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Merge pull request #52 from mattchdt/38-add-support-for-pearpege
38 add support for pearpege
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‎CHANGELOG.md‎

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.2.3] - November, 2025
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### Features
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* Support for Météo-France ensemble model `PE-ARPEGE` (#38)
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## [0.2.2] - August, 2025
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### Features

‎README.md‎

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@@ -50,14 +50,14 @@ Create an account on [the Météo-France API portal](https://portail-api.meteofr
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Meteole allows you to retrieve forecasts for a wide range of weather indicators. Here's how to get started:
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| Characteristics | AROME | AROME-PE | ARPEGE | AROME INSTANTANE | PIAF |
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|------------------|----------------------------|----------------------------|-----------------------------|--------------------------------| -------------------------------|
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| Resolution | 1.3 km | 2.8 km | 10 km | 1.3 km | 1.3 km |
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| Update Frequency | Every 3 hours | Every 6 hours | Every 6 hours | Every 1 hour | Every 10 minutes |
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| Forecast Range | Every hour, up to 51 hours | Every hour, up to 51 hours | Every hour, up to 114 hours | Every 15 minutes, up to 360 minutes | Every 5 minutes, up to 195 minutes |
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| Numbers of scenarios | 1 | 25 | 1 | 1 | 1 |
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The AromePE model is an ensemble model. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere ([from Wikipedia](https://en.wikipedia.org/wiki/Ensemble_forecasting)). It provides 25 scenarios of the possible weather parameters instead of one for the standard determinist models.
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| Characteristics | AROME | AROME-PE | ARPEGE | ARPEGE-PE | AROME INSTANTANE | PIAF |
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|------------------|----------------------------|----------------------------|-----------------------------|--------------------------------| -------------------------------| -------------------------------|
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| Resolution | 1.3 km | 2.8 km | 10 km | 10 km | 1.3 km | 1.3 km |
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| Update Frequency | Every 3 hours | Every 6 hours | Every 6 hours | Every 6 hours | Every 1 hour | Every 10 minutes |
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| Forecast Range | Every hour, up to 51 hours | Every hour, up to 51 hours | Every hour, up to 114 hours | Every hour up to 48 hours, then every 3 hours up to 114 hours | Every 15 minutes, up to 360 minutes | Every 5 minutes, up to 195 minutes |
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| Numbers of scenarios | 1 | 25 | 1 | 35 | 1 | 1 |
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The AromePE and ArpegePE models are ensemble models. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere ([from Wikipedia](https://en.wikipedia.org/wiki/Ensemble_forecasting)). It provides several scenarios of the possible weather parameters instead of one for the standard determinist models.
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*note : the date of the run cannot be more than 4 days in the past. Consequently, change the date of the run in the example below.*
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‎docs/pages/how_to.md‎

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Meteole allows you to retrieve forecasts for a wide range of weather indicators. Here's how to get started with AROME, AROME-PE, AROME INSTANTANE, ARPEGE or PIAF:
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| Characteristics | AROME | AROME-PE | ARPEGE | AROME INSTANTANE | PIAF |
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|------------------|----------------------------|----------------------------|-----------------------------|--------------------------------| -------------------------------|
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| Resolution | 1.3 km | 2.8 km | 10 km | 1.3 km | 1.3 km |
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| Update Frequency | Every 3 hours | Every 6 hours | Every 6 hours | Every 1 hour | Every 10 minutes |
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| Forecast Range | Every hour, up to 51 hours | Every hour, up to 51 hours | Every hour, up to 114 hours | Every 15 minutes, up to 360 minutes | Every 5 minutes, up to 195 minutes |
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| Numbers of scenarios | 1 | 25 | 1 | 1 | 1 |
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The AromePE model is an ensemble model. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere ([from Wikipedia](https://en.wikipedia.org/wiki/Ensemble_forecasting)). It provides 25 scenarios of the possible weather parameters instead of one for the standard determinist models.
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| Characteristics | AROME | AROME-PE | ARPEGE | ARPEGE-PE | AROME INSTANTANE | PIAF |
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|------------------|----------------------------|----------------------------|-----------------------------|--------------------------------| -------------------------------| -------------------------------|
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| Resolution | 1.3 km | 2.8 km | 10 km | 10 km | 1.3 km | 1.3 km |
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| Update Frequency | Every 3 hours | Every 6 hours | Every 6 hours | Every 6 hours | Every 1 hour | Every 10 minutes |
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| Forecast Range | Every hour, up to 51 hours | Every hour, up to 51 hours | Every hour, up to 114 hours | Every hour up to 48 hours, then every 3 hours up to 114 hours | Every 15 minutes, up to 360 minutes | Every 5 minutes, up to 195 minutes |
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| Numbers of scenarios | 1 | 25 | 1 | 35 | 1 | 1 |
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The AromePE and ArpegePE models are ensemble models. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere ([from Wikipedia](https://en.wikipedia.org/wiki/Ensemble_forecasting)). It provides several scenarios of the possible weather parameters instead of one for the standard determinist models.
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*note : the date of the run cannot be more than 4 days in the past. Consequently, change the date of the run in the example below.*
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‎src/meteole/_arpege_ensemble.py‎

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from __future__ import annotations
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import logging
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from typing import Any, final
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from meteole.clients import BaseClient, MeteoFranceClient
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from meteole.forecast import WeatherForecast
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logger = logging.getLogger(__name__)
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AVAILABLE_ARPEGE_TERRITORY: list[str] = ["EUROPE", "GLOBE"]
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ARPEGE_INSTANT_INDICATORS: list[str] = [
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"GEOMETRIC_HEIGHT__GROUND_OR_WATER_SURFACE",
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"ISOTHERMAL_LEVEL_TW27315__GROUND_OR_WATER_SURFACE",
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"ISOTHERMAL_LEVEL_TW27415__GROUND_OR_WATER_SURFACE",
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"ISOTHERMAL_LEVEL_TW27465__GROUND_OR_WATER_SURFACE",
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"ISOTHERMAL_LEVEL_T27315__GROUND_OR_WATER_SURFACE",
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"CLOUD_BASE_HEIGHT__GROUND_OR_WATER_SURFACE",
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"CONVECTIVE_AVAILABLE_POTENTIAL_ENERGY_MAX__GROUND_OR_WATER_SURFACE",
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"CONVECTIVE_AVAILABLE_POTENTIAL_ENERGY_MODEL__GROUND_OR_WATER_SURFACE",
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"INHIBITION_CONVECTIVE__GROUND_OR_WATER_SURFACE",
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"TOTAL_WATER_VAPOUR__GROUND_OR_WATER_SURFACE",
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"DIVERGENCE__ISOBARIC_SURFACE",
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"RELATIVE_HUMIDITY__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"RELATIVE_HUMIDITY__ISOBARIC_SURFACE",
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"PLANETARY_BOUNDARY_LAYER_HEIGHT__GROUND_OR_WATER_SURFACE",
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"LIGHTNING_DENSITY_MEAN_3H__GROUND_OR_WATER_SURFACE",
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"CONVECTIVE_AVAILABLE_POTENTIAL_ENERGY_MEAN_LAYER__GROUND_OR_WATER_SURFACE",
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"LOW_CLOUD_COVER__GROUND_OR_WATER_SURFACE",
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"CONVECTIVE_CLOUD_COVER__GROUND_OR_WATER_SURFACE",
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"HIGH_CLOUD_COVER__GROUND_OR_WATER_SURFACE",
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"MEDIUM_CLOUD_COVER__GROUND_OR_WATER_SURFACE",
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"TOTAL_CLOUD_COVER__GROUND_OR_WATER_SURFACE",
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"PRESSURE__GROUND_OR_WATER_SURFACE",
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"PRESSURE__MEAN_SEA_LEVEL",
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"POTENTIAL_TEMPERATURE__POTENTIAL_VORTICITY_SURFACE_1500",
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"POTENTIAL_TEMPERATURE__POTENTIAL_VORTICITY_SURFACE_2000",
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"MAXIMUM_TEMPERATURE__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"MINIMUM_TEMPERATURE__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"PSEUDO_ADIABATIC_POTENTIAL_TEMPERATURE__ISOBARIC_SURFACE",
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"TEMPERATURE__GROUND_OR_WATER_SURFACE",
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"TEMPERATURE__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"TEMPERATURE__ISOBARIC_SURFACE",
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"U_COMPONENT_OF_WIND_GUST__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"U_COMPONENT_OF_WIND__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"U_COMPONENT_OF_WIND__ISOBARIC_SURFACE",
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"U_COMPONENT_OF_WIND__POTENTIAL_VORTICITY_SURFACE_1500",
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"U_COMPONENT_OF_WIND__POTENTIAL_VORTICITY_SURFACE_2000",
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"MINIMUM_VISIBILITY_180_PRECIPITATING_HYDROMETEORS__GROUND_OR_WATER_SURFACE",
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"MINIMUM_VISIBILITY_PRECIPITATING_HYDROMETEORS__GROUND_OR_WATER_SURFACE",
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"MINIMUM_VISIBILITY_180_NON_PRECIPITATING_HYDROMETEORS__GROUND_OR_WATER_SURFACE",
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"MINIMUM_VISIBILITY_NON_PRECIPITATING_HYDROMETEORS__GROUND_OR_WATER_SURFACE",
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"VERTICAL_VELOCITY_PRESSURE__ISOBARIC_SURFACE",
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"V_COMPONENT_OF_WIND_GUST__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"V_COMPONENT_OF_WIND__SPECIFIC_HEIGHT_LEVEL_ABOVE_GROUND",
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"V_COMPONENT_OF_WIND__ISOBARIC_SURFACE",
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"V_COMPONENT_OF_WIND__POTENTIAL_VORTICITY_SURFACE_1500",
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"V_COMPONENT_OF_WIND__POTENTIAL_VORTICITY_SURFACE_2000",
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"GEOPOTENTIAL__ISOBARIC_SURFACE",
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"GEOPOTENTIAL__POTENTIAL_VORTICITY_SURFACE_1500",
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"GEOPOTENTIAL__POTENTIAL_VORTICITY_SURFACE_2000",
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]
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ARPEGE_OTHER_INDICATORS: list[str] = [
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"TOTAL_WATER_PRECIPITATION__GROUND_OR_WATER_SURFACE",
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"LIGHTNING_DENSITY_CUMULATED__GROUND_OR_WATER_SURFACE",
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"TOTAL_SNOW_PRECIPITATION__GROUND_OR_WATER_SURFACE",
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"TOTAL_PRECIPITATION__GROUND_OR_WATER_SURFACE",
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]
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@final
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class ArpegePEForecast(WeatherForecast):
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"""Access the ARPEGE numerical weather forecast data from Meteo-France API.
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Doc:
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- https://portail-api.meteofrance.fr/web/fr/api/pe-arpege
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Attributes:
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territory: Covered area (e.g., FRANCE, EUROPE, ...).
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precision: Precision value of the forecast.
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capabilities: DataFrame containing details on all available coverage ids.
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"""
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MODEL_NAME: str = "pearpege"
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BASE_ENTRY_POINT: str = "wcs/MF-NWP-GLOBAL-PEARP"
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MODEL_TYPE: str = "ENSEMBLE"
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ENSEMBLE_NUMBERS: int = 35
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DEFAULT_TERRITORY: str = "EUROPE"
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RELATION_TERRITORY_TO_PREC_ARPEGE: dict[str, float] = {"EUROPE": 0.1, "GLOBE": 0.25}
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CLIENT_CLASS: type[BaseClient] = MeteoFranceClient
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INDICATORS: list[str] = ARPEGE_INSTANT_INDICATORS + ARPEGE_OTHER_INDICATORS
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INSTANT_INDICATORS: list[str] = ARPEGE_INSTANT_INDICATORS
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def __init__(
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self,
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client: BaseClient | None = None,
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*,
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territory: str = "EUROPE",
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**kwargs: Any,
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):
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"""Initializes an ArpegeForecast object.
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The `precision` of the forecast is inferred from the specified `territory`.
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Args:
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territory: The ARPEGE territory to fetch. Defaults to "EUROPE".
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api_key: The API key for authentication. Defaults to None.
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token: The API token for authentication. Defaults to None.
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application_id: The Application ID for authentication. Defaults to None.
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Notes:
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- See `MeteoFranceClient` for additional details on the parameters `api_key`, `token`,
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and `application_id`.
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- Available territories are listed in the `AVAILABLE_TERRITORY` constant.
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"""
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super().__init__(
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client=client,
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territory=territory,
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precision=self.RELATION_TERRITORY_TO_PREC_ARPEGE[territory],
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**kwargs,
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)
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def _validate_parameters(self) -> None:
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"""Check the territory and the precision parameters.
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Raise:
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ValueError: At least, one parameter is not good.
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"""
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if self.territory not in AVAILABLE_ARPEGE_TERRITORY:
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raise ValueError(f"The parameter precision must be in {AVAILABLE_ARPEGE_TERRITORY}")

‎src/meteole/forecast.py‎

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df: pd.DataFrame = self._grib_bytes_to_df(grib_binary, temp_dir=temp_dir)
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if self.MODEL_NAME == "pearpege":
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# for unclear reasons, the pearpege API does not accept lat, long
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# parameters unlike the other models API.
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# So we retrieve all the domain and then filter the results
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# for the desired lat, long in _get_data_single_forecast
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df = df.loc[
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(df["latitude"] <= lat[1])
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& (df["latitude"] >= lat[0])
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& (df["longitude"] <= long[1])
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& (df["longitude"] >= long[0])
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]
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# Drop and rename columns
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df.drop(columns=["surface", "valid_time"], errors="ignore", inplace=True)
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df.rename(
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else:
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url = f"{self._model_base_path}/{self._entry_point.replace('xxx', f'{ensemble_number:03}')}/GetCoverage"
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params = {
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"service": "WCS",
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"version": "2.0.1",
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"coverageid": coverage_id,
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"format": "application/wmo-grib",
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"subset": [
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if self.MODEL_NAME == "pearpege":
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# for unclear reasons, the pearpege API does not accept lat, long
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# parameters unlike the other models API.
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# So we retrieve all the domain and then filter the results
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# for the desired lat, long in _get_data_single_forecast
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subset = [
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*([f"pressure({pressure})"] if pressure is not None else []),
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*([f"height({height})"] if height is not None else []),
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f"time({forecast_horizon_in_seconds})",
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]
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else:
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subset = [
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*([f"pressure({pressure})"] if pressure is not None else []),
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*([f"height({height})"] if height is not None else []),
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f"time({forecast_horizon_in_seconds})",
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f"lat({lat[0]},{lat[1]})",
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f"long({long[0]},{long[1]})",
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],
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]
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params = {
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"service": "WCS",
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"version": "2.0.1",
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"coverageid": coverage_id,
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"format": "application/wmo-grib",
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"subset": subset,
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}
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response = self._client.get(url, params=params)
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return response.content

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