Add TAF timing markers and market-aware intraday cues

frontend/components/dashboard/FutureForecastModal.tsx

@@ -285,6 +285,20 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
           tension: 0.3,
         });
       }
+      if ((todayChartData.tafMarkers || []).length > 0) {
+        datasets.push({
+          backgroundColor: "#f59e0b",
+          borderColor: "#f59e0b",
+          borderWidth: 0,
+          data: todayChartData.datasets.tafMarkerPoints,
+          fill: false,
+          label: locale === "en-US" ? "TAF Timing" : "TAF 时段",
+          order: -2,
+          pointHoverRadius: 8,
+          pointRadius: 5,
+          showLine: false,
+        });
+      }
 
       return {
         data: {
@@ -315,6 +329,21 @@ function DailyTemperatureChart({ dateStr }: { dateStr: string }) {
               backgroundColor: "rgba(15, 23, 42, 0.96)",
               borderColor: "rgba(34, 211, 238, 0.2)",
               borderWidth: 1,
+              callbacks: {
+                label: (ctx) => {
+                  const label = String(ctx.dataset.label || "");
+                  if (label === "TAF Timing" || label === "TAF 时段") {
+                    const marker = (todayChartData.tafMarkers || []).find(
+                      (item) => item.index === ctx.dataIndex,
+                    );
+                    if (!marker) return label;
+                    return `${label}: ${marker.summary}`;
+                  }
+                  const value = ctx.parsed.y;
+                  if (value == null) return label;
+                  return `${label}: ${value.toFixed(1)}${detail.temp_symbol || "°C"}`;
+                },
+              },
             },
           },
           responsive: true,
@@ -504,81 +533,154 @@ export function FutureForecastModal() {
       }`
     : "--";
   const upperAirSignal = detail.vertical_profile_signal || {};
+  const tafSignal = detail.taf?.signal || {};
   const topBucketProbability = normalizeMarketValue(topBucket?.probability);
+  const numericEdge = Number(marketScan?.edge_percent);
   const hottestMatchesSettlement =
     hottestBucketLabel !== "--" &&
     settlementBucketLabel !== "--" &&
     hottestBucketLabel === settlementBucketLabel;
   const marketAwareUpperAirCue = useMemo(() => {
-    if (!isToday || !upperAirSignal.source) return null;
+    if (!isToday || (!upperAirSignal.source && !tafSignal.available)) return null;
 
     const crowded = hottestMatchesSettlement && (topBucketProbability || 0) >= 0.3;
     const setup = String(upperAirSignal.heating_setup || "neutral").toLowerCase();
+    const tafSuppression = String(
+      tafSignal.suppression_level || "low",
+    ).toLowerCase();
+    const tafDisruption = String(
+      tafSignal.disruption_level || "low",
+    ).toLowerCase();
+    const signalLabel = String(marketScan?.signal_label || "").toUpperCase();
+    const edgeAbs = Number.isFinite(numericEdge) ? Math.abs(numericEdge) : 0;
+    const strongEdge = edgeAbs >= 8;
+    const reasons: string[] = [];
+    let score = 0;
 
     if (setup === "supportive") {
+      score += 2;
+      reasons.push(
+        locale === "en-US"
+          ? "upper-air structure still supports daytime heating"
+          : "高空结构仍偏支持白天冲高",
+      );
+    } else if (setup === "suppressed") {
+      score -= 2;
+      reasons.push(
+        locale === "en-US"
+          ? "upper-air structure still leans toward capping the peak"
+          : "高空结构更偏向压住峰值",
+      );
+    }
+
+    if (tafSuppression === "high") {
+      score -= 2;
+      reasons.push(
+        locale === "en-US"
+          ? "TAF flags meaningful cloud/rain suppression near the peak window"
+          : "TAF 在峰值窗口提示云雨压温风险偏高",
+      );
+    } else if (tafSuppression === "medium") {
+      score -= 1;
+      reasons.push(
+        locale === "en-US"
+          ? "TAF keeps some cloud/rain suppression risk on the table"
+          : "TAF 仍提示一定的云雨压温风险",
+      );
+    }
+
+    if (tafDisruption === "high") {
+      score -= 1;
+      reasons.push(
+        locale === "en-US"
+          ? "TAF also suggests a noisier afternoon regime"
+          : "TAF 还提示午后扰动偏强",
+      );
+    } else if (tafDisruption === "medium") {
+      score -= 0.5;
+      reasons.push(
+        locale === "en-US"
+          ? "TAF keeps some afternoon timing noise in play"
+          : "TAF 提示午后仍可能有时段性扰动",
+      );
+    }
+
+    if (strongEdge && signalLabel === "BUY YES") {
+      score += 1;
+      reasons.push(
+        locale === "en-US"
+          ? `market edge still leans hotter (${formatSignedPercent(numericEdge)})`
+          : `市场 edge 仍偏向更热一侧（${formatSignedPercent(numericEdge)}）`,
+      );
+    } else if (strongEdge && signalLabel === "BUY NO") {
+      score -= 1;
+      reasons.push(
+        locale === "en-US"
+          ? `market edge still leans cooler (${formatSignedPercent(numericEdge)})`
+          : `市场 edge 仍偏向更冷一侧（${formatSignedPercent(numericEdge)}）`,
+      );
+    }
+
+    if (crowded && score > 0) {
+      score -= 0.5;
+      reasons.push(
+        locale === "en-US"
+          ? "the target bucket is already getting crowded"
+          : "目标区间已经开始变拥挤",
+      );
+    }
+
+    if (score >= 1.5) {
       return {
-        summary: locale === "en-US"
-          ? crowded
-            ? "Upper-air structure still leans warmer, but the target bucket is already crowded. Do not fade lower buckets too early, and do not chase blindly either."
-            : "Upper-air structure still leans warmer, and the market is not fully crowded into the target bucket yet. Do not fade lower buckets too early."
-          : crowded
-            ? "高空结构仍偏支持冲高，但当前目标区间已经较拥挤。不宜过早做更低温区间，也不要盲目继续追价。"
-            : "高空结构仍偏支持冲高，市场最热桶还没完全挤到目标区间。不宜过早做更低温区间。",
-        note: locale === "en-US"
-          ? crowded
-            ? "Warmer side still has structural support, but price is no longer cheap. Wait for surface follow-through before adding."
-            : "Warmer side still has structural support and is not fully overcrowded yet. Fading lower buckets too early is risky."
-          : crowded
-            ? "暖侧仍有结构支撑，但价格已经不算便宜，继续加仓前先等近地面兑现。"
-            : "暖侧仍有结构支撑，且还没有完全过热，过早去做更低温区间风险较高。",
+        summary:
+          locale === "en-US"
+            ? "The combined upper-air, TAF, and market read still leans warmer. Do not fade lower buckets too early."
+            : "高空、TAF 和市场三层信号合并后仍偏暖侧，不宜过早做更低温区间。",
+        note:
+          locale === "en-US"
+            ? `${reasons.slice(0, 2).join("; ")}.`
+            : `${reasons.slice(0, 2).join("；")}。`,
         tone: "warm",
         value: locale === "en-US" ? "Lean warmer" : "偏暖侧",
       };
     }
 
-    if (setup === "suppressed") {
+    if (score <= -1.5) {
       return {
-        summary: locale === "en-US"
-          ? crowded
-            ? "Upper-air structure leans toward capping the high, and the target bucket is already crowded. Chasing higher buckets needs extra caution."
-            : "Upper-air structure leans toward capping the high. Be more careful chasing higher buckets."
-          : crowded
-            ? "高空结构更偏向压住峰值，而且目标区间已经较拥挤。追更高温区间要更谨慎。"
-            : "高空结构更偏向压住峰值，追更高温区间要更谨慎。",
-        note: locale === "en-US"
-          ? crowded
-            ? "Both structure and crowding argue for caution on the hotter side."
-            : "The hotter side needs stronger surface confirmation before adding."
-          : crowded
-            ? "结构和拥挤度都不利于继续追热。"
-            : "更高温区间需要更强的近地面确认后再考虑追价。",
+        summary:
+          locale === "en-US"
+            ? "The combined upper-air, TAF, and market read leans more defensive. Be more careful chasing higher buckets."
+            : "高空、TAF 和市场三层信号合并后更偏防守，追更高温区间要更谨慎。",
+        note:
+          locale === "en-US"
+            ? `${reasons.slice(0, 2).join("; ")}.`
+            : `${reasons.slice(0, 2).join("；")}。`,
         tone: "cold",
         value: locale === "en-US" ? "Lean cautious" : "偏谨慎",
       };
     }
 
     return {
-      summary: locale === "en-US"
-        ? crowded
-          ? "Upper-air structure is neutral, while the target bucket is already crowded. Let surface structure and price action decide before taking a side."
-          : "Upper-air structure is neutral. Let surface structure and price action decide before taking a side."
-        : crowded
-          ? "高空结构偏中性，但目标区间已经较拥挤。先看近地面结构和盘口变化，不急着继续站边。"
-          : "高空结构偏中性，先看近地面结构和盘口变化，不急着站边。",
-      note: locale === "en-US"
-        ? crowded
-          ? "There is no clean edge from the upper-air layer alone, and the market is already leaning. Wait for confirmation."
-          : "There is no clean edge from the upper-air layer alone. Wait for confirmation."
-        : crowded
-          ? "单看高空层没有明确边，而且市场已经先站位，先等确认。"
-          : "单看高空层没有明确边，先等确认。",
+      summary:
+        locale === "en-US"
+          ? "The combined upper-air, TAF, and market read is mixed. Let surface structure and price action decide before taking a side."
+          : "高空、TAF 和市场三层信号目前偏混合，先看近地面结构和盘口变化，不急着站边。",
+      note:
+        locale === "en-US"
+          ? `${reasons.slice(0, 2).join("; ") || "No clean edge from the upper-air layer alone"}.`
+          : `${reasons.slice(0, 2).join("；") || "单看高空层还没有干净的交易边"}。`,
       tone: "",
       value: locale === "en-US" ? "Wait / confirm" : "先观察",
     };
   }, [
+    tafSignal.available,
+    tafSignal.disruption_level,
+    tafSignal.suppression_level,
+    marketScan?.signal_label,
     hottestMatchesSettlement,
     isToday,
     locale,
+    numericEdge,
     topBucketProbability,
     upperAirSignal.heating_setup,
     upperAirSignal.source,

frontend/lib/dashboard-types.ts

@@ -315,7 +315,21 @@ export interface CityDetail {
       available?: boolean;
       source?: string | null;
       peak_window?: string | null;
-      precip_codes?: string[] | null;
+      segments?: Array<{
+        type?: string | null;
+        start_local?: string | null;
+        end_local?: string | null;
+        tokens?: string[] | null;
+      }> | null;
+      markers?: Array<{
+        label_time?: string | null;
+        marker_type?: string | null;
+        start_local?: string | null;
+        end_local?: string | null;
+        suppression_level?: string | null;
+        summary_zh?: string | null;
+        summary_en?: string | null;
+      }> | null;
       low_ceiling_ft?: number | null;
       ceiling_cover?: string | null;
       wind_regimes?: string[] | null;

frontend/lib/dashboard-utils.ts

@@ -334,6 +334,32 @@ export function getTemperatureChartData(
 
   const min = Math.floor(Math.min(...allValues)) - 1;
   const max = Math.ceil(Math.max(...allValues)) + 1;
+  const tafMarkersRaw = Array.isArray(detail.taf?.signal?.markers)
+    ? detail.taf?.signal?.markers || []
+    : [];
+  const tafMarkerValue = max - 0.4;
+  const tafMarkerPoints = new Array(times.length).fill(null);
+  const tafMarkers = tafMarkersRaw
+    .map((marker) => {
+      const labelTime = String(marker?.label_time || "").trim();
+      const index = times.indexOf(labelTime);
+      if (index >= 0) {
+        tafMarkerPoints[index] = tafMarkerValue;
+      }
+      return {
+        endLocal: String(marker?.end_local || "").trim(),
+        index,
+        labelTime,
+        markerType: String(marker?.marker_type || "").trim(),
+        startLocal: String(marker?.start_local || "").trim(),
+        summary:
+          isEnglish(locale)
+            ? String(marker?.summary_en || "").trim()
+            : String(marker?.summary_zh || "").trim(),
+        suppressionLevel: String(marker?.suppression_level || "").trim(),
+      };
+    })
+    .filter((marker) => marker.index >= 0);
 
   const legendParts: string[] = [];
   if (detail.mgm?.temp != null) {
@@ -385,6 +411,21 @@ export function getTemperatureChartData(
         : "台北按 NOAA RCTP 最终完成质控后的最高整度摄氏值结算；图中曲线仅作为结算参考线。",
     );
   }
+  if (tafMarkers.length) {
+    const tafText = tafMarkers
+      .slice(0, 4)
+      .map((marker) =>
+        isEnglish(locale)
+          ? `${marker.markerType} ${marker.startLocal}-${marker.endLocal}`
+          : `${marker.markerType} ${marker.startLocal}-${marker.endLocal}`,
+      )
+      .join(" | ");
+    legendParts.push(
+      isEnglish(locale)
+        ? `TAF timing: ${tafText}`
+        : `TAF 时段: ${tafText}`,
+    );
+  }
 
   return {
     datasets: {
@@ -395,6 +436,7 @@ export function getTemperatureChartData(
       mgmHourlyPoints,
       mgmPoints,
       offset,
+      tafMarkerPoints,
       temps,
     },
     observationLabel:
@@ -408,6 +450,7 @@ export function getTemperatureChartData(
     legendText: legendParts.join(" | "),
     max,
     min,
+    tafMarkers,
     times,
   };
 }

web/analysis_service.py

@@ -295,67 +295,238 @@ def _build_vertical_profile_signal(
 def _build_taf_signal(
     taf_data: Dict[str, Any],
     city: str,
+    local_date: str,
+    utc_offset: int,
     first_peak_h: int,
     last_peak_h: int,
 ) -> Dict[str, Any]:
     if str(city or "").strip().lower() == "hong kong":
         return {}
-    raw_taf = str((taf_data or {}).get("raw_taf") or "").upper().strip()
+    raw_taf = re.sub(r"\s+", " ", str((taf_data or {}).get("raw_taf") or "").upper().strip())
     if not raw_taf:
         return {}
 
-    precip_codes = re.findall(
-        r"\b(?:-|\+)?(?:TSRA|TS|VCTS|SHRA|RA|DZ|SN|SHSN|SHGS)\b",
-        raw_taf,
-    )
-    cloud_matches = re.findall(r"\b(FEW|SCT|BKN|OVC)(\d{3})\b", raw_taf)
-    wind_matches = re.findall(r"\b(\d{3}|VRB)(\d{2,3})(?:G\d{2,3})?KT\b", raw_taf)
-    tempo_tokens = re.findall(r"\b(?:TEMPO|BECMG|PROB30|PROB40|FM\d{6})\b", raw_taf)
+    issue_raw = str((taf_data or {}).get("issue_time") or "").strip()
+    issue_dt = None
+    if issue_raw:
+        try:
+            issue_dt = datetime.fromisoformat(issue_raw.replace("Z", "+00:00"))
+        except Exception:
+            issue_dt = None
+    if issue_dt is None:
+        issue_dt = datetime.now(timezone.utc)
 
+    local_tz = timezone(timedelta(seconds=int(utc_offset or 0)))
+    valid_match = re.search(r"\b(\d{2})(\d{2})/(\d{2})(\d{2})\b", raw_taf)
+    tokens = raw_taf.split()
+    if not valid_match:
+        return {}
+
+    def _infer_utc(day: int, hour: int, minute: int = 0) -> datetime:
+        base = issue_dt
+        year = base.year
+        month = base.month
+        candidate = datetime(year, month, day, hour, minute, tzinfo=timezone.utc)
+        if candidate < base - timedelta(days=20):
+            if month == 12:
+                candidate = datetime(year + 1, 1, day, hour, minute, tzinfo=timezone.utc)
+            else:
+                candidate = datetime(year, month + 1, day, hour, minute, tzinfo=timezone.utc)
+        elif candidate > base + timedelta(days=20):
+            if month == 1:
+                candidate = datetime(year - 1, 12, day, hour, minute, tzinfo=timezone.utc)
+            else:
+                candidate = datetime(year, month - 1, day, hour, minute, tzinfo=timezone.utc)
+        return candidate
+
+    def _parse_period(token: str) -> tuple[Optional[datetime], Optional[datetime]]:
+        match = re.match(r"^(\d{2})(\d{2})/(\d{2})(\d{2})$", token)
+        if not match:
+            return None, None
+        start = _infer_utc(int(match.group(1)), int(match.group(2)))
+        end = _infer_utc(int(match.group(3)), int(match.group(4)))
+        if end <= start:
+            end += timedelta(days=1)
+        return start, end
+
+    valid_start_utc, valid_end_utc = _parse_period(valid_match.group(0))
+    if valid_start_utc is None or valid_end_utc is None:
+        return {}
+
+    segment_indexes: list[int] = []
+    for idx, token in enumerate(tokens):
+        if re.match(r"^FM\d{6}$", token) or token in {"TEMPO", "BECMG", "PROB30", "PROB40"}:
+            segment_indexes.append(idx)
+
+    base_start_idx = 0
+    for idx, token in enumerate(tokens):
+        if token == valid_match.group(0):
+            base_start_idx = idx + 1
+            break
+
+    segments: list[Dict[str, Any]] = []
+    first_segment_idx = segment_indexes[0] if segment_indexes else len(tokens)
+    if base_start_idx < first_segment_idx:
+        segments.append(
+            {
+                "type": "BASE",
+                "start_utc": valid_start_utc,
+                "end_utc": valid_end_utc,
+                "tokens": tokens[base_start_idx:first_segment_idx],
+            }
+        )
+
+    idx_pos = 0
+    while idx_pos < len(segment_indexes):
+        start_idx = segment_indexes[idx_pos]
+        end_idx = segment_indexes[idx_pos + 1] if idx_pos + 1 < len(segment_indexes) else len(tokens)
+        token = tokens[start_idx]
+        seg_type = token
+        seg_start = valid_start_utc
+        seg_end = valid_end_utc
+        payload_start = start_idx + 1
+
+        if re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token):
+            match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", token)
+            seg_type = "FM"
+            seg_start = _infer_utc(int(match.group(1)), int(match.group(2)), int(match.group(3)))
+            if idx_pos + 1 < len(segment_indexes):
+                next_token = tokens[segment_indexes[idx_pos + 1]]
+                next_match = re.match(r"^FM(\d{2})(\d{2})(\d{2})$", next_token)
+                if next_match:
+                    seg_end = _infer_utc(int(next_match.group(1)), int(next_match.group(2)), int(next_match.group(3)))
+                else:
+                    seg_end = valid_end_utc
+            else:
+                seg_end = valid_end_utc
+        elif token in {"TEMPO", "BECMG"}:
+            seg_type = token
+            if payload_start < len(tokens):
+                seg_start, seg_end = _parse_period(tokens[payload_start])
+                payload_start += 1
+        elif token in {"PROB30", "PROB40"}:
+            seg_type = token
+            if payload_start < len(tokens) and tokens[payload_start] == "TEMPO":
+                seg_type = f"{token} TEMPO"
+                payload_start += 1
+            if payload_start < len(tokens):
+                seg_start, seg_end = _parse_period(tokens[payload_start])
+                payload_start += 1
+
+        if seg_start is None or seg_end is None:
+            idx_pos += 1
+            continue
+        if seg_end <= seg_start:
+            seg_end = seg_start + timedelta(hours=1)
+
+        segments.append(
+            {
+                "type": seg_type,
+                "start_utc": seg_start,
+                "end_utc": seg_end,
+                "tokens": tokens[payload_start:end_idx],
+            }
+        )
+        idx_pos += 1
+
+    peak_window_start = datetime.strptime(f"{local_date} {max(0, first_peak_h - 2):02d}:00", "%Y-%m-%d %H:%M").replace(tzinfo=local_tz)
+    peak_window_end = datetime.strptime(f"{local_date} {min(23, last_peak_h + 1):02d}:00", "%Y-%m-%d %H:%M").replace(tzinfo=local_tz)
+
+    precip_rank = {"low": 0, "medium": 1, "high": 2}
+    suppression_level = "low"
+    disruption_level = "low"
     low_ceiling_ft = None
     ceiling_cover = None
-    for cover, base in cloud_matches:
-        if cover not in {"BKN", "OVC"}:
-            continue
-        try:
-            base_ft = int(base) * 100
-        except Exception:
-            continue
-        if low_ceiling_ft is None or base_ft < low_ceiling_ft:
-            low_ceiling_ft = base_ft
-            ceiling_cover = cover
+    wind_regimes: list[str] = []
+    markers: list[Dict[str, Any]] = []
+    active_segments: list[Dict[str, Any]] = []
 
-    direction_buckets: list[str] = []
-    for direction, _speed in wind_matches:
-        if direction == "VRB":
-            direction_buckets.append("variable")
+    def _segment_precip_level(tokens_block: list[str]) -> str:
+        joined = " ".join(tokens_block)
+        if re.search(r"\b(?:-|\+)?(?:TSRA|TS|VCTS|SHRA|SHSN|SHGS)\b", joined):
+            return "high"
+        if re.search(r"\b(?:-|\+)?(?:RA|DZ|SN)\b", joined):
+            return "medium"
+        return "low"
+
+    for segment in segments:
+        start_local = segment["start_utc"].astimezone(local_tz)
+        end_local = segment["end_utc"].astimezone(local_tz)
+        if end_local < peak_window_start or start_local > peak_window_end:
             continue
-        try:
+        active_segments.append(segment)
+        joined = " ".join(segment["tokens"])
+        level = _segment_precip_level(segment["tokens"])
+        if precip_rank[level] > precip_rank[suppression_level]:
+            suppression_level = level
+
+        cloud_matches = re.findall(r"\b(FEW|SCT|BKN|OVC)(\d{3})\b", joined)
+        for cover, base in cloud_matches:
+            if cover not in {"BKN", "OVC"}:
+                continue
+            try:
+                base_ft = int(base) * 100
+            except Exception:
+                continue
+            if low_ceiling_ft is None or base_ft < low_ceiling_ft:
+                low_ceiling_ft = base_ft
+                ceiling_cover = cover
+        if low_ceiling_ft is not None and low_ceiling_ft <= 4000 and suppression_level == "low":
+            suppression_level = "medium"
+
+        wind_matches = re.findall(r"\b(\d{3}|VRB)(\d{2,3})(?:G\d{2,3})?KT\b", joined)
+        segment_regimes = []
+        for direction, _speed in wind_matches:
+            if direction == "VRB":
+                segment_regimes.append("variable")
+                continue
             deg = int(direction)
-        except Exception:
-            continue
-        if 135 <= deg <= 225:
-            direction_buckets.append("southerly")
-        elif deg >= 315 or deg <= 45:
-            direction_buckets.append("northerly")
-        else:
-            direction_buckets.append("cross")
-    unique_buckets = [bucket for bucket in dict.fromkeys(direction_buckets) if bucket]
+            if 135 <= deg <= 225:
+                segment_regimes.append("southerly")
+            elif deg >= 315 or deg <= 45:
+                segment_regimes.append("northerly")
+            else:
+                segment_regimes.append("cross")
+        for item in segment_regimes:
+            if item not in wind_regimes:
+                wind_regimes.append(item)
 
-    suppression_level = "low"
-    if any(code in {"TSRA", "TS", "VCTS", "SHRA", "SHSN", "SHGS"} for code in precip_codes):
-        suppression_level = "high"
-    elif precip_codes or (low_ceiling_ft is not None and low_ceiling_ft <= 4000):
-        suppression_level = "medium"
+        if segment["type"] in {"TEMPO", "BECMG", "PROB30", "PROB40", "PROB30 TEMPO", "PROB40 TEMPO"}:
+            disruption_level = "medium" if disruption_level == "low" else disruption_level
+        if segment["type"] in {"PROB30 TEMPO", "PROB40 TEMPO"} or level == "high":
+            disruption_level = "high"
 
-    disruption_level = "low"
-    if tempo_tokens and suppression_level == "high":
-        disruption_level = "high"
-    elif tempo_tokens or len(unique_buckets) >= 2:
-        disruption_level = "medium"
+        marker_time_local = start_local
+        marker_hour = marker_time_local.strftime("%H:00")
+        hazards = []
+        if level != "low":
+            hazards.append(level)
+        if low_ceiling_ft is not None and segment_regimes is not None:
+            hazards.append("cloud")
+        if segment_regimes:
+            hazards.append("wind")
+        summary_zh = (
+            f"{segment['type']} {start_local.strftime('%H:%M')}-{end_local.strftime('%H:%M')} "
+            f"{'有阵雨/雷暴扰动' if level == 'high' else '有云雨扰动' if level == 'medium' else '以稳定为主'}"
+        )
+        summary_en = (
+            f"{segment['type']} {start_local.strftime('%H:%M')}-{end_local.strftime('%H:%M')} "
+            f"{'shows shower/thunder disruption' if level == 'high' else 'shows cloud/rain disruption' if level == 'medium' else 'stays relatively stable'}"
+        )
+        markers.append(
+            {
+                "label_time": marker_hour,
+                "marker_type": segment["type"],
+                "start_local": start_local.strftime("%H:%M"),
+                "end_local": end_local.strftime("%H:%M"),
+                "suppression_level": level,
+                "summary_zh": summary_zh,
+                "summary_en": summary_en,
+            }
+        )
 
-    wind_shift = len(unique_buckets) >= 2 or "variable" in unique_buckets
-    peak_window = f"{max(0, first_peak_h - 2):02d}:00-{min(23, last_peak_h + 1):02d}:00"
+    wind_shift = len(wind_regimes) >= 2 or "variable" in wind_regimes
+    peak_window = f"{peak_window_start.strftime('%H:%M')}-{peak_window_end.strftime('%H:%M')}"
 
     if suppression_level == "high":
         summary_zh = f"TAF 在峰值窗口（{peak_window}）提示阵雨或雷暴扰动，机场端压温风险偏高。"
@@ -366,7 +537,6 @@ def _build_taf_signal(
     else:
         summary_zh = f"TAF 在峰值窗口（{peak_window}）暂未提示明显云雨压温。"
         summary_en = f"TAF does not flag a strong cloud/rain suppression signal around the peak window ({peak_window})."
-
     if wind_shift:
         summary_zh += " 同时机场预报风向存在阶段性切换。"
         summary_en += " Airport wind direction also shifts by regime during the window."
@@ -379,10 +549,19 @@ def _build_taf_signal(
         "valid_time_from": (taf_data or {}).get("valid_time_from"),
         "valid_time_to": (taf_data or {}).get("valid_time_to"),
         "peak_window": peak_window,
-        "precip_codes": precip_codes,
+        "segments": [
+            {
+                "type": seg["type"],
+                "start_local": seg["start_utc"].astimezone(local_tz).strftime("%H:%M"),
+                "end_local": seg["end_utc"].astimezone(local_tz).strftime("%H:%M"),
+                "tokens": seg["tokens"],
+            }
+            for seg in active_segments
+        ],
+        "markers": markers,
         "low_ceiling_ft": low_ceiling_ft,
         "ceiling_cover": ceiling_cover,
-        "wind_regimes": unique_buckets,
+        "wind_regimes": wind_regimes,
         "wind_shift": wind_shift,
         "suppression_level": suppression_level,
         "disruption_level": disruption_level,
@@ -875,6 +1054,8 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
     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,
     )