bmi-era5 0.1.4

BMI implementation for ERA5 data https://confluence.ecmwf.int/display/CKB/ERA5

bmi_era5

bmi_era5 package is an implementation of the Basic Model Interface (BMI) for the ERA5 dataset. This package uses the CDS API to download the ERA5 dataset and wraps the dataset with BMI for data control and query (currently support 3 dimensional ERA5 dataset). This package is not implemented for people to use but is the key element to convert the ERA5 dataset into a data component for the PyMT modeling framework developed by Community Surface Dynamics Modeling System (CSDMS).

If you have any suggestion to improve the current function, please create a github issue here.

Get Started

Install package

Please make sure to first install the CDS API and then follow the instructions below to install the bmi_era5 package.

Stable Release

The bmi_era5 package and its dependencies can be installed with pip

$ pip install bmi_era5

From Source

After downloading the source code, run the following command from top-level folder (the one that contains setup.py) to install bmi_era5.

$ pip install -e .

Download ERA5 Data

Learn more details of the examples from the tutorial notebook provided in this package. You can also launch binder to test and run the code below.

Example 1: use CDS API to download the ERA5 data

import cdsapi
import xarray
import matplotlib.pyplot as plt

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-single-levels',
    {
        'product_type': 'reanalysis',
        'format': 'netcdf',
        'variable': ['2m_temperature', 'total_precipitation'],
        'year': '2021',
        'month': '01',
        'day': '01',
        'time': [ '00:00', '01:00', '02:00'],
        'area': [ 41, -109, 36, -102],
        'grid': [0.25, 0.25]
    },
    'download.nc')

# load netCDF data
dataset = xarray.open_dataset('download.nc')

# select 2 meter temperature on 2021-01-01 at 00:00 
air_temp = dataset.t2m.isel(time=0)

# plot data
air_temp.plot(figsize=(9,5))
plt.title('2 metre temperature in Colorado on Jan 1st, 2021 at 00:00')

Example 2: use BmiEra5 class to download the ERA5 data (Demonstration of how to use BMI)

from bmi_era5 import BmiEra5
import numpy as np
import matplotlib.pyplot as plt

data_comp = BmiEra5()
data_comp.initialize('config_file.yaml')

# get variable info
for var_name in  data_comp.get_output_var_names():
    var_unit = data_comp.get_var_units(var_name)
    var_location = data_comp.get_var_location(var_name)
    var_type = data_comp.get_var_type(var_name)
    var_grid = data_comp.get_var_grid(var_name)
    var_itemsize = data_comp.get_var_itemsize(var_name)
    var_nbytes = data_comp.get_var_nbytes(var_name)
    print('variable_name: {} \nvar_unit: {} \nvar_location: {} \nvar_type: {} \nvar_grid: {} \nvar_itemsize: {}' 
            '\nvar_nbytes: {} \n'. format(var_name, var_unit, var_location, var_type, var_grid, var_itemsize, var_nbytes))

# get time info
start_time = data_comp.get_start_time()
end_time = data_comp.get_end_time()
time_step = data_comp.get_time_step()
time_unit = data_comp.get_time_units()
time_steps = int((end_time - start_time)/time_step) + 1
print('start_time:{} \nend_time:{} \ntime_step:{} \ntime_unit:{} \ntime_steps:{}'.format(
    start_time, end_time, time_step, time_unit, time_steps))

# get variable grid info 
grid_rank = data_comp.get_grid_rank(var_grid) 
grid_size = data_comp.get_grid_size(var_grid)

grid_shape = np.empty(grid_rank, int)
data_comp.get_grid_shape(var_grid, grid_shape)

grid_spacing = np.empty(grid_rank)
data_comp.get_grid_spacing(var_grid, grid_spacing)

grid_origin = np.empty(grid_rank)
data_comp.get_grid_origin(var_grid, grid_origin)

print('grid_rank: {} \ngrid_size: {} \ngrid_shape: {} \ngrid_spacing: {} \ngrid_origin: {}'.format(
    grid_rank, grid_size, grid_shape, grid_spacing, grid_origin))

# get variable data 
data = np.empty(grid_size, var_type)
data_comp.get_value('2 metre temperature', data)
data_2D = data.reshape(grid_shape)

# get X, Y extent for plot
min_y, min_x = grid_origin
max_y = min_y + grid_spacing[0]*grid_shape[0]
max_x = min_x + grid_spacing[1]*grid_shape[1]
dy = grid_spacing[0]/2
dx = grid_spacing[1]/2
extent = [min_x - dx, max_x + dx, min_y - dy, max_y + dy]

# plot data
fig, ax = plt.subplots(1,1, figsize=(9,5))
im = ax.imshow(data_2D, extent=extent)
cbar = fig.colorbar(im)
cbar.set_label('2 metre temperature [K]')
plt.xlabel('longitude [degree_east]')
plt.ylabel('latitude [degree_north]')
plt.title('2 metre temperature in Colorado on Jan 1st, 2021 at 00:00')