feat(weather): add hourly precipitation summaries and peak timing to weather card

Fetch hourly precipitation probabilities from Open-Meteo alongside daily
forecasts. Generate human-readable precip summaries ("Rain likely 2–5 PM",
"Rain likely all day") for today and each forecast day. Display today's
summary as a styled callout and show peak precipitation hour in forecast rows.
Also fix briefing pipeline to parse all weather location rows (not just first).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/fabledassistant/services/briefing_pipeline.py

@@ -379,7 +379,11 @@ async def run_compilation(
         if item.get("id")
     ]
 
-    weather_card = parse_weather_card_data(weather_rows[0], temp_unit) if weather_rows else None
+    weather_cards = [
+        card for row in weather_rows
+        if (card := parse_weather_card_data(row, temp_unit)) is not None
+    ]
+    weather_card = weather_cards[0] if weather_cards else None
 
     briefing_text, agentic_messages = await run_agentic_briefing(
         user_id, slot, model, conv_id=None, rss_override=filtered_rss,

src/fabledassistant/services/weather.py

@@ -17,6 +17,7 @@ OPEN_METEO_DAILY = (
     "temperature_2m_max,temperature_2m_min,precipitation_sum,"
     "precipitation_probability_max,weathercode,windspeed_10m_max"
 )
+OPEN_METEO_HOURLY = "precipitation_probability"
 
 # WMO weather code → description (subset; covers the most common codes)
 _WMO_CODES: dict[int, str] = {
@@ -93,6 +94,55 @@ def detect_changes(old_days: list[dict], new_days: list[dict]) -> list[str]:
     return changes
 
 
+def _summarize_precip(hourly_probs: list[tuple[int, int]], threshold: int = 30) -> str | None:
+    """Build a human-readable precipitation summary from (hour, probability) pairs.
+
+    Returns None when no significant precipitation is expected.
+    """
+    wet_hours = [(h, p) for h, p in hourly_probs if p >= threshold]
+    if not wet_hours:
+        return None
+
+    peak_hour, peak_prob = max(wet_hours, key=lambda x: x[1])
+    daytime_hours = [h for h, _ in hourly_probs if 6 <= h <= 22]
+    if not daytime_hours:
+        return None
+
+    wet_daytime = [h for h, p in hourly_probs if 6 <= h <= 22 and p >= threshold]
+    if len(wet_daytime) >= 10:
+        return f"Rain likely all day (up to {peak_prob}%)"
+
+    if not wet_daytime:
+        return None
+
+    def _fmt_hour(h: int) -> str:
+        if h == 0 or h == 24:
+            return "12 AM"
+        if h == 12:
+            return "12 PM"
+        return f"{h} AM" if h < 12 else f"{h - 12} PM"
+
+    start = wet_daytime[0]
+    end = wet_daytime[-1]
+    if start == end:
+        return f"{peak_prob}% chance around {_fmt_hour(start)}"
+    return f"Rain likely {_fmt_hour(start)}–{_fmt_hour(end + 1)} (up to {peak_prob}%)"
+
+
+def _extract_hourly_precip_for_date(raw: dict, date_str: str) -> list[tuple[int, int]]:
+    """Extract (hour, probability) pairs for a specific date from cached forecast JSON."""
+    hourly = raw.get("hourly", {})
+    times = hourly.get("precipitation_probability", [])
+    time_labels = hourly.get("time", [])
+    pairs: list[tuple[int, int]] = []
+    prefix = date_str + "T"
+    for i, t in enumerate(time_labels):
+        if t.startswith(prefix) and i < len(times) and times[i] is not None:
+            hour = int(t[11:13])
+            pairs.append((hour, times[i]))
+    return pairs
+
+
 def parse_weather_card_data(
     cache_row,
     temp_unit: str = "C",
@@ -138,6 +188,30 @@ def parse_weather_card_data(
 
     wind_unit = "mph" if imperial else "km/h"
 
+    today_hourly = _extract_hourly_precip_for_date(raw, today_str)
+    today_precip_summary = _summarize_precip(today_hourly)
+
+    def _forecast_day(d: dict) -> dict:
+        entry: dict = {
+            "day": day_label(d["date"]),
+            "condition": d["description"],
+            "high": to_temp(d["temp_max"]),
+            "low": to_temp(d["temp_min"]),
+            "precip_probability": d["precip_probability"],
+            "precip_mm": d["precip_mm"],
+            "windspeed_max": to_wind(d["windspeed_max"]),
+        }
+        hourly = _extract_hourly_precip_for_date(raw, d["date"])
+        summary = _summarize_precip(hourly)
+        if summary:
+            entry["precip_summary"] = summary
+        if hourly:
+            peak = max(hourly, key=lambda x: x[1])
+            if peak[1] >= 30:
+                h = peak[0]
+                entry["precip_peak_hour"] = f"{h} AM" if h < 12 else ("12 PM" if h == 12 else f"{h - 12} PM")
+        return entry
+
     return {
         "location": getattr(cache_row, "location_label", ""),
         "fetched_at": cache_row.fetched_at.isoformat(),
@@ -148,18 +222,8 @@ def parse_weather_card_data(
         "yesterday_high": to_temp(yesterday_day["temp_max"]) if yesterday_day else None,
         "yesterday_low": to_temp(yesterday_day["temp_min"]) if yesterday_day else None,
         "wind_unit": wind_unit,
-        "forecast": [
-            {
-                "day": day_label(d["date"]),
-                "condition": d["description"],
-                "high": to_temp(d["temp_max"]),
-                "low": to_temp(d["temp_min"]),
-                "precip_probability": d["precip_probability"],
-                "precip_mm": d["precip_mm"],
-                "windspeed_max": to_wind(d["windspeed_max"]),
-            }
-            for d in future_days
-        ],
+        "precip_summary": today_precip_summary,
+        "forecast": [_forecast_day(d) for d in future_days],
     }
 
 
@@ -259,12 +323,13 @@ async def fetch_hourly_precip(lat: float, lon: float) -> dict[str, int]:
 
 
 async def _fetch_open_meteo(lat: float, lon: float) -> dict:
-    """Fetch 7-day forecast from Open-Meteo with current conditions and yesterday's data."""
+    """Fetch 7-day forecast from Open-Meteo with current conditions, hourly precip, and yesterday's data."""
     async with httpx.AsyncClient(timeout=15.0) as client:
         resp = await client.get(OPEN_METEO_URL, params={
             "latitude": lat,
             "longitude": lon,
             "daily": OPEN_METEO_DAILY,
+            "hourly": OPEN_METEO_HOURLY,
             "current_weather": "true",
             "past_days": 1,
             "timezone": "auto",