modified: app.py

modified:   static/css/style.css
	modified:   templates/weather.html

app.py

@@ -193,6 +193,22 @@ def _round_temp(k):
 def _clamp(value, min_value, max_value):
     return max(min_value, min(max_value, value))
 
+def _estimate_uv(dt_local, lat, cloud_pct):
+    """Rough UV index estimate for Central Europe when the MOSMIX station doesn't
+    provide the UVI parameter.  Uses time-of-day, season, latitude, and cloud cover."""
+    hour = dt_local.hour + dt_local.minute / 60.0
+    if hour < 5.5 or hour > 20.5:
+        return 0.0
+    noon = 13.0  # approximate solar noon in Germany (CET)
+    hour_factor = max(0.0, math.cos(math.pi * (hour - noon) / 14.0))
+    # Typical clear-sky peak UV at solar noon for ~51°N, Jan–Dec
+    monthly_peak = [1.0, 1.8, 3.5, 5.0, 6.5, 7.5, 7.2, 6.2, 4.5, 2.5, 1.2, 0.8]
+    seasonal = monthly_peak[dt_local.month - 1]
+    lat_factor = max(0.5, 1.0 - (float(lat) - 51.0) * 0.015)
+    cloud_factor = 1.0 - (cloud_pct or 0) / 100.0 * 0.75
+    uv = seasonal * hour_factor * lat_factor * cloud_factor
+    return round(max(0.0, uv), 1)
+
 def uv_risk_info(uv_index):
     if uv_index is None:
         return "–", "na"
@@ -286,9 +302,9 @@ def temp_trend_info(forecast, step_hours=6):
         return None, None
     delta = round(t1 - t0, 1)
     if delta >= 1.0:
-        return delta, "waermer"
+        return delta, "wärmer"
     if delta <= -1.0:
-        return delta, "kaelter"
+        return delta, "kälter"
     return delta, "konstant"
 
 def _parse_warning_datetime(value):
@@ -489,12 +505,15 @@ def get_mosmix_forecast(lat, lon, hours=72):
             wd       = p.get("wind_direction")
             wind_dir = float(wd) if not _isnan(wd) else None
             uv_raw   = p.get("uv_index")
-            uv       = round(float(uv_raw),1) if not _isnan(uv_raw) else None
+            dt_local = pd.Timestamp(date_val).tz_convert(berlin).tz_localize(None)
+            if not _isnan(uv_raw):
+                uv = round(float(uv_raw), 1)
+            else:
+                uv = _estimate_uv(dt_local, lat, clouds)
             uv_label, uv_level = uv_risk_info(uv)
             feels    = feels_like(temp_c, wind_kmh, clouds)
             confidence_score, confidence_label = hour_confidence_score(temp_c, precip, rain_prob, wind_kmh, gust_kmh, clouds)
             a_score = activity_score(temp_c, precip, rain_prob, wind_kmh, gust_kmh, uv)
-            dt_local = pd.Timestamp(date_val).tz_convert(berlin).tz_localize(None)
             forecast.append({
                 "datetime":     dt_local,
                 "temp_c":       temp_c,