Save multiple GeoTIFF files from a GRIB file, one file per day (time dimension)

Question

I am trying to save multiple GeoTIFF files from a GRIB file I have elaborated.

Before converting to raster, the GRIB file I started with is a ERA5 2m hourly temperature over 3 days:

import iris

# Load the GRIB file using iris
cubes = iris.load('download_for_et0.grib')

print(cubes[1])

air_temperature / (K)               (time: 72; latitude: 20; longitude: 28)
    Dimension coordinates:
        time                             x             -              -
        latitude                         -             x              -
        longitude                        -             -              x
    Auxiliary coordinates:
        forecast_period                  x             -              -
    Scalar coordinates:
        height                      2 m
        originating_centre          European Centre for Medium Range Weather Forecasts

From this, I calculate the daily mean and save as a new GRIB file:

# 1st step: Create an auxialiary coordinate
iris.coord_categorisation.add_day_of_year(cube, "time", name="day of year")

# 2nd step: Mean over the auxiliary coordinate  
daily_Tmean = cube.aggregated_by(["day of year"], iris.analysis.MEAN)

#3rd step: Fix cell_methods   
method = iris.coords.CellMethod('mean', coords=['time'], intervals='1 day')
daily_Tmean.cell_methods = daily_Tmean.cell_methods[:-1]
daily_Tmean.add_cell_method(method)

# 4rd step: Remove the auxiliary coordinate
daily_Tmean.remove_coord("day of year")

print(daily_Tmean)
air_temperature / (K)               (time: 3; latitude: 20; longitude: 28)
    Dimension coordinates:
        time                             x            -              -
        latitude                         -            x              -
        longitude                        -            -              x
    Auxiliary coordinates:
        forecast_period                  x            -              -
        forecast_reference_time          x            -              -
    Scalar coordinates:
        height                      2.0 m
    Cell methods:
        mean                        time (1 day)
    Attributes:
        GRIB_PARAM                  GRIB2:d000c000n000

I want to save this daily_Tmean GRIB file as a series of GeoTIFF files, one per day, with %Y-%m-D% format, for example:

2022-07-29.tif
2022-07-30.tif
2022-07-31.tif

I've tried with iris, xarray and rioxarray with no success, I am missing something for sure.

With iris:

import iris

# Load the GRIB file
grib_file = iris.load_cubes('daily_Tmean.grib')

# Loop through the cubes in the grib_file
for cube in grib_file:
    # Check if the cube has a time coordinate
    if 'time' in cube.coords():
        # Get the time value as a string
        time_str = str(cube.coord('time').points[0])
        print(time_str)
        # Create a filename using the time value
        filename = './geotiff_' + time_str + '.tif'
        # Save the cube as a geotiff
        iris.fileformats.grib.save_grib2(cube, file_name)

with xarray:

import xarray as xr

# Open the GRIB file using xarray
ds = xr.open_dataset("daily_Tmean.grib", engine='cfgrib')

# Loop through the time variables in the dataset
for time in ds.time:
    # Extract the data for the current time variable
    data = ds.sel(time=time)
    # Save the data as a geotiff file
    data.to_netcdf("file_" + str(time) + ".tif")

# Close the GRIB file
ds.close()

with rioxarray:

import rioxarray

# Open the GRIB file using rioxarray
ds = rioxarray.open_rasterio("daily_Tmean.grib")

# Get the time dimension
time = ds.band

# Loop through each day in the time dimension
for day in time.dt.floor('D'):
    # Select data for the current day
    day_data = ds.sel(time=slice(day, day + np.timedelta64(1, 'D')))
    
    # Save the data as a separate GeoTIFF file
    day_data.rio.to_raster("path/to/save/file_{}.tif".format(day.strftime("%Y-%m-%d")))

Answer 1

You didn't clearly indicate what's wrong with your code.

My guess is your GRIB file isn't georeferenced. This would mean you should add lat/lon information as a transform to your array before saving it to GeoTiff.

You have an example (for another datasource) here: https://lpdaac.usgs.gov/resources/e-learning/working-ecostress-evapotranspiration-data (look for the 2nd title)