GPM IMERG monthly precipitation (NASA Earthdata)¶

Download a monthly GPM IMERG precipitation granule from GES DISC through one Earthdata Login, then map it. This is a live query: it needs the [earthdata] extra (Python >=3.12) and EDL credentials (EARTHDATA_USERNAME / EARTHDATA_PASSWORD or ~/.netrc). The query is wrapped so the notebook stays safe under nbval-lax when run offline / without credentials. See Authentication and Usage.

Setup¶

Import the unified EarthLens entry point and prepare a local output directory. download() writes the fetched granule(s) into earthdata_output/.

In [ ]:

from pathlib import Path

from earthlens import EarthLens

OUT_DIR = Path('earthdata_output')
OUT_DIR.mkdir(exist_ok=True)

from pathlib import Path from earthlens import EarthLens OUT_DIR = Path('earthdata_output') OUT_DIR.mkdir(exist_ok=True)

Download the IMERG granule¶

Build the request¶

Configure an EarthLens request for the monthly GPM IMERG dataset (GPM_3IMERGM_07), one month of June 2023, over a near-global window. Keeping construction on its own line makes the parameters easy to read and tweak.

In [ ]:

imerg = EarthLens(
    data_source='earthdata',
    dataset='GPM_3IMERGM_07',
    variables=['precipitation'],
    start='2023-06-01',
    end='2023-06-30',
    aoi=[-180.0, -60.0, 180.0, 60.0],
    path=str(OUT_DIR),
)

imerg = EarthLens( data_source='earthdata', dataset='GPM_3IMERGM_07', variables=['precipitation'], start='2023-06-01', end='2023-06-30', aoi=[-180.0, -60.0, 180.0, 60.0], path=str(OUT_DIR), )

Fetch it¶

Run download() to fetch the granule(s) to disk. The call is wrapped in a try/except so the notebook degrades gracefully — printing a skip message instead of erroring — when run offline or without Earthdata credentials.

In [ ]:

paths = None
try:
    paths = imerg.download(progress_bar=False)
    print(len(paths), 'granule(s):', [Path(p).name for p in paths])
except Exception as exc:
    print(f'skipped live query: {type(exc).__name__}: {exc}')

paths = None try: paths = imerg.download(progress_bar=False) print(len(paths), 'granule(s):', [Path(p).name for p in paths]) except Exception as exc: print(f'skipped live query: {type(exc).__name__}: {exc}')

Map the precipitation field¶

If the download succeeded, open the granule with xarray (the data lives in the Grid group) and plot the June 2023 mean precipitation rate. This step is also guarded so a read / plotting surprise does not mask the download above.

In [ ]:

if paths:
    try:
        import matplotlib.pyplot as plt
        import xarray as xr

        ds = xr.open_dataset(paths[0], group='Grid')
        precip = ds['precipitation'].isel(time=0)
        fig, ax = plt.subplots(figsize=(10, 5))
        precip.where(precip >= 0).T.plot(ax=ax, cmap='viridis', robust=True)
        ax.set_title('GPM IMERG monthly precipitation rate, June 2023')
        plt.tight_layout()
        plt.show()
    except Exception as exc:
        print(f'plot step skipped: {type(exc).__name__}: {exc}')

if paths: try: import matplotlib.pyplot as plt import xarray as xr ds = xr.open_dataset(paths[0], group='Grid') precip = ds['precipitation'].isel(time=0) fig, ax = plt.subplots(figsize=(10, 5)) precip.where(precip >= 0).T.plot(ax=ax, cmap='viridis', robust=True) ax.set_title('GPM IMERG monthly precipitation rate, June 2023') plt.tight_layout() plt.show() except Exception as exc: print(f'plot step skipped: {type(exc).__name__}: {exc}')