diff --git a/CHANGES.md b/CHANGES.md index 488f51c..87bd4e3 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,3 +1,10 @@ +## Changes in 0.2.4 (under development) + +* Added support for **common band names** from the [STAC EO extension](https://github.com/stac-extensions/eo?tab=readme-ov-file#common-band-names) + in **Sentinel-2 analysis mode**. The `variables` parameter now accepts standard + spectral names such as `blue`, `green`, `red`, `nir`, and others. + + ## Changes in 0.2.3 (from 2025-10-23) - **Sentinel-3 SLSTR Level-1 RBT products** are now supported in analysis mode. This diff --git a/docs/guide.md b/docs/guide.md index 9d95397..b75e002 100644 --- a/docs/guide.md +++ b/docs/guide.md @@ -90,6 +90,8 @@ bands from multiple resolutions onto the same grid using [affine transformation **Specific Sentinel-2 parameters `**kwargs`:** +- `variables`: The common spectral band names specified in the [STAC EO extension](https://github.com/stac-extensions/eo?tab=readme-ov-file#common-band-names) + are supported for the Sentinel-2 analysis mode. - `resolution`: Target resolution for all spatial data variables / bands. Must be one of `10`, `20`, or `60`. diff --git a/examples/open-sen2.ipynb b/examples/open-sen2.ipynb index 9772664..6ec5f54 100644 --- a/examples/open-sen2.ipynb +++ b/examples/open-sen2.ipynb @@ -50,8 +50,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 970 ms, sys: 139 ms, total: 1.11 s\n", - "Wall time: 2.94 s\n" + "CPU times: user 888 ms, sys: 111 ms, total: 999 ms\n", + "Wall time: 4.88 s\n" ] }, { @@ -72,9 +72,7 @@ "\n", "\n", "\n", - "
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+ "text/plain": [ + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "ds.b01.plot(vmin=0., vmax=0.3)" + ] + }, + { + "cell_type": "markdown", + "id": "58f39f5a-45da-4bcd-a04a-c2e17e078703", + "metadata": {}, + "source": [ + "### Open as Dataset\n", + "\n", + "The function `xarray.open_dataset(path, engine=\"eopf-zarr\", op_mode=\"native\", **kwargs)` flattens the `DataTree` structure and returns a single `xr.Dataset`.\n", + "\n", + "In this process, hierarchical groups within the Zarr product are removed by converting their contents into standalone datasets and merging them into one. To ensure uniqueness, variable and dimension names are prefixed with their original group paths, using an underscore (`_`) as the default separator. For example, a variable named `b02` located in the group `measurements/reflectance/r10m` will be renamed to `measurements_reflectance_r10m_b02` in the returned dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "61771275-2de2-407c-b111-4aa2de4738b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 155 ms, sys: 11.1 ms, total: 167 ms\n", + "Wall time: 2.17 s\n" + ] + }, + { + "data": { + "text/html": [ + "\n", " \n", - "\n", " \n", "\n", " \n", "\n", @@ -2601,9 +3643,7 @@ " <xarray.DatasetView> Size: 482MB\n", - "Dimensions: (y: 10980, x: 10980)\n", - "Coordinates:\n", - " * x (x) int64 88kB 300005 300015 300025 300035 ... 409775 409785 409795\n", - " * y (y) int64 88kB 5000035 5000025 5000015 ... 4890265 4890255 4890245\n", - "Data variables:\n", - " b02 (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>\n", - " b03 (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>\n", - " b04 (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>\n", - " b08 (y, x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>r10m
- y: 10980
- x: 10980
x(x)int64300005 300015 ... 409785 409795array([300005, 300015, 300025, ..., 409775, 409785, 409795], shape=(10980,)) y(y)int645000035 5000025 ... 4890255 4890245array([5000035, 5000025, 5000015, ..., 4890265, 4890255, 4890245],\n", - " shape=(10980,))
b02(y, x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
<xarray.Dataset> Size: 7GB\n", + "Dimensions: (\n", + " conditions_geometry_angle: 2,\n", + " conditions_geometry_band: 13,\n", + " conditions_geometry_y: 23,\n", + " conditions_geometry_x: 23,\n", + " conditions_geometry_detector: 4,\n", + " ...\n", + " quality_mask_r10m_y: 10980,\n", + " quality_mask_r10m_x: 10980,\n", + " quality_mask_r20m_y: 5490,\n", + " quality_mask_r20m_x: 5490,\n", + " quality_mask_r60m_y: 1830,\n", + " quality_mask_r60m_x: 1830)\n", + "Coordinates: (12/44)\n", + " * conditions_geometry_angle (conditions_geometry_angle) <U7 56B ...\n", + " * conditions_geometry_band (conditions_geometry_band) <U3 156B ...\n", + " * conditions_geometry_y (conditions_geometry_y) int64 184B ...\n", + " * conditions_geometry_x (conditions_geometry_x) int64 184B ...\n", + " * conditions_geometry_detector (conditions_geometry_detector) int64 32B ...\n", + " * conditions_mask_detector_footprint_r10m_y (conditions_mask_detector_footprint_r10m_y) int64 88kB ...\n", + " ... ...\n", + " conditions_meteorology_ecmwf_isobaricInhPa float64 8B ...\n", + " conditions_meteorology_ecmwf_number int64 8B ...\n", + " conditions_meteorology_ecmwf_step int64 8B ...\n", + " conditions_meteorology_ecmwf_surface float64 8B ...\n", + " conditions_meteorology_ecmwf_time datetime64[ns] 8B ...\n", + " conditions_meteorology_ecmwf_valid_time datetime64[ns] 8B ...\n", + "Data variables: (12/62)\n", + " conditions_geometry_mean_sun_angles (conditions_geometry_angle) float64 16B dask.array<chunksize=(2,), meta=np.ndarray>\n", + " conditions_geometry_mean_viewing_incidence_angles (conditions_geometry_band, conditions_geometry_angle) float64 208B dask.array<chunksize=(13, 2), meta=np.ndarray>\n", + " conditions_geometry_sun_angles (conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x) float64 8kB dask.array<chunksize=(2, 23, 23), meta=np.ndarray>\n", + " conditions_geometry_viewing_incidence_angles (conditions_geometry_band, conditions_geometry_detector, conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x) float64 440kB dask.array<chunksize=(7, 4, 2, 23, 23), meta=np.ndarray>\n", + " conditions_mask_detector_footprint_r10m_b02 (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>\n", + " conditions_mask_detector_footprint_r10m_b03 (conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x) uint8 121MB dask.array<chunksize=(1830, 1830), meta=np.ndarray>\n", + " ... ...\n", + " quality_mask_r20m_b11 (quality_mask_r20m_y, quality_mask_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", + " quality_mask_r20m_b12 (quality_mask_r20m_y, quality_mask_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", + " quality_mask_r20m_b8a (quality_mask_r20m_y, quality_mask_r20m_x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", + " quality_mask_r60m_b01 (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>\n", + " quality_mask_r60m_b09 (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>\n", + " quality_mask_r60m_b10 (quality_mask_r60m_y, quality_mask_r60m_x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>\n", + "Attributes: (3)xarray.Dataset
- conditions_geometry_angle: 2
- conditions_geometry_band: 13
- conditions_geometry_y: 23
- conditions_geometry_x: 23
- conditions_geometry_detector: 4
- conditions_mask_detector_footprint_r10m_y: 10980
- conditions_mask_detector_footprint_r10m_x: 10980
- conditions_mask_detector_footprint_r20m_y: 5490
- conditions_mask_detector_footprint_r20m_x: 5490
- conditions_mask_detector_footprint_r60m_y: 1830
- conditions_mask_detector_footprint_r60m_x: 1830
- conditions_mask_l1c_classification_y: 1830
- conditions_mask_l1c_classification_x: 1830
- conditions_meteorology_cams_latitude: 9
- conditions_meteorology_cams_longitude: 9
- conditions_meteorology_ecmwf_latitude: 9
- conditions_meteorology_ecmwf_longitude: 9
- measurements_r10m_y: 10980
- measurements_r10m_x: 10980
- measurements_r20m_y: 5490
- measurements_r20m_x: 5490
- measurements_r60m_y: 1830
- measurements_r60m_x: 1830
- quality_l1c_quicklook_band: 3
- quality_l1c_quicklook_y: 10980
- quality_l1c_quicklook_x: 10980
- quality_mask_r10m_y: 10980
- quality_mask_r10m_x: 10980
- quality_mask_r20m_y: 5490
- quality_mask_r20m_x: 5490
- quality_mask_r60m_y: 1830
- quality_mask_r60m_x: 1830
conditions_geometry_angle(conditions_geometry_angle)<U7'zenith' 'azimuth'array(['zenith', 'azimuth'], dtype='<U7') conditions_geometry_band(conditions_geometry_band)<U3'b01' 'b02' 'b03' ... 'b11' 'b12'array(['b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07', 'b08', 'b8a', 'b09',\n", + " 'b10', 'b11', 'b12'], dtype='<U3') conditions_geometry_y(conditions_geometry_y)int645000040 4995040 ... 4895040 4890040array([5000040, 4995040, 4990040, 4985040, 4980040, 4975040, 4970040, 4965040,\n", + " 4960040, 4955040, 4950040, 4945040, 4940040, 4935040, 4930040, 4925040,\n", + " 4920040, 4915040, 4910040, 4905040, 4900040, 4895040, 4890040]) conditions_geometry_x(conditions_geometry_x)int64300000 305000 ... 405000 410000array([300000, 305000, 310000, 315000, 320000, 325000, 330000, 335000, 340000,\n", + " 345000, 350000, 355000, 360000, 365000, 370000, 375000, 380000, 385000,\n", + " 390000, 395000, 400000, 405000, 410000]) conditions_geometry_detector(conditions_geometry_detector)int641 2 3 4array([1, 2, 3, 4]) conditions_mask_detector_footprint_r10m_y(conditions_mask_detector_footprint_r10m_y)int645000035 5000025 ... 4890255 4890245array([5000035, 5000025, 5000015, ..., 4890265, 4890255, 4890245],\n", + " shape=(10980,)) conditions_mask_detector_footprint_r10m_x(conditions_mask_detector_footprint_r10m_x)int64300005 300015 ... 409785 409795array([300005, 300015, 300025, ..., 409775, 409785, 409795], shape=(10980,)) conditions_mask_detector_footprint_r20m_y(conditions_mask_detector_footprint_r20m_y)int645000030 5000010 ... 4890270 4890250array([5000030, 5000010, 4999990, ..., 4890290, 4890270, 4890250],\n", + " shape=(5490,)) conditions_mask_detector_footprint_r20m_x(conditions_mask_detector_footprint_r20m_x)int64300010 300030 ... 409770 409790array([300010, 300030, 300050, ..., 409750, 409770, 409790], shape=(5490,)) conditions_mask_detector_footprint_r60m_y(conditions_mask_detector_footprint_r60m_y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", + " shape=(1830,)) conditions_mask_detector_footprint_r60m_x(conditions_mask_detector_footprint_r60m_x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) conditions_mask_l1c_classification_y(conditions_mask_l1c_classification_y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", + " shape=(1830,)) conditions_mask_l1c_classification_x(conditions_mask_l1c_classification_x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) conditions_meteorology_cams_latitude(conditions_meteorology_cams_latitude)float6445.13 45.0 44.88 ... 44.28 44.16
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4 , 44.279, 44.16 ]) conditions_meteorology_cams_longitude(conditions_meteorology_cams_longitude)float646.457 6.634 6.811 ... 7.695 7.872
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([6.457 , 6.633875, 6.81075 , 6.987625, 7.1645 , 7.341375, 7.51825 ,\n", + " 7.695125, 7.872 ]) conditions_meteorology_ecmwf_latitude(conditions_meteorology_ecmwf_latitude)float6445.13 45.0 44.88 ... 44.28 44.16
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4 , 44.279, 44.16 ]) conditions_meteorology_ecmwf_longitude(conditions_meteorology_ecmwf_longitude)float646.457 6.634 6.811 ... 7.695 7.872
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([6.457 , 6.633875, 6.81075 , 6.987625, 7.1645 , 7.341375, 7.51825 ,\n", + " 7.695125, 7.872 ]) measurements_r10m_y(measurements_r10m_y)int645000035 5000025 ... 4890255 4890245array([5000035, 5000025, 5000015, ..., 4890265, 4890255, 4890245],\n", + " shape=(10980,)) measurements_r10m_x(measurements_r10m_x)int64300005 300015 ... 409785 409795array([300005, 300015, 300025, ..., 409775, 409785, 409795], shape=(10980,)) measurements_r20m_y(measurements_r20m_y)int645000030 5000010 ... 4890270 4890250array([5000030, 5000010, 4999990, ..., 4890290, 4890270, 4890250],\n", + " shape=(5490,)) measurements_r20m_x(measurements_r20m_x)int64300010 300030 ... 409770 409790array([300010, 300030, 300050, ..., 409750, 409770, 409790], shape=(5490,)) measurements_r60m_y(measurements_r60m_y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", + " shape=(1830,)) measurements_r60m_x(measurements_r60m_x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) quality_l1c_quicklook_band(quality_l1c_quicklook_band)int641 2 3array([1, 2, 3]) quality_l1c_quicklook_y(quality_l1c_quicklook_y)int645000035 5000025 ... 4890255 4890245array([5000035, 5000025, 5000015, ..., 4890265, 4890255, 4890245],\n", + " shape=(10980,)) quality_l1c_quicklook_x(quality_l1c_quicklook_x)int64300005 300015 ... 409785 409795array([300005, 300015, 300025, ..., 409775, 409785, 409795], shape=(10980,)) quality_mask_r10m_y(quality_mask_r10m_y)int645000035 5000025 ... 4890255 4890245array([5000035, 5000025, 5000015, ..., 4890265, 4890255, 4890245],\n", + " shape=(10980,)) quality_mask_r10m_x(quality_mask_r10m_x)int64300005 300015 ... 409785 409795array([300005, 300015, 300025, ..., 409775, 409785, 409795], shape=(10980,)) quality_mask_r20m_y(quality_mask_r20m_y)int645000030 5000010 ... 4890270 4890250array([5000030, 5000010, 4999990, ..., 4890290, 4890270, 4890250],\n", + " shape=(5490,)) quality_mask_r20m_x(quality_mask_r20m_x)int64300010 300030 ... 409770 409790array([300010, 300030, 300050, ..., 409750, 409770, 409790], shape=(5490,)) quality_mask_r60m_y(quality_mask_r60m_y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", + " shape=(1830,)) quality_mask_r60m_x(quality_mask_r60m_x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) conditions_meteorology_cams_isobaricInhPa()float64...
- long_name :
- pressure
- positive :
- down
- standard_name :
- air_pressure
- stored_direction :
- decreasing
- units :
- hPa
[1 values with dtype=float64] conditions_meteorology_cams_number()int64...
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
[1 values with dtype=int64] conditions_meteorology_cams_step()int64...
- long_name :
- time since forecast_reference_time
- standard_name :
- forecast_period
- units :
- nanoseconds
[1 values with dtype=int64] conditions_meteorology_cams_surface()float64...
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
[1 values with dtype=float64] conditions_meteorology_cams_time()datetime64[ns]...
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
[1 values with dtype=datetime64[ns]] conditions_meteorology_cams_valid_time()datetime64[ns]...
- long_name :
- time
- standard_name :
- time
[1 values with dtype=datetime64[ns]] conditions_meteorology_ecmwf_isobaricInhPa()float64...
- long_name :
- pressure
- positive :
- down
- standard_name :
- air_pressure
- stored_direction :
- decreasing
- units :
- hPa
[1 values with dtype=float64] conditions_meteorology_ecmwf_number()int64...
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
[1 values with dtype=int64] conditions_meteorology_ecmwf_step()int64...
- long_name :
- time since forecast_reference_time
- standard_name :
- forecast_period
- units :
- nanoseconds
[1 values with dtype=int64] conditions_meteorology_ecmwf_surface()float64...
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
[1 values with dtype=float64] conditions_meteorology_ecmwf_time()datetime64[ns]...
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
[1 values with dtype=datetime64[ns]] conditions_meteorology_ecmwf_valid_time()datetime64[ns]...
- long_name :
- time
- standard_name :
- time
[1 values with dtype=datetime64[ns]]
conditions_geometry_mean_sun_angles(conditions_geometry_angle)float64dask.array<chunksize=(2,), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['angle'], 'dimensions': ['angle']}
- unit :
- deg
\n", "
\n", " \n", - "\n", " \n", "\n", @@ -3054,57 +4181,47 @@ " \n", "
\n", "\n", " \n", " \n", "Bytes \n", - "114.98 MiB \n", - "3.19 MiB \n", + "16 B \n", + "16 B \n", "\n", " \n", "Shape \n", - "(10980, 10980) \n", - "(1830, 1830) \n", + "(2,) \n", + "(2,) \n", "\n", " \n", "Dask graph \n", - "36 chunks in 2 graph layers \n", + "1 chunks in 2 graph layers \n", "\n", " \n", " \n", "Data type \n", - "uint8 numpy.ndarray \n", + "float64 numpy.ndarray \n", "\n", - " \n", "\n", + " \n", "\n", " \n", " \n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", "\n", " \n", - " \n", + " \n", "\n", " \n", - " 10980 \n", - "10980 \n", + "2 \n", + "1 \n", " b03(y, x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_geometry_mean_viewing_incidence_angles(conditions_geometry_band, conditions_geometry_angle)float64dask.array<chunksize=(13, 2), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['angle', 'band'], 'dimensions': ['band', 'angle']}
- unit :
- deg
\n", "
\n", " \n", - "\n", " \n", "\n", @@ -3119,57 +4236,47 @@ " \n", "
\n", "\n", " \n", " \n", "Bytes \n", - "114.98 MiB \n", - "3.19 MiB \n", + "208 B \n", + "208 B \n", "\n", " \n", "Shape \n", - "(10980, 10980) \n", - "(1830, 1830) \n", + "(13, 2) \n", + "(13, 2) \n", "\n", " \n", "Dask graph \n", - "36 chunks in 2 graph layers \n", + "1 chunks in 2 graph layers \n", "\n", " \n", " \n", "Data type \n", - "uint8 numpy.ndarray \n", + "float64 numpy.ndarray \n", "\n", - " \n", "\n", + " \n", "\n", " \n", - " \n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", "\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", "\n", " \n", - " \n", + " \n", "\n", " \n", - " 10980 \n", - "10980 \n", + "2 \n", + "13 \n", " b04(y, x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_geometry_sun_angles(conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x)float64dask.array<chunksize=(2, 23, 23), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['angle', 'y', 'x'], 'dimensions': ['angle', 'y', 'x']}
\n", "
\n", " \n", + "\n", " \n", "\n", @@ -3184,57 +4291,165 @@ " \n", "
\n", "\n", " \n", " \n", "Bytes \n", - "114.98 MiB \n", - "3.19 MiB \n", + "8.27 kiB \n", + "8.27 kiB \n", "\n", " \n", "Shape \n", - "(10980, 10980) \n", - "(1830, 1830) \n", + "(2, 23, 23) \n", + "(2, 23, 23) \n", "\n", " \n", "Dask graph \n", - "36 chunks in 2 graph layers \n", + "1 chunks in 2 graph layers \n", "\n", " \n", " \n", "Data type \n", - "uint8 numpy.ndarray \n", + "float64 numpy.ndarray \n", "\n", - " \n", + "\n", + " \n", "\n", " \n", - " \n", + "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", "\n", " \n", - " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", "\n", " \n", - " 10980 \n", - "10980 \n", + "23 \n", + "23 \n", + "2 \n", + " conditions_geometry_viewing_incidence_angles(conditions_geometry_band, conditions_geometry_detector, conditions_geometry_angle, conditions_geometry_y, conditions_geometry_x)float64dask.array<chunksize=(7, 4, 2, 23, 23), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['angle', 'y', 'x', 'detector', 'band'], 'dimensions': ['band', 'detector', 'angle', 'y', 'x']}
\n", + "
\n", + " \n", - "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " Array \n", + "Chunk \n", + "\n", + " \n", + " \n", + "Bytes \n", + "429.81 kiB \n", + "231.44 kiB \n", + "\n", + " \n", + "Shape \n", + "(13, 4, 2, 23, 23) \n", + "(7, 4, 2, 23, 23) \n", + "\n", + " \n", + "Dask graph \n", + "2 chunks in 2 graph layers \n", + "\n", + " \n", + " \n", + "Data type \n", + "float64 numpy.ndarray \n", + "\n", + " \n", "\n", + "\n", + " \n", + " \n", "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 4 \n", + "13 \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " 23 \n", + "23 \n", + "2 \n", " b08(y, x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r10m_b02(conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -3299,462 +4514,7 @@ "\n", " \n", " \n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 181MB\n", - "Dimensions: (y: 5490, x: 5490)\n", - "Coordinates:\n", - " * x (x) int64 44kB 300010 300030 300050 300070 ... 409750 409770 409790\n", - " * y (y) int64 44kB 5000030 5000010 4999990 ... 4890290 4890270 4890250\n", - "Data variables:\n", - " b05 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b06 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b07 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b11 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b12 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b8a (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>r20m
- y: 5490
- x: 5490
x(x)int64300010 300030 ... 409770 409790array([300010, 300030, 300050, ..., 409750, 409770, 409790], shape=(5490,)) y(y)int645000030 5000010 ... 4890270 4890250array([5000030, 5000010, 4999990, ..., 4890290, 4890270, 4890250],\n", - " shape=(5490,))
b05(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r10m_b03(conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -3769,14 +4529,14 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "28.74 MiB \n", - "817.60 kiB \n", + "114.98 MiB \n", + "3.19 MiB \n", "\n", " \n", "Shape \n", - "(5490, 5490) \n", - "(915, 915) \n", + "(10980, 10980) \n", + "(1830, 1830) \n", "\n", " \n", - "Dask graph \n", @@ -3814,12 +4574,12 @@ "\n", "\n", " \n", - " 5490 \n", - "5490 \n", + "10980 \n", + "10980 \n", "\n", " \n", " b06(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r10m_b04(conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -3834,14 +4594,14 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "28.74 MiB \n", - "817.60 kiB \n", + "114.98 MiB \n", + "3.19 MiB \n", "\n", " \n", "Shape \n", - "(5490, 5490) \n", - "(915, 915) \n", + "(10980, 10980) \n", + "(1830, 1830) \n", "\n", " \n", - "Dask graph \n", @@ -3879,12 +4639,12 @@ "\n", "\n", " \n", - " 5490 \n", - "5490 \n", + "10980 \n", + "10980 \n", "\n", " \n", " b07(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r10m_b08(conditions_mask_detector_footprint_r10m_y, conditions_mask_detector_footprint_r10m_x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -3899,14 +4659,14 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "28.74 MiB \n", - "817.60 kiB \n", + "114.98 MiB \n", + "3.19 MiB \n", "\n", " \n", "Shape \n", - "(5490, 5490) \n", - "(915, 915) \n", + "(10980, 10980) \n", + "(1830, 1830) \n", "\n", " \n", - "Dask graph \n", @@ -3944,12 +4704,12 @@ "\n", "\n", " \n", - " 5490 \n", - "5490 \n", + "10980 \n", + "10980 \n", "\n", " \n", " b11(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r20m_b05(conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4014,7 +4774,7 @@ "\n", " \n", " \n", - "
b12(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r20m_b06(conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4079,7 +4839,7 @@ "\n", " \n", " \n", - "
b8a(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r20m_b07(conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4144,459 +4904,72 @@ "\n", " \n", " \n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 10MB\n", - "Dimensions: (y: 1830, x: 1830)\n", - "Coordinates:\n", - " * x (x) int64 15kB 300030 300090 300150 300210 ... 409650 409710 409770\n", - " * y (y) int64 15kB 5000010 4999950 4999890 ... 4890390 4890330 4890270\n", - "Data variables:\n", - " b01 (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>\n", - " b09 (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>\n", - " b10 (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>r60m
- y: 1830
- x: 1830
x(x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) y(y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", - " shape=(1830,))
b01(y, x)uint8dask.array<chunksize=(305, 305), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [1830, 1830]
- proj:transform :
- [60.0, 0.0, 300000.0, 0.0, -60.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + " \n", + "
5490 \n", + "5490 \n", + "\n", + " \n", + " \n", + " conditions_mask_detector_footprint_r20m_b12(conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4611,14 +4984,14 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "3.19 MiB \n", - "90.84 kiB \n", + "28.74 MiB \n", + "817.60 kiB \n", "\n", " \n", "Shape \n", - "(1830, 1830) \n", - "(305, 305) \n", + "(5490, 5490) \n", + "(915, 915) \n", "\n", " \n", - "Dask graph \n", @@ -4656,12 +5029,12 @@ "\n", "\n", " \n", - " 1830 \n", - "1830 \n", + "5490 \n", + "5490 \n", "\n", " \n", " b09(y, x)uint8dask.array<chunksize=(305, 305), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [1830, 1830]
- proj:transform :
- [60.0, 0.0, 300000.0, 0.0, -60.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r20m_b8a(conditions_mask_detector_footprint_r20m_y, conditions_mask_detector_footprint_r20m_x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4676,14 +5049,14 @@ " \n", "
\n", " \n", " \n", "Bytes \n", - "3.19 MiB \n", - "90.84 kiB \n", + "28.74 MiB \n", + "817.60 kiB \n", "\n", " \n", "Shape \n", - "(1830, 1830) \n", - "(305, 305) \n", + "(5490, 5490) \n", + "(915, 915) \n", "\n", " \n", - "Dask graph \n", @@ -4721,12 +5094,12 @@ "\n", "\n", " \n", - " 1830 \n", - "1830 \n", + "5490 \n", + "5490 \n", "\n", " \n", " b10(y, x)uint8dask.array<chunksize=(305, 305), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [1830, 1830]
- proj:transform :
- [60.0, 0.0, 300000.0, 0.0, -60.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", + "
conditions_mask_detector_footprint_r60m_b01(conditions_mask_detector_footprint_r60m_y, conditions_mask_detector_footprint_r60m_x)uint8dask.array<chunksize=(305, 305), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'long_name': 'detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12'}
- dtype :
- <u1
- long_name :
- detector footprint mask provided in the final reference frame (ground geometry). 0 = no detector, 1-12 = detector 1-12
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [1830, 1830]
- proj:transform :
- [60.0, 0.0, 300000.0, 0.0, -60.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", "
\n", " \n", " \n", @@ -4791,34721 +5164,257 @@ "\n", " \n", " \n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 0B\n", - "Dimensions: ()\n", - "Data variables:\n", - " *empty*l1c_classification
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 3MB\n", - "Dimensions: (y: 1830, x: 1830)\n", - "Coordinates:\n", - " * x (x) int64 15kB 300030 300090 300150 300210 ... 409650 409710 409770\n", - " * y (y) int64 15kB 5000010 4999950 4999890 ... 4890390 4890330 4890270\n", - "Data variables:\n", - " b00 (y, x) uint8 3MB dask.array<chunksize=(305, 305), meta=np.ndarray>r60m
- y: 1830
- x: 1830
x(x)int64300030 300090 ... 409710 409770array([300030, 300090, 300150, ..., 409650, 409710, 409770], shape=(1830,)) y(y)int645000010 4999950 ... 4890330 4890270array([5000010, 4999950, 4999890, ..., 4890390, 4890330, 4890270],\n", - " shape=(1830,))
b00(y, x)uint8dask.array<chunksize=(305, 305), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4], 'flag_meanings': ['OPAQUE', 'CIRRUS', 'SNOW_ICE'], 'long_name': 'cloud classification mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4]
- flag_meanings :
- ['OPAQUE', 'CIRRUS', 'SNOW_ICE']
- long_name :
- cloud classification mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [1830, 1830]
- proj:transform :
- [60.0, 0.0, 300000.0, 0.0, -60.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "3.19 MiB \n", - "90.84 kiB \n", - "\n", - " \n", - "Shape \n", - "(1830, 1830) \n", - "(305, 305) \n", - "\n", - " \n", - "Dask graph \n", - "36 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "uint8 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 1830 \n", - "1830 \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 0B\n", - "Dimensions: ()\n", - "Data variables:\n", - " *empty*meteorology
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 4kB\n", - "Dimensions: (latitude: 9, longitude: 9)\n", - "Coordinates:\n", - " isobaricInhPa float64 8B ...\n", - " * latitude (latitude) float64 72B 45.13 45.0 44.88 ... 44.4 44.28 44.16\n", - " * longitude (longitude) float64 72B 6.457 6.634 6.811 ... 7.695 7.872\n", - " number int64 8B ...\n", - " step int64 8B ...\n", - " surface float64 8B ...\n", - " time datetime64[ns] 8B ...\n", - " valid_time datetime64[ns] 8B ...\n", - "Data variables:\n", - " aod1240 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " aod469 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " aod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " aod670 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " aod865 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " bcaod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " duaod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " omaod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " ssaod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " suaod550 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " z (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - "Attributes: (7)cams
- latitude: 9
- longitude: 9
isobaricInhPa()float64...
- long_name :
- pressure
- positive :
- down
- standard_name :
- air_pressure
- stored_direction :
- decreasing
- units :
- hPa
[1 values with dtype=float64] latitude(latitude)float6445.13 45.0 44.88 ... 44.28 44.16
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4 , 44.279, 44.16 ]) longitude(longitude)float646.457 6.634 6.811 ... 7.695 7.872
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([6.457 , 6.633875, 6.81075 , 6.987625, 7.1645 , 7.341375, 7.51825 ,\n", - " 7.695125, 7.872 ]) number()int64...
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
[1 values with dtype=int64] step()int64...
- long_name :
- time since forecast_reference_time
- standard_name :
- forecast_period
- units :
- nanoseconds
[1 values with dtype=int64] surface()float64...
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
[1 values with dtype=float64] time()datetime64[ns]...
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
[1 values with dtype=datetime64[ns]] valid_time()datetime64[ns]...
- long_name :
- time
- standard_name :
- time
[1 values with dtype=datetime64[ns]]
aod1240(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- unknown
- GRIB_cfVarName :
- aod1240
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Total Aerosol Optical Depth at 1240nm
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 210216
- GRIB_shortName :
- aod1240
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- ~
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Total Aerosol Optical Depth at 1240nm', 'standard_name': 'unknown', 'units': '~'}
- long_name :
- Total Aerosol Optical Depth at 1240nm
- standard_name :
- unknown
- units :
- ~
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " aod469(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- unknown
- GRIB_cfVarName :
- aod469
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Total Aerosol Optical Depth at 469nm
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 210213
- GRIB_shortName :
- aod469
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- ~
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Total Aerosol Optical Depth at 469nm', 'standard_name': 'unknown', 'units': '~'}
- long_name :
- Total Aerosol Optical Depth at 469nm
- standard_name :
- unknown
- units :
- ~
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- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Geopotential
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 129
- GRIB_shortName :
- z
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- m**2 s**-2
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Geopotential', 'standard_name': 'geopotential', 'units': 'm**2 s**-2'}
- long_name :
- Geopotential
- standard_name :
- geopotential
- units :
- m**2 s**-2
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - "
- Conventions :
- CF-1.7
- GRIB_centre :
- ecmf
- GRIB_centreDescription :
- European Centre for Medium-Range Weather Forecasts
- GRIB_edition :
- 1
- GRIB_subCentre :
- 0
- history :
- 2025-04-30T14:26 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.34.1 with {"source": "../../mnt/data/eopf-conversion-gb4cp/S2B_MSIL1C_20250430T101559_N0511_R065_T32TLQ_20250430T124542.SAFE/GRANULE/L1C_T32TLQ_A042562_20250430T102505/AUX_DATA/AUX_CAMSFO", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}
- institution :
- European Centre for Medium-Range Weather Forecasts
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 2kB\n", - "Dimensions: (latitude: 9, longitude: 9)\n", - "Coordinates:\n", - " isobaricInhPa float64 8B ...\n", - " * latitude (latitude) float64 72B 45.13 45.0 44.88 ... 44.4 44.28 44.16\n", - " * longitude (longitude) float64 72B 6.457 6.634 6.811 ... 7.695 7.872\n", - " number int64 8B ...\n", - " step int64 8B ...\n", - " surface float64 8B ...\n", - " time datetime64[ns] 8B ...\n", - " valid_time datetime64[ns] 8B ...\n", - "Data variables:\n", - " msl (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " r (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " tco3 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " tcwv (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " u10 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - " v10 (latitude, longitude) float32 324B dask.array<chunksize=(9, 9), meta=np.ndarray>\n", - "Attributes: (7)ecmwf
- latitude: 9
- longitude: 9
isobaricInhPa()float64...
- long_name :
- pressure
- positive :
- down
- standard_name :
- air_pressure
- stored_direction :
- decreasing
- units :
- hPa
[1 values with dtype=float64] latitude(latitude)float6445.13 45.0 44.88 ... 44.28 44.16
- long_name :
- latitude
- standard_name :
- latitude
- stored_direction :
- decreasing
- units :
- degrees_north
array([45.126, 45.005, 44.884, 44.763, 44.642, 44.521, 44.4 , 44.279, 44.16 ]) longitude(longitude)float646.457 6.634 6.811 ... 7.695 7.872
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degrees_east
array([6.457 , 6.633875, 6.81075 , 6.987625, 7.1645 , 7.341375, 7.51825 ,\n", - " 7.695125, 7.872 ]) number()int64...
- long_name :
- ensemble member numerical id
- standard_name :
- realization
- units :
- 1
[1 values with dtype=int64] step()int64...
- long_name :
- time since forecast_reference_time
- standard_name :
- forecast_period
- units :
- nanoseconds
[1 values with dtype=int64] surface()float64...
- long_name :
- original GRIB coordinate for key: level(surface)
- units :
- 1
[1 values with dtype=float64] time()datetime64[ns]...
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
[1 values with dtype=datetime64[ns]] valid_time()datetime64[ns]...
- long_name :
- time
- standard_name :
- time
[1 values with dtype=datetime64[ns]]
msl(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- air_pressure_at_mean_sea_level
- GRIB_cfVarName :
- msl
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Mean sea level pressure
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 151
- GRIB_shortName :
- msl
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- Pa
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Mean sea level pressure', 'standard_name': 'air_pressure_at_mean_sea_level', 'units': 'Pa'}
- long_name :
- Mean sea level pressure
- standard_name :
- air_pressure_at_mean_sea_level
- units :
- Pa
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " r(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- relative_humidity
- GRIB_cfVarName :
- r
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Relative humidity
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 157
- GRIB_shortName :
- r
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- isobaricInhPa
- GRIB_units :
- %
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Relative humidity', 'standard_name': 'relative_humidity', 'units': '%'}
- long_name :
- Relative humidity
- standard_name :
- relative_humidity
- units :
- %
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " tco3(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- atmosphere_mass_content_of_ozone
- GRIB_cfVarName :
- tco3
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Total column ozone
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 206
- GRIB_shortName :
- tco3
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- kg m**-2
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Total column ozone', 'standard_name': 'atmosphere_mass_content_of_ozone', 'units': 'kg m**-2'}
- long_name :
- Total column ozone
- standard_name :
- atmosphere_mass_content_of_ozone
- units :
- kg m**-2
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " tcwv(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- lwe_thickness_of_atmosphere_mass_content_of_water_vapor
- GRIB_cfVarName :
- tcwv
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- Total column vertically-integrated water vapour
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 137
- GRIB_shortName :
- tcwv
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- kg m**-2
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': 'Total column vertically-integrated water vapour', 'standard_name': 'lwe_thickness_of_atmosphere_mass_content_of_water_vapor', 'units': 'kg m**-2'}
- long_name :
- Total column vertically-integrated water vapour
- standard_name :
- lwe_thickness_of_atmosphere_mass_content_of_water_vapor
- units :
- kg m**-2
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " u10(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- unknown
- GRIB_cfVarName :
- u10
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- 10 metre U wind component
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 165
- GRIB_shortName :
- 10u
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- m s**-1
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': '10 metre U wind component', 'standard_name': 'unknown', 'units': 'm s**-1'}
- long_name :
- 10 metre U wind component
- standard_name :
- unknown
- units :
- m s**-1
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - " v10(latitude, longitude)float32dask.array<chunksize=(9, 9), meta=np.ndarray>
- GRIB_NV :
- 0
- GRIB_Nx :
- 9
- GRIB_Ny :
- 9
- GRIB_cfName :
- unknown
- GRIB_cfVarName :
- v10
- GRIB_dataType :
- fc
- GRIB_gridDefinitionDescription :
- Latitude/Longitude Grid
- GRIB_gridType :
- regular_ll
- GRIB_iDirectionIncrementInDegrees :
- 0.177
- GRIB_iScansNegatively :
- 0
- GRIB_jDirectionIncrementInDegrees :
- 0.121
- GRIB_jPointsAreConsecutive :
- 0
- GRIB_jScansPositively :
- 0
- GRIB_latitudeOfFirstGridPointInDegrees :
- 45.126
- GRIB_latitudeOfLastGridPointInDegrees :
- 44.16
- GRIB_longitudeOfFirstGridPointInDegrees :
- 6.457
- GRIB_longitudeOfLastGridPointInDegrees :
- 7.872
- GRIB_missingValue :
- 3.4028234663852886e+38
- GRIB_name :
- 10 metre V wind component
- GRIB_numberOfPoints :
- 81
- GRIB_paramId :
- 166
- GRIB_shortName :
- 10v
- GRIB_stepType :
- instant
- GRIB_stepUnits :
- 0
- GRIB_totalNumber :
- 0
- GRIB_typeOfLevel :
- surface
- GRIB_units :
- m s**-1
- _eopf_attrs :
- {'coordinates': ['number', 'time', 'step', 'surface', 'latitude', 'longitude', 'valid_time', 'isobaricInhPa'], 'dimensions': ['latitude', 'longitude'], 'long_name': '10 metre V wind component', 'standard_name': 'unknown', 'units': 'm s**-1'}
- long_name :
- 10 metre V wind component
- standard_name :
- unknown
- units :
- m s**-1
\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "324 B \n", - "324 B \n", - "\n", - " \n", - "Shape \n", - "(9, 9) \n", - "(9, 9) \n", - "\n", - " \n", - "Dask graph \n", - "1 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "float32 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 9 \n", - "9 \n", - "
- Conventions :
- CF-1.7
- GRIB_centre :
- ecmf
- GRIB_centreDescription :
- European Centre for Medium-Range Weather Forecasts
- GRIB_edition :
- 1
- GRIB_subCentre :
- 0
- history :
- 2025-04-30T14:26 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.34.1 with {"source": "../../mnt/data/eopf-conversion-gb4cp/S2B_MSIL1C_20250430T101559_N0511_R065_T32TLQ_20250430T124542.SAFE/GRANULE/L1C_T32TLQ_A042562_20250430T102505/AUX_DATA/AUX_ECMWFT", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}
- institution :
- European Centre for Medium-Range Weather Forecasts
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- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "114.98 MiB \n", - "3.19 MiB \n", - "\n", - " \n", - "Shape \n", - "(10980, 10980) \n", - "(1830, 1830) \n", - "\n", - " \n", - "Dask graph \n", - "36 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "uint8 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 10980 \n", - "10980 \n", - " b08(y, x)uint8dask.array<chunksize=(1830, 1830), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [10980, 10980]
- proj:transform :
- [10.0, 0.0, 300000.0, 0.0, -10.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "114.98 MiB \n", - "3.19 MiB \n", - "\n", - " \n", - "Shape \n", - "(10980, 10980) \n", - "(1830, 1830) \n", - "\n", - " \n", - "Dask graph \n", - "36 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "uint8 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 10980 \n", - "10980 \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " <xarray.DatasetView> Size: 181MB\n", - "Dimensions: (y: 5490, x: 5490)\n", - "Coordinates:\n", - " * x (x) int64 44kB 300010 300030 300050 300070 ... 409750 409770 409790\n", - " * y (y) int64 44kB 5000030 5000010 4999990 ... 4890290 4890270 4890250\n", - "Data variables:\n", - " b05 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b06 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b07 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b11 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b12 (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>\n", - " b8a (y, x) uint8 30MB dask.array<chunksize=(915, 915), meta=np.ndarray>r20m
- y: 5490
- x: 5490
x(x)int64300010 300030 ... 409770 409790array([300010, 300030, 300050, ..., 409750, 409770, 409790], shape=(5490,)) y(y)int645000030 5000010 ... 4890270 4890250array([5000030, 5000010, 4999990, ..., 4890290, 4890270, 4890250],\n", - " shape=(5490,))
b05(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "28.74 MiB \n", - "817.60 kiB \n", - "\n", - " \n", - "Shape \n", - "(5490, 5490) \n", - "(915, 915) \n", - "\n", - " \n", - "Dask graph \n", - "36 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "uint8 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 5490 \n", - "5490 \n", - " b06(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " Array \n", - "Chunk \n", - "\n", - " \n", - " \n", - "Bytes \n", - "28.74 MiB \n", - "817.60 kiB \n", - "\n", - " \n", - "Shape \n", - "(5490, 5490) \n", - "(915, 915) \n", - "\n", - " \n", - "Dask graph \n", - "36 chunks in 2 graph layers \n", - "\n", - " \n", - " \n", - "Data type \n", - "uint8 numpy.ndarray \n", - "\n", - " \n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " 5490 \n", - "5490 \n", - " b07(y, x)uint8dask.array<chunksize=(915, 915), meta=np.ndarray>
- _eopf_attrs :
- {'coordinates': ['x', 'y'], 'dimensions': ['y', 'x'], 'dtype': '<u1', 'flag_masks': [1, 2, 4, 8, 16, 32, 64, 128], 'flag_meanings': ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A'], 'long_name': 'quality mask provided in the final reference frame (ground geometry)'}
- dtype :
- <u1
- flag_masks :
- [1, 2, 4, 8, 16, 32, 64, 128]
- flag_meanings :
- ['ANC_LOST', 'ANC_DEG', 'MSI_LOST', 'MSI_DEG', 'QT_DEFECTIVE_PIXELS', 'QT_NODATA_PIXELS', 'QT_PARTIALLY_CORRECTED_PIXELS', 'QT_SATURATED_PIXELS_L1A']
- long_name :
- quality mask provided in the final reference frame (ground geometry)
- proj:bbox :
- [300000.0, 4890240.0, 409800.0, 5000040.0]
- proj:epsg :
- 32632
- proj:shape :
- [5490, 5490]
- proj:transform :
- [20.0, 0.0, 300000.0, 0.0, -20.0, 5000040.0, 0.0, 0.0, 1.0]
- proj:wkt2 :
- PROJCS["WGS 84 / UTM zone 32N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",9],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32632"]]
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- long_name :
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'S2B_OPER_GIP_R2WAFI_MPC__20170206T103047_V20170101T000000_21000101T000000_B8A', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103047_V20170101T000000_21000101T000000_B01', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103039_V20170101T000000_21000101T000000_B07', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103040_V20170101T000000_21000101T000000_B06', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103047_V20170101T000000_21000101T000000_B09', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103047_V20170101T000000_21000101T000000_B08', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103040_V20170101T000000_21000101T000000_B11', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103040_V20170101T000000_21000101T000000_B05', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103040_V20170101T000000_21000101T000000_B12', 'S2B_OPER_GIP_R2WAFI_MPC__20170206T103039_V20170101T000000_21000101T000000_B03', 'S2B_OPER_GIP_RESPAR_MPC__20170206T103032_V20170101T000000_21000101T000000_B00', 'S2B_OPER_GIP_SPAMOD_MPC__20250428T000037_V20250430T000000_21000101T000000_B00', 'S2B_OPER_GIP_TILPAR_MPC__20170206T103032_V20170101T000000_21000101T000000_B00', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B8A', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B01', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B11', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B08', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B03', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B07', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B02', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B12', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B04', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B06', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B09', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B05', 'S2B_OPER_GIP_VIEDIR_MPC__20170512T114736_V20170322T000000_21000101T000000_B10'], 'used DEM file name': 'S2__OPER_DEM_GLOBEF_PDMC_19800101T000000_S19800101T000000', 'used ECMWF file names': 'S2__OPER_AUX_ECMWFD_ADG__20250430T000000_V20250430T090000_20250502T030000', 'used GRI file list': ['\\n '], 'used IERS file name': 'S2__OPER_AUX_UT1UTC_PDMC_20250424T000000_V20250425T000000_20260424T000000'}, 'organisation': 'ESA', 'output': 'S2MSIL1C_etc', 'processor': 'Sentinel-2 IPF', 'type': 'Level-1C Product', 'version': '???'}], 'meteo': {'source': 'ECMWF', 'type': 'FORECAST'}, 'multispectral_registration_assessment': 'null', 'onboard_equalization_flag': 'null', 'optical_crosstalk_correction_flag': 'null', 'percentage_of_degraded_MSI_data': 0.0, 'planimetric_stability_assessment_from_AOCS': 'null', 'product_quality_status': 'PASSED,PASSED,PASSED,PASSED,PASSED', 'reflectance_correction_factor_from_the_Sun-Earth_distance_variation_computed_using_the_acquisition_date': 0.987756572639482, 'spectral_band_of_reference': 3}
- stac_discovery :
- {'assets': {'analytic': {'eo:bands': [{'center_wavelength': 0.4423, 'common_name': 'coastal', 'full_width_half_max': 0.02, 'name': '01', 'solar_illumination': '1874.3'}, {'center_wavelength': 0.4923, 'common_name': 'blue', 'full_width_half_max': 0.065, 'name': '02', 'solar_illumination': '1959.75'}, {'center_wavelength': 0.559, 'common_name': 'green', 'full_width_half_max': 0.035, 'name': '03', 'solar_illumination': '1824.93'}, {'center_wavelength': 0.665, 'common_name': 'red', 'full_width_half_max': 0.03, 'name': '04', 'solar_illumination': '1512.79'}, {'center_wavelength': 0.7038, 'full_width_half_max': 0.015, 'name': '05', 'solar_illumination': '1425.78'}, {'center_wavelength': 0.7391000000000001, 'full_width_half_max': 0.015, 'name': '06', 'solar_illumination': '1291.13'}, {'center_wavelength': 0.7797000000000001, 'full_width_half_max': 0.02, 'name': '07', 'solar_illumination': '1175.57'}, {'center_wavelength': 0.833, 'common_name': 'nir', 'full_width_half_max': 0.105, 'name': '08', 'solar_illumination': '1041.28'}, {'center_wavelength': 0.864, 'full_width_half_max': 0.02, 'name': '8A', 'solar_illumination': '953.93'}, {'center_wavelength': 0.9432, 'full_width_half_max': 0.02, 'name': '09', 'solar_illumination': '817.58'}, {'center_wavelength': 1.3769, 'common_name': 'cirrus', 'full_width_half_max': 0.03, 'name': '10', 'solar_illumination': '365.41'}, {'center_wavelength': 1.6104, 'common_name': 'swir16', 'full_width_half_max': 0.09, 'name': '11', 'solar_illumination': '247.08'}, {'center_wavelength': 2.1856999999999998, 'common_name': 'swir22', 'full_width_half_max': 0.18, 'name': '12', 'solar_illumination': '87.75'}], 'eo:cloud_cover': 14.7082520546716, 'eo:snow_cover': 11.4217589091851, 'href': 'null'}}, 'bbox': [7.871918704100081, 44.142863623969056, 6.806013472393584, 45.148073182871244], 'geometry': {'coordinates': [[[6.806013472393584, 44.142863623969056], [6.847172984568832, 44.257699806043775], [6.895765992662161, 44.40461676259632], [6.945798496598133, 44.55119099205477], [6.995257370182014, 44.697946240047614], [7.042942343005017, 44.84518269067407], [7.092044923604431, 44.99198102161969], [7.137123557319143, 45.13650884990956], [7.852592618382666, 45.148073182871244], [7.871918704100081, 44.15979663371134], [6.806013472393584, 44.142863623969056]]], 'type': 'Polygon'}, 'id': 'S2B_MSIL1C_20250430T101559_N0511_R065_T32TLQ_20250430T124542.SAFE', 'links': [{'href': './.zattrs.json', 'rel': 'self', 'type': 'application/json'}], 'properties': {'bands': [{'center_wavelength': 442.3, 'common_name': 'coastal', 'full_width_half_max': 0.02, 'name': 'b01', 'solar_illumination': 1874.3}, {'center_wavelength': 492.3, 'common_name': 'blue', 'full_width_half_max': 0.065, 'name': 'b02', 'solar_illumination': 1959.75}, {'center_wavelength': 559.0, 'common_name': 'green', 'full_width_half_max': 0.035, 'name': 'b03', 'solar_illumination': 1824.93}, {'center_wavelength': 665.0, 'common_name': 'red', 'full_width_half_max': 0.03, 'name': 'b04', 'solar_illumination': 1512.79}, {'center_wavelength': 703.8, 'common_name': 'rededge', 'full_width_half_max': 0.015, 'name': 'b05', 'solar_illumination': 1425.78}, {'center_wavelength': 739.1, 'common_name': 'rededge', 'full_width_half_max': 0.015, 'name': 'b06', 'solar_illumination': 1291.13}, {'center_wavelength': 779.7, 'common_name': 'rededge', 'full_width_half_max': 0.02, 'name': 'b07', 'solar_illumination': 1175.57}, {'center_wavelength': 833.0, 'common_name': 'nir', 'full_width_half_max': 0.105, 'name': 'b08', 'solar_illumination': 1041.28}, {'center_wavelength': 864.0, 'common_name': 'nir08', 'full_width_half_max': 0.02, 'name': 'b8a', 'solar_illumination': 953.93}, {'center_wavelength': 943.2, 'common_name': 'nir09', 'full_width_half_max': 0.02, 'name': 'b09', 'solar_illumination': 817.58}, {'center_wavelength': 1376.9, 'common_name': 'cirrus', 'full_width_half_max': 0.03, 'name': 'b10', 'solar_illumination': 365.41}, {'center_wavelength': 1610.4, 'common_name': 'swir16', 'full_width_half_max': 0.09, 'name': 'b11', 'solar_illumination': 247.08}, {'center_wavelength': 2185.7, 'common_name': 'swir22', 'full_width_half_max': 0.18, 'name': 'b12', 'solar_illumination': 87.75}], 'constellation': 'sentinel-2', 'created': '2025-04-30T12:45:42+00:00', 'datetime': None, 'end_datetime': '2025-04-30T10:15:59.024000+00:00', 'eo:cloud_cover': 14.7082520546716, 'eo:snow_cover': 11.4217589091851, 'eopf:baseline': '05.11', 'eopf:data_take_id': 'GS2B_20250430T101559_042562_N05.11', 'eopf:image_size': [{'columns': 10980, 'name': 'bands 02, 03, 04, 08', 'rows': 10980, 'start_offset': 5000040.0, 'track_offset': 300000.0}, {'columns': 5490, 'name': 'bands 05, 06, 07, 8A, 11, 12', 'rows': 5490, 'start_offset': 5000040.0, 'track_offset': 300000.0}, {'columns': 1830, 'name': 'bands 01, 09, 10', 'rows': 1830, 'start_offset': 5000040.0, 'track_offset': 300000.0}], 'eopf:instrument_mode': 'INS-NOBS', 'eopf:resolutions': {'bands 01, 09, 10': '60', 'bands 02, 03, 04, 08': '10', 'bands 05, 06, 07, 8A, 11, 12': '20'}, 'instrument': 'msi', 'mission': 'copernicus', 'platform': 'sentinel-2b', 'processing:expression': 'systematic', 'processing:facility': 'ESA', 'processing:level': 'L1C', 'processing:lineage': 'IPF L1C processor', 'processing:software': {'Sentinel-2 IPF': ' '}, 'processing:version': '', 'product:timeline': 'NRT', 'product:timeliness': 'PT3H', 'product:timeliness_category': 'NRT', 'product:type': 'S02MSIL1C', 'proj:bbox': [300000.0, 4890240.0, 409800.0, 5000040.0], 'proj:epsg': 32632, 'providers': [{'name': 'L1C Processor', 'roles': ['processor']}, {'name': 'ESA', 'roles': ['producer']}], 'sat:absolute_orbit': 42562, 'sat:orbit_state': 'descending', 'sat:platform_international_designator': '2015-028A', 'sat:relative_orbit': 65, 'sci:doi': '10.5270/S2_-742ikth', 'start_datetime': '2025-04-30T10:15:59.024000+00:00'}, 'stac_extensions': ['https://stac-extensions.github.io/eopf/v1.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.1.0/schema.json', 'https://stac-extensions.github.io/sat/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.2.0/schema.json', 'https://stac-extensions.github.io/product/v0.1.0/schema.json'], 'stac_version': '1.0.0', 'type': 'Feature'}
\n", - "Group: /\n", - "│ Attributes: (3)\n", - "├── Group: /conditions\n", - "│ ├── Group: /conditions/geometry\n", - "│ │ Dimensions: (angle: 2, band: 13, y: 23, x: 23,\n", - "│ │ detector: 4)\n", - "│ │ Coordinates:\n", - "│ │ * angle (angle) \n", - "│ │ mean_viewing_incidence_angles (band, angle) float64 208B dask.array \n", - "│ │ sun_angles (angle, y, x) float64 8kB dask.array \n", - "│ │ viewing_incidence_angles (band, detector, angle, y, x) float64 440kB dask.array \n", - "│ ├── Group: /conditions/mask\n", - "│ │ ├── Group: /conditions/mask/detector_footprint\n", - "│ │ │ ├── Group: /conditions/mask/detector_footprint/r10m\n", - "│ │ │ │ Dimensions: (y: 10980, x: 10980)\n", - "│ │ │ │ Coordinates:\n", - "│ │ │ │ * x (x) int64 88kB 300005 300015 300025 300035 ... 409775 409785 409795\n", - "│ │ │ │ * y (y) int64 88kB 5000035 5000025 5000015 ... 4890265 4890255 4890245\n", - "│ │ │ │ Data variables:\n", - "│ │ │ │ b02 (y, x) uint8 121MB dask.array \n", - "│ │ │ │ b03 (y, x) uint8 121MB dask.array \n", - "│ │ │ │ b04 (y, x) uint8 121MB dask.array \n", - "│ │ │ │ b08 (y, x) uint8 121MB dask.array \n", - "│ │ │ ├── Group: /conditions/mask/detector_footprint/r20m\n", - "│ │ │ │ Dimensions: (y: 5490, x: 5490)\n", - "│ │ │ │ Coordinates:\n", - "│ │ │ │ * x (x) int64 44kB 300010 300030 300050 300070 ... 409750 409770 409790\n", - "│ │ │ │ * y (y) int64 44kB 5000030 5000010 4999990 ... 4890290 4890270 4890250\n", - "│ │ │ │ Data variables:\n", - "│ │ │ │ b05 (y, x) uint8 30MB dask.array \n", - "│ │ │ │ b06 (y, x) uint8 30MB dask.array \n", - "│ │ │ │ b07 (y, x) uint8 30MB dask.array \n", - "│ │ │ │ b11 (y, x) uint8 30MB dask.array \n", - "│ │ │ │ b12 (y, x) uint8 30MB dask.array \n", - "│ │ │ │ b8a (y, x) uint8 30MB dask.array \n", - "│ │ │ └── Group: /conditions/mask/detector_footprint/r60m\n", - "│ │ │ Dimensions: (y: 1830, x: 1830)\n", - "│ │ │ Coordinates:\n", - "│ │ │ * x (x) int64 15kB 300030 300090 300150 300210 ... 409650 409710 409770\n", - "│ │ │ * y (y) int64 15kB 5000010 4999950 4999890 ... 4890390 4890330 4890270\n", - "│ │ │ Data variables:\n", - "│ │ │ b01 (y, x) uint8 3MB dask.array \n", - "│ │ │ b09 (y, x) uint8 3MB dask.array \n", - "│ │ │ b10 (y, x) uint8 3MB dask.array \n", - "│ │ └── Group: /conditions/mask/l1c_classification\n", - "│ │ └── Group: /conditions/mask/l1c_classification/r60m\n", - "│ │ Dimensions: (y: 1830, x: 1830)\n", - "│ │ Coordinates:\n", - "│ │ * x (x) int64 15kB 300030 300090 300150 300210 ... 409650 409710 409770\n", - "│ │ * y (y) int64 15kB 5000010 4999950 4999890 ... 4890390 4890330 4890270\n", - "│ │ Data variables:\n", - "│ │ b00 (y, x) uint8 3MB dask.array \n", - "│ └── Group: /conditions/meteorology\n", - "│ ├── Group: /conditions/meteorology/cams\n", - "│ │ Dimensions: (latitude: 9, longitude: 9)\n", - "│ │ Coordinates:\n", - "│ │ isobaricInhPa float64 8B ...\n", - "│ │ * latitude (latitude) float64 72B 45.13 45.0 44.88 ... 44.4 44.28 44.16\n", - "│ │ * longitude (longitude) float64 72B 6.457 6.634 6.811 ... 7.695 7.872\n", - "│ │ number int64 8B ...\n", - "│ │ step int64 8B ...\n", - "│ │ surface float64 8B ...\n", - "│ │ time datetime64[ns] 8B ...\n", - "│ │ valid_time datetime64[ns] 8B ...\n", - "│ │ Data variables:\n", - "│ │ aod1240 (latitude, longitude) float32 324B dask.array \n", - "│ │ aod469 (latitude, longitude) float32 324B dask.array \n", - "│ │ aod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ aod670 (latitude, longitude) float32 324B dask.array \n", - "│ │ aod865 (latitude, longitude) float32 324B dask.array \n", - "│ │ bcaod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ duaod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ omaod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ ssaod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ suaod550 (latitude, longitude) float32 324B dask.array \n", - "│ │ z (latitude, longitude) float32 324B dask.array \n", - "│ │ Attributes: (7)\n", - "│ └── Group: /conditions/meteorology/ecmwf\n", - "│ Dimensions: (latitude: 9, longitude: 9)\n", - "│ Coordinates:\n", - "│ isobaricInhPa float64 8B ...\n", - "│ * latitude (latitude) float64 72B 45.13 45.0 44.88 ... 44.4 44.28 44.16\n", - "│ * longitude (longitude) float64 72B 6.457 6.634 6.811 ... 7.695 7.872\n", - "│ number int64 8B ...\n", - "│ step int64 8B ...\n", - "│ surface float64 8B ...\n", - "│ time datetime64[ns] 8B ...\n", - "│ valid_time datetime64[ns] 8B ...\n", - "│ Data variables:\n", - "│ msl (latitude, longitude) float32 324B dask.array \n", - "│ r (latitude, longitude) float32 324B dask.array \n", - "│ tco3 (latitude, longitude) float32 324B dask.array \n", - "│ tcwv (latitude, longitude) float32 324B dask.array \n", - "│ u10 (latitude, longitude) float32 324B dask.array \n", - "│ v10 (latitude, longitude) float32 324B dask.array \n", - "│ Attributes: (7)\n", - "├── Group: /measurements\n", - "│ └── Group: /measurements/reflectance\n", - "│ ├── Group: /measurements/reflectance/r10m\n", - "│ │ Dimensions: (y: 10980, x: 10980)\n", - "│ │ Coordinates:\n", - "│ │ * x (x) int64 88kB 300005 300015 300025 300035 ... 409775 409785 409795\n", - "│ │ * y (y) int64 88kB 5000035 5000025 5000015 ... 4890265 4890255 4890245\n", - "│ │ Data variables:\n", - "│ │ b02 (y, x) float64 964MB dask.array \n", - "│ │ b03 (y, x) float64 964MB dask.array \n", - "│ │ b04 (y, x) float64 964MB dask.array \n", - "│ │ b08 (y, x) float64 964MB dask.array \n", - "│ ├── Group: /measurements/reflectance/r20m\n", - "│ │ Dimensions: (y: 5490, x: 5490)\n", - "│ │ Coordinates:\n", - "│ │ * x (x) int64 44kB 300010 300030 300050 300070 ... 409750 409770 409790\n", - "│ │ * y (y) int64 44kB 5000030 5000010 4999990 ... 4890290 4890270 4890250\n", - "│ │ Data variables:\n", - "│ │ b05 (y, x) float64 241MB dask.array \n", - "│ │ b06 (y, x) float64 241MB dask.array \n", - "│ │ b07 (y, x) float64 241MB dask.array \n", - "│ │ b11 (y, x) float64 241MB dask.array \n", - "│ │ b12 (y, x) float64 241MB dask.array \n", - "│ │ b8a (y, x) float64 241MB dask.array \n", - "│ └── Group: /measurements/reflectance/r60m\n", - "│ Dimensions: (y: 1830, x: 1830)\n", - "│ Coordinates:\n", - "│ * x (x) int64 15kB 300030 300090 300150 300210 ... 409650 409710 409770\n", - "│ * y (y) int64 15kB 5000010 4999950 4999890 ... 4890390 4890330 4890270\n", - "│ Data variables:\n", - "│ b01 (y, x) float64 27MB dask.array \n", - "│ b09 (y, x) float64 27MB dask.array \n", - "│ b10 (y, x) float64 27MB dask.array \n", - "└── Group: /quality\n", - " ├── Group: /quality/l1c_quicklook\n", - " │ └── Group: /quality/l1c_quicklook/r10m\n", - " │ Dimensions: (band: 3, y: 10980, x: 10980)\n", - " │ Coordinates:\n", - " │ * band (band) int64 24B 1 2 3\n", - " │ * x (x) int64 88kB 300005 300015 300025 300035 ... 409775 409785 409795\n", - " │ * y (y) int64 88kB 5000035 5000025 5000015 ... 4890265 4890255 4890245\n", - " │ Data variables:\n", - " │ tci (band, y, x) uint8 362MB dask.array \n", - " └── Group: /quality/mask\n", - " ├── Group: /quality/mask/r10m\n", - " │ Dimensions: (y: 10980, x: 10980)\n", - " │ Coordinates:\n", - " │ * x (x) int64 88kB 300005 300015 300025 300035 ... 409775 409785 409795\n", - " │ * y (y) int64 88kB 5000035 5000025 5000015 ... 4890265 4890255 4890245\n", - " │ Data variables:\n", - " │ b02 (y, x) uint8 121MB dask.array \n", - " │ b03 (y, x) uint8 121MB dask.array \n", - " │ b04 (y, x) uint8 121MB dask.array \n", - " │ b08 (y, x) uint8 121MB dask.array \n", - " ├── Group: /quality/mask/r20m\n", - " │ Dimensions: (y: 5490, x: 5490)\n", - " │ Coordinates:\n", - " │ * x (x) int64 44kB 300010 300030 300050 300070 ... 409750 409770 409790\n", - " │ * y (y) int64 44kB 5000030 5000010 4999990 ... 4890290 4890270 4890250\n", - " │ Data variables:\n", - " │ b05 (y, x) uint8 30MB dask.array