diff --git a/bot_listener.py b/bot_listener.py
index 4e9ba88c..85375064 100644
--- a/bot_listener.py
+++ b/bot_listener.py
@@ -118,33 +118,37 @@ def start_bot():
             )
         
         bot.send_message(message.chat.id, rank_text, parse_mode="HTML")
-
     @bot.message_handler(commands=["deb"])
     def deb_accuracy(message):
-        """查询 DEB 融合预测的历史准确率"""
+        """查询 DEB 融合预测的近 7 天准确率。"""
         try:
             parts = message.text.split(maxsplit=1)
             if len(parts) < 2:
                 bot.reply_to(
-                    message, "❓ 用法: <code>/deb ankara</code>", parse_mode="HTML"
+                    message,
+                    "❌ 用法: <code>/deb ankara</code>",
+                    parse_mode="HTML",
                 )
                 return
 
-            from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
+            from datetime import datetime as _dt, timedelta as _td
+            import os as _os
+
+            from src.analysis.deb_algorithm import load_history
+            from src.data_collection.city_registry import ALIASES
+
             city_input = parts[1].strip().lower()
             city_name = ALIASES.get(city_input, city_input)
 
-            from src.analysis.deb_algorithm import load_history
-            import os as _os
-
-            # 获取详细历史数据
             project_root = _os.path.dirname(_os.path.abspath(__file__))
             history_file = _os.path.join(project_root, "data", "daily_records.json")
             data = load_history(history_file)
 
             if city_name not in data or not data[city_name]:
                 bot.reply_to(
-                    message, f"❌ 暂无 {city_name} 的历史数据", parse_mode="HTML"
+                    message,
+                    f"❌ 暂无 {city_name} 的历史数据。",
+                    parse_mode="HTML",
                 )
                 return
 
@@ -152,28 +156,40 @@ def start_bot():
                 return
 
             city_data = data[city_name]
-            from datetime import datetime as _dt
+            today = _dt.now().date()
+            today_str = today.strftime("%Y-%m-%d")
+            cutoff_date = today - _td(days=6)
 
-            today_str = _dt.now().strftime("%Y-%m-%d")
+            recent_items = []
+            for date_str, record in city_data.items():
+                try:
+                    row_date = _dt.strptime(date_str, "%Y-%m-%d").date()
+                except Exception:
+                    continue
+                if row_date >= cutoff_date:
+                    recent_items.append((date_str, record, row_date))
 
-            lines = [f"📊 <b>DEB 准确率报告 - {city_name.title()}</b>\n"]
+            recent_items.sort(key=lambda item: item[0])
 
-            # 逐日明细
-            lines.append("<b>📅 逐日记录：</b>")
+            lines = [
+                f"📊 <b>DEB 准确率报告 - {city_name.title()}</b>",
+                "",
+                "📅 <b>近7日记录：</b>",
+            ]
             total_days = 0
             hits = 0
             deb_errors = []
-            signed_errors = []  # 有正负的误差 (DEB - 实测)
+            signed_errors = []
             model_errors = {}
 
-            for date_str in sorted(city_data.keys()):
-                record = city_data[date_str]
+            for date_str, record, _row_date in recent_items:
                 actual = record.get("actual_high")
                 deb_pred = record.get("deb_prediction")
-                forecasts = record.get("forecasts", {})
+                forecasts = record.get("forecasts", {}) or {}
 
                 if actual is None:
                     continue
+
                 try:
                     actual = float(actual)
                     if deb_pred is not None:
@@ -181,18 +197,16 @@ def start_bot():
                 except Exception:
                     continue
 
-                # 如果没有存 DEB 预测值，用当天各模型平均值回算
                 if deb_pred is None and forecasts:
-                    valid_preds = [
-                        float(v) for v in forecasts.values() if v is not None
-                    ]
+                    valid_preds = [float(v) for v in forecasts.values() if v is not None]
                     if valid_preds:
                         deb_pred = round(sum(valid_preds) / len(valid_preds), 1)
 
                 actual_wu = round(actual)
 
-                # DEB 命中判断
-                if deb_pred is not None and date_str != today_str:
+                if date_str == today_str:
+                    lines.append(f"  {date_str}: 📍 今天进行中 (实测暂 {actual:.1f})")
+                elif deb_pred is not None:
                     total_days += 1
                     deb_wu = round(deb_pred)
                     hit = deb_wu == actual_wu
@@ -201,111 +215,97 @@ def start_bot():
                     err = deb_pred - actual
                     deb_errors.append(abs(err))
                     signed_errors.append(err)
-                    icon = "✅" if hit else "❌"
-                    retro = "≈" if "deb_prediction" not in record else ""
-                    # 错误类型标签
-                    if not hit:
-                        err_label = (
-                            f" 低估{abs(err):.1f}°"
-                            if err < 0
-                            else f" 高估{abs(err):.1f}°"
-                        )
-                    else:
-                        err_label = f" 偏差{abs(err):.1f}°"
-                    mu_val = record.get("mu")
-                    mu_str = f" | μ: {mu_val}" if mu_val is not None else ""
-                    lines.append(
-                        f"  {date_str}: DEB {retro}{deb_pred}→<b>{deb_wu}</b> vs 实测 {actual}→<b>{actual_wu}</b> {icon}{err_label}{mu_str}"
-                    )
-                elif date_str == today_str:
-                    lines.append(f"  {date_str}: 📍 今天进行中 (实测暂 {actual})")
 
-                # 各模型误差统计
+                    if hit:
+                        result_icon = "✅"
+                        err_text = f"偏差{abs(err):.1f}°"
+                    elif err < 0:
+                        result_icon = "❌"
+                        err_text = f"低估{abs(err):.1f}°"
+                    else:
+                        result_icon = "❌"
+                        err_text = f"高估{abs(err):.1f}°"
+
+                    retro = "≈" if "deb_prediction" not in record else ""
+                    lines.append(
+                        f"  {date_str}: DEB {retro}{deb_pred:.1f}→{deb_wu} vs 实测 {actual:.1f}→{actual_wu} "
+                        f"{result_icon} {err_text}"
+                    )
+
                 if date_str != today_str and actual is not None:
                     for model, pred in forecasts.items():
-                        if pred is not None:
-                            if model not in model_errors:
-                                model_errors[model] = []
-                            model_errors[model].append(abs(float(pred) - actual))
+                        if pred is None:
+                            continue
+                        try:
+                            model_errors.setdefault(model, []).append(abs(float(pred) - actual))
+                        except Exception:
+                            continue
 
-            # 汇总
             if total_days > 0:
                 hit_rate = hits / total_days * 100
                 deb_mae = sum(deb_errors) / len(deb_errors)
+                lines.append("")
                 lines.append(
-                    f"\n🎯 <b>DEB 总战绩</b>：WU命中 {hits}/{total_days} (<b>{hit_rate:.0f}%</b>) | MAE: {deb_mae:.1f}°"
+                    f"🎯 <b>DEB 总战绩：</b>WU命中 {hits}/{total_days} (<b>{hit_rate:.0f}%</b>) | MAE: {deb_mae:.1f}°"
                 )
 
-                # --- 概率引擎 μ 的战绩 ---
-                from src.analysis.deb_algorithm import get_mu_accuracy
-                mu_acc = get_mu_accuracy(city_name)
-                if mu_acc:
-                    mu_mae, mu_hr, avg_brier, mu_total, _ = mu_acc
-                    lines.append(
-                        f"🎲 <b>概率引擎 (μ)</b>：WU命中 <b>{mu_hr:.0f}%</b> | MAE: {mu_mae:.1f}°"
-                    )
-                    if avg_brier is not None:
-                        # Brier Score 范围是 0 (完美) 到 2 (全错)
-                        bs_eval = "极佳" if avg_brier < 0.2 else ("良好" if avg_brier < 0.4 else "需校准")
-                        lines.append(f"  ▪ Brier评分: {avg_brier:.3f} ({bs_eval})")
-
-
-                # 和各模型 MAE 对比
                 if model_errors:
-                    lines.append("\n📈 <b>模型 MAE 对比</b>：")
-                    model_maes = {
-                        m: sum(e) / len(e) for m, e in model_errors.items() if e
-                    }
-                    sorted_models = sorted(model_maes.items(), key=lambda x: x[1])
-                    for m, mae in sorted_models:
+                    lines.append("")
+                    lines.append("📈 <b>模型 MAE 对比：</b>")
+                    model_maes = {m: sum(e) / len(e) for m, e in model_errors.items() if e}
+                    sorted_models = sorted(model_maes.items(), key=lambda item: item[1])
+                    for model, mae in sorted_models:
                         tag = " ⭐" if mae <= deb_mae else ""
-                        lines.append(f"  {m}: {mae:.1f}°{tag}")
+                        lines.append(f"  {model}: {mae:.1f}°{tag}")
                     lines.append(f"  <b>DEB融合: {deb_mae:.1f}°</b>")
 
-                # 偏差模式分析
                 mean_bias = sum(signed_errors) / len(signed_errors)
                 underest = sum(1 for e in signed_errors if e < -0.3)
                 overest = sum(1 for e in signed_errors if e > 0.3)
+                accurate = total_days - underest - overest
 
-                lines.append("\n🔍 <b>偏差分析</b>：")
+                lines.append("")
+                lines.append("🔍 <b>偏差分析：</b>")
                 if abs(mean_bias) > 0.3:
-                    bias_dir = "低估" if mean_bias < 0 else "高估"
-                    lines.append(f"  ⚠️ 系统性{bias_dir}：平均偏差 {mean_bias:+.1f}°")
+                    bias_label = "系统性低估" if mean_bias < 0 else "系统性高估"
+                    lines.append(f"  ⚠️ {bias_label}：平均偏差 {mean_bias:+.1f}°")
                 else:
-                    lines.append(f"  ✅ 无明显系统偏差（平均 {mean_bias:+.1f}°）")
-                lines.append(
-                    f"  低估 {underest} 次 | 高估 {overest} 次 | 准确 {total_days - underest - overest} 次"
-                )
+                    lines.append(f"  ✅ 整体无明显系统偏差：平均偏差 {mean_bias:+.1f}°")
+                lines.append(f"  低估 {underest} 次 | 高估 {overest} 次 | 准确 {accurate} 次")
 
-                # 可操作建议
-                lines.append("\n💡 <b>建议</b>：")
+                lines.append("")
+                lines.append("💡 <b>建议：</b>")
                 if underest > overest and abs(mean_bias) > 0.5:
                     lines.append(
-                        f"  该城市模型集体低估趋势明显（{mean_bias:+.1f}°），实际最高温可能比 DEB 融合值高 {abs(mean_bias):.0f}-{abs(mean_bias) + 0.5:.0f}°。交易时建议适当看高。"
+                        f"  该城市模型集体低估趋势明显（{mean_bias:+.1f}°），实际最高温可能比 DEB 融合值高 "
+                        f"{abs(mean_bias):.0f}-{abs(mean_bias) + 0.5:.0f}°。交易时建议适当看高。"
                     )
                 elif overest > underest and abs(mean_bias) > 0.5:
                     lines.append(
-                        f"  该城市模型集体高估趋势明显（{mean_bias:+.1f}°），实际最高温可能比 DEB 融合值低。交易时建议适当看低。"
+                        f"  该城市模型集体高估趋势明显（{mean_bias:+.1f}°），实际最高温可能低于 DEB 融合值。交易时注意追高风险。"
                     )
                 elif deb_mae > 1.5:
-                    lines.append(
-                        f"  该城市预报波动大 (MAE {deb_mae:.1f}°)，建议观望或轻仓。"
-                    )
+                    lines.append(f"  近期模型波动较大（MAE {deb_mae:.1f}°），建议降低对单一日预测的信任度。")
                 elif hit_rate >= 60:
-                    lines.append("  DEB 表现良好，可作为主要参考。")
+                    lines.append("  DEB 近期表现稳定，可继续作为主要参考。")
                 else:
-                    lines.append("  数据积累中，建议结合 AI 分析综合判断。")
+                    lines.append("  近期准确率一般，建议结合主站实测与周边站点共同判断。")
 
-                lines.append("\n📝 MAE = 平均绝对误差，越小越准。⭐ = 优于 DEB 融合。")
+                lines.append("")
+                lines.append("📝 MAE = 平均绝对误差，越小越准。⭐ = 优于 DEB 融合。")
+                lines.append("🗓 统计窗口：近7天滚动样本。")
             else:
-                lines.append("\n⏳ 尚无完整的 DEB 预测记录，明天起开始统计。")
+                lines.append("")
+                lines.append("⏳ 近7天尚无完整的 DEB 预测记录。")
 
-            lines.append(f"\n💳 本次消耗 <code>{DEB_QUERY_COST}</code> 积分。")
+            lines.append("")
+            lines.append(f"💳 本次消耗 <code>{DEB_QUERY_COST}</code> 积分。")
             bot.reply_to(message, "\n".join(lines), parse_mode="HTML")
         except Exception as e:
             bot.reply_to(message, f"❌ 查询失败: {e}")
 
     @bot.message_handler(commands=["city"])
+
     def get_city_info(message):
         """查询指定城市的天气详情"""
         try:
diff --git a/frontend/app/globals.css b/frontend/app/globals.css
index 52c8295d..6e1481cb 100644
--- a/frontend/app/globals.css
+++ b/frontend/app/globals.css
@@ -123,3 +123,132 @@
     }
   }
 }
+
+
+@layer components {
+  .nearby-marker {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    background: rgba(15, 23, 42, 0.9);
+    color: rgba(226, 232, 240, 0.92);
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    border-radius: 8px;
+    padding: 4px 10px;
+    font-size: 11px;
+    font-weight: 600;
+    box-shadow: 0 10px 28px rgba(2, 6, 23, 0.45);
+    backdrop-filter: blur(10px);
+    white-space: nowrap;
+  }
+
+  .nearby-name {
+    color: rgba(148, 163, 184, 0.95);
+  }
+
+  .nearby-temp {
+    color: #fff;
+    font-weight: 800;
+  }
+
+  .nearby-unit {
+    color: rgba(148, 163, 184, 0.85);
+    font-size: 9px;
+  }
+
+  .wind-info {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    margin-left: 4px;
+    padding-left: 6px;
+    border-left: 1px solid rgba(255, 255, 255, 0.1);
+    color: rgba(34, 211, 238, 0.9);
+    font-size: 10px;
+  }
+
+  .wind-arrow {
+    display: inline-block;
+    transform-origin: center;
+  }
+
+  .city-marker {
+    position: relative;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    cursor: pointer;
+  }
+
+  .marker-bubble {
+    position: relative;
+    min-width: 46px;
+    padding: 5px 10px;
+    border-radius: 12px;
+    font-size: 13px;
+    font-weight: 800;
+    text-align: center;
+    color: white;
+    border: 1px solid transparent;
+    box-shadow: 0 8px 24px rgba(0, 0, 0, 0.4);
+  }
+
+  .marker-bubble::after {
+    content: "";
+    position: absolute;
+    bottom: -6px;
+    left: 50%;
+    transform: translateX(-50%);
+    width: 0;
+    height: 0;
+    border-left: 6px solid transparent;
+    border-right: 6px solid transparent;
+    border-top: 6px solid transparent;
+  }
+
+  .marker-bubble.risk-high {
+    background: linear-gradient(135deg, #dc2626, #ef4444);
+    border-color: rgba(239, 68, 68, 0.5);
+  }
+
+  .marker-bubble.risk-high::after { border-top-color: #ef4444; }
+
+  .marker-bubble.risk-medium {
+    background: linear-gradient(135deg, #d97706, #f59e0b);
+    border-color: rgba(245, 158, 11, 0.5);
+  }
+
+  .marker-bubble.risk-medium::after { border-top-color: #f59e0b; }
+
+  .marker-bubble.risk-low {
+    background: linear-gradient(135deg, #059669, #10b981);
+    border-color: rgba(16, 185, 129, 0.5);
+  }
+
+  .marker-bubble.risk-low::after { border-top-color: #10b981; }
+
+  .marker-name {
+    margin-top: 8px;
+    font-size: 10px;
+    font-weight: 700;
+    color: rgba(255, 255, 255, 0.88);
+    text-shadow: 0 1px 4px rgba(0,0,0,0.8);
+  }
+
+  .city-marker.selected .marker-bubble {
+    animation: markerGlow 2s ease-in-out infinite;
+  }
+
+  .custom-scrollbar::-webkit-scrollbar {
+    width: 8px;
+  }
+  .custom-scrollbar::-webkit-scrollbar-thumb {
+    background: rgba(51, 65, 85, 0.75);
+    border-radius: 9999px;
+  }
+}
+
+@keyframes markerGlow {
+  0%, 100% { box-shadow: 0 6px 18px rgba(0,0,0,0.4); }
+  50% { box-shadow: 0 10px 26px rgba(34,211,238,0.35); }
+}
diff --git a/frontend/lib/types.ts b/frontend/lib/types.ts
index e2528d45..d3dbb834 100644
--- a/frontend/lib/types.ts
+++ b/frontend/lib/types.ts
@@ -238,6 +238,10 @@ export interface CityAnalysis {
     clouds_raw: Array<{ cover: string; base: number | null }>;
     visibility_mi: number | null;
     wx_desc: string | null;
+    raw_metar?: string | null;
+    report_time?: string | null;
+    receipt_time?: string | null;
+    obs_time_epoch?: number | null;
   };
   mgm?: {
     temp?: number | null;
@@ -317,6 +321,7 @@ export interface CityDetail {
     weather_gov: WeatherGovData;
     mgm: any;
     mgm_nearby: any[];
+    nearby_source?: string;
   };
   timeseries: {
     metar_recent_obs: any[];
diff --git a/frontend/public/legacy/index.html b/frontend/public/legacy/index.html
index e46673a7..8ac00933 100644
--- a/frontend/public/legacy/index.html
+++ b/frontend/public/legacy/index.html
@@ -193,7 +193,7 @@
     <div id="historyModal" class="modal-overlay hidden">
         <div class="modal-content">
             <div class="modal-header">
-                <h2>📊 <span id="historyModalTitle">历史准确率</span></h2>
+                <h2>&#128202; <span id="historyModalTitle">&#x5386;&#x53F2;&#x51C6;&#x786E;&#x7387;</span></h2>
                 <button class="modal-close" id="historyModalClose">✕</button>
             </div>
             <div class="modal-body">
diff --git a/frontend/public/static/app.js b/frontend/public/static/app.js
index 38ed7485..9a0bedde 100644
--- a/frontend/public/static/app.js
+++ b/frontend/public/static/app.js
@@ -40,6 +40,10 @@ let tempChart = null;
 const AUTO_REFRESH_MS = 60 * 60 * 1000; // 1 hour
 let selectedForecastDate = null;
 let nearbyLayerGroup = null;
+let autoNearbyCity = null;
+let autoNearbyLoading = false;
+const AUTO_NEARBY_MIN_ZOOM = 8;
+const AUTO_NEARBY_MAX_DISTANCE_M = 120000;
 
 // ──────────────────────────────────────────────────────────
 //  Map Setup
@@ -74,6 +78,7 @@ function initMap() {
 
   // Handle zoom-based visibility for local stations and minor cities
   map.on("zoomend", updateMapVisibility);
+  map.on("moveend", maybeAutoShowNearbyStations);
 }
 
 function updateMapVisibility() {
@@ -93,6 +98,73 @@ function updateMapVisibility() {
   Object.values(markers).forEach(({ marker }) => {
     if (!map.hasLayer(marker)) map.addLayer(marker);
   });
+
+  maybeAutoShowNearbyStations();
+}
+
+function getNearestCityForCenter() {
+  if (!map) return null;
+  const center = map.getCenter();
+  let best = null;
+
+  Object.entries(markers).forEach(([cityName, entry]) => {
+    const city = entry.city;
+    if (city?.lat == null || city?.lon == null) return;
+    const distance = map.distance(center, L.latLng(city.lat, city.lon));
+    if (distance > AUTO_NEARBY_MAX_DISTANCE_M) return;
+    if (!best || distance < best.distance) {
+      best = { cityName, distance };
+    }
+  });
+
+  return best?.cityName || null;
+}
+
+async function maybeAutoShowNearbyStations() {
+  if (!map || !nearbyLayerGroup) return;
+  if (map.getZoom() < AUTO_NEARBY_MIN_ZOOM) {
+    autoNearbyCity = null;
+    nearbyLayerGroup.clearLayers();
+    return;
+  }
+
+  const targetCity = getNearestCityForCenter();
+  if (!targetCity) {
+    autoNearbyCity = null;
+    nearbyLayerGroup.clearLayers();
+    return;
+  }
+
+  if (
+    autoNearbyCity === targetCity &&
+    nearbyLayerGroup.getLayers().length > 0
+  ) {
+    return;
+  }
+
+  autoNearbyCity = targetCity;
+
+  if (cityDataCache[targetCity]) {
+    renderNearbyStations(cityDataCache[targetCity], true);
+    return;
+  }
+
+  if (autoNearbyLoading) return;
+  autoNearbyLoading = true;
+  try {
+    const data = await fetchCityDetail(targetCity, false);
+    cityDataCache[targetCity] = data;
+    saveCache();
+    renderNearbyStations(data, true);
+    if (data.current?.temp != null) {
+      updateMarkerTemp(targetCity, data.current.temp);
+      updateCityListInfo(data);
+    }
+  } catch (e) {
+    console.error(`Auto nearby load failed for ${targetCity}:`, e);
+  } finally {
+    autoNearbyLoading = false;
+  }
 }
 
 // ──────────────────────────────────────────────────────────
@@ -258,13 +330,31 @@ async function fetchCityDetail(cityName, force = false) {
 // ──────────────────────────────────────────────────────────
 //  Nearby Map Stations Rendering
 // ──────────────────────────────────────────────────────────
-function renderNearbyStations(data) {
+function renderNearbyStations(data, preserveView = false) {
   if (!nearbyLayerGroup) return;
   nearbyLayerGroup.clearLayers();
 
-  if (!data.mgm_nearby || data.mgm_nearby.length === 0) {
+  const allNearby = Array.isArray(data.mgm_nearby) ? data.mgm_nearby : [];
+  const isAnkara = String(data.name || "").toLowerCase() === "ankara";
+  const ankaraPriority = [
+    "Airport (MGM/17128)",
+    "Ankara (Bölge/Center)",
+    "Etimesgut",
+    "Keçiören",
+    "Pursaklar",
+    "Çubuk",
+    "Kalecik",
+  ];
+  const ankaraNearby = isAnkara
+    ? ankaraPriority
+        .map((name) => allNearby.find((st) => st?.name === name))
+        .filter(Boolean)
+    : allNearby;
+  const nearbyStations = ankaraNearby.length > 0 ? ankaraNearby : allNearby;
+
+  if (nearbyStations.length === 0) {
     // Regular city zoom-in
-    if (data.lat != null && data.lon != null) {
+    if (!preserveView && data.lat != null && data.lon != null) {
       map.flyTo([data.lat, data.lon], 10, {
         animate: true,
         duration: 1.5,
@@ -281,7 +371,7 @@ function renderNearbyStations(data) {
     latLngs.push([data.lat, data.lon]);
   }
 
-  data.mgm_nearby.forEach((st) => {
+  nearbyStations.forEach((st) => {
     // Filter out stations too far if needed, but il handles grouping nicely.
     // Skip if it is the exact same marker as main (though coordinates might slightly differ)
     const sym = data.temp_symbol || "°C";
@@ -289,7 +379,7 @@ function renderNearbyStations(data) {
     let windHtml = "";
     if (st.wind_dir != null) {
       const rot = (parseFloat(st.wind_dir) + 180) % 360;
-      const speedRaw = parseFloat(st.wind_speed);
+      const speedRaw = parseFloat(st.wind_speed ?? st.wind_speed_kt);
       const speed = !isNaN(speedRaw) ? `${speedRaw.toFixed(1)}k` : "";
       windHtml = `
         <div class="wind-info">
@@ -317,6 +407,10 @@ function renderNearbyStations(data) {
     latLngs.push([st.lat, st.lon]);
   });
 
+  if (preserveView) {
+    return;
+  }
+
   if (latLngs.length > 1) {
     const bounds = L.latLngBounds(latLngs);
     map.flyToBounds(bounds, {
@@ -363,12 +457,7 @@ async function loadCityDetail(cityName, force = false) {
 
     // Update marker and list
     if (data.current?.temp != null) {
-      const displayTemp =
-        data.current.max_so_far != null &&
-        data.current.max_so_far >= data.current.temp
-          ? data.current.max_so_far
-          : data.current.temp;
-      updateMarkerTemp(cityName, displayTemp);
+      updateMarkerTemp(cityName, data.current.temp);
       updateCityListInfo(data);
     }
   } catch (e) {
@@ -879,7 +968,7 @@ function renderProbabilities(data) {
 
   let html = "";
   if (mu != null) {
-    html += `<div style="font-size:11px;color:var(--text-muted);margin-bottom:6px;">期望值 μ = ${mu}${data.temp_symbol}</div>`;
+    html += `<div style="font-size:11px;color:var(--text-muted);margin-bottom:6px;">动态分布中心= ${mu}${data.temp_symbol}</div>`;
   }
 
   probs.forEach((p, i) => {
@@ -895,7 +984,6 @@ function renderProbabilities(data) {
   });
   container.innerHTML = html;
 
-  // Animate bars
   requestAnimationFrame(() => {
     container.querySelectorAll(".prob-bar-fill").forEach((bar, i) => {
       const pct = Math.round(probs[i].probability * 100);
@@ -922,7 +1010,9 @@ function formatCents(price) {
   const n = Number(price);
   if (!Number.isFinite(n)) return "--";
   const cents = Math.round(n * 1000) / 10;
-  return Number.isInteger(cents) ? `${cents.toFixed(0)}c` : `${cents.toFixed(1)}c`;
+  return Number.isInteger(cents)
+    ? `${cents.toFixed(0)}c`
+    : `${cents.toFixed(1)}c`;
 }
 
 function renderModels(data) {
@@ -1080,6 +1170,7 @@ function closePanel() {
   document
     .querySelectorAll(".city-item")
     .forEach((el) => el.classList.remove("active"));
+  maybeAutoShowNearbyStations();
 }
 
 // ──────────────────────────────────────────────────────────
@@ -1095,12 +1186,7 @@ function startAutoRefresh() {
         cityDataCache[selectedCity] = data;
         renderPanel(data);
         if (data.current?.temp != null) {
-          const displayTemp =
-            data.current.max_so_far != null &&
-            data.current.max_so_far >= data.current.temp
-              ? data.current.max_so_far
-              : data.current.temp;
-          updateMarkerTemp(selectedCity, displayTemp);
+          updateMarkerTemp(selectedCity, data.current.temp);
           updateCityListInfo(data);
         }
         flashLiveBadge();
@@ -1177,66 +1263,110 @@ async function openHistoryModal() {
   modal.classList.remove("hidden");
   title.textContent = `历史准确率对账 - ${selectedCity.toUpperCase()}`;
   statsDiv.innerHTML =
-    '<span style="color:var(--text-muted)">正在获取底层数据库...</span>';
+    '<span style="color:var(--text-muted)">正在获取历史数据...</span>';
 
   try {
     const res = await fetch(`/api/history/${encodeURIComponent(selectedCity)}`);
     const json = await res.json();
     const data = json.history || [];
+    const cutoff = new Date();
+    cutoff.setHours(0, 0, 0, 0);
+    cutoff.setDate(cutoff.getDate() - 14);
+    const recentData = data.filter((row) => {
+      if (!row?.date) return false;
+      const rowDate = new Date(`${row.date}T00:00:00`);
+      return !Number.isNaN(rowDate.getTime()) && rowDate >= cutoff;
+    });
 
-    if (data.length === 0) {
+    if (recentData.length === 0) {
       statsDiv.innerHTML =
-        '<span style="color:var(--text-muted)">暂无该城市历史数据</span>';
+        '<span style="color:var(--text-muted)">\u8fd115\u5929\u6682\u65e0\u8be5\u57ce\u5e02\u5386\u53f2\u6570\u636e</span>';
       if (historyChartInst) historyChartInst.destroy();
       return;
     }
 
-    // Compute stats
     let hits = 0;
-    let debErrors = [];
-    let muErrors = [];
-
+    const debErrors = [];
     const dates = [];
     const actuals = [];
     const debs = [];
-    const mus = [];
     const mgms = [];
+    const cityLocalDate = cityDataCache?.[selectedCity]?.local_date || null;
+    const settledData = recentData.filter((row) => {
+      if (!row?.date) return false;
+      return cityLocalDate
+        ? row.date < cityLocalDate
+        : row.date < new Date().toISOString().slice(0, 10);
+    });
 
-    data.forEach((row) => {
+    recentData.forEach((row) => {
       dates.push(row.date);
       actuals.push(row.actual);
       debs.push(row.deb);
-      mus.push(row.mu);
       mgms.push(row.mgm);
+    });
 
+    settledData.forEach((row) => {
       if (row.actual != null && row.deb != null) {
         debErrors.push(Math.abs(row.actual - row.deb));
         if (Math.round(row.actual) === Math.round(row.deb)) {
           hits++;
         }
       }
-      if (row.actual != null && row.mu != null) {
-        muErrors.push(Math.abs(row.actual - row.mu));
-      }
     });
 
     const hitRate = debErrors.length
       ? ((hits / debErrors.length) * 100).toFixed(0)
-      : 0;
+      : "--";
     const debMae = debErrors.length
       ? (debErrors.reduce((a, b) => a + b, 0) / debErrors.length).toFixed(1)
-      : "-";
-    const muMae = muErrors.length
-      ? (muErrors.reduce((a, b) => a + b, 0) / muErrors.length).toFixed(1)
-      : "-";
+      : "--";
+    const hasMgm =
+      selectedCity === "ankara" && mgms.some((value) => value != null);
 
     statsDiv.innerHTML = `
-      <div class="h-stat-card"><span class="label">DEB 结算胜率 (WU)</span><span class="val">${hitRate}%</span></div>
-      <div class="h-stat-card"><span class="label">DEB MAE</span><span class="val">${debMae}°</span></div>
-      <div class="h-stat-card"><span class="label">μ (概率) MAE</span><span class="val">${muMae}°</span></div>
-      <div class="h-stat-card"><span class="label">有效样本数</span><span class="val">${data.length}天</span></div>
+      <div class="h-stat-card"><span class="label">DEB ???? (WU)</span><span class="val">${hitRate === "--" ? "--" : `${hitRate}%`}</span></div>
+      <div class="h-stat-card"><span class="label">DEB MAE</span><span class="val">${debMae}?</span></div>
+      <div class="h-stat-card"><span class="label">?15??????</span><span class="val">${settledData.length}?</span></div>
     `;
 
+    const datasets = [
+      {
+        label: "实测最高温",
+        data: actuals,
+        borderColor: "#f87171",
+        backgroundColor: "rgba(248, 113, 113, 0.1)",
+        borderWidth: 2,
+        tension: 0.2,
+        pointRadius: 4,
+        pointBackgroundColor: "#f87171",
+        pointBorderColor: "#fff",
+        zIndex: 10,
+      },
+      {
+        label: "DEB 融合",
+        data: debs,
+        borderColor: "#34d399",
+        backgroundColor: "transparent",
+        borderWidth: 2,
+        borderDash: [5, 4],
+        tension: 0.2,
+        pointRadius: 3,
+      },
+    ];
+
+    if (hasMgm) {
+      datasets.push({
+        label: "MGM 官方预报",
+        data: mgms,
+        borderColor: "#fb923c",
+        backgroundColor: "transparent",
+        borderWidth: 2,
+        tension: 0.2,
+        pointRadius: 3,
+      });
+    }
+
     if (historyChartInst) historyChartInst.destroy();
     const ctx = document.getElementById("historyChart").getContext("2d");
 
@@ -1244,50 +1374,7 @@ async function openHistoryModal() {
       type: "line",
       data: {
         labels: dates,
-        datasets: [
-          {
-            label: "实测最高温",
-            data: actuals,
-            borderColor: "#f87171", // red
-            backgroundColor: "rgba(248, 113, 113, 0.1)",
-            borderWidth: 2,
-            tension: 0.2,
-            pointRadius: 4,
-            pointBackgroundColor: "#f87171",
-            pointBorderColor: "#fff",
-            zIndex: 10,
-          },
-          {
-            label: "DEB 融合",
-            data: debs,
-            borderColor: "#34d399", // emerald
-            backgroundColor: "transparent",
-            borderWidth: 2,
-            borderDash: [5, 4],
-            tension: 0.2,
-            pointRadius: 3,
-          },
-          {
-            label: "μ (概率锚定)",
-            data: mus,
-            borderColor: "#a78bfa", // purple
-            backgroundColor: "transparent",
-            borderWidth: 2,
-            borderDash: [2, 2],
-            tension: 0.2,
-            pointRadius: 3,
-          },
-          {
-            label: "MGM 官方预报",
-            data: mgms,
-            borderColor: "#fb923c", // orange
-            backgroundColor: "transparent",
-            borderWidth: 2,
-            tension: 0.2,
-            pointRadius: 3,
-            hidden: false, // Show by default for Ankara
-          },
-        ],
+        datasets,
       },
       options: {
         responsive: true,
@@ -1387,11 +1474,7 @@ document.addEventListener("DOMContentLoaded", async () => {
     cities.forEach((c) => {
       const cached = cityDataCache[c.name];
       if (cached && cached.current?.temp != null) {
-        c._temp =
-          cached.current.max_so_far != null &&
-          cached.current.max_so_far >= cached.current.temp
-            ? cached.current.max_so_far
-            : cached.current.temp;
+        c._temp = cached.current.temp;
       }
     });
 
diff --git a/src/data_collection/weather_sources.py b/src/data_collection/weather_sources.py
index 19d59977..53f221c0 100644
--- a/src/data_collection/weather_sources.py
+++ b/src/data_collection/weather_sources.py
@@ -759,7 +759,12 @@ class WeatherDataCollector:
                         "lat": lat,
                         "lon": lon,
                         "temp": round(display_temp, 1),
-                        "istNo": icao # 用 ICAO ID 作为标识
+                        "istNo": icao,  # 用 ICAO ID 作为标识
+                        "icao": icao,
+                        "wind_dir": obs.get("wdir"),
+                        "wind_speed": obs.get("wspd"),
+                        "wind_speed_kt": obs.get("wspd"),
+                        "raw_metar": obs.get("rawOb"),
                     })
             
             if results:
diff --git a/web/app.py b/web/app.py
index a103fdb1..13b81ab0 100644
--- a/web/app.py
+++ b/web/app.py
@@ -591,6 +591,110 @@ def _normalize_city_or_404(name: str) -> str:
     return city
 
 
+def _build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        "name": data.get("name"),
+        "display_name": data.get("display_name"),
+        "icao": data.get("risk", {}).get("icao"),
+        "local_time": data.get("local_time"),
+        "temp_symbol": data.get("temp_symbol"),
+        "current": {
+            "temp": data.get("current", {}).get("temp"),
+            "obs_time": data.get("current", {}).get("obs_time"),
+        },
+        "deb": {
+            "prediction": data.get("deb", {}).get("prediction"),
+        },
+        "risk": {
+            "level": data.get("risk", {}).get("level"),
+            "warning": data.get("risk", {}).get("warning"),
+        },
+        "updated_at": data.get("updated_at"),
+    }
+
+
+def _build_city_detail_payload(data: Dict[str, Any]) -> Dict[str, Any]:
+    distribution = data.get("probabilities", {}).get("distribution", []) or []
+    primary_bucket = distribution[0] if distribution else None
+    return {
+        "city": data.get("name"),
+        "fetched_at": data.get("updated_at"),
+        "overview": {
+            "name": data.get("name"),
+            "display_name": data.get("display_name"),
+            "icao": data.get("risk", {}).get("icao"),
+            "airport": data.get("risk", {}).get("airport"),
+            "lat": data.get("lat"),
+            "lon": data.get("lon"),
+            "local_time": data.get("local_time"),
+            "local_date": data.get("local_date"),
+            "temp_symbol": data.get("temp_symbol"),
+            "current_temp": data.get("current", {}).get("temp"),
+            "deb_prediction": data.get("deb", {}).get("prediction"),
+            "risk_level": data.get("risk", {}).get("level"),
+            "risk_warning": data.get("risk", {}).get("warning"),
+            "updated_at": data.get("updated_at"),
+        },
+        "official": {
+            "available": bool(data.get("current", {}).get("temp") is not None),
+            "metar": {
+                "observation_time": data.get("current", {}).get("obs_time"),
+                "obs_age_min": data.get("current", {}).get("obs_age_min"),
+                "report_time": data.get("current", {}).get("report_time"),
+                "receipt_time": data.get("current", {}).get("receipt_time"),
+                "raw_metar": data.get("current", {}).get("raw_metar"),
+                "current": data.get("current"),
+            },
+            "weather_gov": {},
+            "mgm": data.get("mgm") or {},
+            "mgm_nearby": data.get("mgm_nearby") or [],
+            "nearby_source": "mgm" if data.get("name") == "ankara" else "metar_cluster",
+        },
+        "timeseries": {
+            "metar_recent_obs": data.get("metar_recent_obs") or [],
+            "metar_today_obs": data.get("metar_today_obs") or [],
+            "hourly": data.get("hourly") or {},
+            "mgm_hourly": (data.get("mgm") or {}).get("hourly", []),
+            "forecast_daily": (data.get("forecast") or {}).get("daily", []),
+        },
+        "models": data.get("multi_model") or {},
+        "probabilities": data.get("probabilities") or {"mu": None, "distribution": []},
+        "market_scan": {
+            "available": False,
+            "reason": "Market layer is not available on the current backend build.",
+            "primary_market": None,
+            "selected_date": data.get("local_date"),
+            "selected_condition_id": None,
+            "selected_slug": None,
+            "temperature_bucket": primary_bucket,
+            "model_probability": (
+                (primary_bucket.get("probability") / 100.0)
+                if isinstance(primary_bucket, dict) and primary_bucket.get("probability") is not None
+                else None
+            ),
+            "market_price": None,
+            "edge_percent": None,
+            "signal_label": "MONITOR",
+            "confidence": "low",
+            "yes_token": None,
+            "no_token": None,
+            "yes_buy": None,
+            "yes_sell": None,
+            "no_buy": None,
+            "no_sell": None,
+            "last_trade_price": None,
+            "liquidity": None,
+            "volume": None,
+            "sparkline": [p.get("probability", 0) for p in distribution[:8] if isinstance(p, dict)],
+            "recent_trades": [],
+            "websocket": {},
+        },
+        "risk": data.get("risk"),
+        "ai_analysis": data.get("ai_analysis") or "",
+        "errors": {},
+    }
+
+
 @app.get("/api/history/{name}")
 async def city_history(name: str):
     """Return historical accuracy data (DEB, mu, actuals) for a city."""
@@ -625,6 +729,20 @@ async def city_history(name: str):
         })
     return {"history": out}
 
+
+@app.get("/api/city/{name}/summary")
+async def city_summary(name: str, force_refresh: bool = False):
+    city = _normalize_city_or_404(name)
+    data = _analyze(city, force_refresh=force_refresh)
+    return _build_city_summary_payload(data)
+
+
+@app.get("/api/city/{name}/detail")
+async def city_detail_aggregate(name: str, force_refresh: bool = False):
+    city = _normalize_city_or_404(name)
+    data = _analyze(city, force_refresh=force_refresh)
+    return _build_city_detail_payload(data)
+
 # ──────────────────────────────────────────────────────────
 #  Entrypoint
 # ──────────────────────────────────────────────────────────