Optimize DWD ICON data downloads for faster performance

- Reduce parameters to 8 essential variables (temp, wind, precip, snow, clouds, cape, gusts)
- Cut forecast hours from 10 to 8 key intervals for 4-day forecast
- Fix timezone comparison issues in daily forecast generation
- Remove problematic LPI parameter causing 404 errors
- Use default humidity values to avoid extra downloads
- Optimize synthetic data generation to 6-hour intervals

🤖 Generated with [Claude Code](https://claude.ai/code)

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

app.py

@@ -233,23 +233,16 @@ def fetch_dwd_icon_data(lat, lon):
         run_date = get_latest_dwd_run()
         print(f"Using DWD ICON run: {run_date.strftime('%Y-%m-%d %H:%M UTC')}")
         
-        # Define parameters to download
+        # Define ESSENTIAL parameters only for faster downloads
         parameters = {
             't_2m': 'Temperature at 2m',
-            'relhum_2m': 'Relative humidity at 2m', 
             'u_10m': 'U-component of wind at 10m',
             'v_10m': 'V-component of wind at 10m',
-            'pmsl': 'Pressure at mean sea level',
             'tot_prec': 'Total precipitation',
-            'rain_con': 'Convective rain',
-            'rain_gsp': 'Grid-scale rain',
-            'snow_con': 'Convective snow',
             'snow_gsp': 'Grid-scale snow',
-            'cape_con': 'Convective Available Potential Energy',
             'clct': 'Total cloud cover',
-            'asob_s': 'Net shortwave radiation at surface',
-            'vmax_10m': 'Wind gusts at 10m',
-            'lpi_con': 'Lightning Potential Index'
+            'cape_con': 'Convective Available Potential Energy',
+            'vmax_10m': 'Wind gusts at 10m'
         }
         
         # Download coordinate files first
@@ -371,8 +364,8 @@ def fetch_fallback_data(lat, lon):
     current_time = datetime.now(timezone.utc)
     forecast_hours = []
     
-    # Generate forecast times
-    for i in range(0, 97, 3):  # Every 3 hours for 4 days
+    # Generate forecast times - 4 days with 6-hour intervals
+    for i in range(0, 97, 6):  # Every 6 hours for 4 days
         forecast_time = current_time + timedelta(hours=i)
         forecast_hours.append(forecast_time)
     
@@ -515,15 +508,8 @@ def process_real_dwd_data(dwd_data, lat, lon):
             else:
                 temperature.append(15.0)  # Default
             
-            # Humidity (convert from fraction to percentage if needed)
-            rh = data['relhum_2m'][i]
-            if rh is not None:
-                if rh <= 1.0:  # Fraction
-                    humidity.append(rh * 100)
-                else:  # Already percentage
-                    humidity.append(rh)
-            else:
-                humidity.append(60.0)  # Default
+            # Humidity - use default since we don't download it for speed
+            humidity.append(60.0)  # Default humidity for faster processing
             
             # Wind components
             u_10m = data['u_10m'][i] if data['u_10m'][i] is not None else 0.0
@@ -970,23 +956,25 @@ def generate_daily_detailed_forecasts(forecast_data, events):
     temperatures = forecast_data['temperature']
     precipitation = forecast_data.get('precipitation', [0] * len(temperatures))
     wind_speeds = forecast_data['wind_speed']
-    humidity = forecast_data['humidity']
+    humidity = forecast_data.get('humidity', [60] * len(temperatures))
     cloud_cover = forecast_data.get('cloud_cover', [50] * len(temperatures))
     
-    # Process 4 days of forecasts
+    # Process 4 days of forecasts - fix timezone comparison
     for day_offset in range(4):
-        target_date = current_time + timedelta(days=day_offset)
+        # Use timezone-naive datetime for comparison
+        base_time = current_time.replace(tzinfo=None) if current_time.tzinfo else current_time
+        target_date = base_time + timedelta(days=day_offset)
         
         # Get day and night periods
         day_start = target_date.replace(hour=6, minute=0, second=0, microsecond=0)
         day_end = target_date.replace(hour=18, minute=0, second=0, microsecond=0)
         night_end = (target_date + timedelta(days=1)).replace(hour=6, minute=0, second=0, microsecond=0)
         
-        # Find data for this day
+        # Find data for this day - handle timezone-aware timestamps
         day_indices = [i for i, ts in enumerate(timestamps) 
-                      if day_start <= ts < day_end]
+                      if day_start <= (ts.replace(tzinfo=None) if ts.tzinfo else ts) < day_end]
         night_indices = [i for i, ts in enumerate(timestamps) 
-                        if day_end <= ts < night_end]
+                        if day_end <= (ts.replace(tzinfo=None) if ts.tzinfo else ts) < night_end]
         
         if not day_indices and not night_indices:
             continue