Fetching Up to date forecast

this notebook show a simple way to fetch the up to date forecast of the ARPEGE 0.1° model from the météo France, hosted on meteo.data.gouv.fr.

TODO : Refocator all of that

import pandas as pd

base_url = "https://object.data.gouv.fr/meteofrance-pnt/pnt/{date}T{time}Z/arpege/01/SP1/arpege__01__SP1__{forecast}__{date}T{time}Z.grib2"

forecast_horizons = ["000H012H", "013H024H"]
date = pd.Timestamp("today").strftime("%Y-%m-%d")
time = "00:00:00"

url = base_url.format(forecast=forecast_horizons[0], date=date, time=time)
print(url)

https://object.data.gouv.fr/meteofrance-pnt/pnt/2024-06-14T00:00:00Z/arpege/01/SP1/arpege__01__SP1__000H012H__2024-06-14T00:00:00Z.grib2

import requests

tmp_dir = "/tmp"
for forecast in forecast_horizons:
    url = base_url.format(forecast=forecast, date=date, time=time)
    print(url)
    filename = f"{tmp_dir}/arpege__01__SP1__{forecast}__{date}T{time}Z.grib2"
    r = requests.get(url, allow_redirects=True)
    open(filename, "wb").write(r.content)

https://object.data.gouv.fr/meteofrance-pnt/pnt/2024-06-14T00:00:00Z/arpege/01/SP1/arpege__01__SP1__000H012H__2024-06-14T00:00:00Z.grib2
https://object.data.gouv.fr/meteofrance-pnt/pnt/2024-06-14T00:00:00Z/arpege/01/SP1/arpege__01__SP1__013H024H__2024-06-14T00:00:00Z.grib2

import xarray as xr

KEYS_FILTER_SSPD = {
    "typeOfLevel": "surface",
    "cfVarName": "ssrd",
}
KEYS_FILTER_WIND = {
    "typeOfLevel": "heightAboveGround",
    "level": 10,
    "cfVarName": "si10",
}
KEYS_FILTER_T2M = {
    "typeOfLevel": "heightAboveGround",
    "level": 2,
    "cfVarName": "t2m",
}

da_suns = []
da_winds = []
for forecast_horizon in forecast_horizons:
    filename = f"{tmp_dir}/arpege__01__SP1__{forecast_horizon}__{date}T{time}Z.grib2"
    da_tmp = xr.open_dataset(
        filename, engine="cfgrib", backend_kwargs={"filter_by_keys": KEYS_FILTER_SSPD}
    ).ssrd
    da_suns.append(da_tmp)
    da_tmp = xr.open_dataset(
        filename, engine="cfgrib", backend_kwargs={"filter_by_keys": KEYS_FILTER_WIND}
    ).si10
    da_winds.append(da_tmp)

da_sun = xr.concat(da_suns, dim="step")
da_wind = xr.concat(da_winds, dim="step")

Ignoring index file '/tmp/arpege__01__SP1__000H012H__2024-06-14T00:00:00Z.grib2.789e2.idx' older than GRIB file
Ignoring index file '/tmp/arpege__01__SP1__000H012H__2024-06-14T00:00:00Z.grib2.d5933.idx' older than GRIB file
Ignoring index file '/tmp/arpege__01__SP1__013H024H__2024-06-14T00:00:00Z.grib2.789e2.idx' older than GRIB file
Ignoring index file '/tmp/arpege__01__SP1__013H024H__2024-06-14T00:00:00Z.grib2.d5933.idx' older than GRIB file

da_sun.mean(["latitude", "longitude"]).plot(x="valid_time")

[<matplotlib.lines.Line2D at 0x7a824e840090>]

da_wind.mean(["latitude", "longitude"]).plot(x="valid_time")

[<matplotlib.lines.Line2D at 0x7a824e8b3890>]

Regions Selections

This section shows how to select the regions of interest.

import yaml

filename_bounds = "./france_bounds.yml"
bounds = yaml.safe_load(open(filename_bounds, "r"))
min_lon = bounds["min_lon"]
max_lon = bounds["max_lon"]
min_lat = bounds["min_lat"]
max_lat = bounds["max_lat"]
bounds

{'min_lon': -4.79542,
 'max_lon': 9.55996,
 'min_lat': 41.36484,
 'max_lat': 51.089}

regions_names = yaml.safe_load(open("./regions.yml", "r"))
regions_names

['Île-de-France',
 'Centre-Val de Loire',
 'Bourgogne-Franche-Comté',
 'Normandie',
 'Hauts-de-France',
 'Grand Est',
 'Pays de la Loire',
 'Bretagne',
 'Nouvelle-Aquitaine',
 'Occitanie',
 'Auvergne-Rhône-Alpes',
 "Provence-Alpes-Côte d'Azur",
 'Corse']

masks = xr.load_dataarray("./mask_france_regions.nc")
masks

<xarray.DataArray (longitude: 143, latitude: 97)> Size: 111kB
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
Coordinates:
  * longitude  (longitude) float64 1kB -4.7 -4.6 -4.5 -4.4 ... 9.2 9.3 9.4 9.5
  * latitude   (latitude) float64 776B 51.0 50.9 50.8 50.7 ... 41.6 41.5 41.4

xarray.DataArray

• longitude: 143
• latitude: 97

• nan nan nan nan nan nan nan nan ... 12.0 nan nan nan nan nan nan nan

  array([[nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         ...,
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan]])

• Coordinates: (2)
  • longitude
    (longitude)
    float64
    -4.7 -4.6 -4.5 -4.4 ... 9.3 9.4 9.5

    array([-4.700000e+00, -4.600000e+00, -4.500000e+00, -4.400000e+00,
           -4.300000e+00, -4.200000e+00, -4.100000e+00, -4.000000e+00,
           -3.900000e+00, -3.800000e+00, -3.700000e+00, -3.600000e+00,
           -3.500000e+00, -3.400000e+00, -3.300000e+00, -3.200000e+00,
           -3.100000e+00, -3.000000e+00, -2.900000e+00, -2.800000e+00,
           -2.700000e+00, -2.600000e+00, -2.500000e+00, -2.400000e+00,
           -2.300000e+00, -2.200000e+00, -2.100000e+00, -2.000000e+00,
           -1.900000e+00, -1.800000e+00, -1.700000e+00, -1.600000e+00,
           -1.500000e+00, -1.400000e+00, -1.300000e+00, -1.200000e+00,
           -1.100000e+00, -1.000000e+00, -9.000000e-01, -8.000000e-01,
           -7.000000e-01, -6.000000e-01, -5.000000e-01, -4.000000e-01,
           -3.000000e-01, -2.000000e-01, -1.000000e-01,  1.858236e-13,
            1.000000e-01,  2.000000e-01,  3.000000e-01,  4.000000e-01,
            5.000000e-01,  6.000000e-01,  7.000000e-01,  8.000000e-01,
            9.000000e-01,  1.000000e+00,  1.100000e+00,  1.200000e+00,
            1.300000e+00,  1.400000e+00,  1.500000e+00,  1.600000e+00,
            1.700000e+00,  1.800000e+00,  1.900000e+00,  2.000000e+00,
            2.100000e+00,  2.200000e+00,  2.300000e+00,  2.400000e+00,
            2.500000e+00,  2.600000e+00,  2.700000e+00,  2.800000e+00,
            2.900000e+00,  3.000000e+00,  3.100000e+00,  3.200000e+00,
            3.300000e+00,  3.400000e+00,  3.500000e+00,  3.600000e+00,
            3.700000e+00,  3.800000e+00,  3.900000e+00,  4.000000e+00,
            4.100000e+00,  4.200000e+00,  4.300000e+00,  4.400000e+00,
            4.500000e+00,  4.600000e+00,  4.700000e+00,  4.800000e+00,
            4.900000e+00,  5.000000e+00,  5.100000e+00,  5.200000e+00,
            5.300000e+00,  5.400000e+00,  5.500000e+00,  5.600000e+00,
            5.700000e+00,  5.800000e+00,  5.900000e+00,  6.000000e+00,
            6.100000e+00,  6.200000e+00,  6.300000e+00,  6.400000e+00,
            6.500000e+00,  6.600000e+00,  6.700000e+00,  6.800000e+00,
            6.900000e+00,  7.000000e+00,  7.100000e+00,  7.200000e+00,
            7.300000e+00,  7.400000e+00,  7.500000e+00,  7.600000e+00,
            7.700000e+00,  7.800000e+00,  7.900000e+00,  8.000000e+00,
            8.100000e+00,  8.200000e+00,  8.300000e+00,  8.400000e+00,
            8.500000e+00,  8.600000e+00,  8.700000e+00,  8.800000e+00,
            8.900000e+00,  9.000000e+00,  9.100000e+00,  9.200000e+00,
            9.300000e+00,  9.400000e+00,  9.500000e+00])

  • latitude
    (latitude)
    float64
    51.0 50.9 50.8 ... 41.6 41.5 41.4

    array([51. , 50.9, 50.8, 50.7, 50.6, 50.5, 50.4, 50.3, 50.2, 50.1, 50. , 49.9,
           49.8, 49.7, 49.6, 49.5, 49.4, 49.3, 49.2, 49.1, 49. , 48.9, 48.8, 48.7,
           48.6, 48.5, 48.4, 48.3, 48.2, 48.1, 48. , 47.9, 47.8, 47.7, 47.6, 47.5,
           47.4, 47.3, 47.2, 47.1, 47. , 46.9, 46.8, 46.7, 46.6, 46.5, 46.4, 46.3,
           46.2, 46.1, 46. , 45.9, 45.8, 45.7, 45.6, 45.5, 45.4, 45.3, 45.2, 45.1,
           45. , 44.9, 44.8, 44.7, 44.6, 44.5, 44.4, 44.3, 44.2, 44.1, 44. , 43.9,
           43.8, 43.7, 43.6, 43.5, 43.4, 43.3, 43.2, 43.1, 43. , 42.9, 42.8, 42.7,
           42.6, 42.5, 42.4, 42.3, 42.2, 42.1, 42. , 41.9, 41.8, 41.7, 41.6, 41.5,
           41.4])

• Indexes: (2)
  • longitude
    PandasIndex

    PandasIndex(Index([-4.6999999999998145,  -4.599999999999815,  -4.499999999999815,
            -4.399999999999816,  -4.299999999999816,  -4.199999999999816,
            -4.099999999999817, -3.9999999999998166, -3.8999999999998165,
           -3.7999999999998164,
           ...
             8.600000000000172,   8.700000000000172,   8.800000000000171,
              8.90000000000017,    9.00000000000017,    9.10000000000017,
              9.20000000000017,    9.30000000000017,   9.400000000000169,
             9.500000000000169],
          dtype='float64', name='longitude', length=143))

  • latitude
    PandasIndex

    PandasIndex(Index([ 51.00000000000027,  50.90000000000027,  50.80000000000027,
           50.700000000000266, 50.600000000000264,  50.50000000000026,
            50.40000000000026,  50.30000000000026,  50.20000000000026,
            50.10000000000026, 50.000000000000256, 49.900000000000254,
            49.80000000000025,  49.70000000000025,  49.60000000000025,
            49.50000000000025,  49.40000000000025, 49.300000000000246,
           49.200000000000244,  49.10000000000024,  49.00000000000024,
            48.90000000000024,  48.80000000000024,  48.70000000000024,
           48.600000000000236, 48.500000000000234,  48.40000000000023,
            48.30000000000023,  48.20000000000023,  48.10000000000023,
            48.00000000000023, 47.900000000000226, 47.800000000000225,
            47.70000000000022,  47.60000000000022,  47.50000000000022,
            47.40000000000022,  47.30000000000022, 47.200000000000216,
           47.100000000000215,  47.00000000000021,  46.90000000000021,
            46.80000000000021,  46.70000000000021,  46.60000000000021,
           46.500000000000206, 46.400000000000205,   46.3000000000002,
             46.2000000000002,   46.1000000000002,   46.0000000000002,
             45.9000000000002, 45.800000000000196, 45.700000000000195,
            45.60000000000019,  45.50000000000019,  45.40000000000019,
            45.30000000000019,  45.20000000000019, 45.100000000000186,
           45.000000000000185,  44.90000000000018,  44.80000000000018,
            44.70000000000018,  44.60000000000018,  44.50000000000018,
           44.400000000000176, 44.300000000000175,  44.20000000000017,
            44.10000000000017,  44.00000000000017,  43.90000000000017,
            43.80000000000017, 43.700000000000166, 43.600000000000165,
            43.50000000000016,  43.40000000000016,  43.30000000000016,
            43.20000000000016,  43.10000000000016, 43.000000000000156,
           42.900000000000155,  42.80000000000015,  42.70000000000015,
            42.60000000000015,  42.50000000000015,  42.40000000000015,
           42.300000000000146, 42.200000000000145,  42.10000000000014,
            42.00000000000014,  41.90000000000014,  41.80000000000014,
            41.70000000000014, 41.600000000000136, 41.500000000000135,
           41.400000000000134],
          dtype='float64', name='latitude'))

• Attributes: (0)

da_wind_france = da_wind.sel(
    longitude=slice(min_lon, max_lon), latitude=slice(max_lat, min_lat)
)
da_wind_region = da_wind_france.groupby(masks).mean(["latitude", "longitude"])
# relabel the regions groups
da_wind_region["group"] = regions_names
# change the name of the groups
da_wind_region = da_wind_region.rename(group="region")
da_wind_region

<xarray.DataArray 'si10' (step: 25, region: 13)> Size: 1kB
array([[3.633834 , 4.726924 , 2.890073 , 6.462578 , 4.6430645, 2.5707262,
        5.893275 , 4.5465994, 3.987221 , 1.9507447, 3.1654837, 1.6676582,
        2.0362241],
       [3.2270477, 4.5367227, 3.1619112, 5.9841156, 4.9005775, 2.5648384,
        5.5866203, 4.4071183, 4.0192533, 1.9807669, 3.222778 , 1.7768989,
        2.3054676],
       [3.563212 , 4.9562073, 3.6203182, 5.6698127, 5.4124393, 2.6153326,
        5.2358146, 4.3605576, 4.083691 , 1.9310851, 3.28721  , 1.727055 ,
        2.2345347],
       [4.003104 , 5.397727 , 4.08255  , 5.3970237, 5.878717 , 2.577101 ,
        4.7475724, 4.2789207, 4.08367  , 1.8213174, 3.2024512, 1.7382587,
        2.168975 ],
       [4.489919 , 5.513933 , 4.3274903, 5.1862683, 5.875749 , 2.9034426,
        4.276406 , 4.189905 , 4.0086737, 1.8521403, 3.1398833, 1.7972649,
        2.0536613],
       [4.7292566, 5.4604716, 4.472719 , 5.031581 , 5.7862563, 3.5444107,
        3.7230563, 4.0997047, 3.7875435, 1.8912587, 3.2018595, 1.7687811,
        1.945979 ],
       [4.794839 , 5.1926055, 4.6337023, 4.969603 , 5.718553 , 4.10932  ,
        3.7716787, 4.2422333, 3.6012459, 1.8309088, 3.3185728, 1.5955588,
...
        7.831558 , 8.190574 , 4.9081326, 4.0079556, 3.741763 , 2.7064288,
        1.9084607],
       [6.0472727, 6.5705533, 4.2902894, 5.991585 , 4.7212768, 4.5449133,
        7.984231 , 8.034786 , 4.8415146, 3.89688  , 3.526491 , 2.6235993,
        1.3268776],
       [5.4628553, 6.6761065, 4.471928 , 6.7887735, 4.6536603, 4.8045125,
        7.8817663, 7.6242557, 4.7331285, 3.5951288, 3.3250184, 2.6223977,
        1.1473461],
       [5.478565 , 6.6750846, 4.699337 , 7.140038 , 4.5736847, 5.102801 ,
        7.726341 , 6.9882126, 4.671682 , 3.4135349, 3.2307334, 2.6370065,
        1.2340739],
       [6.2646194, 6.649729 , 4.681647 , 7.6925907, 4.961998 , 5.2455564,
        7.3468404, 6.5835176, 4.766712 , 3.3322113, 3.4290023, 2.7505903,
        1.3582473],
       [6.5186   , 6.928161 , 4.544454 , 8.074212 , 5.6381497, 5.2747464,
        6.6815963, 6.314526 , 4.6499653, 3.4512608, 3.813115 , 2.8217368,
        1.4309248],
       [6.653599 , 7.106466 , 4.980348 , 7.927352 , 6.568499 , 5.2746086,
        6.219347 , 5.915178 , 4.480625 , 3.382714 , 4.1069565, 2.831832 ,
        1.6329246]], dtype=float32)
Coordinates:
    time               datetime64[ns] 8B 2024-06-14
  * step               (step) timedelta64[ns] 200B 00:00:00 ... 1 days 00:00:00
    heightAboveGround  float64 8B 10.0
    valid_time         (step) datetime64[ns] 200B 2024-06-14 ... 2024-06-15
  * region             (region) <U26 1kB 'Île-de-France' ... 'Corse'
Attributes: (12/29)
    GRIB_paramId:                             207
    GRIB_dataType:                            fc
    GRIB_numberOfPoints:                      386061
    GRIB_typeOfLevel:                         heightAboveGround
    GRIB_stepUnits:                           1
    GRIB_stepType:                            instant
    ...                                       ...
    GRIB_name:                                10 metre wind speed
    GRIB_shortName:                           10si
    GRIB_units:                               m s**-1
    long_name:                                10 metre wind speed
    units:                                    m s**-1
    standard_name:                            unknown

xarray.DataArray

'si10'

• step: 25
• region: 13

• 3.634 4.727 2.89 6.463 4.643 2.571 ... 4.481 3.383 4.107 2.832 1.633

  array([[3.633834 , 4.726924 , 2.890073 , 6.462578 , 4.6430645, 2.5707262,
          5.893275 , 4.5465994, 3.987221 , 1.9507447, 3.1654837, 1.6676582,
          2.0362241],
         [3.2270477, 4.5367227, 3.1619112, 5.9841156, 4.9005775, 2.5648384,
          5.5866203, 4.4071183, 4.0192533, 1.9807669, 3.222778 , 1.7768989,
          2.3054676],
         [3.563212 , 4.9562073, 3.6203182, 5.6698127, 5.4124393, 2.6153326,
          5.2358146, 4.3605576, 4.083691 , 1.9310851, 3.28721  , 1.727055 ,
          2.2345347],
         [4.003104 , 5.397727 , 4.08255  , 5.3970237, 5.878717 , 2.577101 ,
          4.7475724, 4.2789207, 4.08367  , 1.8213174, 3.2024512, 1.7382587,
          2.168975 ],
         [4.489919 , 5.513933 , 4.3274903, 5.1862683, 5.875749 , 2.9034426,
          4.276406 , 4.189905 , 4.0086737, 1.8521403, 3.1398833, 1.7972649,
          2.0536613],
         [4.7292566, 5.4604716, 4.472719 , 5.031581 , 5.7862563, 3.5444107,
          3.7230563, 4.0997047, 3.7875435, 1.8912587, 3.2018595, 1.7687811,
          1.945979 ],
         [4.794839 , 5.1926055, 4.6337023, 4.969603 , 5.718553 , 4.10932  ,
          3.7716787, 4.2422333, 3.6012459, 1.8309088, 3.3185728, 1.5955588,
  ...
          7.831558 , 8.190574 , 4.9081326, 4.0079556, 3.741763 , 2.7064288,
          1.9084607],
         [6.0472727, 6.5705533, 4.2902894, 5.991585 , 4.7212768, 4.5449133,
          7.984231 , 8.034786 , 4.8415146, 3.89688  , 3.526491 , 2.6235993,
          1.3268776],
         [5.4628553, 6.6761065, 4.471928 , 6.7887735, 4.6536603, 4.8045125,
          7.8817663, 7.6242557, 4.7331285, 3.5951288, 3.3250184, 2.6223977,
          1.1473461],
         [5.478565 , 6.6750846, 4.699337 , 7.140038 , 4.5736847, 5.102801 ,
          7.726341 , 6.9882126, 4.671682 , 3.4135349, 3.2307334, 2.6370065,
          1.2340739],
         [6.2646194, 6.649729 , 4.681647 , 7.6925907, 4.961998 , 5.2455564,
          7.3468404, 6.5835176, 4.766712 , 3.3322113, 3.4290023, 2.7505903,
          1.3582473],
         [6.5186   , 6.928161 , 4.544454 , 8.074212 , 5.6381497, 5.2747464,
          6.6815963, 6.314526 , 4.6499653, 3.4512608, 3.813115 , 2.8217368,
          1.4309248],
         [6.653599 , 7.106466 , 4.980348 , 7.927352 , 6.568499 , 5.2746086,
          6.219347 , 5.915178 , 4.480625 , 3.382714 , 4.1069565, 2.831832 ,
          1.6329246]], dtype=float32)

• Coordinates: (5)
  • time
    ()
    datetime64[ns]
    2024-06-14

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2024-06-14T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00 ... 1 days 00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([             0,  3600000000000,  7200000000000, 10800000000000,
           14400000000000, 18000000000000, 21600000000000, 25200000000000,
           28800000000000, 32400000000000, 36000000000000, 39600000000000,
           43200000000000, 46800000000000, 50400000000000, 54000000000000,
           57600000000000, 61200000000000, 64800000000000, 68400000000000,
           72000000000000, 75600000000000, 79200000000000, 82800000000000,
           86400000000000], dtype='timedelta64[ns]')

  • heightAboveGround
    ()
    float64
    10.0

    long_name :

    height above the surface

    units :

    m

    positive :

    up

    standard_name :

    height

    array(10.)

  • valid_time
    (step)
    datetime64[ns]
    2024-06-14 ... 2024-06-15

    standard_name :

    time

    long_name :

    time

    array(['2024-06-14T00:00:00.000000000', '2024-06-14T01:00:00.000000000',
           '2024-06-14T02:00:00.000000000', '2024-06-14T03:00:00.000000000',
           '2024-06-14T04:00:00.000000000', '2024-06-14T05:00:00.000000000',
           '2024-06-14T06:00:00.000000000', '2024-06-14T07:00:00.000000000',
           '2024-06-14T08:00:00.000000000', '2024-06-14T09:00:00.000000000',
           '2024-06-14T10:00:00.000000000', '2024-06-14T11:00:00.000000000',
           '2024-06-14T12:00:00.000000000', '2024-06-14T13:00:00.000000000',
           '2024-06-14T14:00:00.000000000', '2024-06-14T15:00:00.000000000',
           '2024-06-14T16:00:00.000000000', '2024-06-14T17:00:00.000000000',
           '2024-06-14T18:00:00.000000000', '2024-06-14T19:00:00.000000000',
           '2024-06-14T20:00:00.000000000', '2024-06-14T21:00:00.000000000',
           '2024-06-14T22:00:00.000000000', '2024-06-14T23:00:00.000000000',
           '2024-06-15T00:00:00.000000000'], dtype='datetime64[ns]')

  • region
    (region)
    <U26
    'Île-de-France' ... 'Corse'

    array(['Île-de-France', 'Centre-Val de Loire', 'Bourgogne-Franche-Comté',
           'Normandie', 'Hauts-de-France', 'Grand Est', 'Pays de la Loire',
           'Bretagne', 'Nouvelle-Aquitaine', 'Occitanie', 'Auvergne-Rhône-Alpes',
           "Provence-Alpes-Côte d'Azur", 'Corse'], dtype='<U26')

• Indexes: (2)
  • step
    PandasIndex

    PandasIndex(TimedeltaIndex(['0 days 00:00:00', '0 days 01:00:00', '0 days 02:00:00',
                    '0 days 03:00:00', '0 days 04:00:00', '0 days 05:00:00',
                    '0 days 06:00:00', '0 days 07:00:00', '0 days 08:00:00',
                    '0 days 09:00:00', '0 days 10:00:00', '0 days 11:00:00',
                    '0 days 12:00:00', '0 days 13:00:00', '0 days 14:00:00',
                    '0 days 15:00:00', '0 days 16:00:00', '0 days 17:00:00',
                    '0 days 18:00:00', '0 days 19:00:00', '0 days 20:00:00',
                    '0 days 21:00:00', '0 days 22:00:00', '0 days 23:00:00',
                    '1 days 00:00:00'],
                   dtype='timedelta64[ns]', name='step', freq=None))

  • region
    PandasIndex

    PandasIndex(Index(['Île-de-France', 'Centre-Val de Loire', 'Bourgogne-Franche-Comté',
           'Normandie', 'Hauts-de-France', 'Grand Est', 'Pays de la Loire',
           'Bretagne', 'Nouvelle-Aquitaine', 'Occitanie', 'Auvergne-Rhône-Alpes',
           'Provence-Alpes-Côte d'Azur', 'Corse'],
          dtype='object', name='region'))

• Attributes: (29)

  GRIB_paramId :

  207

  GRIB_dataType :

  fc

  GRIB_numberOfPoints :

  386061

  GRIB_typeOfLevel :

  heightAboveGround

  GRIB_stepUnits :

  1

  GRIB_stepType :

  instant

  GRIB_gridType :

  regular_ll

  GRIB_NV :

  0

  GRIB_Nx :

  741

  GRIB_Ny :

  521

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  si10

  GRIB_gridDefinitionDescription :

  Latitude/longitude

  GRIB_iDirectionIncrementInDegrees :

  0.1

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.1

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  72.0

  GRIB_latitudeOfLastGridPointInDegrees :

  20.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  328.0

  GRIB_longitudeOfLastGridPointInDegrees :

  42.0

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  10 metre wind speed

  GRIB_shortName :

  10si

  GRIB_units :

  m s**-1

  long_name :

  10 metre wind speed

  units :

  m s**-1

  standard_name :

  unknown

df_wind_regions = da_wind_region.to_dataframe()
df_wind_regions = df_wind_regions.set_index("valid_time", append=True)
# remove the step level
df_wind_regions = df_wind_regions.droplevel("step")
# remove the time level
df_wind = df_wind_regions["si10"].unstack("region")
df_wind.plot(title=f"Wind speed at 10m on {time.date()}")

<Axes: title={'center': 'Wind speed at 10m on 2024-06-14'}, xlabel='valid_time'>

Same for the sun

da_sun_france = da_sun.sel(
    longitude=slice(min_lon, max_lon), latitude=slice(max_lat, min_lat)
)
da_sun_region = da_sun_france.groupby(masks).mean(["latitude", "longitude"])
# relabel the regions groups
da_sun_region["group"] = regions_names
# change the name of the groups
da_sun_region = da_sun_region.rename(group="region")
da_sun_region

<xarray.DataArray 'ssrd' (step: 24, region: 13)> Size: 1kB
array([[4.81964017e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
        4.81963983e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
        4.81964017e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
        4.81964017e-11],
       [9.63928035e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
        9.63927965e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
        9.63928035e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
        9.63928035e-11],
       [1.44589216e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
        1.44589216e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
        1.44589202e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
        1.44589216e-10],
       [2.97205475e+02, 1.80127926e+01, 9.15356628e+02, 3.76368561e+01,
        1.25227722e+03, 2.87917822e+03, 1.92882751e-10, 1.92882765e-10,
        1.92882751e-10, 1.92882751e-10, 3.54682861e+02, 3.55898010e+02,
        1.60612769e+03],
       [1.89948496e+04, 1.37849727e+04, 3.67006445e+04, 3.12361621e+04,
        3.71646719e+04, 5.94732539e+04, 2.99723496e+04, 2.79847812e+04,
        1.06043076e+04, 3.34486367e+04, 4.66420820e+04, 1.09222797e+05,
        1.77529188e+05],
...
       [1.65587530e+07, 1.33618860e+07, 9.86733200e+06, 1.46184300e+07,
        1.37320470e+07, 9.72893100e+06, 1.79774720e+07, 1.70122940e+07,
        1.09404450e+07, 1.80745660e+07, 1.50217520e+07, 1.62290090e+07,
        2.73906760e+07],
       [1.65587530e+07, 1.33618860e+07, 9.86733200e+06, 1.46184690e+07,
        1.37320470e+07, 9.72893100e+06, 1.79774720e+07, 1.70136420e+07,
        1.09404450e+07, 1.80745660e+07, 1.50217520e+07, 1.62290090e+07,
        2.73906760e+07],
       [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
        1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
        1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
        2.73906760e+07],
       [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
        1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
        1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
        2.73906760e+07],
       [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
        1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
        1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
        2.73906760e+07]], dtype=float32)
Coordinates:
    time        datetime64[ns] 8B 2024-06-14
  * step        (step) timedelta64[ns] 192B 01:00:00 ... 1 days 00:00:00
    surface     float64 8B 0.0
    valid_time  (step) datetime64[ns] 192B 2024-06-14T01:00:00 ... 2024-06-15
  * region      (region) <U26 1kB 'Île-de-France' ... 'Corse'
Attributes: (12/29)
    GRIB_paramId:                             169
    GRIB_dataType:                            fc
    GRIB_numberOfPoints:                      386061
    GRIB_typeOfLevel:                         surface
    GRIB_stepUnits:                           1
    GRIB_stepType:                            accum
    ...                                       ...
    GRIB_name:                                Surface short-wave (solar) radi...
    GRIB_shortName:                           ssrd
    GRIB_units:                               J m**-2
    long_name:                                Surface short-wave (solar) radi...
    units:                                    J m**-2
    standard_name:                            surface_downwelling_shortwave_f...

xarray.DataArray

'ssrd'

• step: 24
• region: 13

• 4.82e-11 4.82e-11 4.82e-11 4.82e-11 ... 1.502e+07 1.623e+07 2.739e+07

  array([[4.81964017e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
          4.81963983e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
          4.81964017e-11, 4.81964017e-11, 4.81964017e-11, 4.81964017e-11,
          4.81964017e-11],
         [9.63928035e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
          9.63927965e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
          9.63928035e-11, 9.63928035e-11, 9.63928035e-11, 9.63928035e-11,
          9.63928035e-11],
         [1.44589216e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
          1.44589216e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
          1.44589202e-10, 1.44589216e-10, 1.44589216e-10, 1.44589216e-10,
          1.44589216e-10],
         [2.97205475e+02, 1.80127926e+01, 9.15356628e+02, 3.76368561e+01,
          1.25227722e+03, 2.87917822e+03, 1.92882751e-10, 1.92882765e-10,
          1.92882751e-10, 1.92882751e-10, 3.54682861e+02, 3.55898010e+02,
          1.60612769e+03],
         [1.89948496e+04, 1.37849727e+04, 3.67006445e+04, 3.12361621e+04,
          3.71646719e+04, 5.94732539e+04, 2.99723496e+04, 2.79847812e+04,
          1.06043076e+04, 3.34486367e+04, 4.66420820e+04, 1.09222797e+05,
          1.77529188e+05],
  ...
         [1.65587530e+07, 1.33618860e+07, 9.86733200e+06, 1.46184300e+07,
          1.37320470e+07, 9.72893100e+06, 1.79774720e+07, 1.70122940e+07,
          1.09404450e+07, 1.80745660e+07, 1.50217520e+07, 1.62290090e+07,
          2.73906760e+07],
         [1.65587530e+07, 1.33618860e+07, 9.86733200e+06, 1.46184690e+07,
          1.37320470e+07, 9.72893100e+06, 1.79774720e+07, 1.70136420e+07,
          1.09404450e+07, 1.80745660e+07, 1.50217520e+07, 1.62290090e+07,
          2.73906760e+07],
         [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
          1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
          1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
          2.73906760e+07],
         [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
          1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
          1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
          2.73906760e+07],
         [1.65587180e+07, 1.33618780e+07, 9.86734300e+06, 1.46184780e+07,
          1.37320490e+07, 9.72891400e+06, 1.79774640e+07, 1.70136380e+07,
          1.09404370e+07, 1.80745860e+07, 1.50217340e+07, 1.62290220e+07,
          2.73906760e+07]], dtype=float32)

• Coordinates: (5)
  • time
    ()
    datetime64[ns]
    2024-06-14

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array('2024-06-14T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    01:00:00 ... 1 days 00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([ 3600000000000,  7200000000000, 10800000000000, 14400000000000,
           18000000000000, 21600000000000, 25200000000000, 28800000000000,
           32400000000000, 36000000000000, 39600000000000, 43200000000000,
           46800000000000, 50400000000000, 54000000000000, 57600000000000,
           61200000000000, 64800000000000, 68400000000000, 72000000000000,
           75600000000000, 79200000000000, 82800000000000, 86400000000000],
          dtype='timedelta64[ns]')

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • valid_time
    (step)
    datetime64[ns]
    2024-06-14T01:00:00 ... 2024-06-15

    standard_name :

    time

    long_name :

    time

    array(['2024-06-14T01:00:00.000000000', '2024-06-14T02:00:00.000000000',
           '2024-06-14T03:00:00.000000000', '2024-06-14T04:00:00.000000000',
           '2024-06-14T05:00:00.000000000', '2024-06-14T06:00:00.000000000',
           '2024-06-14T07:00:00.000000000', '2024-06-14T08:00:00.000000000',
           '2024-06-14T09:00:00.000000000', '2024-06-14T10:00:00.000000000',
           '2024-06-14T11:00:00.000000000', '2024-06-14T12:00:00.000000000',
           '2024-06-14T13:00:00.000000000', '2024-06-14T14:00:00.000000000',
           '2024-06-14T15:00:00.000000000', '2024-06-14T16:00:00.000000000',
           '2024-06-14T17:00:00.000000000', '2024-06-14T18:00:00.000000000',
           '2024-06-14T19:00:00.000000000', '2024-06-14T20:00:00.000000000',
           '2024-06-14T21:00:00.000000000', '2024-06-14T22:00:00.000000000',
           '2024-06-14T23:00:00.000000000', '2024-06-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • region
    (region)
    <U26
    'Île-de-France' ... 'Corse'

    array(['Île-de-France', 'Centre-Val de Loire', 'Bourgogne-Franche-Comté',
           'Normandie', 'Hauts-de-France', 'Grand Est', 'Pays de la Loire',
           'Bretagne', 'Nouvelle-Aquitaine', 'Occitanie', 'Auvergne-Rhône-Alpes',
           "Provence-Alpes-Côte d'Azur", 'Corse'], dtype='<U26')

• Indexes: (2)
  • step
    PandasIndex

    PandasIndex(TimedeltaIndex(['0 days 01:00:00', '0 days 02:00:00', '0 days 03:00:00',
                    '0 days 04:00:00', '0 days 05:00:00', '0 days 06:00:00',
                    '0 days 07:00:00', '0 days 08:00:00', '0 days 09:00:00',
                    '0 days 10:00:00', '0 days 11:00:00', '0 days 12:00:00',
                    '0 days 13:00:00', '0 days 14:00:00', '0 days 15:00:00',
                    '0 days 16:00:00', '0 days 17:00:00', '0 days 18:00:00',
                    '0 days 19:00:00', '0 days 20:00:00', '0 days 21:00:00',
                    '0 days 22:00:00', '0 days 23:00:00', '1 days 00:00:00'],
                   dtype='timedelta64[ns]', name='step', freq=None))

  • region
    PandasIndex

    PandasIndex(Index(['Île-de-France', 'Centre-Val de Loire', 'Bourgogne-Franche-Comté',
           'Normandie', 'Hauts-de-France', 'Grand Est', 'Pays de la Loire',
           'Bretagne', 'Nouvelle-Aquitaine', 'Occitanie', 'Auvergne-Rhône-Alpes',
           'Provence-Alpes-Côte d'Azur', 'Corse'],
          dtype='object', name='region'))

• Attributes: (29)

  GRIB_paramId :

  169

  GRIB_dataType :

  fc

  GRIB_numberOfPoints :

  386061

  GRIB_typeOfLevel :

  surface

  GRIB_stepUnits :

  1

  GRIB_stepType :

  accum

  GRIB_gridType :

  regular_ll

  GRIB_NV :

  0

  GRIB_Nx :

  741

  GRIB_Ny :

  521

  GRIB_cfName :

  surface_downwelling_shortwave_flux_in_air

  GRIB_cfVarName :

  ssrd

  GRIB_gridDefinitionDescription :

  Latitude/longitude

  GRIB_iDirectionIncrementInDegrees :

  0.1

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.1

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  72.0

  GRIB_latitudeOfLastGridPointInDegrees :

  20.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  328.0

  GRIB_longitudeOfLastGridPointInDegrees :

  42.0

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  Surface short-wave (solar) radiation downwards

  GRIB_shortName :

  ssrd

  GRIB_units :

  J m**-2

  long_name :

  Surface short-wave (solar) radiation downwards

  units :

  J m**-2

  standard_name :

  surface_downwelling_shortwave_flux_in_air

df_sun_regions = da_sun_region.to_dataframe()
df_sun_regions = df_sun_regions.set_index("valid_time", append=True)
df_sun_regions = df_sun_regions.droplevel("step")
df_unstacked = df_sun_regions["ssrd"].unstack("region")

zero_pad = df_unstacked.iloc[0].copy().to_frame().T
zero_pad[:] = 0
zero_pad.index = zero_pad.index - pd.Timedelta("1H")
df_unstacked = pd.concat([zero_pad, df_unstacked], axis=0)
df_instant_flux = df_unstacked.diff().dropna()
df_instant_flux.index -= pd.Timedelta("1H")
df_instant_flux.plot(title=f"Sunshine duration on {time.date()}")

<Axes: title={'center': 'Sunshine duration on 2024-06-14'}>

Prediction of the ENR production

sun_model = pd.read_csv("sun_model_2_params.csv", index_col=0)["0"]
sun_model

Intercept                     12518.539356
auvergne_rhône_alpes             -0.003394
bourgogne_franche_comté           0.007265
bretagne                         -0.005534
centre_val_de_loire               0.006064
corse                            -0.000185
grand_est                         0.003932
hauts_de_france                  -0.004527
normandie                         0.011918
nouvelle_aquitaine                0.024502
occitanie                         0.030147
pays_de_la_loire                 -0.002904
provence_alpes_côte_d_azur        0.010969
île_de_france                    -0.004461
Name: 0, dtype: float64

wind_model = pd.read_csv("wind_model_2_params.csv", index_col=0)["0"]
wind_model

Intercept                    -87065.603543
auvergne_rhône_alpes           6864.873864
bourgogne_franche_comté       -6060.317404
bretagne                       7631.077211
centre_val_de_loire           14372.517590
corse                          -452.219525
grand_est                     17476.839471
hauts_de_france               17141.512065
normandie                      1358.174748
nouvelle_aquitaine             3480.879194
occitanie                      3917.939519
pays_de_la_loire              -2783.430051
provence_alpes_côte_d_azur     -252.119737
île_de_france                 -7465.879598
Name: 0, dtype: float64

region_names = [
    col.replace(" ", "_").replace("'", "_").replace("-", "_").lower()
    for col in df_instant_flux.columns
]
df_instant_flux.columns = region_names
df_instant_flux["Intercept"] = 1
df_instant_production = df_instant_flux * sun_model
df_instant_production.sum(axis=1).plot(title="Instant production")

<Axes: title={'center': 'Instant production'}>

region_names = [
    col.replace(" ", "_").replace("'", "_").replace("-", "_").lower()
    for col in df_wind.columns
]
df_wind.columns = region_names
df_wind["Intercept"] = 1
df_wind_production = df_wind * wind_model
df_wind_production.sum(axis=1).plot(title="Wind production")

<Axes: title={'center': 'Wind production'}, xlabel='valid_time'>

Conclusion

Nous avons vu comment récupérer les prévisions de la météo France pour le modèle ARPEGE 0.1°.

Nous avons vu comment sélectionner les régions et comment calculer les prévisions de production d’énergie renouvelable grâces aux modèles entrainés dans les notebooks précédents.