bmi-era5 0.2.1

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

bmi_era5

Please note: Starting with release v0.2.0, the New CDS platform is now supported.

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. It currently supports 3-dimensional ERA5 datasets defined with dimensions such as valid_time (or date), latitude, and longitude.

This package is not implemented for people to use but is the key element to convert the ERA5 dataset into a data component (pymt_era5) 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.

Install package

Stable Release

The bmi_era5 package and its dependencies can be installed with pip

$ pip install bmi_era5

or conda

$ conda install -c conda-forge bmi_era5

From Source

After downloading the source code, run the following command from top-level folder to install bmi_era5.

$ pip install -e .

Citation

Please include the following references when citing this software package:

Gan, T., Tucker, G.E., Hutton, E.W.H., Piper, M.D., Overeem, I., Kettner, A.J., Campforts, B., Moriarty, J.M., Undzis, B., Pierce, E., McCready, L., 2024: CSDMS Data Components: data–model integration tools for Earth surface processes modeling. Geosci. Model Dev., 17, 2165–2185. https://doi.org/10.5194/gmd-17-2165-2024

Gan, T. (2024). CSDMS ERA5 Data Component. Zenodo. https://doi.org/10.5281/zenodo.10368878

Quick Start

Below shows how to use two methods to download the ERA5 datasets.

You can learn more details from the tutorial notebook. To run this notebook, please go to the CSDMS EKT Lab and follow the instruction in the "Lab notes" section.

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"],
        "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(valid_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
var_name = data_comp.get_output_var_names()[0]
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(f"{var_name=}")
print(f"{var_unit=}")
print(f"{var_location=}")
print(f"{var_type=}")
print(f"{var_grid=}")
print(f"{var_itemsize=}")
print(f"{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(f"{start_time=}")
print(f"{end_time=}")
print(f"{time_step=}")
print(f"{time_unit=}")
print(f"{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(f"{grid_rank=}")
print(f"{grid_size=}")
print(f"{grid_shape=}")
print(f"{grid_spacing=}")
print(f"{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] - 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")

# finalize the data component
data_comp.finalize()