Update app/climate_data.py

app/climate_data.py

@@ -32,6 +32,7 @@ logger = logging.getLogger(__name__)
 MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
 CLIMATE_ZONES = ["0A", "0B", "1A", "1B", "2A", "2B", "3A", "3B", "3C", "4A", "4B", "4C", "5A", "5B", "5C", "6A", "6B", "7", "8"]
 AU_CCH_DIR = "au_cch"  # Relative path to au_cch folder
+SKY_CLEARNESS_CONSTANT = 5.535e-6  # Constant k for Perez model Sky Clearness Index
 
 # Location mapping for Australian climate projections
 LOCATION_MAPPING = {
@@ -357,9 +358,75 @@ class ClimateDataManager:
                 "pressure": 101325.0
             }
     
+    def _calculate_dew_point(self, dry_bulb: float, relative_humidity: float, atmospheric_pressure: float) -> float:
+        """
+        Calculate dew point temperature using August-Roche-Magnus formula.
+        
+        Args:
+            dry_bulb: Dry bulb temperature in °C
+            relative_humidity: Relative humidity in %
+            atmospheric_pressure: Atmospheric pressure in Pa (not used in simplified formula)
+            
+        Returns:
+            Dew point temperature in °C
+        """
+        try:
+            # Step 1: Calculate saturation vapor pressure (hPa)
+            es = 6.1078 * 10 ** ((7.5 * dry_bulb) / (237.3 + dry_bulb))
+            es = es * 100  # Convert to Pa
+            
+            # Step 2: Calculate actual vapor pressure
+            e = (relative_humidity * es) / 100
+            
+            # Step 3: Calculate dew point temperature
+            if e <= 0:
+                logger.warning(f"Invalid vapor pressure {e} Pa, returning dry bulb temperature {dry_bulb}°C as dew point")
+                return dry_bulb
+            ln_term = math.log(e / 610.78)
+            dew_point = (237.3 * ln_term) / (7.5 - ln_term)
+            
+            # Ensure dew point does not exceed dry bulb temperature
+            dew_point = min(dew_point, dry_bulb)
+            
+            return round(dew_point, 1)
+        except (ValueError, ZeroDivisionError) as e:
+            logger.warning(f"Error calculating dew point: {str(e)}, returning dry bulb temperature {dry_bulb}°C")
+            return dry_bulb
+    
+    def _calculate_sky_clearness_index(self, diffuse_horizontal: float, global_horizontal: float, direct_normal: float) -> Optional[float]:
+        """
+        Calculate Sky Clearness Index using the Perez model.
+        
+        Args:
+            diffuse_horizontal: Diffuse horizontal irradiance in W/m²
+            global_horizontal: Global horizontal irradiance in W/m²
+            direct_normal: Direct normal irradiance in W/m²
+            
+        Returns:
+            Sky Clearness Index (dimensionless) or None if undefined (e.g., nighttime)
+        """
+        try:
+            # Handle nighttime or invalid data
+            if global_horizontal <= 0 or diffuse_horizontal < 0 or direct_normal < 0:
+                return None
+            
+            # Cap diffuse_horizontal to global_horizontal to handle potential measurement errors
+            diffuse_horizontal = min(diffuse_horizontal, global_horizontal)
+            
+            # Calculate Sky Clearness Index using Perez model
+            k = SKY_CLEARNESS_CONSTANT
+            if global_horizontal == 0:
+                return None  # Avoid division by zero
+            epsilon = ((diffuse_horizontal / global_horizontal) + (k * direct_normal)) / (1 + k)
+            
+            return round(epsilon, 3)
+        except Exception as e:
+            logger.warning(f"Error calculating Sky Clearness Index: {str(e)}, returning None")
+            return None
+    
     def _process_hourly_data(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
         """
-        Process hourly data from EPW DataFrame.
+        Process hourly data from EPW DataFrame, including dew point and Sky Clearness Index.
         
         Args:
             df: DataFrame containing EPW data
@@ -372,7 +439,7 @@ class ClimateDataManager:
         try:
             # Ensure numeric columns
             numeric_columns = [
-                "dry_bulb_temp", "relative_humidity", "atmospheric_pressure",
+                "dry_bulb_temp", "dew_point_temp", "relative_humidity", "atmospheric_pressure",
                 "global_horizontal_radiation", "direct_normal_radiation",
                 "diffuse_horizontal_radiation", "wind_speed", "wind_direction"
             ]
@@ -391,18 +458,32 @@ class ClimateDataManager:
                 if pd.isna(row["month"]) or pd.isna(row["day"]) or pd.isna(row["hour"]) or pd.isna(row["dry_bulb_temp"]):
                     continue  # Skip rows with missing critical data
                 
+                # Calculate dew point temperature
+                dry_bulb = float(row["dry_bulb_temp"]) if not pd.isna(row["dry_bulb_temp"]) else 20.0
+                relative_humidity = float(row["relative_humidity"]) if not pd.isna(row["relative_humidity"]) else 50.0
+                atmospheric_pressure = float(row["atmospheric_pressure"]) if not pd.isna(row["atmospheric_pressure"]) else 101325.0
+                dew_point = self._calculate_dew_point(dry_bulb, relative_humidity, atmospheric_pressure)
+                
+                # Calculate Sky Clearness Index
+                diffuse_horizontal = float(row["diffuse_horizontal_radiation"]) if not pd.isna(row["diffuse_horizontal_radiation"]) else 0.0
+                global_horizontal = float(row["global_horizontal_radiation"]) if not pd.isna(row["global_horizontal_radiation"]) else 0.0
+                direct_normal = float(row["direct_normal_radiation"]) if not pd.isna(row["direct_normal_radiation"]) else 0.0
+                sky_clearness_index = self._calculate_sky_clearness_index(diffuse_horizontal, global_horizontal, direct_normal)
+                
                 record = {
                     "month": int(row["month"]),
                     "day": int(row["day"]),
                     "hour": int(row["hour"]),
                     "dry_bulb": float(row["dry_bulb_temp"]) if not pd.isna(row["dry_bulb_temp"]) else 20.0,
+                    "dew_point": dew_point,
                     "relative_humidity": float(row["relative_humidity"]) if not pd.isna(row["relative_humidity"]) else 50.0,
                     "atmospheric_pressure": float(row["atmospheric_pressure"]) if not pd.isna(row["atmospheric_pressure"]) else 101325.0,
                     "global_horizontal_radiation": float(row["global_horizontal_radiation"]) if not pd.isna(row["global_horizontal_radiation"]) else 0.0,
                     "direct_normal_radiation": float(row["direct_normal_radiation"]) if not pd.isna(row["direct_normal_radiation"]) else 0.0,
                     "diffuse_horizontal_radiation": float(row["diffuse_horizontal_radiation"]) if not pd.isna(row["diffuse_horizontal_radiation"]) else 0.0,
                     "wind_speed": float(row["wind_speed"]) if not pd.isna(row["wind_speed"]) else 0.0,
-                    "wind_direction": float(row["wind_direction"]) if not pd.isna(row["wind_direction"]) else 0.0
+                    "wind_direction": float(row["wind_direction"]) if not pd.isna(row["wind_direction"]) else 0.0,
+                    "sky_clearness_index": sky_clearness_index if sky_clearness_index is not None else None
                 }
                 hourly_data.append(record)
             
@@ -769,13 +850,15 @@ def display_climate_summary(climate_data: Dict[str, Any]):
                 "Day": record["day"],
                 "Hour": record["hour"],
                 "Dry Bulb Temp (°C)": f"{record['dry_bulb']:.1f}",
+                "Dew Point Temp (°C)": f"{record['dew_point']:.1f}" if record['dew_point'] is not None else "N/A",
                 "Relative Humidity (%)": f"{record['relative_humidity']:.1f}",
                 "Atmospheric Pressure (Pa)": f"{record['atmospheric_pressure']:.1f}",
                 "Global Horizontal Radiation (W/m²)": f"{record['global_horizontal_radiation']:.1f}",
                 "Direct Normal Radiation (W/m²)": f"{record['direct_normal_radiation']:.1f}",
                 "Diffuse Horizontal Radiation (W/m²)": f"{record['diffuse_horizontal_radiation']:.1f}",
                 "Wind Speed (m/s)": f"{record['wind_speed']:.1f}",
-                "Wind Direction (°)": f"{record['wind_direction']:.1f}"
+                "Wind Direction (°)": f"{record['wind_direction']:.1f}",
+                "Sky Clearness Index": f"{record['sky_clearness_index']:.3f}" if record['sky_clearness_index'] is not None else "N/A"
             }
             for record in climate_data["hourly_data"]
         ]
@@ -812,10 +895,12 @@ def display_climate_help():
     EPW (EnergyPlus Weather) files contain hourly weather data for a specific location, including:
     
     * Dry-bulb temperature
+    * Dew point temperature
     * Relative humidity
     * Solar radiation (direct and diffuse)
     * Wind speed and direction
     * Atmospheric pressure
+    * Sky Clearness Index (calculated)
     
     **Where to Find EPW Files:**
     
@@ -838,7 +923,7 @@ def display_climate_help():
     * Typical/Extreme periods
     * Ground temperatures by depth
     * Monthly average temperatures and solar radiation
-    * Hourly data statistics with downloadable tables
+    * Hourly data statistics with downloadable tables (including dew point and Sky Clearness Index)
     
     This information will be used throughout the calculation process.
     """)