Skip to content

get_jasmin_era5

Modifications

This is a modification of someone else's package.

The modifications are:

  • Model level data has a different file name format for years 2000-2006, so if you try and load it normally, you get the following error: 
    ERA5 Model level data - 2000-2006: WARNING
==========================================
ERA5 data for 2000-2006 was found to suffer from statospheric cold biases.
As such users should make use of the ERA5.1 data for this period.
Please see the associated dataset catalgoue records in the CEDA Data Catalogue for links to the ERA5.1 datasets.
    To fix this, you can select the archive i.e. for years 2000-2006, you can call era5=Find_era5(archive=1).
  • Now accepts the variable sp (surface pressure) and computes by taking exponential of lnsp.
  • Changed H to h in date stuff to avoid annoying warning: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead
  • Gives warning messages if the variable selected does not exist.

Initial README

A small python package to find and load ECMWF ERA5 data from the BADC archive on jasmin

Requirements: numpy, pandas and xarray

Installation: 

git clone https://github.com/w-k-jones/get_jasmin_era5.git
pip install get_jasmin_era5/

Example usage:

Import and initialise era5 object:

from get_jasmin_era5 import Find_era5
era5 = Find_era5()
Load temperature (t) for one time step:

era5["t", "2020-06-01-12:00:00"]

OUT:
<xarray.Dataset>
Dimensions:    (longitude: 1440, latitude: 721, level: 137, time: 1)
Coordinates:
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * level      (level) int32 1 2 3 4 5 6 7 8 ... 130 131 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01T12:00:00
Data variables:
    t          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 137, 721, 1440), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-12-18 00:24:54 GMT by grib_to_netcdf-2.19.1: grib_to_n...

Load temperature (t) for all time steps on 2020/6/1:

era5["t", "2020-06-01":"2020-06-02"]

OUT: 
<xarray.Dataset>
Dimensions:    (longitude: 1440, latitude: 721, level: 137, time: 24)
Coordinates:
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * level      (level) int32 1 2 3 4 5 6 7 8 ... 130 131 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01 ... 2020-06-01T23:00:00
Data variables:
    t          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 137, 721, 1440), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-12-18 00:18:51 GMT by grib_to_netcdf-2.19.1: grib_to_n...

Slicing across the prime meridian and using ascending latitude values is also enabled:

era5["t", "2020-06-01":"2020-06-02", None, -90:90, -60:60]

OUT:
<xarray.Dataset>
Dimensions:    (longitude: 721, latitude: 481, level: 137, time: 24)
Coordinates:
  * longitude  (longitude) float32 270.0 270.2 270.5 270.8 ... 89.5 89.75 90.0
  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... -59.5 -59.75 -60.0
  * level      (level) int32 1 2 3 4 5 6 7 8 ... 130 131 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01 ... 2020-06-01T23:00:00
Data variables:
    t          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 137, 481, 721), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-12-18 00:18:51 GMT by grib_to_netcdf-2.19.1: grib_to_n...

Load temperature (t), specific humidity (q), 2m temperature (2t) and surface height (z) for every three hours on 2020/6/1, for the 100th level downward, and between 90-270 degrees longtiude and -60-60 degrees latitude:

era5[("t", "q", "2t", "z"), "2020-06-01":"2020-06-02":"3H", 100:, 90:270, -60:60]

OUT:
<xarray.Dataset>
Dimensions:    (longitude: 721, latitude: 481, level: 38, time: 8)
Coordinates:
  * longitude  (longitude) float32 90.0 90.25 90.5 90.75 ... 269.5 269.8 270.0
  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... -59.5 -59.75 -60.0
  * level      (level) int32 100 101 102 103 104 105 ... 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01 ... 2020-06-01T21:00:00
Data variables:
    q          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 38, 481, 721), meta=np.ndarray>
    t          (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 38, 481, 721), meta=np.ndarray>
    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(1, 481, 721), meta=np.ndarray>
    z          (latitude, longitude) float32 dask.array<chunksize=(481, 721), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-12-18 00:18:45 GMT by grib_to_netcdf-2.19.1: grib_to_n...

Calculate pressure levels for the previous example:

era5.pl["2020-06-01":"2020-06-02":"3H", 100:, 90:270, -60:60]

OUT:
<xarray.DataArray (time: 8, level: 38, latitude: 481, longitude: 721)>
array([[[[ 57872.48474194,  57851.04773663,  57737.57761903, ...,
           59308.89351633,  59309.34814012,  59312.7428857 ],
         [ 57819.39331036,  57858.96549122,  57897.17048227, ...,
           59314.18639922,  59313.68863594,  59313.68863594],
         [ 57719.0044118 ,  57721.79188617,  57792.87911958, ...,
           59317.08669994,  59320.93275089,  59319.48260053],
         ...,
...
         [ 97330.26117977,  97328.03725574,  97325.90697063, ...,
          100171.16415324, 100144.3366066 , 100115.30854566],
         [ 97374.73185699,  97376.86994535,  97377.99361223, ...,
          100127.62206184, 100100.7945152 , 100078.54747172],
         [ 97401.85592684,  97413.84170684,  97422.48769918, ...,
          100085.22704703, 100067.38103215, 100052.87480492]]]])
Coordinates:
  * level      (level) int64 100 101 102 103 104 105 ... 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01 ... 2020-06-01T21:00:00
  * longitude  (longitude) float32 90.0 90.25 90.5 90.75 ... 269.5 269.8 270.0
  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... -59.5 -59.75 -60.0

Calculate geopotential heights for the same example:

era5.gz["2020-06-01":"2020-06-02":"3H", 100:, 90:270, -60:60]

OUT:
<xarray.DataArray (time: 8, level: 38, latitude: 481, longitude: 721)>
array([[[[5.11737957e+04, 5.20996878e+04, 5.44298830e+04, ...,
          4.13218602e+04, 4.12940971e+04, 4.12325377e+04],
         [5.19596141e+04, 5.17204796e+04, 5.12408262e+04, ...,
          4.13251723e+04, 4.13314302e+04, 4.13199145e+04],
         [5.43623733e+04, 5.43935385e+04, 5.32772366e+04, ...,
          4.13783058e+04, 4.12982295e+04, 4.12765517e+04],
         ...,
...
         [1.01424387e+02, 1.01402244e+02, 9.25518078e+01, ...,
          7.53837186e+01, 7.54326293e+01, 8.43107208e+01],
         [9.25974757e+01, 9.25697266e+01, 9.25488523e+01, ...,
          8.42433633e+01, 1.19629061e+02, 1.19669439e+02],
         [1.01434764e+02, 1.01407126e+02, 9.25514372e+01, ...,
          1.19581624e+02, 9.31415615e+01, 6.66779947e+01]]]])
Coordinates:
  * level      (level) int64 100 101 102 103 104 105 ... 132 133 134 135 136 137
  * time       (time) datetime64[ns] 2020-06-01 ... 2020-06-01T21:00:00
  * longitude  (longitude) float32 90.0 90.25 90.5 90.75 ... 269.5 269.8 270.0
  * latitude   (latitude) float32 60.0 59.75 59.5 59.25 ... -59.5 -59.75 -60.0

It will also load ensemble datasets. By default, if you pass the "enda" argument it will load the ensemble mean:

era5[("2t","2d"), "2020-06-01", None, None, None, "enda"]

OUT:
<xarray.Dataset>
Dimensions:    (longitude: 1440, latitude: 721, time: 1)
Coordinates:
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * time       (time) datetime64[ns] 2020-06-01
Data variables:
    d2m        (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2020-12-22 18:23:11 GMT by grib_to_netcdf-2.19.1: grib_to_n...

However you can also get the ensemble members using .enda:

era5.enda[("2t", "2d"), "2020-06-01":"2020-06-02":"3H", 100:, 90:270, -60:60]

OUT:
<xarray.Dataset>
Dimensions:          (longitude: 721, latitude: 481, time: 8,
                      ensemble_member: 10)
Coordinates:
  * longitude        (longitude) float32 90.0 90.25 90.5 ... 269.5 269.8 270.0
  * latitude         (latitude) float32 60.0 59.75 59.5 ... -59.5 -59.75 -60.0
  * time             (time) datetime64[ns] 2020-06-01 ... 2020-06-01T21:00:00
  * ensemble_member  (ensemble_member) int64 1 2 3 4 5 6 7 8 9 10
Data variables:
    t2m              (ensemble_member, time, latitude, longitude) float32 dask.array<chunksize=(1, 1, 481, 721), meta=np.ndarray>
    d2m              (ensemble_member, time, latitude, longitude) float32 dask.array<chunksize=(1, 1, 481, 721), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2021-01-15 04:09:35 GMT by grib_to_netcdf-2.19.1: grib_to_n...