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Add open multi-model forecast sources

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This commit is contained in:
2569718930@qq.com
2026-04-17 00:27:57 +08:00
parent 8db933a47c
commit 7909a16002
4 changed files with 200 additions and 28 deletions
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src/data_collection/nws_open_meteo_sources.py
+144 -26
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@@ -2,13 +2,143 @@ from __future__ import annotations
import time
from datetime import datetime, timedelta
from typing import Dict, Optional
from typing import Any, Dict, Optional
from loguru import logger
from src.utils.metrics import record_source_call
OPEN_METEO_MULTI_MODEL_SPECS: Dict[str, Dict[str, Any]] = {
"ecmwf_ifs025": {
"label": "ECMWF",
"provider": "ECMWF",
"model": "IFS",
"tier": "global",
"resolution_km": 25,
"horizon": "15d",
},
"ecmwf_aifs025_single": {
"label": "ECMWF AIFS",
"provider": "ECMWF",
"model": "AIFS single",
"tier": "global_ai",
"resolution_km": 25,
"horizon": "15d",
},
"gfs_seamless": {
"label": "GFS",
"provider": "NOAA",
"model": "GFS Seamless",
"tier": "global",
"resolution_km": None,
"horizon": "16d",
},
"icon_seamless": {
"label": "ICON",
"provider": "DWD",
"model": "ICON Seamless",
"tier": "global",
"resolution_km": None,
"horizon": "7d",
},
"icon_eu": {
"label": "ICON-EU",
"provider": "DWD",
"model": "ICON-EU",
"tier": "regional_europe",
"resolution_km": 7,
"horizon": "5d",
},
"icon_d2": {
"label": "ICON-D2",
"provider": "DWD",
"model": "ICON-D2",
"tier": "short_range_europe",
"resolution_km": 2.2,
"horizon": "2d",
},
"gem_seamless": {
"label": "GEM",
"provider": "ECCC",
"model": "GEM Seamless",
"tier": "global",
"resolution_km": None,
"horizon": "10d",
},
"gem_global": {
"label": "GDPS",
"provider": "ECCC",
"model": "GEM Global / GDPS",
"tier": "global",
"resolution_km": 15,
"horizon": "10d",
},
"gem_regional": {
"label": "RDPS",
"provider": "ECCC",
"model": "GEM Regional / RDPS",
"tier": "regional_north_america",
"resolution_km": 10,
"horizon": "3d",
},
"gem_hrdps_continental": {
"label": "HRDPS",
"provider": "ECCC",
"model": "HRDPS Continental",
"tier": "short_range_north_america",
"resolution_km": 2.5,
"horizon": "2d",
},
"jma_seamless": {
"label": "JMA",
"provider": "JMA",
"model": "JMA Seamless",
"tier": "global",
"resolution_km": None,
"horizon": "11d",
},
}
OPEN_METEO_MULTI_MODEL_ORDER = tuple(OPEN_METEO_MULTI_MODEL_SPECS.keys())
def _parse_open_meteo_multi_model_daily(
daily: Dict[str, Any],
) -> tuple[list[str], Dict[str, Dict[str, float]], Dict[str, Dict[str, Any]], Dict[str, str]]:
dates = daily.get("time", [])
if not isinstance(dates, list):
dates = []
model_metadata: Dict[str, Dict[str, Any]] = {}
model_keys: Dict[str, str] = {}
daily_forecasts: Dict[str, Dict[str, float]] = {}
for day_idx, date_str in enumerate(dates):
day_data: Dict[str, float] = {}
for model_key, spec in OPEN_METEO_MULTI_MODEL_SPECS.items():
label = str(spec["label"])
key = f"temperature_2m_max_{model_key}"
values = daily.get(key, [])
if not isinstance(values, list):
continue
if day_idx >= len(values) or values[day_idx] is None:
continue
try:
day_data[label] = round(float(values[day_idx]), 1)
except (TypeError, ValueError):
continue
model_metadata[label] = {
**spec,
"open_meteo_model": model_key,
}
model_keys[label] = model_key
if day_data:
daily_forecasts[str(date_str)] = day_data
return [str(d) for d in dates], daily_forecasts, model_metadata, model_keys
class NwsOpenMeteoSourceMixin:
def fetch_nws(self, lat: float, lon: float) -> Optional[Dict]:
"""
@@ -468,10 +598,10 @@ class NwsOpenMeteoSourceMixin:
用于真正的多模型共识评分
模型列表:
- ECMWF IFS (欧洲中期天气预报中心)
- ECMWF IFS / AIFS
- GFS (美国 NOAA)
- ICON (德国气象局 DWD)
- GEM (加拿大气象局)
- ICON Seamless / ICON-EU / ICON-D2 (德国气象局 DWD)
- GEM Seamless / GDPS / RDPS / HRDPS (加拿大 ECCC)
- JMA (日本气象厅)
返回 3 天的预报数据，支持今日+明日共识分析
@@ -510,7 +640,7 @@ class NwsOpenMeteoSourceMixin:
return dict(cached_data)
try:
url = "https://api.open-meteo.com/v1/forecast"
models = "ecmwf_ifs025,gfs_seamless,icon_seamless,gem_seamless,jma_seamless"
models = ",".join(OPEN_METEO_MULTI_MODEL_ORDER)
params = {
"latitude": lat,
"longitude": lon,
@@ -532,27 +662,11 @@ class NwsOpenMeteoSourceMixin:
data = response.json()
daily = data.get("daily", {})
dates = daily.get("time", [])
model_labels = {
"ecmwf_ifs025": "ECMWF",
"gfs_seamless": "GFS",
"icon_seamless": "ICON",
"gem_seamless": "GEM",
"jma_seamless": "JMA",
}
# 按天提取每个模型的预报
daily_forecasts = {} # {"2026-02-23": {"ECMWF": 7.9, "GFS": 6.5, ...}, ...}
for day_idx, date_str in enumerate(dates):
day_data = {}
for model_key, label in model_labels.items():
key = f"temperature_2m_max_{model_key}"
values = daily.get(key, [])
if day_idx < len(values) and values[day_idx] is not None:
day_data[label] = round(values[day_idx], 1)
if day_data:
daily_forecasts[date_str] = day_data
if not isinstance(daily, dict):
daily = {}
dates, daily_forecasts, model_metadata, model_keys = (
_parse_open_meteo_multi_model_daily(daily)
)
if not daily_forecasts:
logger.warning("Multi-model: 无有效模型数据")
@@ -570,10 +684,14 @@ class NwsOpenMeteoSourceMixin:
result = {
"source": "multi_model",
"provider": "open-meteo",
"forecasts": forecasts, # 今天 {"ECMWF": 12.3, "GFS": 11.8, ...} (向后兼容)
"daily_forecasts": daily_forecasts, # 按天 {"2026-02-23": {...}, "2026-02-24": {...}}
"model_metadata": model_metadata,
"model_keys": model_keys,
"dates": dates,
"unit": "fahrenheit" if use_fahrenheit else "celsius",
"attribution": "Open-Meteo forecast model API; underlying models from ECMWF, DWD, ECCC, NOAA and JMA.",
}
with self._multi_model_cache_lock:
self._multi_model_cache[cache_key] = {
src/data_collection/weather_sources.py
+2 -2
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@@ -161,8 +161,8 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC", "900")
)
self.multi_model_cache_version = str(
os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_VERSION", "v2")
).strip() or "v2"
os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_VERSION", "v3")
).strip() or "v3"
self._open_meteo_cache: Dict[str, Dict] = {}
self._ensemble_cache: Dict[str, Dict] = {}
self._multi_model_cache: Dict[str, Dict] = {}
tests/test_multi_model_sources.py
+46
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@@ -0,0 +1,46 @@
from src.data_collection.nws_open_meteo_sources import (
OPEN_METEO_MULTI_MODEL_ORDER,
_parse_open_meteo_multi_model_daily,
)
def test_multi_model_parser_exposes_open_recommended_models():
daily = {
"time": ["2026-04-17", "2026-04-18"],
"temperature_2m_max_ecmwf_ifs025": [20.1, 21.1],
"temperature_2m_max_ecmwf_aifs025_single": [20.2, 21.2],
"temperature_2m_max_icon_eu": [20.3, 21.3],
"temperature_2m_max_icon_d2": [20.4, None],
"temperature_2m_max_gem_global": [19.8, 20.8],
"temperature_2m_max_gem_regional": [21.0, 22.0],
"temperature_2m_max_gem_hrdps_continental": [21.5, None],
}
dates, forecasts, metadata, model_keys = _parse_open_meteo_multi_model_daily(daily)
assert dates == ["2026-04-17", "2026-04-18"]
assert forecasts["2026-04-17"]["ECMWF"] == 20.1
assert forecasts["2026-04-17"]["ECMWF AIFS"] == 20.2
assert forecasts["2026-04-17"]["ICON-EU"] == 20.3
assert forecasts["2026-04-17"]["ICON-D2"] == 20.4
assert forecasts["2026-04-17"]["GDPS"] == 19.8
assert forecasts["2026-04-17"]["RDPS"] == 21.0
assert forecasts["2026-04-17"]["HRDPS"] == 21.5
assert "ICON-D2" not in forecasts["2026-04-18"]
assert metadata["ECMWF AIFS"]["provider"] == "ECMWF"
assert metadata["HRDPS"]["resolution_km"] == 2.5
assert model_keys["RDPS"] == "gem_regional"
def test_multi_model_order_includes_legacy_and_new_sources():
assert "ecmwf_ifs025" in OPEN_METEO_MULTI_MODEL_ORDER
assert "ecmwf_aifs025_single" in OPEN_METEO_MULTI_MODEL_ORDER
assert "gfs_seamless" in OPEN_METEO_MULTI_MODEL_ORDER
assert "icon_seamless" in OPEN_METEO_MULTI_MODEL_ORDER
assert "icon_eu" in OPEN_METEO_MULTI_MODEL_ORDER
assert "icon_d2" in OPEN_METEO_MULTI_MODEL_ORDER
assert "gem_seamless" in OPEN_METEO_MULTI_MODEL_ORDER
assert "gem_global" in OPEN_METEO_MULTI_MODEL_ORDER
assert "gem_regional" in OPEN_METEO_MULTI_MODEL_ORDER
assert "gem_hrdps_continental" in OPEN_METEO_MULTI_MODEL_ORDER
assert "jma_seamless" in OPEN_METEO_MULTI_MODEL_ORDER
web/analysis_service.py
+8
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@@ -2313,6 +2313,14 @@ def _analyze(
},
"source_forecasts": {
"weather_gov": raw.get("nws") or {},
"open_meteo_multi_model": {
"source": mm.get("source"),
"provider": mm.get("provider"),
"dates": mm.get("dates") or [],
"model_metadata": mm.get("model_metadata") or {},
"model_keys": mm.get("model_keys") or {},
"attribution": mm.get("attribution"),
} if isinstance(mm, dict) and mm else {},
},
"multi_model": {k: v for k, v in current_forecasts.items() if v is not None},
"multi_model_daily": multi_model_daily,