feat: implement METAR data collection service and dashboard infrastructure

.env.example

@@ -45,7 +45,13 @@ OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC=7200
 OPEN_METEO_MULTI_MODEL_CACHE_VERSION=v2
 OPEN_METEO_RATE_LIMIT_COOLDOWN_SEC=900
 OPEN_METEO_RATE_CACHE_TTL_SEC=3600
-OPEN_METEO_MIN_CALL_INTERVAL_SEC=3
+OPEN_METEO_MIN_CALL_INTERVAL_SEC=1
+POLYWEATHER_HTTP_TIMEOUT_SEC=8
+POLYWEATHER_HTTP_RETRY_COUNT=0
+POLYWEATHER_HTTP_RETRY_BACKOFF_SEC=0.2
+POLYWEATHER_OPEN_METEO_TIMEOUT_SEC=5
+POLYWEATHER_METAR_TIMEOUT_SEC=4
+POLYWEATHER_METAR_CLUSTER_TIMEOUT_SEC=3.5
 METAR_CACHE_TTL_SEC=600
 METEOBLUE_CACHE_TTL_SEC=7200
 POLYWEATHER_LGBM_ENABLED=false

data/probability_training_snapshots.jsonl

@@ -126,3 +126,30 @@
 {"city": "chengdu", "timestamp": "2026-04-08T07:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 24.3, "raw_sigma": 1.1821289062499998, "deb_prediction": 23.1, "ensemble": {"p10": 21.8, "median": 23.0, "p90": 24.5}, "multi_model": {"Open-Meteo": 22.5, "ECMWF": 23.9, "GFS": 23.0, "ICON": 22.5, "GEM": 23.7, "JMA": 23.2}, "max_so_far": 24.0, "observation": {"current_temp": 24.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 15.183333333333334}, "peak_status": "before", "prob_snapshot": [{"v": 24, "p": 0.442}, {"v": 25, "p": 0.386}, {"v": 26, "p": 0.172}], "shadow_prob_snapshot": [{"v": 24, "p": 0.394}, {"v": 25, "p": 0.355}, {"v": 26, "p": 0.19}, {"v": 27, "p": 0.06}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 24.3, "calibrated_sigma": 1.354078466151897}
 {"city": "chengdu", "timestamp": "2026-04-08T08:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 24.3, "raw_sigma": 0.7283767361111106, "deb_prediction": 23.1, "ensemble": {"p10": 21.8, "median": 22.9, "p90": 24.2}, "multi_model": {"Open-Meteo": 22.5, "ECMWF": 23.9, "GFS": 22.5, "ICON": 22.5, "GEM": 23.7, "JMA": 23.2}, "max_so_far": 24.0, "observation": {"current_temp": 23.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 16.133333333333333}, "peak_status": "in_window", "prob_snapshot": [{"v": 24, "p": 0.547}, {"v": 25, "p": 0.397}, {"v": 26, "p": 0.056}], "shadow_prob_snapshot": [{"v": 24, "p": 0.521}, {"v": 25, "p": 0.399}, {"v": 26, "p": 0.08}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 24.3, "calibrated_sigma": 0.8140063140878262}
 {"city": "tokyo", "timestamp": "2026-04-08T08:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 17.810000000000002, "raw_sigma": 0.23200683593750038, "deb_prediction": 17.1, "ensemble": {"p10": 17.4, "median": 18.3, "p90": 19.1}, "multi_model": {"Open-Meteo": 16.1, "ECMWF": 16.3, "GFS": 17.6, "ICON": 18.2, "GEM": 18.3, "JMA": 16.1}, "max_so_far": 17.0, "observation": {"current_temp": 16.0, "humidity": null, "wind_speed_kt": 17.0, "visibility_mi": null, "local_hour": 17.133333333333333}, "peak_status": "past", "prob_snapshot": [{"v": 18, "p": 0.909}, {"v": 17, "p": 0.091}], "shadow_prob_snapshot": [{"v": 18, "p": 0.892}, {"v": 17, "p": 0.108}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 17.810000000000002, "calibrated_sigma": 0.25}
+{"city": "ankara", "timestamp": "2026-04-11T11:20:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 8.3, "raw_sigma": 0.5468749999999998, "deb_prediction": 7.9, "ensemble": {"p10": 6.8, "median": 7.4, "p90": 8.2}, "multi_model": {"Open-Meteo": 7.5, "ECMWF": 7.5, "GFS": 8.3, "ICON": 7.5, "GEM": 8.2, "JMA": 8.3}, "max_so_far": 8.0, "observation": {"current_temp": 8.0, "humidity": null, "wind_speed_kt": 6.0, "visibility_mi": null, "local_hour": 14.45}, "peak_status": "before", "prob_snapshot": [{"v": 8, "p": 0.615}, {"v": 9, "p": 0.37}, {"v": 10, "p": 0.015}], "shadow_prob_snapshot": [{"v": 8, "p": 0.594}, {"v": 9, "p": 0.381}, {"v": 10, "p": 0.024}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 8.3, "calibrated_sigma": 0.5978357923824302}
+{"city": "istanbul", "timestamp": "2026-04-11T14:20:00+03:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 11.3, "raw_sigma": 0.42968750000000056, "deb_prediction": 10.8, "ensemble": {"p10": 9.7, "median": 10.4, "p90": 10.8}, "multi_model": {"Open-Meteo": 11.3, "ECMWF": 11.0, "GFS": 10.3, "ICON": 11.3, "GEM": 11.1, "JMA": 9.6}, "max_so_far": 11.0, "observation": {"current_temp": 11.0, "humidity": 57.8, "wind_speed_kt": 10.0, "visibility_mi": null, "local_hour": 14.45}, "peak_status": "before", "prob_snapshot": [{"v": 11, "p": 0.671}, {"v": 12, "p": 0.329}], "shadow_prob_snapshot": [{"v": 11, "p": 0.654}, {"v": 12, "p": 0.346}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 11.3, "calibrated_sigma": 0.46787549747087}
+{"city": "hong kong", "timestamp": "2026-04-11T19:10:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 27.900000000000002, "raw_sigma": 0.10084635416666676, "deb_prediction": 27.3, "ensemble": {"p10": 27.0, "median": 27.3, "p90": 27.8}, "multi_model": {"Open-Meteo": 26.8, "HKO(港天文)": 29.0, "ECMWF": 26.7, "GFS": 26.5, "ICON": 26.8, "GEM": 27.2, "JMA": 28.2}, "max_so_far": 27.6, "observation": {"current_temp": 26.7, "humidity": 82.0, "wind_speed_kt": 4.3, "visibility_mi": null, "local_hour": 19.45}, "peak_status": "past", "prob_snapshot": [{"v": 27, "p": 0.839}, {"v": 28, "p": 0.161}], "shadow_prob_snapshot": [{"v": 27, "p": 0.769}, {"v": 28, "p": 0.231}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 27.900000000000002, "calibrated_sigma": 0.13614257812500014}
+{"city": "taipei", "timestamp": "2026-04-11T11:00:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.10876464843750025, "deb_prediction": 29.5, "ensemble": {"p10": 28.9, "median": 29.4, "p90": 29.8}, "multi_model": {"Open-Meteo": 27.6, "ECMWF": 31.0, "GFS": 32.4, "ICON": 27.6, "GEM": 29.7, "JMA": 28.9}, "max_so_far": 33.0, "observation": {"current_temp": 27.0, "humidity": null, "wind_speed_kt": 2.0, "visibility_mi": null, "local_hour": 19.45}, "peak_status": "past", "prob_snapshot": [{"v": 33, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "istanbul", "timestamp": "2026-04-11T14:20:00+03:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 11.1, "raw_sigma": 0.8500000000000005, "deb_prediction": 10.9, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 11.3, "ECMWF": 11.0, "GFS": 10.8, "ICON": 11.3, "GEM": 11.1, "JMA": 9.6}, "max_so_far": 11.0, "observation": {"current_temp": 11.0, "humidity": 57.8, "wind_speed_kt": 10.0, "visibility_mi": null, "local_hour": 14.85}, "peak_status": "before", "prob_snapshot": [{"v": 11, "p": 0.582}, {"v": 12, "p": 0.355}, {"v": 13, "p": 0.063}], "shadow_prob_snapshot": [{"v": 11, "p": 0.563}, {"v": 12, "p": 0.361}, {"v": 13, "p": 0.076}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 11.1, "calibrated_sigma": 0.9012761535978395}
+{"city": "istanbul", "timestamp": "2026-04-11T14:20:00+03:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 11.3, "raw_sigma": 0.42968750000000056, "deb_prediction": 10.9, "ensemble": {"p10": 9.7, "median": 10.4, "p90": 10.8}, "multi_model": {"Open-Meteo": 11.3, "ECMWF": 11.0, "GFS": 10.8, "ICON": 11.3, "GEM": 11.1, "JMA": 9.6}, "max_so_far": 11.0, "observation": {"current_temp": 11.0, "humidity": 57.8, "wind_speed_kt": 10.0, "visibility_mi": null, "local_hour": 14.85}, "peak_status": "before", "prob_snapshot": [{"v": 11, "p": 0.671}, {"v": 12, "p": 0.329}], "shadow_prob_snapshot": [{"v": 11, "p": 0.656}, {"v": 12, "p": 0.344}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 11.3, "calibrated_sigma": 0.4631927411757732}
+{"city": "hong kong", "timestamp": "2026-04-11T19:40:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 27.900000000000002, "raw_sigma": 0.375, "deb_prediction": 27.3, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 26.8, "HKO(港天文)": 29.0, "ECMWF": 26.7, "GFS": 26.5, "ICON": 26.8, "GEM": 27.2, "JMA": 28.2}, "max_so_far": 27.6, "observation": {"current_temp": 26.7, "humidity": 82.0, "wind_speed_kt": 2.7, "visibility_mi": null, "local_hour": 19.85}, "peak_status": "past", "prob_snapshot": [{"v": 27, "p": 0.603}, {"v": 28, "p": 0.397}], "shadow_prob_snapshot": [{"v": 27, "p": 0.597}, {"v": 28, "p": 0.403}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 27.900000000000002, "calibrated_sigma": 0.39311002429138}
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+{"city": "hong kong", "timestamp": "2026-04-11T19:40:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 28.999999999999996, "raw_sigma": 0.3299999999999999, "deb_prediction": 27.9, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 26.8, "HKO(港天文)": 29.0}, "max_so_far": 27.6, "observation": {"current_temp": 26.7, "humidity": 82.0, "wind_speed_kt": 2.7, "visibility_mi": null, "local_hour": 19.9}, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 0.5}, {"v": 29, "p": 0.5}], "shadow_prob_snapshot": [{"v": 28, "p": 0.5}, {"v": 29, "p": 0.5}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 28.999999999999996, "calibrated_sigma": 0.33392493649594973}
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+{"city": "taipei", "timestamp": "2026-04-11T11:30:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.3, "deb_prediction": 27.6, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 27.6}, "max_so_far": 33.0, "observation": {"current_temp": 27.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 19.9}, "peak_status": "past", "prob_snapshot": [{"v": 33, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "taipei", "timestamp": "2026-04-11T11:30:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.10938964843750025, "deb_prediction": 29.5, "ensemble": {"p10": 28.9, "median": 29.4, "p90": 29.8}, "multi_model": {"Open-Meteo": 27.6, "ECMWF": 31.0, "GFS": 32.4, "ICON": 27.6, "GEM": 29.7, "JMA": 28.9}, "max_so_far": 33.0, "observation": {"current_temp": 27.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 19.9}, "peak_status": "past", "prob_snapshot": [{"v": 33, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "ankara", "timestamp": "2026-04-11T12:00:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 8.3, "raw_sigma": 1.0, "deb_prediction": 7.5, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 7.5}, "max_so_far": 8.0, "observation": {"current_temp": 8.0, "humidity": null, "wind_speed_kt": 5.0, "visibility_mi": null, "local_hour": 14.983333333333333}, "peak_status": "before", "prob_snapshot": [{"v": 8, "p": 0.475}, {"v": 9, "p": 0.395}, {"v": 10, "p": 0.131}], "shadow_prob_snapshot": [{"v": 8, "p": 0.451}, {"v": 9, "p": 0.389}, {"v": 10, "p": 0.16}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 8.3, "calibrated_sigma": 1.1241186693749348}
+{"city": "ankara", "timestamp": "2026-04-11T12:00:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 8.3, "raw_sigma": 0.5468749999999998, "deb_prediction": 7.9, "ensemble": {"p10": 6.8, "median": 7.4, "p90": 8.2}, "multi_model": {"Open-Meteo": 7.5, "ECMWF": 7.5, "GFS": 8.2, "ICON": 7.5, "GEM": 8.2, "JMA": 8.3}, "max_so_far": 8.0, "observation": {"current_temp": 8.0, "humidity": null, "wind_speed_kt": 5.0, "visibility_mi": null, "local_hour": 14.983333333333333}, "peak_status": "before", "prob_snapshot": [{"v": 8, "p": 0.615}, {"v": 9, "p": 0.37}, {"v": 10, "p": 0.015}], "shadow_prob_snapshot": [{"v": 8, "p": 0.594}, {"v": 9, "p": 0.381}, {"v": 10, "p": 0.024}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 8.3, "calibrated_sigma": 0.5978357923824302}
+{"city": "istanbul", "timestamp": "2026-04-11T14:50:00+03:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 11.3, "raw_sigma": 1.0, "deb_prediction": 11.3, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 11.3}, "max_so_far": 11.0, "observation": {"current_temp": 10.0, "humidity": 57.5, "wind_speed_kt": 10.0, "visibility_mi": null, "local_hour": 14.983333333333333}, "peak_status": "before", "prob_snapshot": [{"v": 11, "p": 0.475}, {"v": 12, "p": 0.395}, {"v": 13, "p": 0.131}], "shadow_prob_snapshot": [{"v": 11, "p": 0.469}, {"v": 12, "p": 0.394}, {"v": 13, "p": 0.137}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 11.3, "calibrated_sigma": 1.0267976073543543}
+{"city": "istanbul", "timestamp": "2026-04-11T14:50:00+03:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 11.3, "raw_sigma": 0.4494809751157413, "deb_prediction": 10.9, "ensemble": {"p10": 9.7, "median": 10.4, "p90": 10.8}, "multi_model": {"Open-Meteo": 11.3, "ECMWF": 11.0, "GFS": 10.8, "ICON": 11.3, "GEM": 11.1, "JMA": 9.6}, "max_so_far": 11.0, "observation": {"current_temp": 10.0, "humidity": 57.5, "wind_speed_kt": 10.0, "visibility_mi": null, "local_hour": 15.0}, "peak_status": "before", "prob_snapshot": [{"v": 11, "p": 0.662}, {"v": 12, "p": 0.338}], "shadow_prob_snapshot": [{"v": 11, "p": 0.647}, {"v": 12, "p": 0.353}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 11.3, "calibrated_sigma": 0.4837415114258855}
+{"city": "hong kong", "timestamp": "2026-04-11T19:50:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 28.999999999999996, "raw_sigma": 0.3, "deb_prediction": 29.0, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"HKO(港天文)": 29.0}, "max_so_far": 27.6, "observation": {"current_temp": 26.8, "humidity": 81.0, "wind_speed_kt": 3.2, "visibility_mi": null, "local_hour": 20.0}, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 0.5}, {"v": 29, "p": 0.5}], "shadow_prob_snapshot": [{"v": 28, "p": 0.5}, {"v": 29, "p": 0.5}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 28.999999999999996, "calibrated_sigma": 0.29193323618665046}
+{"city": "hong kong", "timestamp": "2026-04-11T19:50:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": 27.900000000000002, "raw_sigma": 0.09375000000000008, "deb_prediction": 27.4, "ensemble": {"p10": 27.0, "median": 27.3, "p90": 27.8}, "multi_model": {"HKO(港天文)": 29.0, "ECMWF": 26.7, "GFS": 26.5, "ICON": 26.8, "GEM": 27.2, "JMA": 28.2}, "max_so_far": 27.6, "observation": {"current_temp": 26.8, "humidity": 81.0, "wind_speed_kt": 3.2, "visibility_mi": null, "local_hour": 20.0}, "peak_status": "past", "prob_snapshot": [{"v": 27, "p": 0.857}, {"v": 28, "p": 0.143}], "shadow_prob_snapshot": [{"v": 27, "p": 0.785}, {"v": 28, "p": 0.215}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 27.900000000000002, "calibrated_sigma": 0.12656250000000013}
+{"city": "taipei", "timestamp": "2026-04-11T11:30:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.3, "deb_prediction": 27.6, "ensemble": {"p10": null, "median": null, "p90": null}, "multi_model": {"Open-Meteo": 27.6}, "max_so_far": 33.0, "observation": {"current_temp": 27.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 20.0}, "peak_status": "past", "prob_snapshot": [{"v": 33, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "taipei", "timestamp": "2026-04-11T11:30:00.000Z", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.10938964843750025, "deb_prediction": 29.5, "ensemble": {"p10": 28.9, "median": 29.4, "p90": 29.8}, "multi_model": {"Open-Meteo": 27.6, "ECMWF": 31.0, "GFS": 32.4, "ICON": 27.6, "GEM": 29.7, "JMA": 28.9}, "max_so_far": 33.0, "observation": {"current_temp": 27.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 20.0}, "peak_status": "past", "prob_snapshot": [{"v": 33, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "taipei", "timestamp": "2026-04-11T19:50:00+08:00", "date": "2026-04-11", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.10938964843749983, "deb_prediction": 30.3, "ensemble": {"p10": 28.8, "median": 29.3, "p90": 29.7}, "multi_model": {"Open-Meteo": 29.7, "ECMWF": 30.9, "GFS": 32.4, "ICON": 29.7, "GEM": 29.6, "JMA": 29.3}, "max_so_far": 32.1, "observation": {"current_temp": 27.6, "humidity": 73.0, "wind_speed_kt": 2.9, "visibility_mi": null, "local_hour": 20.1}, "peak_status": "past", "prob_snapshot": [{"v": 32, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}

frontend/app/api/city/[name]/route.ts

@@ -20,7 +20,8 @@ export async function GET(
 
   const { name } = await context.params;
   const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
-  const url = `${API_BASE}/api/city/${encodeURIComponent(name)}?force_refresh=${forceRefresh}`;
+  const depth = req.nextUrl.searchParams.get("depth") ?? "panel";
+  const url = `${API_BASE}/api/city/${encodeURIComponent(name)}?force_refresh=${forceRefresh}&depth=${encodeURIComponent(depth)}`;
 
   try {
     const auth = await buildBackendRequestHeaders(req, {

frontend/components/dashboard/PolyWeatherDashboard.tsx

@@ -45,8 +45,12 @@ const FutureForecastModal = dynamic(
 function DashboardScreen() {
   const store = useDashboardStore();
   const { t } = useI18n();
+  const activeSummary = store.selectedCity
+    ? store.citySummariesByName[store.selectedCity] || null
+    : null;
   const activeCityName =
     store.selectedDetail?.display_name ||
+    activeSummary?.display_name ||
     store.cities.find((city) => city.name === store.selectedCity)?.display_name ||
     store.selectedCity ||
     "";
@@ -77,6 +81,11 @@ function DashboardScreen() {
   const showLoading =
     store.loadingState.cities ||
     store.loadingState.refresh;
+  const showCitySyncToast =
+    store.loadingState.cityDetail &&
+    activeCityName &&
+    !store.selectedDetail &&
+    !activeSummary;
 
   return (
     <div className={styles.root}>
@@ -84,7 +93,7 @@ function DashboardScreen() {
       <HeaderBar />
       <CitySidebar />
       <DetailPanel />
-      {store.loadingState.cityDetail && activeCityName ? (
+      {showCitySyncToast ? (
         <div className="city-loading-toast" role="status" aria-live="polite">
           <span className="city-loading-dot" aria-hidden="true" />
           <span className="city-loading-copy">

frontend/hooks/useDashboardStore.tsx

@@ -30,10 +30,14 @@ interface DashboardStoreValue extends DashboardState {
   closeFutureModal: () => void;
   closeHistory: () => void;
   closePanel: () => void;
-  ensureCityDetail: (cityName: string, force?: boolean) => Promise<CityDetail>;
+  ensureCityDetail: (
+    cityName: string,
+    force?: boolean,
+    depth?: "panel" | "full",
+  ) => Promise<CityDetail>;
   futureModalDate: string | null;
   loadCities: () => Promise<void>;
-  openFutureModal: (dateStr: string, forceRefresh?: boolean) => void;
+  openFutureModal: (dateStr: string, forceRefresh?: boolean) => Promise<void>;
   openHistory: () => Promise<void>;
   openTodayModal: (forceRefresh?: boolean) => Promise<void>;
   registerMapStopMotion: (stopMotion: () => void) => void;
@@ -90,6 +94,7 @@ const SELECTED_CITY_STORAGE_KEY = "polyWeather_selected_city_v1";
 const BACKGROUND_SUMMARY_REFRESH_MS = 30_000;
 const EAGER_CITY_SUMMARIES_ENABLED =
   process.env.NEXT_PUBLIC_POLYWEATHER_EAGER_CITY_SUMMARIES === "true";
+type CityDetailDepth = "panel" | "full";
 
 function countAvailableModels(
   detail?: CityDetail | null,
@@ -128,6 +133,19 @@ function hasSparseDetailCoverage(
   );
 }
 
+function normalizeDetailDepth(detail?: CityDetail | null): CityDetailDepth {
+  return detail?.detail_depth === "panel" ? "panel" : "full";
+}
+
+function detailSatisfiesDepth(
+  detail: CityDetail | null | undefined,
+  depth: CityDetailDepth,
+) {
+  if (!detail) return false;
+  if (depth === "panel") return true;
+  return normalizeDetailDepth(detail) === "full";
+}
+
 export function DashboardStoreProvider({
   children,
 }: {
@@ -171,6 +189,7 @@ export function DashboardStoreProvider({
   const hydratedProCacheRef = useRef(false);
   const backgroundSummaryCheckAtRef = useRef<Record<string, number>>({});
   const citySummariesRef = useRef<Record<string, CitySummary>>({});
+  const selectedCityRef = useRef<string | null>(null);
   const selectedDetail =
     selectedCity && proAccess.subscriptionActive
       ? cityDetailsByName[selectedCity] || null
@@ -210,6 +229,10 @@ export function DashboardStoreProvider({
     citySummariesRef.current = citySummariesByName;
   }, [citySummariesByName]);
 
+  useEffect(() => {
+    selectedCityRef.current = selectedCity;
+  }, [selectedCity]);
+
   useEffect(() => {
     proAccessRef.current = proAccess;
   }, [proAccess]);
@@ -273,6 +296,7 @@ export function DashboardStoreProvider({
 
         const latestDetail = await dashboardClient.getCityDetail(cityName, {
           force: false,
+          depth: normalizeDetailDepth(cached),
         });
         const detail = latestDetail;
 
@@ -295,13 +319,19 @@ export function DashboardStoreProvider({
       .catch(() => {});
   };
 
-  const ensureCityDetail = async (cityName: string, force = false) => {
+  const ensureCityDetail = async (
+    cityName: string,
+    force = false,
+    depth: CityDetailDepth = "panel",
+  ) => {
     const cached = cityDetailsByName[cityName];
     const cachedMeta = cityDetailMetaByName[cityName];
+    const hasRequestedDepth = detailSatisfiesDepth(cached, depth);
     const cachedIsSparse = hasSparseDetailCoverage(cached, cached?.local_date);
     if (
       !force &&
       cached &&
+      hasRequestedDepth &&
       !cachedIsSparse &&
       dashboardClient.isCityDetailFresh(cachedMeta)
     ) {
@@ -309,7 +339,7 @@ export function DashboardStoreProvider({
       return cached;
     }
 
-    if (!force && cached) {
+    if (!force && cached && hasRequestedDepth) {
       try {
         const summary = await dashboardClient.getCitySummary(cityName);
         const revision = getCityRevision(summary);
@@ -317,6 +347,7 @@ export function DashboardStoreProvider({
           if (cachedIsSparse) {
             const latestDetail = await dashboardClient.getCityDetail(cityName, {
               force: true,
+              depth,
             });
             const detail = latestDetail;
             setCityDetailsByName((current) => ({
@@ -352,6 +383,7 @@ export function DashboardStoreProvider({
 
     const latestDetail = await dashboardClient.getCityDetail(cityName, {
       force,
+      depth,
     });
     const detail = latestDetail;
     setCityDetailsByName((current) => ({
@@ -381,7 +413,7 @@ export function DashboardStoreProvider({
 
     let cancelled = false;
     setLoadingState((current) => ({ ...current, cityDetail: true }));
-    void ensureCityDetail(selectedCity, false)
+    void ensureCityDetail(selectedCity, false, "panel")
       .then((detail) => {
         if (cancelled) return;
         setSelectedForecastDate(detail.local_date);
@@ -577,26 +609,35 @@ export function DashboardStoreProvider({
     };
   }, [cities]);
 
+  const ensureCitySummary = async (cityName: string, force = false) => {
+    const existing = citySummariesRef.current[cityName];
+    if (!force && existing) {
+      return existing;
+    }
+    const summary = await dashboardClient.getCitySummary(cityName, { force });
+    setCitySummariesByName((current) => ({
+      ...current,
+      [cityName]: summary,
+    }));
+    return summary;
+  };
+
   const selectCity = async (cityName: string) => {
     setSelectedCity(cityName);
     setIsPanelOpen(true);
     setSelectedForecastDate(null);
     setFutureModalDate(null);
 
+    const summaryPromise = !citySummariesRef.current[cityName]
+      ? ensureCitySummary(cityName).catch(() => null)
+      : Promise.resolve(citySummariesRef.current[cityName]);
+
     if (proAccessRef.current.loading) {
       setLoadingState((current) => ({ ...current, cityDetail: true }));
-      if (!citySummariesRef.current[cityName]) {
-        try {
-          const summary = await dashboardClient.getCitySummary(cityName);
-          setCitySummariesByName((current) => ({
-            ...current,
-            [cityName]: summary,
-          }));
-        } catch {
-        } finally {
-          setLoadingState((current) => ({ ...current, cityDetail: false }));
-        }
-      } else {
+      try {
+        await summaryPromise;
+      } catch {
+      } finally {
         setLoadingState((current) => ({ ...current, cityDetail: false }));
       }
       return;
@@ -605,18 +646,10 @@ export function DashboardStoreProvider({
     const access = proAccessRef.current;
     if (!access.authenticated || !access.subscriptionActive) {
       setLoadingState((current) => ({ ...current, cityDetail: true }));
-      if (!citySummariesRef.current[cityName]) {
-        try {
-          const summary = await dashboardClient.getCitySummary(cityName);
-          setCitySummariesByName((current) => ({
-            ...current,
-            [cityName]: summary,
-          }));
-        } catch {
-        } finally {
-          setLoadingState((current) => ({ ...current, cityDetail: false }));
-        }
-      } else {
+      try {
+        await summaryPromise;
+      } catch {
+      } finally {
         setLoadingState((current) => ({ ...current, cityDetail: false }));
       }
       return;
@@ -629,20 +662,28 @@ export function DashboardStoreProvider({
     );
     setLoadingState((current) => ({ ...current, cityDetail: true }));
     try {
-      const detail = await ensureCityDetail(cityName, needsDetailRefresh);
-      setSelectedForecastDate(detail.local_date);
-      if (access.authenticated && access.subscriptionActive) {
-        // 预热市场数据，不做 await 阻塞，后台静默拉取
-        void ensureCityMarketScan(
-          cityName,
-          false,
-          null,
-          detail.local_date,
-        ).catch(() => {});
-      }
-    } finally {
-      setLoadingState((current) => ({ ...current, cityDetail: false }));
+      await summaryPromise;
+    } catch {
     }
+
+    void ensureCityDetail(cityName, needsDetailRefresh, "panel")
+      .then((detail) => {
+        if (selectedCityRef.current !== cityName) return;
+        setSelectedForecastDate(detail.local_date);
+        if (access.authenticated && access.subscriptionActive) {
+          // 预热市场数据，不做 await 阻塞，后台静默拉取
+          void ensureCityMarketScan(
+            cityName,
+            false,
+            null,
+            detail.local_date,
+          ).catch(() => {});
+        }
+      })
+      .finally(() => {
+        if (selectedCityRef.current !== cityName) return;
+        setLoadingState((current) => ({ ...current, cityDetail: false }));
+      });
   };
 
   useEffect(() => {
@@ -678,7 +719,7 @@ export function DashboardStoreProvider({
     if (!selectedCity) return;
     setLoadingState((current) => ({ ...current, refresh: true }));
     try {
-      const detail = await ensureCityDetail(selectedCity, true);
+      const detail = await ensureCityDetail(selectedCity, true, "panel");
       setSelectedForecastDate(detail.local_date);
     } finally {
       setLoadingState((current) => ({ ...current, refresh: false }));
@@ -696,6 +737,7 @@ export function DashboardStoreProvider({
         if (access.authenticated && access.subscriptionActive) {
           const latestDetail = await dashboardClient.getCityDetail(selectedCity, {
             force: true,
+            depth: "panel",
           });
           const detail = latestDetail;
           setCityDetailsByName({ [selectedCity]: detail });
@@ -779,16 +821,31 @@ export function DashboardStoreProvider({
       loadCities,
       loadingState,
       proAccess,
-      openFutureModal: (dateStr: string, forceRefresh = false) => {
+      openFutureModal: async (dateStr: string, forceRefresh = false) => {
         mapStopMotionRef.current();
-        setFutureModalDate(dateStr);
         if (!selectedCity || !proAccess.subscriptionActive) return;
         const cachedDetail = cityDetailsByName[selectedCity];
-        const needsDetailRefresh =
-          !forceRefresh && hasSparseDetailCoverage(cachedDetail, dateStr);
-        if (needsDetailRefresh) {
-          void ensureCityDetail(selectedCity, true).catch(() => {});
+        const hasFullCachedDetail =
+          detailSatisfiesDepth(cachedDetail, "full") &&
+          !hasSparseDetailCoverage(cachedDetail, dateStr);
+
+        if (!hasFullCachedDetail || forceRefresh) {
+          setLoadingState((current) => ({
+            ...current,
+            refresh: true,
+          }));
+          try {
+            await ensureCityDetail(selectedCity, true, "full");
+          } catch {
+          } finally {
+            setLoadingState((current) => ({
+              ...current,
+              refresh: false,
+            }));
+          }
         }
+
+        setFutureModalDate(dateStr);
         const cacheKey = getMarketScanCacheKey(selectedCity, dateStr);
         setLoadingState((current) => ({ ...current, marketScan: true }));
         void ensureCityMarketScan(
@@ -810,25 +867,30 @@ export function DashboardStoreProvider({
 
         mapStopMotionRef.current();
         const cachedDetail = cityDetailsByName[selectedCity];
-        if (cachedDetail?.local_date) {
+        const hasFullCachedDetail =
+          detailSatisfiesDepth(cachedDetail, "full") &&
+          !hasSparseDetailCoverage(cachedDetail, cachedDetail?.local_date);
+        if (hasFullCachedDetail && cachedDetail?.local_date) {
           setSelectedForecastDate(cachedDetail.local_date);
           setFutureModalDate(cachedDetail.local_date);
         }
         if (!proAccess.subscriptionActive) return;
         const needsDetailRefresh =
-          !forceRefresh &&
+          forceRefresh ||
+          !detailSatisfiesDepth(cachedDetail, "full") ||
           hasSparseDetailCoverage(cachedDetail, cachedDetail?.local_date);
 
         setLoadingState((current) => ({
           ...current,
-          refresh: !cachedDetail?.local_date,
+          refresh: needsDetailRefresh,
           marketScan: true,
         }));
 
         try {
           const detail = await ensureCityDetail(
             selectedCity,
-            Boolean(forceRefresh || needsDetailRefresh),
+            needsDetailRefresh,
+            "full",
           );
           setSelectedForecastDate(detail.local_date);
           setFutureModalDate(detail.local_date);

frontend/lib/dashboard-client.ts

@@ -29,6 +29,10 @@ function normalizeCityName(cityName: string) {
   return encodeURIComponent(String(cityName).replace(/\s/g, "-"));
 }
 
+function normalizeDetailDepth(depth?: "panel" | "full") {
+  return depth === "full" ? "full" : "panel";
+}
+
 async function fetchJson<T>(url: string): Promise<T> {
   const response = await fetch(url, {
     headers: { Accept: "application/json" },
@@ -155,17 +159,21 @@ export const dashboardClient = {
     return request;
   },
 
-  async getCityDetail(cityName: string, options?: { force?: boolean }) {
+  async getCityDetail(
+    cityName: string,
+    options?: { force?: boolean; depth?: "panel" | "full" },
+  ) {
     const force = options?.force ?? false;
+    const depth = normalizeDetailDepth(options?.depth);
     if (!force) {
-      const requestKey = `${cityName}::cached`;
+      const requestKey = `${cityName}::${depth}::cached`;
       const existing = pendingCityDetailRequests.get(requestKey);
       if (existing) {
         return existing;
       }
 
       const request = fetchJson<CityDetail>(
-        `/api/city/${normalizeCityName(cityName)}?force_refresh=false`,
+        `/api/city/${normalizeCityName(cityName)}?force_refresh=false&depth=${depth}`,
       ).finally(() => {
         pendingCityDetailRequests.delete(requestKey);
       });
@@ -176,6 +184,7 @@ export const dashboardClient = {
 
     const params = new URLSearchParams({
       force_refresh: "true",
+      depth,
       _ts: String(Date.now()),
     });
     return fetchJson<CityDetail>(

frontend/lib/dashboard-types.ts

@@ -327,6 +327,7 @@ export interface AiAnalysisStructured {
 export interface CityDetail {
   name: string;
   display_name: string;
+  detail_depth?: "panel" | "full";
   lat: number;
   lon: number;
   temp_symbol: string;

src/data_collection/city_registry.py

@@ -149,21 +149,20 @@ CITY_REGISTRY = {
     },
     "taipei": {
         "name": "Taipei",
-        "lat": 25.0670,
-        "lon": 121.5525,
+        "lat": 25.0377,
+        "lon": 121.5149,
         "icao": "RCSS",
-        "settlement_source": "wunderground",
-        "settlement_station_code": "RCSS",
-        "settlement_station_label": "Taipei Songshan Airport Station",
-        "settlement_url": "https://www.wunderground.com/history/daily/tw/taipei/RCSS",
+        "settlement_source": "cwa",
+        "settlement_station_code": "466920",
+        "settlement_station_label": "中央气象署台北站",
         "tz_offset": 28800,
         "use_fahrenheit": False,
         "is_major": True,
         "risk_level": "low",
         "risk_emoji": "🟢",
-        "airport_name": "台北松山机场",
-        "distance_km": 4.1,
-        "warning": "市场现按 Wunderground 台北松山机场站整度°C口径结算；以历史页当日最终完成后的最高整度摄氏值为准。",
+        "airport_name": "中央气象署台北站",
+        "distance_km": 0.0,
+        "warning": "结算按交通部中央气象署台北站口径，不应混用松山机场 METAR 作为结算主源。",
     },
     "shanghai": {
         "name": "Shanghai",

src/data_collection/metar_sources.py

@@ -248,7 +248,11 @@ class MetarSourceMixin:
                 "hours": 24,
                 "_t": int(time.time()),
             }
-            response = self.session.get(url, params=params, timeout=self.timeout)
+            response = self.session.get(
+                url,
+                params=params,
+                timeout=getattr(self, "metar_timeout_sec", self.timeout),
+            )
             response.raise_for_status()
             data = response.json()
             if not data:
@@ -295,7 +299,11 @@ class MetarSourceMixin:
             headers = {
                 "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
             }
-            resp = self.session.get(url, headers=headers, timeout=self.timeout)
+            resp = self.session.get(
+                url,
+                headers=headers,
+                timeout=getattr(self, "metar_cluster_timeout_sec", self.timeout),
+            )
             if resp.status_code != 200:
                 logger.warning(f"METAR cluster fetch HTTP {resp.status_code} for {icaos}")
                 return []

src/data_collection/nws_open_meteo_sources.py

@@ -208,7 +208,7 @@ class NwsOpenMeteoSourceMixin:
             response = self._http_get(
                 url,
                 params=params,
-                timeout=self.timeout,
+                timeout=getattr(self, "open_meteo_timeout_sec", self.timeout),
             )
             response.raise_for_status()
             data = response.json()
@@ -369,7 +369,7 @@ class NwsOpenMeteoSourceMixin:
             response = self._http_get(
                 url,
                 params=params,
-                timeout=self.timeout,
+                timeout=getattr(self, "open_meteo_timeout_sec", self.timeout),
             )
             response.raise_for_status()
             data = response.json()
@@ -526,7 +526,7 @@ class NwsOpenMeteoSourceMixin:
             response = self._http_get(
                 url,
                 params=params,
-                timeout=self.timeout,
+                timeout=getattr(self, "open_meteo_timeout_sec", self.timeout),
             )
             response.raise_for_status()
             data = response.json()

src/data_collection/settlement_sources.py

@@ -4,6 +4,7 @@ import csv
 import math
 import threading
 import time
+from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List, Optional
 
@@ -226,19 +227,31 @@ class SettlementSourceMixin:
 
         try:
             base = "https://data.weather.gov.hk/weatherAPI/hko_data/regional-weather"
-            temp_csv = self._http_get(f"{base}/latest_1min_temperature.csv", timeout=self.timeout)
-            temp_csv.raise_for_status()
-            maxmin_csv = self._http_get(f"{base}/latest_since_midnight_maxmin.csv", timeout=self.timeout)
-            maxmin_csv.raise_for_status()
-            humidity_csv = self._http_get(f"{base}/latest_1min_humidity.csv", timeout=self.timeout)
-            humidity_csv.raise_for_status()
-            wind_csv = self._http_get(f"{base}/latest_10min_wind.csv", timeout=self.timeout)
-            wind_csv.raise_for_status()
+            csv_urls = {
+                "temp": f"{base}/latest_1min_temperature.csv",
+                "maxmin": f"{base}/latest_since_midnight_maxmin.csv",
+                "humidity": f"{base}/latest_1min_humidity.csv",
+                "wind": f"{base}/latest_10min_wind.csv",
+            }
 
-            temp_rows = self._csv_rows(temp_csv.text)
-            maxmin_rows = self._csv_rows(maxmin_csv.text)
-            humidity_rows = self._csv_rows(humidity_csv.text)
-            wind_rows = self._csv_rows(wind_csv.text)
+            def _fetch_csv(url: str):
+                response = self._http_get(url, timeout=self.timeout)
+                response.raise_for_status()
+                return response
+
+            fetched_csv = {}
+            with ThreadPoolExecutor(max_workers=4) as executor:
+                future_map = {
+                    executor.submit(_fetch_csv, url): key
+                    for key, url in csv_urls.items()
+                }
+                for future, key in future_map.items():
+                    fetched_csv[key] = future.result()
+
+            temp_rows = self._csv_rows(fetched_csv["temp"].text)
+            maxmin_rows = self._csv_rows(fetched_csv["maxmin"].text)
+            humidity_rows = self._csv_rows(fetched_csv["humidity"].text)
+            wind_rows = self._csv_rows(fetched_csv["wind"].text)
 
             temp_row = self._pick_station_row(temp_rows, candidate_names)
             maxmin_row = self._pick_station_row(maxmin_rows, candidate_names)
@@ -626,6 +639,8 @@ class SettlementSourceMixin:
                     station_name=station_name,
                     station_candidates=station_candidates,
                 )
+            if settlement_source == "cwa":
+                return self.fetch_cwa_taipei_settlement_current()
             if settlement_source == "noaa":
                 station_code = (
                     str(city_meta.get("settlement_station_code") or "").strip()
@@ -643,8 +658,5 @@ class SettlementSourceMixin:
         except Exception as exc:
             logger.warning(f"Settlement source dispatch failed city={city}: {exc}")
         if normalized == "taipei":
-            return self.fetch_noaa_station_settlement_current(
-                station_code="RCTP",
-                station_name="Taiwan Taoyuan International Airport",
-            )
+            return self.fetch_cwa_taipei_settlement_current()
         return None

src/data_collection/weather_sources.py

@@ -113,12 +113,24 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
     def __init__(self, config: dict):
         self.config = config
 
-        self.timeout = 30  # 增加超时以支持高延迟 VPS
+        # Keep external calls short so one degraded source cannot block the whole city pipeline.
+        self.timeout = max(
+            2.0, float(os.getenv("POLYWEATHER_HTTP_TIMEOUT_SEC", "8"))
+        )
         self.http_retry_count = max(
-            0, int(os.getenv("POLYWEATHER_HTTP_RETRY_COUNT", "1"))
+            0, int(os.getenv("POLYWEATHER_HTTP_RETRY_COUNT", "0"))
         )
         self.http_retry_backoff_sec = max(
-            0.0, float(os.getenv("POLYWEATHER_HTTP_RETRY_BACKOFF_SEC", "0.35"))
+            0.0, float(os.getenv("POLYWEATHER_HTTP_RETRY_BACKOFF_SEC", "0.2"))
+        )
+        self.open_meteo_timeout_sec = max(
+            2.0, float(os.getenv("POLYWEATHER_OPEN_METEO_TIMEOUT_SEC", "5"))
+        )
+        self.metar_timeout_sec = max(
+            2.0, float(os.getenv("POLYWEATHER_METAR_TIMEOUT_SEC", "4"))
+        )
+        self.metar_cluster_timeout_sec = max(
+            2.0, float(os.getenv("POLYWEATHER_METAR_CLUSTER_TIMEOUT_SEC", "3.5"))
         )
         self.session = httpx.Client(
             timeout=self.timeout,
@@ -151,7 +163,7 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
         self._open_meteo_rl_lock = threading.Lock()
         # Open-Meteo burst control: avoid hammering API with many cities at once.
         self._open_meteo_min_interval_sec: float = float(
-            os.getenv("OPEN_METEO_MIN_CALL_INTERVAL_SEC", "3")
+            os.getenv("OPEN_METEO_MIN_CALL_INTERVAL_SEC", "1")
         )
         self._open_meteo_last_call_ts: float = 0.0
         self._open_meteo_call_lock = threading.Lock()
@@ -728,8 +740,18 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
             hko_forecast = self.fetch_hko_forecast()
             if hko_forecast:
                 results["hko_forecast"] = hko_forecast
+        elif settlement_source == "cwa":
+            cwa_forecast = self.fetch_cwa_taipei_forecast()
+            if cwa_forecast is not None:
+                results["cwa_forecast"] = cwa_forecast
 
-    def _attach_turkish_mgm_data(self, results: Dict, city_lower: str) -> None:
+    def _attach_turkish_mgm_data(
+        self,
+        results: Dict,
+        city_lower: str,
+        *,
+        include_nearby: bool = True,
+    ) -> None:
         if city_lower not in self.TURKISH_PROVINCES:
             return
         istno, province = self.TURKISH_PROVINCES[city_lower]
@@ -737,15 +759,22 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
         if not mgm_data:
             return
         results["mgm"] = mgm_data
-        results["nearby_source"] = "mgm"
-        nearby = self.fetch_mgm_nearby_stations(province, root_ist_no=istno)
-        if nearby:
-            results["mgm_nearby"] = nearby
+        if include_nearby:
+            results["nearby_source"] = "mgm"
+            nearby = self.fetch_mgm_nearby_stations(province, root_ist_no=istno)
+            if nearby:
+                results["mgm_nearby"] = nearby
 
     def _attach_global_nearby_cluster(
         self, results: Dict, city_lower: str, use_fahrenheit: bool
     ) -> None:
-        if city_lower not in self.CITY_METAR_CLUSTERS or "mgm_nearby" in results:
+        city_meta = self.CITY_REGISTRY.get(str(city_lower or "").strip().lower()) or {}
+        settlement_source = str(city_meta.get("settlement_source") or "").strip().lower()
+        if (
+            city_lower not in self.CITY_METAR_CLUSTERS
+            or "mgm_nearby" in results
+            or settlement_source in {"hko", "cwa"}
+        ):
             return
         cluster_icaos = self.CITY_METAR_CLUSTERS[city_lower]
         cluster_data = self.fetch_metar_nearby_cluster(
@@ -854,21 +883,26 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
         lat: float,
         lon: float,
         use_fahrenheit: bool,
+        *,
+        include_ensemble: bool = True,
+        include_multi_model: bool = True,
     ) -> None:
         if use_fahrenheit:
             nws_data = self.fetch_nws(lat, lon)
             if nws_data:
                 results["nws"] = nws_data
 
-        ensemble_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
-        if ensemble_data:
-            results["ensemble"] = ensemble_data
+        if include_ensemble:
+            ensemble_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
+            if ensemble_data:
+                results["ensemble"] = ensemble_data
 
-        multi_model_data = self.fetch_multi_model(
-            lat, lon, city=city, use_fahrenheit=use_fahrenheit
-        )
-        if multi_model_data:
-            results["multi_model"] = multi_model_data
+        if include_multi_model:
+            multi_model_data = self.fetch_multi_model(
+                lat, lon, city=city, use_fahrenheit=use_fahrenheit
+            )
+            if multi_model_data:
+                results["multi_model"] = multi_model_data
 
     def fetch_all_sources(
         self,
@@ -877,6 +911,10 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
         lon: float = None,
         country: str = None,
         force_refresh: bool = False,
+        include_taf: bool = True,
+        include_nearby: bool = True,
+        include_ensemble: bool = True,
+        include_multi_model: bool = True,
     ) -> Dict:
         """
         Fetch weather data from all available sources
@@ -910,23 +948,34 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
                     )
                     if metar_data:
                         results["metar"] = metar_data
-                if supports_aviationweather and city_lower != "hong kong":
+                if include_taf and supports_aviationweather and city_lower != "hong kong":
                     taf_data = self.fetch_taf(city, utc_offset=utc_offset)
                     if taf_data:
                         results["taf"] = taf_data
 
-                self._attach_turkish_mgm_data(results, city_lower)
-                self._attach_china_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_japan_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_korea_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_russia_official_nearby(results, city_lower, use_fahrenheit)
-                if city_lower == "warsaw":
-                    self._attach_warsaw_official_nearby(results, use_fahrenheit)
-                self._attach_global_nearby_cluster(
-                    results, city_lower, use_fahrenheit
+                self._attach_turkish_mgm_data(
+                    results,
+                    city_lower,
+                    include_nearby=include_nearby,
                 )
+                if include_nearby:
+                    self._attach_china_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_japan_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_korea_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_russia_official_nearby(results, city_lower, use_fahrenheit)
+                    if city_lower == "warsaw":
+                        self._attach_warsaw_official_nearby(results, use_fahrenheit)
+                    self._attach_global_nearby_cluster(
+                        results, city_lower, use_fahrenheit
+                    )
                 self._attach_nws_and_models(
-                    results, city, lat, lon, use_fahrenheit
+                    results,
+                    city,
+                    lat,
+                    lon,
+                    use_fahrenheit,
+                    include_ensemble=include_ensemble,
+                    include_multi_model=include_multi_model,
                 )
             else:
                 fallback_utc_offset = int(
@@ -940,30 +989,41 @@ class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSour
                     )
                     if metar_data:
                         results["metar"] = metar_data
-                if supports_aviationweather and city_lower != "hong kong":
+                if include_taf and supports_aviationweather and city_lower != "hong kong":
                     taf_data = self.fetch_taf(city, utc_offset=fallback_utc_offset)
                     if taf_data:
                         results["taf"] = taf_data
 
-                self._attach_turkish_mgm_data(results, city_lower)
-                self._attach_china_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_japan_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_korea_official_nearby(results, city_lower, use_fahrenheit)
-                self._attach_russia_official_nearby(results, city_lower, use_fahrenheit)
-                if city_lower == "warsaw":
-                    self._attach_warsaw_official_nearby(results, use_fahrenheit)
-                self._attach_global_nearby_cluster(
-                    results, city_lower, use_fahrenheit
+                self._attach_turkish_mgm_data(
+                    results,
+                    city_lower,
+                    include_nearby=include_nearby,
                 )
+                if include_nearby:
+                    self._attach_china_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_japan_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_korea_official_nearby(results, city_lower, use_fahrenheit)
+                    self._attach_russia_official_nearby(results, city_lower, use_fahrenheit)
+                    if city_lower == "warsaw":
+                        self._attach_warsaw_official_nearby(results, use_fahrenheit)
+                    self._attach_global_nearby_cluster(
+                        results, city_lower, use_fahrenheit
+                    )
                 self._attach_nws_and_models(
-                    results, city, lat, lon, use_fahrenheit
+                    results,
+                    city,
+                    lat,
+                    lon,
+                    use_fahrenheit,
+                    include_ensemble=include_ensemble,
+                    include_multi_model=include_multi_model,
                 )
         else:
             if supports_aviationweather:
                 metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
                 if metar_data:
                     results["metar"] = metar_data
-            if supports_aviationweather and city_lower != "hong kong":
+            if include_taf and supports_aviationweather and city_lower != "hong kong":
                 taf_data = self.fetch_taf(city)
                 if taf_data:
                     results["taf"] = taf_data

web/analysis_service.py

@@ -6,6 +6,7 @@ import os
 import re
 import time as _time
 import threading
+from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime, timezone, timedelta
 from typing import Dict, Any, Optional
 
@@ -28,7 +29,7 @@ from web.core import (
 from src.analysis.deb_algorithm import calculate_dynamic_weights
 from src.analysis.settlement_rounding import apply_city_settlement
 from src.data_collection.country_networks import build_country_network_snapshot
-from src.data_collection.city_registry import ALIASES
+from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
 from src.models.lgbm_daily_high import predict_lgbm_daily_high
 
 TURKISH_MGM_CITIES = {"ankara", "istanbul"}
@@ -42,6 +43,8 @@ _ANALYSIS_CACHE_STATS: Dict[str, Any] = {
     "last_cache_miss_at": None,
     "last_city": None,
 }
+_SUMMARY_CACHE_LOCK = threading.Lock()
+_SUMMARY_CACHE: Dict[str, Dict[str, Any]] = {}
 _GROQ_COMMENTARY_CACHE_LOCK = threading.Lock()
 _GROQ_COMMENTARY_CACHE: Dict[str, Dict[str, Any]] = {}
 _GROQ_COMMENTARY_CACHE_TTL_SEC = int(
@@ -77,6 +80,26 @@ def get_analysis_cache_stats() -> Dict[str, Any]:
     return stats
 
 
+def _analysis_ttl_for_city(city: str) -> int:
+    return CACHE_TTL_ANKARA if city.lower() in TURKISH_MGM_CITIES else CACHE_TTL
+
+
+def _get_cached_summary(city: str, ttl: int) -> Optional[Dict[str, Any]]:
+    now_ts = _time.time()
+    with _SUMMARY_CACHE_LOCK:
+        cached = _SUMMARY_CACHE.get(city)
+        if cached and now_ts - float(cached.get("t", 0)) < ttl:
+            payload = cached.get("d")
+            if isinstance(payload, dict):
+                return dict(payload)
+    return None
+
+
+def _set_cached_summary(city: str, payload: Dict[str, Any]) -> None:
+    with _SUMMARY_CACHE_LOCK:
+        _SUMMARY_CACHE[city] = {"t": _time.time(), "d": dict(payload)}
+
+
 def _groq_commentary_enabled() -> bool:
     enabled = str(
         os.getenv("POLYWEATHER_GROQ_COMMENTARY_ENABLED", "false")
@@ -1027,6 +1050,7 @@ def _analyze(
     city: str,
     force_refresh: bool = False,
     include_llm_commentary: bool = False,
+    detail_mode: str = "full",
 ) -> Dict[str, Any]:
     """Fetch, analyse, and return structured weather data for one city."""
     # Check cache
@@ -1057,11 +1081,17 @@ def _analyze(
     )
 
     # ── 1. Fetch raw data ──
+    is_panel_mode = str(detail_mode or "full").strip().lower() == "panel"
+
     raw = _weather.fetch_all_sources(
         city,
         lat=lat,
         lon=lon,
         force_refresh=force_refresh,
+        include_taf=not is_panel_mode,
+        include_nearby=not is_panel_mode,
+        include_ensemble=not is_panel_mode,
+        include_multi_model=not is_panel_mode,
     )
     om = raw.get("open-meteo", {})
     metar = raw.get("metar", {})
@@ -1083,7 +1113,11 @@ def _analyze(
     if not isinstance(mm, dict):
         mm = {}
     risk = CITY_RISK_PROFILES.get(city, {})
-    network_snapshot = build_country_network_snapshot(city, raw)
+    network_snapshot = (
+        build_country_network_snapshot(city, raw)
+        if not is_panel_mode
+        else {}
+    )
 
     # ── 2. Current conditions (city-specific settlement source first, then METAR/MGM fallback) ──
     mc = metar.get("current", {}) if metar else {}
@@ -1541,20 +1575,28 @@ def _analyze(
                 h_boundary_layer_height[i] if i < len(h_boundary_layer_height) else None
             )
 
-    vertical_profile_signal = _build_vertical_profile_signal(
-        next_48h_hourly,
-        local_date_str,
-        local_hour,
-        first_peak_h,
-        last_peak_h,
+    vertical_profile_signal = (
+        _build_vertical_profile_signal(
+            next_48h_hourly,
+            local_date_str,
+            local_hour,
+            first_peak_h,
+            last_peak_h,
+        )
+        if not is_panel_mode
+        else {}
     )
-    taf_signal = _build_taf_signal(
-        taf if isinstance(taf, dict) else {},
-        city,
-        local_date_str,
-        int(utc_offset or 0),
-        first_peak_h,
-        last_peak_h,
+    taf_signal = (
+        _build_taf_signal(
+            taf if isinstance(taf, dict) else {},
+            city,
+            local_date_str,
+            int(utc_offset or 0),
+            first_peak_h,
+            last_peak_h,
+        )
+        if not is_panel_mode
+        else {"available": False}
     )
 
     # ── 13. Cloud description (METAR primary, MGM fallback) ──
@@ -1703,6 +1745,7 @@ def _analyze(
     # ── Assemble result ──
     city_meta = CITIES.get(city, {}) or {}
     result = {
+        "detail_depth": "panel" if is_panel_mode else "full",
         "name": city,
         "display_name": str(city_meta.get("display_name") or city_meta.get("name") or city.title()),
         "lat": lat,
@@ -1844,6 +1887,273 @@ def _normalize_city_or_404(name: str) -> str:
     return city
 
 
+def _analyze_summary(city: str, force_refresh: bool = False) -> Dict[str, Any]:
+    ttl = _analysis_ttl_for_city(city)
+
+    if not force_refresh:
+        cached_detail = _cache.get(city)
+        if cached_detail and _time.time() - cached_detail["t"] < ttl:
+            return cached_detail["d"]
+        cached_summary = _get_cached_summary(city, ttl)
+        if cached_summary:
+            return cached_summary
+
+    info = CITIES[city]
+    lat, lon, is_f = info["lat"], info["lon"], info["f"]
+    sym = "°F" if is_f else "°C"
+    settlement_source = str(info.get("settlement_source") or "metar").strip().lower() or "metar"
+    settlement_source_label = SETTLEMENT_SOURCE_LABELS.get(
+        settlement_source,
+        settlement_source.upper(),
+    )
+
+    if force_refresh:
+        try:
+            _weather._evict_city_caches(  # type: ignore[attr-defined]
+                city=city,
+                lat=lat,
+                lon=lon,
+                use_fahrenheit=is_f,
+            )
+        except Exception:
+            pass
+
+    default_utc_offset = int(info.get("tz", 0) or 0)
+
+    def _safe_call(fn):
+        try:
+            return fn()
+        except Exception:
+            return None
+
+    jobs = {
+        "settlement_current": lambda: _weather.fetch_settlement_current(city) or {},
+        "open_meteo": lambda: _weather.fetch_from_open_meteo(lat, lon, use_fahrenheit=is_f) or {},
+    }
+    if _weather._supports_aviationweather(city):  # type: ignore[attr-defined]
+        jobs["metar"] = lambda: _weather.fetch_metar(
+            city,
+            use_fahrenheit=is_f,
+            utc_offset=default_utc_offset,
+        ) or {}
+    if city in TURKISH_MGM_CITIES:
+        istno, _province = _weather.TURKISH_PROVINCES.get(city, (None, None))  # type: ignore[attr-defined]
+        if istno:
+            jobs["mgm"] = lambda istno=istno: _weather.fetch_from_mgm(str(istno)) or {}
+    if is_f:
+        jobs["nws"] = lambda: _weather.fetch_nws(lat, lon) or {}
+    if settlement_source == "hko":
+        jobs["hko_forecast"] = lambda: _weather.fetch_hko_forecast()
+
+    fetched: Dict[str, Any] = {}
+    with ThreadPoolExecutor(max_workers=min(6, len(jobs))) as executor:
+        future_map = {
+            executor.submit(_safe_call, fn): key
+            for key, fn in jobs.items()
+        }
+        for future, key in [(future, key) for future, key in future_map.items()]:
+            fetched[key] = future.result()
+
+    settlement_current = fetched.get("settlement_current") or {}
+    open_meteo = fetched.get("open_meteo") or {}
+    utc_offset = open_meteo.get("utc_offset")
+    if utc_offset is None:
+        utc_offset = default_utc_offset
+    try:
+        utc_offset = int(utc_offset or 0)
+    except Exception:
+        utc_offset = default_utc_offset
+    metar = fetched.get("metar") or {}
+    mgm = fetched.get("mgm") or {}
+    nws = fetched.get("nws") or {}
+    hko_forecast = fetched.get("hko_forecast")
+
+    sc_cur = settlement_current.get("current") or {}
+    mc = metar.get("current") or {}
+    mg_cur = mgm.get("current") or {}
+    use_settlement_current = settlement_source in {"hko", "cwa", "noaa", "wunderground"} and bool(sc_cur)
+    primary_current = sc_cur if use_settlement_current else mc
+
+    cur_temp = _sf(primary_current.get("temp"))
+    if cur_temp is None:
+        cur_temp = _sf(mc.get("temp"))
+    if cur_temp is None:
+        cur_temp = _sf(mg_cur.get("temp"))
+
+    max_so_far = _sf(primary_current.get("max_temp_so_far"))
+    if max_so_far is None:
+        max_so_far = _sf(mc.get("max_temp_so_far"))
+    if max_so_far is None:
+        max_so_far = _sf(mg_cur.get("mgm_max_temp"))
+
+    max_temp_time = primary_current.get("max_temp_time")
+    if not max_temp_time and not use_settlement_current:
+        max_temp_time = mc.get("max_temp_time")
+    if not max_temp_time:
+        mgm_time = str(mg_cur.get("time") or "")
+        if " " in mgm_time:
+            max_temp_time = mgm_time.split(" ")[1][:5]
+
+    raw_settlement_max = max_so_far
+    wu_settle = (
+        apply_city_settlement(city.lower(), raw_settlement_max)
+        if raw_settlement_max is not None
+        else None
+    )
+    display_settlement_max = (
+        wu_settle
+        if settlement_source == "wunderground" and wu_settle is not None
+        else raw_settlement_max
+    )
+
+    obs_time_str = ""
+    obs_age_min = None
+    obs_t = ""
+    if use_settlement_current:
+        obs_t = str(settlement_current.get("observation_time") or "").strip()
+    if not obs_t:
+        obs_t = str(metar.get("observation_time") or "").strip()
+    if obs_t and "T" in obs_t:
+        try:
+            dt = datetime.fromisoformat(obs_t.replace("Z", "+00:00"))
+            if dt.tzinfo is None:
+                dt = dt.replace(tzinfo=timezone.utc)
+            local_dt = dt.astimezone(timezone(timedelta(seconds=utc_offset)))
+            obs_time_str = local_dt.strftime("%H:%M")
+            obs_age_min = int(
+                (datetime.now(timezone.utc) - dt.astimezone(timezone.utc)).total_seconds() / 60
+            )
+        except Exception:
+            obs_time_str = str(obs_t)[:16]
+
+    om_daily = (open_meteo.get("daily") or {}) if isinstance(open_meteo, dict) else {}
+    om_hourly = (open_meteo.get("hourly") or {}) if isinstance(open_meteo, dict) else {}
+    maxtemps = om_daily.get("temperature_2m_max", [])[:5]
+    om_today = _sf(maxtemps[0]) if maxtemps else None
+    nws_high = _sf((nws or {}).get("today_high")) if isinstance(nws, dict) else None
+    mgm_high = _sf((mgm or {}).get("today_high")) if isinstance(mgm, dict) else None
+
+    if om_today is None:
+        fallback_high = (
+            nws_high
+            if nws_high is not None
+            else mgm_high
+            if mgm_high is not None
+            else max_so_far
+            if max_so_far is not None
+            else cur_temp
+        )
+        if fallback_high is not None:
+            om_today = float(fallback_high)
+
+    current_forecasts: Dict[str, float] = {}
+    if om_today is not None:
+        current_forecasts["Open-Meteo"] = om_today
+    if nws_high is not None:
+        current_forecasts["NWS"] = nws_high
+    if mgm_high is not None:
+        current_forecasts["MGM"] = mgm_high
+    if hko_forecast is not None:
+        current_forecasts["HKO"] = _sf(hko_forecast)
+    current_forecasts = {
+        model_name: value
+        for model_name, value in current_forecasts.items()
+        if value is not None and not _is_excluded_model_name(model_name)
+    }
+
+    deb_val = None
+    if current_forecasts:
+        blended, _weights_info = calculate_dynamic_weights(city, current_forecasts)
+        if blended is not None:
+            deb_val = blended
+    if deb_val is None:
+        deb_val = om_today
+
+    local_time_full = (open_meteo.get("current") or {}).get("local_time", "")
+    now_utc = datetime.now(timezone.utc)
+    local_now = now_utc + timedelta(seconds=utc_offset)
+    local_date_str = local_now.strftime("%Y-%m-%d")
+    local_hour = local_now.hour
+    local_minute = local_now.minute
+    try:
+        if local_time_full:
+            local_date_str = str(local_time_full).split(" ")[0]
+            tp = str(local_time_full).split(" ")[1].split(":")
+            local_hour = int(tp[0])
+            local_minute = int(tp[1]) if len(tp) > 1 else 0
+    except Exception:
+        pass
+    local_time_str = f"{local_hour:02d}:{local_minute:02d}"
+    local_hour_frac = local_hour + local_minute / 60.0
+
+    settlement_today_obs = []
+    if use_settlement_current:
+        explicit_obs = settlement_current.get("today_obs") or []
+        for item in explicit_obs:
+            if isinstance(item, dict):
+                raw_time = str(item.get("time") or "").strip()
+                raw_temp = _sf(item.get("temp"))
+            elif isinstance(item, (list, tuple)) and len(item) >= 2:
+                raw_time = str(item[0] or "").strip()
+                raw_temp = _sf(item[1])
+            else:
+                continue
+            if raw_time and raw_temp is not None:
+                settlement_today_obs.append({"time": raw_time, "temp": raw_temp})
+        if not settlement_today_obs and obs_time_str and cur_temp is not None:
+            settlement_today_obs.append({"time": obs_time_str, "temp": cur_temp})
+        if max_temp_time and max_so_far is not None and str(max_temp_time) != str(obs_time_str):
+            settlement_today_obs.append({"time": str(max_temp_time), "temp": max_so_far})
+
+    metar_today_obs_payload = [
+        {"time": obs_time, "temp": obs_temp}
+        for obs_time, obs_temp in ((metar.get("today_obs") or []) if isinstance(metar, dict) else [])
+    ]
+
+    deviation_monitor = _build_deviation_monitor(
+        current_temp=cur_temp,
+        deb_prediction=deb_val,
+        om_today=om_today,
+        hourly_times=om_hourly.get("time", []) if isinstance(om_hourly, dict) else [],
+        hourly_temps=om_hourly.get("temperature_2m", []) if isinstance(om_hourly, dict) else [],
+        local_date=local_date_str,
+        local_hour_frac=local_hour_frac,
+        observation_points=(
+            settlement_today_obs if settlement_today_obs else metar_today_obs_payload
+        ),
+    )
+
+    risk = CITY_RISK_PROFILES.get(city, {})
+    city_meta = CITY_REGISTRY.get(city, {}) or {}
+    result = {
+        "name": city,
+        "display_name": str(city_meta.get("display_name") or city_meta.get("name") or city.title()),
+        "temp_symbol": sym,
+        "local_time": local_time_str,
+        "local_date": local_date_str,
+        "risk": {
+            "level": risk.get("risk_level", "low"),
+            "warning": risk.get("warning", ""),
+            "icao": risk.get("icao", ""),
+        },
+        "current": {
+            "temp": _sf(cur_temp),
+            "max_so_far": _sf(display_settlement_max),
+            "max_temp_time": max_temp_time,
+            "wu_settlement": _sf(wu_settle),
+            "settlement_source": settlement_source,
+            "settlement_source_label": settlement_source_label,
+            "obs_time": obs_time_str or None,
+            "obs_age_min": obs_age_min,
+        },
+        "deb": {"prediction": _sf(deb_val)},
+        "deviation_monitor": deviation_monitor or {},
+        "updated_at": datetime.now(timezone.utc).isoformat(),
+    }
+    _set_cached_summary(city, result)
+    return result
+
+
 def _build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
     return {
         "name": data.get("name"),

web/routes.py

@@ -19,6 +19,7 @@ from src.data_collection.city_registry import ALIASES
 from src.utils.metrics import export_prometheus_metrics
 from web.analysis_service import (
     _analyze,
+    _analyze_summary,
     _build_city_detail_payload,
     _build_city_summary_payload,
 )
@@ -425,10 +426,17 @@ async def list_cities(request: Request):
 
 
 @router.get("/api/city/{name}")
-async def city_detail(request: Request, name: str, force_refresh: bool = False):
+async def city_detail(
+    request: Request,
+    name: str,
+    force_refresh: bool = False,
+    depth: str = "panel",
+):
     _assert_entitlement(request)
     city = _normalize_city_or_404(name)
-    return await run_in_threadpool(_analyze, city, force_refresh)
+    normalized_depth = str(depth or "panel").strip().lower()
+    detail_mode = "full" if normalized_depth == "full" else "panel"
+    return await run_in_threadpool(_analyze, city, force_refresh, False, detail_mode)
 
 
 @router.get("/api/history/{name}")
@@ -1021,7 +1029,7 @@ async def payment_reconcile_latest(request: Request):
 @router.get("/api/city/{name}/summary")
 async def city_summary(request: Request, name: str, force_refresh: bool = False):
     city = _normalize_city_or_404(name)
-    data = await run_in_threadpool(_analyze, city, force_refresh, False)
+    data = await run_in_threadpool(_analyze_summary, city, force_refresh)
     return await run_in_threadpool(_build_city_summary_payload, data)