feat: introduce WeatherDataCollector for multi-source weather data retrieval from OpenWeatherMap, Visual Crossing, and METAR.

2026-02-22 09:54:47 +08:00
parent 2dcc700cd7
commit 6cc16c938d
2 changed files with 193 additions and 58 deletions
@@ -37,8 +37,10 @@ def analyze_weather_trend(weather_data, temp_symbol):
        forecast_highs.append(mb["today_high"])
    if nws.get("today_high") is not None:
        forecast_highs.append(nws["today_high"])
-    if mgm.get("today_high") is not None:
-        forecast_highs.append(mgm["today_high"])
+    # 加入多模型预报 (ECMWF, GFS, ICON, GEM, JMA)
+    for mv in weather_data.get("multi_model", {}).get("forecasts", {}).values():
+        if mv is not None:
+            forecast_highs.append(mv)
    
    forecast_highs = [h for h in forecast_highs if h is not None]
    # 取预报中的最高值作为风险防御基准
@@ -58,17 +60,25 @@ def analyze_weather_trend(weather_data, temp_symbol):
        local_hour = datetime.now().hour

    # === 模型共识评分 ===
+    # 主要来源: 多模型预报 (ECMWF, GFS, ICON, GEM, JMA)
+    multi_model = weather_data.get("multi_model", {})
+    mm_forecasts = multi_model.get("forecasts", {})
+    
    labeled_forecasts = []
-    om_today = daily.get("temperature_2m_max", [None])[0]
-    if om_today is not None:
-        labeled_forecasts.append(("OM", om_today))
+    for model_name, model_val in mm_forecasts.items():
+        if model_val is not None:
+            labeled_forecasts.append((model_name, model_val))
+    
+    # 额外独立源 (如有)
    if mb.get("today_high") is not None:
        labeled_forecasts.append(("MB", mb["today_high"]))
    if nws.get("today_high") is not None:
        labeled_forecasts.append(("NWS", nws["today_high"]))
-    if mgm.get("today_high") is not None:
-        labeled_forecasts.append(("MGM", mgm["today_high"]))
-    # 集合预报数据 (仅用于不确定性区间展示，不参与共识评分，避免与 OM 双重计数)
+    
+    # Open-Meteo 确定性预报（用于后续偏差检测，不重复加入共识）
+    om_today = daily.get("temperature_2m_max", [None])[0]
+    
+    # 集合预报数据 (仅用于不确定性区间展示)
    ensemble = weather_data.get("ensemble", {})
    ens_median = ensemble.get("median")

@@ -128,20 +138,36 @@ def analyze_weather_trend(weather_data, temp_symbol):
        )
        # 确定性预报 vs 集合分布偏差检测
        if om_today is not None:
+            actual_reached = max_so_far is not None and max_so_far >= om_today - 0.5
            if om_today > ens_p90:
-                delta = om_today - ens_median
-                insights.append(
-                    f"⚡ <b>预报偏高警告</b>：确定性预报 {om_today}{temp_symbol} "
-                    f"超过了集合 90% 上限 ({ens_p90}{temp_symbol})，"
-                    f"比中位数高 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
-                )
+                if actual_reached:
+                    # 实测已达到预报值 → 确定性预报是对的，集合偏保守
+                    insights.append(
+                        f"✅ <b>预报验证</b>：确定性预报 {om_today}{temp_symbol} 已被实测验证 "
+                        f"(实测最高 {max_so_far}{temp_symbol})，集合预报偏保守。"
+                    )
+                else:
+                    # 还没到最高温，存在偏高风险
+                    delta = om_today - ens_median
+                    insights.append(
+                        f"⚡ <b>预报偏高警告</b>：确定性预报 {om_today}{temp_symbol} "
+                        f"超过了集合 90% 上限 ({ens_p90}{temp_symbol})，"
+                        f"比中位数高 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
+                    )
            elif om_today < ens_p10:
-                delta = ens_median - om_today
-                insights.append(
-                    f"⚡ <b>预报偏低警告</b>：确定性预报 {om_today}{temp_symbol} "
-                    f"低于集合 90% 下限 ({ens_p10}{temp_symbol})，"
-                    f"比中位数低 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
-                )
+                if max_so_far is not None and max_so_far >= ens_median:
+                    # 实测已超过中位数 → 确定性预报偏低，集合更准
+                    insights.append(
+                        f"✅ <b>预报验证</b>：实测最高 {max_so_far}{temp_symbol} "
+                        f"已超过确定性预报 {om_today}{temp_symbol}，集合中位数 {ens_median}{temp_symbol} 更准确。"
+                    )
+                else:
+                    delta = ens_median - om_today
+                    insights.append(
+                        f"⚡ <b>预报偏低警告</b>：确定性预报 {om_today}{temp_symbol} "
+                        f"低于集合 90% 下限 ({ens_p10}{temp_symbol})，"
+                        f"比中位数低 {delta:.1f}°。实际高温更可能接近 {ens_median}{temp_symbol}。"
+                    )

    # === 核心判断：实测是否已超预报 ===
    is_breakthrough = False
@@ -224,13 +250,14 @@ def analyze_weather_trend(weather_data, temp_symbol):
            # 正在峰值窗口内
            if is_breakthrough:
                insights.append(f"🔥 <b>极端升温</b>：正处于最热时段，温度已经超过所有预报，还在继续往上走！")
-            elif diff_max <= 0.8:
+            elif max_so_far is not None and forecast_high - max_so_far <= 0.8:
                insights.append(f"⚖️ <b>到顶了</b>：正处于最热时段，温度基本到位，接下来会在这个水平上下浮动。")
            else:
                insights.append(f"⏳ <b>最热时段进行中</b>：虽然在最热时段了，但离预报最高温还差一些，继续观察。")
        elif local_hour < first_peak_h:
            # 还没到峰值窗口
-            if diff_max > 1.2:
+            gap_to_high = forecast_high - (max_so_far if max_so_far is not None else curr_temp)
+            if gap_to_high > 1.2:
                insights.append(f"📈 <b>还在升温</b>：离最热时段还有 {first_peak_h - local_hour} 小时，温度还会继续往上走。")
            else:
                insights.append(f"🌅 <b>快到最热了</b>：马上就要进入最热时段，温度已经接近预报高位了。")
@@ -257,7 +284,7 @@ def analyze_weather_trend(weather_data, temp_symbol):

        # 4. 云层遮挡分析 (仅在升温期/峰值期有意义)
        clouds = metar.get("current", {}).get("clouds", [])
-        if clouds and local_hour <= last_peak_h + 1:
+        if clouds and not is_peak_passed:
            main_cloud = clouds[-1]
            cover = main_cloud.get("cover", "")
            if cover == "OVC":
@@ -265,8 +292,7 @@ def analyze_weather_trend(weather_data, temp_symbol):
            elif cover == "BKN":
                insights.append(f"🌥️ <b>云比较多</b>：天空大部分被云挡住了，日照不足，升温会比较慢。")
            elif cover in ["SKC", "CLR", "FEW"]:
-                if not is_peak_passed:
-                    insights.append(f"☀️ <b>大晴天</b>：阳光直射，没什么云，有利于温度继续往上冲。")
+                insights.append(f"☀️ <b>大晴天</b>：阳光直射，没什么云，有利于温度继续往上冲。")

        # 5. 特殊天气现象
        wx_desc = metar.get("current", {}).get("wx_desc")
@@ -324,12 +350,14 @@ def analyze_weather_trend(weather_data, temp_symbol):
                if 315 <= wd or wd <= 45:
                    insights.append(f"🌬️ <b>吹北风</b>（{wind_source} {wd:.0f}°）：从北方来的冷空气，会压制升温。")
                elif 135 <= wd <= 225:
-                    if diff_max > 0.5 or (is_breakthrough and curr_temp >= max_so_far):
-                        if is_peak_passed and not is_breakthrough:
-                            insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：南方的暖空气还在吹过来，但最热时段已过，后劲不足了。")
-                        else:
-                            status = "温度还有继续上涨的空间" if not is_breakthrough else "可能把温度推得更高"
-                            insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，{status}。")
+                    gap_to_forecast = forecast_high - (max_so_far if max_so_far is not None else curr_temp)
+                    if is_peak_passed and not is_breakthrough:
+                        insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：南方的暖空气还在吹过来，但最热时段已过，后劲不足了。")
+                    elif gap_to_forecast > 0.5 or is_breakthrough:
+                        status = "温度还有继续上涨的空间" if not is_breakthrough else "可能把温度推得更高"
+                        insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，{status}。")
+                    else:
+                        insights.append(f"🔥 <b>吹南风</b>（{wind_source} {wd:.0f}°）：南方的暖空气正在吹过来，但温度已接近预报峰值。")
                elif 225 < wd < 315:
                    if wd <= 260:
                        insights.append(f"🌬️ <b>吹西南风</b>（{wind_source} {wd:.0f}°）：带有一定暖湿气流，对升温有轻微帮助。")
@@ -351,20 +379,22 @@ def analyze_weather_trend(weather_data, temp_symbol):
        except (TypeError, ValueError):
            pass

-        # 7. 模型准确度预警
+        # 7. 模型准确度预警（使用多模型数据）
        if is_peak_passed and max_so_far is not None:
            model_checks = []
-            if om_high and om_high > max_so_far + 1.5:
-                model_checks.append(f"Open-Meteo ({om_high}{temp_symbol})")
+            for m_name, m_val in mm_forecasts.items():
+                if m_val is not None and m_val > max_so_far + 1.5:
+                    model_checks.append(f"{m_name} ({m_val}{temp_symbol})")
+            # 附加源也查一下
            mb_h = mb.get("today_high")
            if mb_h and mb_h > max_so_far + 1.5:
-                model_checks.append(f"Meteoblue ({mb_h}{temp_symbol})")
+                model_checks.append(f"MB ({mb_h}{temp_symbol})")
            nws_h = nws.get("today_high")
            if nws_h and nws_h > max_so_far + 1.5:
                model_checks.append(f"NWS ({nws_h}{temp_symbol})")
            
            if model_checks:
-                insights.append(f"⚠️ <b>预报偏高了</b>：实测远低于 " + "、".join(model_checks) + "，这些预报今天报高了。")
+                insights.append(f"⚠️ <b>预报偏高了</b>：实测远低于 " + "、".join(model_checks) + "，这些模型今天报高了。")

        # 8. MGM 气压分析 (仅安卡拉)
        mgm_pressure = mgm.get("current", {}).get("pressure")
@@ -369,14 +369,33 @@ class WeatherDataCollector:
                        "station_name": latest.get("istasyonAd") or latest.get("adi") or latest.get("merkezAd") or "Ankara Esenboğa"
                    }
            
-            # 2. 每日预报
-            daily_resp = self.session.get(f"{base_url}/tahminler/gunluk?istno={istno}", headers=headers, timeout=self.timeout)
-            if daily_resp.status_code == 200:
-                forecasts = daily_resp.json()
-                if forecasts and isinstance(forecasts, list):
-                    today = forecasts[0]
-                    results["today_high"] = today.get("enYuksekGun1")
-                    results["today_low"] = today.get("enDusukGun1")
+            # 2. 每日预报（尝试两个可能的 API 路径）
+            forecast_urls = [
+                f"{base_url}/tahminler/gunluk?istno={istno}",
+                f"https://servis.mgm.gov.tr/api/tahminler/gunluk?istno={istno}",
+            ]
+            for forecast_url in forecast_urls:
+                try:
+                    daily_resp = self.session.get(forecast_url, headers=headers, timeout=self.timeout)
+                    if daily_resp.status_code == 200:
+                        forecasts = daily_resp.json()
+                        if forecasts and isinstance(forecasts, list):
+                            today = forecasts[0]
+                            high_val = today.get("enYuksekGun1")
+                            low_val = today.get("enDusukGun1")
+                            if high_val is not None:
+                                results["today_high"] = high_val
+                                results["today_low"] = low_val
+                                logger.info(f"📋 MGM 每日预报: 最高 {high_val}°C, 最低 {low_val}°C (from {forecast_url})")
+                                break
+                            else:
+                                # 记录所有可用字段，方便调试
+                                available_keys = [k for k in today.keys() if "yuksek" in k.lower() or "sicaklik" in k.lower() or "gun" in k.lower()]
+                                logger.warning(f"MGM 每日预报: enYuksekGun1 为空，可用字段: {available_keys}")
+                    else:
+                        logger.debug(f"MGM forecast URL {forecast_url} returned {daily_resp.status_code}")
+                except Exception as e:
+                    logger.debug(f"MGM forecast URL {forecast_url} failed: {e}")
            
            return results if "current" in results else None
        except Exception as e:
@@ -618,6 +637,86 @@ class WeatherDataCollector:
            logger.warning(f"Ensemble API 请求失败: {e}")
            return None

+    def fetch_multi_model(
+        self,
+        lat: float,
+        lon: float,
+        use_fahrenheit: bool = False,
+    ) -> Optional[Dict]:
+        """
+        从 Open-Meteo 获取多个独立 NWP 模型的预报
+        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)
+        - GFS (美国 NOAA)
+        - ICON (德国气象局 DWD)
+        - GEM (加拿大气象局)
+        - JMA (日本气象厅)
+        """
+        try:
+            url = "https://api.open-meteo.com/v1/forecast"
+            models = "ecmwf_ifs025,gfs_seamless,icon_seamless,gem_seamless,jma_seamless"
+            params = {
+                "latitude": lat,
+                "longitude": lon,
+                "daily": "temperature_2m_max",
+                "models": models,
+                "timezone": "auto",
+                "forecast_days": 1,
+                "_t": int(time.time()),
+            }
+            if use_fahrenheit:
+                params["temperature_unit"] = "fahrenheit"
+
+            response = self.session.get(
+                url,
+                params=params,
+                headers={"Cache-Control": "no-cache"},
+                timeout=self.timeout,
+            )
+            response.raise_for_status()
+            data = response.json()
+
+            # Open-Meteo 多模型返回格式:
+            # "daily": {
+            #   "temperature_2m_max_ecmwf_ifs025": [12.3],
+            #   "temperature_2m_max_gfs_seamless": [11.8],
+            #   ...
+            # }
+            daily = data.get("daily", {})
+            
+            model_labels = {
+                "ecmwf_ifs025": "ECMWF",
+                "gfs_seamless": "GFS",
+                "icon_seamless": "ICON",
+                "gem_seamless": "GEM",
+                "jma_seamless": "JMA",
+            }
+            
+            forecasts = {}
+            for model_key, label in model_labels.items():
+                key = f"temperature_2m_max_{model_key}"
+                values = daily.get(key, [])
+                if values and values[0] is not None:
+                    forecasts[label] = round(values[0], 1)
+
+            if not forecasts:
+                logger.warning("Multi-model: 无有效模型数据")
+                return None
+
+            labels_str = ", ".join([f"{k}={v}" for k, v in forecasts.items()])
+            logger.info(f"🔬 Multi-model ({len(forecasts)}个): {labels_str}")
+            
+            return {
+                "source": "multi_model",
+                "forecasts": forecasts,  # {"ECMWF": 12.3, "GFS": 11.8, ...}
+                "unit": "fahrenheit" if use_fahrenheit else "celsius",
+            }
+        except Exception as e:
+            logger.warning(f"Multi-model API 请求失败: {e}")
+            return None
+
    def fetch_from_meteoblue(
        self,
        lat: float,
@@ -725,22 +824,23 @@ class WeatherDataCollector:
        """
        使用 Open-Meteo Geocoding API 获取城市坐标 (免费, 无需 Key)
        """
-        # 预设常用城市坐标，避免网络波动导致启动失败
+        # 坐标使用 METAR 机场位置（Polymarket 以机场数据结算）
        static_coords = {
-            "london": {"lat": 51.5074, "lon": -0.1278},
-            "new york": {"lat": 40.7128, "lon": -74.0060},
+            "london": {"lat": 51.5053, "lon": 0.0553},        # EGLC London City
+            "paris": {"lat": 49.0097, "lon": 2.5478},         # LFPG Charles de Gaulle
+            "new york": {"lat": 40.7750, "lon": -73.8750},    # KLGA LaGuardia
            "new york's central park": {"lat": 40.7812, "lon": -73.9665},
-            "nyc": {"lat": 40.7128, "lon": -74.0060},
-            "seattle": {"lat": 47.6062, "lon": -122.3321},
-            "chicago": {"lat": 41.8781, "lon": -87.6298},
-            "dallas": {"lat": 32.7767, "lon": -96.7970},
-            "miami": {"lat": 25.7617, "lon": -80.1918},
-            "atlanta": {"lat": 33.7490, "lon": -84.3880},
-            "seoul": {"lat": 37.5665, "lon": 126.9780},
-            "toronto": {"lat": 43.6532, "lon": -79.3832},
-            "ankara": {"lat": 39.9334, "lon": 32.8597},
-            "wellington": {"lat": -41.2865, "lon": 174.7762},
-            "buenos aires": {"lat": -34.6037, "lon": -58.3816},
+            "nyc": {"lat": 40.7750, "lon": -73.8750},         # KLGA LaGuardia
+            "seattle": {"lat": 47.4499, "lon": -122.3118},    # KSEA Sea-Tac
+            "chicago": {"lat": 41.9769, "lon": -87.9081},     # KORD O'Hare
+            "dallas": {"lat": 32.8459, "lon": -96.8509},      # KDAL Love Field
+            "miami": {"lat": 25.7933, "lon": -80.2906},       # KMIA International
+            "atlanta": {"lat": 33.6367, "lon": -84.4281},     # KATL Hartsfield-Jackson
+            "seoul": {"lat": 37.4691, "lon": 126.4510},       # RKSI Incheon
+            "toronto": {"lat": 43.6759, "lon": -79.6294},     # CYYZ Pearson
+            "ankara": {"lat": 40.1281, "lon": 32.9950},       # LTAC Esenboğa
+            "wellington": {"lat": -41.3272, "lon": 174.8053}, # NZWN Wellington
+            "buenos aires": {"lat": -34.8222, "lon": -58.5358}, # SAEZ Ezeiza
        }

        normalized_city = city.lower().strip()
@@ -895,6 +995,11 @@ class WeatherDataCollector:
                ens_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
                if ens_data:
                    results["ensemble"] = ens_data
+                
+                # 多模型预报 (所有城市通用，用于共识评分)
+                mm_data = self.fetch_multi_model(lat, lon, use_fahrenheit=use_fahrenheit)
+                if mm_data:
+                    results["multi_model"] = mm_data
            else:
                # Open-Meteo 失败时，仍然尝试获取 METAR 和 NWS
                metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)