def METHOD_NAME():
    for dataset in ['ppt', 'tmean']:
        # Create the colormap for this dataset
        colormap = make_cmap(datasets[dataset]['colors'],
                             datasets[dataset]['position'],
                             bit=True)
        for month in range(1, 13):
            month_str = str(month).zfill(2)
            input_file = './source/{}_{}.tif'.format(dataset, month_str)
            # Open all 12 rasters of this data set, one per month
            with rasterio.open(input_file, 'r') as src:
                arr = src.read(1)
                # Normalize the values and create an rgba value for
                # each cell
                img = normalize_to_rgba(dataset, arr, colormap)
                # We need to write out each rgba band separately, so
                # create 4 arrays of the same shape as the src
                # to contain them
                shp = arr.shape
                r = np.ndarray(shape=shp, dtype=np.uint8)
                g = np.ndarray(shape=shp, dtype=np.uint8)
                b = np.ndarray(shape=shp, dtype=np.uint8)
                a = np.ndarray(shape=shp, dtype=np.uint8)
                # Walk the rows and columns, extracting the rgba values
                # individualy but placed at the same index in the new array
                for i in range(0, shp[0]):
                    for j in range(0, shp[1]):
                        cell = img[i][j]
                        r[i][j] = cell[0]
                        g[i][j] = cell[1]
                        b[i][j] = cell[2]
                        a[i][j] = cell[3]
                # Copy the TIFF headers from the source file, but update to a
                # photometic rgba byte raster, the target for our output
                meta = copy(src.meta)
                meta.update({
                    'dtype': 'uint8',
                    'count': 4,
                    'photometric': 'rgb',
                    'nodata': None,
                    'alpha': 'YES'
                })
                # Write out the rgba raster, which is suitable for
                # tiling against gdal2tiles.py
                out = './output/{}_{}_color.tif'.format(dataset, month_str)
                with rasterio.open(out, 'w', **meta) as out:
                    out.write(r, 1)
                    out.write(g, 2)
                    out.write(b, 3)
                    out.write(a, 4)