Read CAPE from GFS GRIB2 in Python with xarray: use the cfgrib engine and filter typeOfLevel=surface

Troubleshoot empty CAPE in GFS GRIB2 with xarray. Learn why engines differ and how cfgrib plus typeOfLevel=surface makes CAPE readable. Quick, practical steps.

Reading GRIB2 from GFS sometimes turns into a head-scratcher when a parameter appears to be present but resolves to an empty dataset in Python. A common example is CAPE (Convective Available Potential Energy): the file downloads fine, Temperature opens fine, but CAPE shows no data. Below is a concise walkthrough of why this happens and how to open the same file so the CAPE field is actually readable.

Reproducing the issue

The file comes from the GFS 0.25° 1-hour product. Opening it with xarray using one engine leads to an empty result for CAPE, while other variables like TMP work as expected.

import xarray as xr
src_path = 'gfs.t18z.pgrb2.0p25.f005'
raw_view = xr.open_dataset(src_path, engine='gribberish')

With this approach, CAPE shows no data, despite the same file yielding data when selecting another weather parameter such as Temperature (TMP).

What is going on

The behavior is engine-dependent. The same GRIB2 file can be parsed differently by different backends. In this case, CAPE is present in the file, but opening it with one engine results in an empty dataset for that field. Switching to another engine and applying a level filter makes the data visible.

The fix

Open the GRIB2 with a different backend and filter by the appropriate level metadata. The following call reveals the CAPE data:

import xarray as xr
path_to_grib = 'gfs.t18z.pgrb2.0p25.f005'
met_data = xr.open_dataset(
    path_to_grib,
    engine='cfgrib',
    filter_by_keys={'typeOfLevel': 'surface'}
)

This switches the decoding engine and narrows the selection to the relevant level, after which CAPE becomes available.

Why this matters

GRIB decoding is not entirely uniform across engines. When a parameter like CAPE appears empty while others in the same file load fine, it is often a parsing pathway issue rather than a missing field. Knowing how to change engine and filter by GRIB metadata (such as typeOfLevel) saves time and prevents false assumptions about data availability.

Takeaways

If a GFS GRIB2 parameter looks empty in Python, try a different engine and apply a key filter. For CAPE from this source, opening with the cfgrib engine and specifying typeOfLevel as surface returns the expected data. Keep this pattern at hand when handling other weather parameters that seem to vanish during decoding.