=ax, color="blue")
era5_daily_mean.t2m.isel(**isel_kwargs).plot(label="Daily mean", ax=ax, color="orange")

era5_daily_mean_p12.t2m.isel(**isel_kwargs).plot(label="Daily mean (12h shift)", ax=ax, color="purple")

# # To add the daily spread as orange dotted lines:
# upper_d = era5_daily_mean.t2m + era5_daily_std.t2m
# lower_d = era5_daily_mean.t2m - era5_daily_std.t2m
# upper_d.isel(
#     **isel_kwargs).plot(ax=ax, label='Daily standard deviation spread', linestyle='--', color='orange'
#
# lower_d.isel(**isel_kwargs).plot(ax=ax, linestyle='--', color='orange')

# Add the rolling mean as green line:
era5_rolling.t2m.isel(**isel_kwargs).plot(label="Rolling mean", ax=ax, color="green")

# Add the monthly mean as a black solid line and spread as black dotted lines:
era5_monthly_mean.t2m.isel(**isel_kwargs).plot(label="Monthly mean", ax=ax, color="black")
upper_m = era5_monthly_mean.t2m + era5_monthly_std.t2m
lower_m = era5_monthly_mean.t2m - era5_monthly_std.t2m
upper_m.isel(**isel_kwargs).plot(ax=ax, label="Monthly standard deviation spread", linestyle="--", color="black")
lower_m.isel(**isel_kwargs).plot(ax=ax, linestyle="--", color="black")


# figure = fig[0].get_figure()
ax.legend(loc=2)
ax.set_title("Aggregation methods")
plt.show()

../../_images/notebooks_temporal_02-era5-daily-monthly-statistics_14_0.png

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