pymt_era5 is a package that converts ERA5 datasets into a reusable, plug-and-play data component for PyMT modeling framework developed by Community Surface Dynamics Modeling System (CSDMS). This allows ERA5 datasets (currently support 3 dimensional data) to be easily coupled with other datasets or models that expose a Basic Model Interface.

Installation

Install the pymt in a new environment:

$ conda config --add channels conda-forge
$ conda create -n pymt -c conda-forge python=3 pymt
$ conda activate pymt

Please make sure to first install the CDS API and then install the pymt_era5 using pip:

pip install pymt_era5

or conda

conda install -c conda-forge pymt_era5

Coding Example

You can learn more details about the coding example from the tutorial notebook.

import matplotlib.pyplot as plt
import numpy as np

from pymt.models import Era5

# initiate a data component
data_comp = Era5()
data_comp.initialize('config_file.yaml')

# get variable info
for var_name in data_comp.output_var_names:
    var_unit = data_comp.var_units(var_name)
    var_location = data_comp.var_location(var_name)
    var_type = data_comp.var_type(var_name)
    var_grid = data_comp.var_grid(var_name)
    var_itemsize = data_comp.var_itemsize(var_name)
    var_nbytes = data_comp.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.start_time
end_time = data_comp.end_time
time_step = data_comp.time_step
time_units = data_comp.time_units
time_steps = int((end_time - start_time)/time_step) + 1

print('start_time: {} \nend_time: {} \ntime_step: {} \ntime_units: {} \ntime_steps: {}'.format(
    start_time, end_time, time_step, time_units, time_steps))

# get variable grid info
grid_type = data_comp.grid_type(var_grid)
grid_rank = data_comp.grid_ndim(var_grid)
grid_shape = data_comp.grid_shape(var_grid)
grid_spacing = data_comp.grid_spacing(var_grid)
grid_origin = data_comp.grid_origin(var_grid)

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

# get variable data
data = data_comp.get_value('2 metre temperature')
data_2D = data.reshape(grid_shape)
print(data.shape, data_2D.shape)

# get X, Y extent for plot
min_y, min_x = grid_origin
max_y = min_y + grid_spacing[0]*(grid_shape[0]-1)
max_x = min_x + grid_spacing[1]*(grid_shape[1]-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')

# complete the example by finalizing the component
data_comp.finalize()