Add comprehensive weather variable support with 30+ meteorological parameters

- Expand variable mapping to include all DWD ICON Global variables:
* Temperature: min/max, dewpoint, soil temp, skin temp
* Wind: direction, gusts, u/v components
* Pressure: sea level, surface pressure
* Precipitation: rain, snow, convective types
* Cloud cover: total, low/mid/high levels
* Solar radiation: direct, diffuse, longwave
* Atmospheric: visibility, boundary layer height, CAPE/CIN
* Moisture: specific humidity, total water vapor, cloud water/ice

- Create comprehensive 9-panel visualization dashboard:
* Temperature panel with min/max and dewpoint
* Humidity/moisture with dual y-axis
* Wind with speed, direction, and gusts
* Pressure with sea level and surface
* Precipitation with rain/snow breakdown
* Multi-level cloud cover visualization
* Solar radiation components
* Atmospheric conditions panel
* Enhanced data summary with variable counts

- Add proper unit conversions for all variables:
* Temperature: Kelvin to Celsius
* Pressure: Pa to hPa
* Precipitation: kg/m²/s to mm/h
* Radiation: J/m² to W/m²
* Visibility: m to km
* Wind direction calculation from u/v components

- Update UI with detailed variable descriptions and enhanced interface

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

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

app.py

@@ -119,12 +119,62 @@ def get_forecast_data(lat, lon, forecast_hour="00"):
         # Extract common meteorological variables
         variables = {}
         var_mapping = {
-            'temperature': ['t_2m', 't_s', 'temp_2m', 'temperature_2m', 't2m'],
-            'humidity': ['relhum_2m', 'rh_2m', 'humidity_2m', 'rh2m', 'qv_2m'],
-            'wind_u': ['u_10m', 'u10m', 'wind_u_10m', 'u10'],
-            'wind_v': ['v_10m', 'v10m', 'wind_v_10m', 'v10'],
-            'pressure': ['pmsl', 'msl', 'pressure_msl', 'ps'],
-            'precipitation': ['tot_prec', 'tp', 'precipitation', 'rain_gsp']
+            # Core temperature variables
+            'temperature': ['t_2m', 't_s', 'temp_2m', 'temperature_2m', 't2m', 'T_2M'],
+            'temp_min': ['tmin_2m', 't_min_2m', 'T_MIN_2M'],
+            'temp_max': ['tmax_2m', 't_max_2m', 'T_MAX_2M'],
+            
+            # Humidity and moisture
+            'humidity': ['relhum_2m', 'rh_2m', 'humidity_2m', 'rh2m', 'RELHUM_2M'],
+            'specific_humidity': ['qv_2m', 'qv_s', 'QV_2M'],
+            'dewpoint': ['td_2m', 'dewpoint_2m', 'TD_2M'],
+            
+            # Wind components and derived
+            'wind_u': ['u_10m', 'u10m', 'wind_u_10m', 'u10', 'U_10M'],
+            'wind_v': ['v_10m', 'v10m', 'wind_v_10m', 'v10', 'V_10M'],
+            'wind_gust': ['vmax_10m', 'wind_gust', 'gust', 'VMAX_10M'],
+            
+            # Pressure variables
+            'pressure': ['pmsl', 'msl', 'pressure_msl', 'ps', 'PMSL'],
+            'surface_pressure': ['ps', 'sp', 'surface_pressure', 'PS'],
+            
+            # Precipitation types
+            'precipitation': ['tot_prec', 'tp', 'precipitation', 'rain_gsp', 'TOT_PREC'],
+            'rain': ['rain_gsp', 'rain_con', 'RAIN_GSP'],
+            'snow': ['snow_gsp', 'snow_con', 'SNOW_GSP'],
+            'convective_precip': ['rain_con', 'snow_con', 'RAIN_CON'],
+            
+            # Cloud variables
+            'cloud_cover': ['clct', 'tcc', 'total_cloud_cover', 'CLCT'],
+            'low_cloud': ['clcl', 'lcc', 'low_cloud_cover', 'CLCL'],
+            'mid_cloud': ['clcm', 'mcc', 'mid_cloud_cover', 'CLCM'],
+            'high_cloud': ['clch', 'hcc', 'high_cloud_cover', 'CLCH'],
+            
+            # Radiation variables
+            'solar_radiation': ['asob_s', 'ssr', 'surface_solar_radiation', 'ASOB_S'],
+            'direct_radiation': ['aswdir_s', 'direct_solar_radiation', 'ASWDIR_S'],
+            'diffuse_radiation': ['aswdif_s', 'diffuse_solar_radiation', 'ASWDIF_S'],
+            'longwave_radiation': ['athb_s', 'surface_thermal_radiation', 'ATHB_S'],
+            'net_radiation': ['aswdir_s', 'net_surface_radiation'],
+            
+            # Visibility and atmospheric conditions
+            'visibility': ['vis', 'visibility', 'VIS'],
+            'fog': ['fog', 'FOG'],
+            
+            # Boundary layer and atmospheric stability
+            'boundary_layer_height': ['hpbl', 'pbl_height', 'HPBL'],
+            'cape': ['cape_ml', 'cape', 'CAPE_ML'],
+            'cin': ['cin_ml', 'cin', 'CIN_ML'],
+            
+            # Additional surface variables
+            'soil_temperature': ['t_soil', 'soil_temp', 'T_SOIL'],
+            'skin_temperature': ['t_skin', 'skin_temp', 'T_S'],
+            'albedo': ['alb_rad', 'albedo', 'ALB_RAD'],
+            
+            # Integrated atmospheric variables
+            'total_water_vapor': ['tqv', 'tcwv', 'total_column_water_vapor', 'TQV'],
+            'total_cloud_water': ['tqc', 'total_cloud_water', 'TQC'],
+            'total_cloud_ice': ['tqi', 'total_cloud_ice', 'TQI'],
         }
         
         extracted_vars = {}
@@ -149,22 +199,62 @@ def get_forecast_data(lat, lon, forecast_hour="00"):
                     except Exception:
                         continue
         
-        # Convert temperature from Kelvin to Celsius if needed
-        if 'temperature' in extracted_vars:
-            temp_vals = extracted_vars['temperature']
-            if np.mean(temp_vals) > 200:  # Likely in Kelvin
-                extracted_vars['temperature'] = temp_vals - 273.15
+        # Unit conversions and processing
+        
+        # Convert temperature variables from Kelvin to Celsius if needed
+        temp_vars = ['temperature', 'temp_min', 'temp_max', 'dewpoint', 'soil_temperature', 'skin_temperature']
+        for temp_var in temp_vars:
+            if temp_var in extracted_vars:
+                temp_vals = extracted_vars[temp_var]
+                if np.mean(temp_vals) > 200:  # Likely in Kelvin
+                    extracted_vars[temp_var] = temp_vals - 273.15
         
-        # Calculate wind speed from u and v components
+        # Calculate wind speed and direction from u and v components
         if 'wind_u' in extracted_vars and 'wind_v' in extracted_vars:
-            wind_speed = np.sqrt(extracted_vars['wind_u']**2 + extracted_vars['wind_v']**2)
+            u_vals = extracted_vars['wind_u']
+            v_vals = extracted_vars['wind_v']
+            wind_speed = np.sqrt(u_vals**2 + v_vals**2)
+            wind_direction = (270 - np.degrees(np.arctan2(v_vals, u_vals))) % 360
             extracted_vars['wind_speed'] = wind_speed
+            extracted_vars['wind_direction'] = wind_direction
+        
+        # Convert relative humidity and cloud cover from fraction to percentage if needed
+        percentage_vars = ['humidity', 'cloud_cover', 'low_cloud', 'mid_cloud', 'high_cloud']
+        for pct_var in percentage_vars:
+            if pct_var in extracted_vars:
+                vals = extracted_vars[pct_var]
+                if np.max(vals) <= 1.0:  # Likely in fraction
+                    extracted_vars[pct_var] = vals * 100
+        
+        # Convert pressure from Pa to hPa if needed
+        pressure_vars = ['pressure', 'surface_pressure']
+        for press_var in pressure_vars:
+            if press_var in extracted_vars:
+                press_vals = extracted_vars[press_var]
+                if np.mean(press_vals) > 50000:  # Likely in Pa
+                    extracted_vars[press_var] = press_vals / 100  # Convert to hPa
         
-        # Convert relative humidity from fraction to percentage if needed
-        if 'humidity' in extracted_vars:
-            humidity_vals = extracted_vars['humidity']
-            if np.max(humidity_vals) <= 1.0:  # Likely in fraction
-                extracted_vars['humidity'] = humidity_vals * 100
+        # Convert precipitation from kg/m²/s to mm/h if needed
+        precip_vars = ['precipitation', 'rain', 'snow', 'convective_precip']
+        for precip_var in precip_vars:
+            if precip_var in extracted_vars:
+                precip_vals = extracted_vars[precip_var]
+                if np.max(precip_vals) < 1:  # Likely in kg/m²/s
+                    extracted_vars[precip_var] = precip_vals * 3600  # Convert to mm/h
+        
+        # Convert radiation from J/m² to W/m² if needed (assuming 3-hour accumulation)
+        radiation_vars = ['solar_radiation', 'direct_radiation', 'diffuse_radiation', 'longwave_radiation']
+        for rad_var in radiation_vars:
+            if rad_var in extracted_vars:
+                rad_vals = extracted_vars[rad_var]
+                if np.max(rad_vals) > 10000:  # Likely accumulated energy
+                    extracted_vars[rad_var] = rad_vals / 10800  # Convert J/m² to W/m² (3h = 10800s)
+        
+        # Convert visibility from m to km if needed
+        if 'visibility' in extracted_vars:
+            vis_vals = extracted_vars['visibility']
+            if np.mean(vis_vals) > 1000:  # Likely in meters
+                extracted_vars['visibility'] = vis_vals / 1000  # Convert to km
         
         # Get time coordinates
         if 'time' in ds.coords:
@@ -199,11 +289,39 @@ def get_forecast_data(lat, lon, forecast_hour="00"):
             'nearest_grid_lon': float(grid_lons[nearest_idx])
         }
         
-        # Add additional variables if available
-        if 'pressure' in extracted_vars:
-            result['pressure'] = extracted_vars['pressure'][:max_hours]
-        if 'precipitation' in extracted_vars:
-            result['precipitation'] = extracted_vars['precipitation'][:max_hours]
+        # Add all available additional variables
+        additional_vars = [
+            # Temperature variables
+            'temp_min', 'temp_max', 'dewpoint', 'soil_temperature', 'skin_temperature',
+            
+            # Wind variables
+            'wind_direction', 'wind_gust',
+            
+            # Pressure variables
+            'pressure', 'surface_pressure',
+            
+            # Precipitation variables
+            'precipitation', 'rain', 'snow', 'convective_precip',
+            
+            # Cloud variables
+            'cloud_cover', 'low_cloud', 'mid_cloud', 'high_cloud',
+            
+            # Radiation variables
+            'solar_radiation', 'direct_radiation', 'diffuse_radiation', 'longwave_radiation',
+            
+            # Atmospheric variables
+            'visibility', 'boundary_layer_height', 'cape', 'cin',
+            
+            # Moisture variables
+            'specific_humidity', 'total_water_vapor', 'total_cloud_water', 'total_cloud_ice',
+            
+            # Other surface variables
+            'albedo', 'fog'
+        ]
+        
+        for var in additional_vars:
+            if var in extracted_vars:
+                result[var] = extracted_vars[var][:max_hours]
         
         return result
         
@@ -234,37 +352,139 @@ def get_forecast_data(lat, lon, forecast_hour="00"):
         }
 
 def create_forecast_plot(forecast_data):
-    """Create forecast visualization plots"""
+    """Create comprehensive forecast visualization plots"""
     if isinstance(forecast_data, str):
         return forecast_data
     
-    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 8))
+    # Create a larger figure with more subplots for all variables
+    fig = plt.figure(figsize=(16, 12))
     
     timestamps = forecast_data['timestamps']
     
-    # Temperature plot
-    ax1.plot(timestamps, forecast_data['temperature'], 'r-', linewidth=2)
-    ax1.set_title('Temperature Forecast (°C)')
-    ax1.set_ylabel('Temperature (°C)')
+    # Create a 3x3 grid of subplots
+    gs = fig.add_gridspec(3, 3, hspace=0.4, wspace=0.3)
+    
+    # Temperature plot with min/max if available
+    ax1 = fig.add_subplot(gs[0, 0])
+    ax1.plot(timestamps, forecast_data['temperature'], 'r-', linewidth=2, label='Temperature')
+    if 'temp_max' in forecast_data:
+        ax1.plot(timestamps, forecast_data['temp_max'], 'r--', linewidth=1, alpha=0.7, label='Max')
+    if 'temp_min' in forecast_data:
+        ax1.plot(timestamps, forecast_data['temp_min'], 'b--', linewidth=1, alpha=0.7, label='Min')
+    if 'dewpoint' in forecast_data:
+        ax1.plot(timestamps, forecast_data['dewpoint'], 'c-', linewidth=1, alpha=0.8, label='Dewpoint')
+    ax1.set_title('Temperature (°C)')
+    ax1.set_ylabel('°C')
     ax1.grid(True, alpha=0.3)
-    ax1.tick_params(axis='x', rotation=45)
-    
-    # Humidity plot
-    ax2.plot(timestamps, forecast_data['humidity'], 'b-', linewidth=2)
-    ax2.set_title('Humidity Forecast (%)')
-    ax2.set_ylabel('Humidity (%)')
+    ax1.legend(fontsize=8)
+    ax1.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Humidity and moisture
+    ax2 = fig.add_subplot(gs[0, 1])
+    ax2.plot(timestamps, forecast_data['humidity'], 'b-', linewidth=2, label='Rel. Humidity')
+    if 'specific_humidity' in forecast_data:
+        ax2_twin = ax2.twinx()
+        ax2_twin.plot(timestamps, forecast_data['specific_humidity'], 'g-', linewidth=1, alpha=0.7, label='Spec. Humidity')
+        ax2_twin.set_ylabel('g/kg', color='g')
+        ax2_twin.tick_params(axis='y', labelcolor='g')
+    ax2.set_title('Humidity (%)')
+    ax2.set_ylabel('%')
     ax2.grid(True, alpha=0.3)
-    ax2.tick_params(axis='x', rotation=45)
-    
-    # Wind speed plot
-    ax3.plot(timestamps, forecast_data['wind_speed'], 'g-', linewidth=2)
-    ax3.set_title('Wind Speed Forecast (m/s)')
-    ax3.set_ylabel('Wind Speed (m/s)')
+    ax2.legend(fontsize=8)
+    ax2.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Wind speed, direction, and gusts
+    ax3 = fig.add_subplot(gs[0, 2])
+    ax3.plot(timestamps, forecast_data['wind_speed'], 'g-', linewidth=2, label='Wind Speed')
+    if 'wind_gust' in forecast_data:
+        ax3.plot(timestamps, forecast_data['wind_gust'], 'orange', linewidth=1, alpha=0.7, label='Gusts')
+    if 'wind_direction' in forecast_data:
+        ax3_twin = ax3.twinx()
+        ax3_twin.scatter(timestamps, forecast_data['wind_direction'], c='purple', s=10, alpha=0.6, label='Direction')
+        ax3_twin.set_ylabel('Direction (°)', color='purple')
+        ax3_twin.set_ylim(0, 360)
+        ax3_twin.tick_params(axis='y', labelcolor='purple')
+    ax3.set_title('Wind (m/s)')
+    ax3.set_ylabel('m/s')
     ax3.grid(True, alpha=0.3)
-    ax3.tick_params(axis='x', rotation=45)
-    
-    # Summary info
-    ax4.axis('off')
+    ax3.legend(fontsize=8)
+    ax3.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Pressure
+    ax4 = fig.add_subplot(gs[1, 0])
+    if 'pressure' in forecast_data:
+        ax4.plot(timestamps, forecast_data['pressure'], 'purple', linewidth=2, label='Sea Level')
+    if 'surface_pressure' in forecast_data:
+        ax4.plot(timestamps, forecast_data['surface_pressure'], 'indigo', linewidth=1, alpha=0.7, label='Surface')
+    ax4.set_title('Pressure (hPa)')
+    ax4.set_ylabel('hPa')
+    ax4.grid(True, alpha=0.3)
+    ax4.legend(fontsize=8)
+    ax4.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Precipitation
+    ax5 = fig.add_subplot(gs[1, 1])
+    if 'precipitation' in forecast_data:
+        ax5.bar(timestamps, forecast_data['precipitation'], alpha=0.7, color='blue', label='Total', width=0.1)
+    if 'rain' in forecast_data:
+        ax5.bar(timestamps, forecast_data['rain'], alpha=0.5, color='lightblue', label='Rain', width=0.08)
+    if 'snow' in forecast_data:
+        ax5.bar(timestamps, forecast_data['snow'], alpha=0.5, color='white', edgecolor='gray', label='Snow', width=0.06)
+    ax5.set_title('Precipitation (mm/h)')
+    ax5.set_ylabel('mm/h')
+    ax5.grid(True, alpha=0.3)
+    ax5.legend(fontsize=8)
+    ax5.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Cloud cover
+    ax6 = fig.add_subplot(gs[1, 2])
+    if 'cloud_cover' in forecast_data:
+        ax6.fill_between(timestamps, forecast_data['cloud_cover'], alpha=0.3, color='gray', label='Total')
+    if 'low_cloud' in forecast_data:
+        ax6.plot(timestamps, forecast_data['low_cloud'], 'brown', linewidth=1, label='Low')
+    if 'mid_cloud' in forecast_data:
+        ax6.plot(timestamps, forecast_data['mid_cloud'], 'orange', linewidth=1, label='Mid')
+    if 'high_cloud' in forecast_data:
+        ax6.plot(timestamps, forecast_data['high_cloud'], 'lightblue', linewidth=1, label='High')
+    ax6.set_title('Cloud Cover (%)')
+    ax6.set_ylabel('%')
+    ax6.set_ylim(0, 100)
+    ax6.grid(True, alpha=0.3)
+    ax6.legend(fontsize=8)
+    ax6.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Solar radiation
+    ax7 = fig.add_subplot(gs[2, 0])
+    if 'solar_radiation' in forecast_data:
+        ax7.fill_between(timestamps, forecast_data['solar_radiation'], alpha=0.3, color='yellow', label='Solar')
+    if 'direct_radiation' in forecast_data:
+        ax7.plot(timestamps, forecast_data['direct_radiation'], 'orange', linewidth=1, label='Direct')
+    if 'diffuse_radiation' in forecast_data:
+        ax7.plot(timestamps, forecast_data['diffuse_radiation'], 'gold', linewidth=1, label='Diffuse')
+    ax7.set_title('Solar Radiation (W/m²)')
+    ax7.set_ylabel('W/m²')
+    ax7.grid(True, alpha=0.3)
+    ax7.legend(fontsize=8)
+    ax7.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Additional atmospheric parameters
+    ax8 = fig.add_subplot(gs[2, 1])
+    if 'visibility' in forecast_data:
+        ax8.plot(timestamps, forecast_data['visibility'], 'teal', linewidth=2, label='Visibility (km)')
+    if 'boundary_layer_height' in forecast_data:
+        ax8_twin = ax8.twinx()
+        ax8_twin.plot(timestamps, forecast_data['boundary_layer_height'], 'brown', linewidth=1, alpha=0.7, label='BL Height (m)')
+        ax8_twin.set_ylabel('BL Height (m)', color='brown')
+        ax8_twin.tick_params(axis='y', labelcolor='brown')
+    ax8.set_title('Atmospheric Conditions')
+    ax8.set_ylabel('Visibility (km)')
+    ax8.grid(True, alpha=0.3)
+    ax8.legend(fontsize=8)
+    ax8.tick_params(axis='x', rotation=45, labelsize=8)
+    
+    # Summary info panel
+    ax9 = fig.add_subplot(gs[2, 2])
+    ax9.axis('off')
     
     # Check if we have real data or fallback
     data_source = "Real DWD ICON Data" if 'error' not in forecast_data else "Fallback Synthetic Data"
@@ -273,34 +493,53 @@ def create_forecast_plot(forecast_data):
     # Grid point info
     grid_info = ""
     if 'nearest_grid_lat' in forecast_data and 'nearest_grid_lon' in forecast_data:
-        grid_info = f"Nearest Grid: {forecast_data['nearest_grid_lat']:.2f}°N, {forecast_data['nearest_grid_lon']:.2f}°E\n"
+        grid_info = f"Grid: {forecast_data['nearest_grid_lat']:.2f}°N, {forecast_data['nearest_grid_lon']:.2f}°E\n"
+    
+    # Count available variables
+    available_vars = []
+    var_categories = {
+        'Temperature': ['temperature', 'temp_min', 'temp_max', 'dewpoint'],
+        'Wind': ['wind_speed', 'wind_direction', 'wind_gust'],
+        'Pressure': ['pressure', 'surface_pressure'],
+        'Precipitation': ['precipitation', 'rain', 'snow'],
+        'Clouds': ['cloud_cover', 'low_cloud', 'mid_cloud', 'high_cloud'],
+        'Radiation': ['solar_radiation', 'direct_radiation', 'diffuse_radiation'],
+        'Atmosphere': ['visibility', 'boundary_layer_height', 'cape', 'humidity']
+    }
+    
+    for category, vars_list in var_categories.items():
+        count = sum(1 for var in vars_list if var in forecast_data)
+        if count > 0:
+            available_vars.append(f"{category}: {count}")
     
     summary_text = f"""
-    Location: {forecast_data['lat']:.2f}°N, {forecast_data['lon']:.2f}°E
-    {grid_info}Data Source: {data_source}
-    Forecast: {forecast_info}
-    
-    Current Conditions:
-    Temperature: {forecast_data['temperature'][0]:.1f}°C
-    Humidity: {forecast_data['humidity'][0]:.1f}%
-    Wind Speed: {forecast_data['wind_speed'][0]:.1f} m/s
-    
-    Forecast Range:
-    Temp: {min(forecast_data['temperature']):.1f}°C to {max(forecast_data['temperature']):.1f}°C
-    Humidity: {min(forecast_data['humidity']):.1f}% to {max(forecast_data['humidity']):.1f}%
-    Wind: {min(forecast_data['wind_speed']):.1f} to {max(forecast_data['wind_speed']):.1f} m/s
-    """
+Location: {forecast_data['lat']:.2f}°N, {forecast_data['lon']:.2f}°E
+{grid_info}
+Data: {data_source}
+Forecast: {forecast_info}
+
+Available Variables:
+{chr(10).join(available_vars)}
+
+Current Conditions:
+Temp: {forecast_data['temperature'][0]:.1f}°C
+Humidity: {forecast_data['humidity'][0]:.1f}%
+Wind: {forecast_data['wind_speed'][0]:.1f} m/s
+"""
+    
+    # Add pressure if available
+    if 'pressure' in forecast_data:
+        summary_text += f"Pressure: {forecast_data['pressure'][0]:.1f} hPa\n"
     
     # Add error info if present
     if 'error' in forecast_data:
-        summary_text += f"\n\nNote: Using fallback data due to:\n{forecast_data['error'][:100]}..."
+        summary_text += f"\nNote: Using fallback data\nReason: {forecast_data['error'][:80]}..."
     
     color = 'lightgreen' if 'error' not in forecast_data else 'lightyellow'
-    ax4.text(0.1, 0.9, summary_text, transform=ax4.transAxes, fontsize=9,
+    ax9.text(0.05, 0.95, summary_text, transform=ax9.transAxes, fontsize=8,
             verticalalignment='top', bbox=dict(boxstyle='round', facecolor=color, alpha=0.7))
     
     plt.tight_layout()
-    plt.subplots_adjust(hspace=0.3)
     
     return fig
 
@@ -353,7 +592,17 @@ def create_attribution_text():
 # Create the Gradio interface
 with gr.Blocks(title="DWD ICON Global Weather Forecast") as app:
     gr.Markdown("# 🌦️ DWD ICON Global Weather Forecast")
-    gr.Markdown("Click on the map to select a location and view the 4-day weather forecast from the DWD ICON Global model.")
+    gr.Markdown("""
+    **Comprehensive Weather Forecasting Dashboard** - Click on the map to select any location and view detailed 4-day forecasts with:
+    
+    📊 **9 Weather Panels**: Temperature, Humidity/Moisture, Wind, Pressure, Precipitation, Cloud Cover, Solar Radiation, Atmospheric Conditions, and Data Summary
+    
+    🔢 **30+ Weather Variables**: Temperature (min/max/dewpoint), Wind (speed/direction/gusts), Pressure (sea level/surface), 
+    Precipitation (rain/snow/convective), Cloud layers (low/mid/high/total), Solar radiation (direct/diffuse/longwave), 
+    Visibility, Boundary layer height, Atmospheric stability (CAPE/CIN), and more!
+    
+    🎯 **Real DWD ICON Data** from the German Weather Service via OpenClimateFix
+    """)
     
     with gr.Row():
         with gr.Column(scale=2):