Update app.py

app.py

@@ -1557,7 +1557,163 @@ def predict_storm_route_and_intensity_with_oceanic_data(
 # -----------------------------
 # FIXED: ADVANCED ML FEATURES WITH ROBUST ERROR HANDLING
 # -----------------------------
+def calculate_environmental_steering_speed(lat, lon, month, oni_value, slp_data):
+    """Calculate storm forward speed based on environmental steering"""
+    base_speed = 0.15  # Default speed in degrees/hour
+    
+    # Latitude effects
+    if lat < 20:
+        speed_factor = 0.8  # Slower in tropics
+    elif lat < 30:
+        speed_factor = 1.2  # Faster in subtropics
+    else:
+        speed_factor = 1.5  # Fast in mid-latitudes
+    
+    # Pressure gradient effects (if SLP data available)
+    if slp_data and slp_data['success']:
+        try:
+            # Calculate approximate pressure gradient (simplified)
+            slp_value = oceanic_manager.interpolate_data_to_point(slp_data, lat, lon, 'slp')
+            if not np.isnan(slp_value):
+                slp_hpa = slp_value if slp_value > 500 else slp_value / 100
+                if slp_hpa < 1008:  # Low pressure - faster motion
+                    speed_factor *= 1.2
+                elif slp_hpa > 1015:  # High pressure - slower motion
+                    speed_factor *= 0.8
+        except:
+            pass
+    
+    return base_speed * speed_factor
+
+def calculate_motion_tendency(lat, lon, month, oni_value, hour, slp_data):
+    """Calculate motion tendency with environmental steering"""
+    # Base climatological motion
+    ridge_position = 32 + 4 * np.sin(2 * np.pi * (month - 6) / 4)
+    
+    if lat < ridge_position - 10:
+        base_lat_tendency = 0.05  # Poleward
+        base_lon_tendency = -0.12  # Westward
+    elif lat > ridge_position - 3:
+        base_lat_tendency = 0.15  # Strong poleward (recurvature)
+        base_lon_tendency = 0.08   # Eastward
+    else:
+        base_lat_tendency = 0.08   # Moderate poleward
+        base_lon_tendency = -0.06  # Moderate westward
+    
+    # ENSO steering effects
+    if oni_value > 0.5:  # El Niño
+        base_lon_tendency += 0.03  # More eastward
+        base_lat_tendency += 0.01  # Slightly more poleward
+    elif oni_value < -0.5:  # La Niña
+        base_lon_tendency -= 0.04  # More westward
+    
+    # Add realistic motion uncertainty
+    motion_uncertainty = 0.02 + (hour / 120) * 0.03
+    lat_noise = np.random.normal(0, motion_uncertainty)
+    lon_noise = np.random.normal(0, motion_uncertainty)
+    
+    return base_lat_tendency + lat_noise, base_lon_tendency + lon_noise
+
+def calculate_environmental_intensity_change(
+    current_intensity, environmental_limit, hour, lat, lon, month, oni_value, sst_data
+):
+    """Calculate intensity change based on environmental conditions"""
+    
+    # Base intensity tendency based on development stage
+    if hour <= 48:  # Development phase
+        if current_intensity < environmental_limit * 0.6:
+            base_tendency = 3.5  # Rapid development possible
+        elif current_intensity < environmental_limit * 0.8:
+            base_tendency = 2.0  # Moderate development
+        else:
+            base_tendency = 0.5  # Near limit
+    elif hour <= 120:  # Mature phase
+        if current_intensity < environmental_limit:
+            base_tendency = 1.0  # Slow intensification
+        else:
+            base_tendency = -0.5  # Slight weakening
+    else:  # Extended phase
+        base_tendency = -2.0  # General weakening trend
+    
+    # Environmental limit constraint
+    if current_intensity >= environmental_limit:
+        base_tendency = min(base_tendency, -1.0)  # Force weakening if over limit
+    
+    # SST effects on development rate
+    if sst_data and sst_data['success']:
+        try:
+            sst_value = oceanic_manager.interpolate_data_to_point(sst_data, lat, lon, 'sst')
+            if not np.isnan(sst_value):
+                sst_celsius = sst_value if sst_value < 50 else sst_value - 273.15
+                if sst_celsius >= 29.5:  # Very warm - enhanced development
+                    base_tendency += 1.5
+                elif sst_celsius >= 28.0:  # Warm - normal development
+                    base_tendency += 0.5
+                elif sst_celsius < 26.5:  # Cool - inhibited development
+                    base_tendency -= 2.0
+        except:
+            pass
+    
+    # Land interaction
+    if lon < 110 or (120 < lon < 125 and lat > 20):  # Near land masses
+        base_tendency -= 8.0
+    
+    # High latitude weakening
+    if lat > 35:
+        base_tendency -= 10.0
+    elif lat > 30:
+        base_tendency -= 4.0
+    
+    # Add realistic intensity uncertainty
+    intensity_noise = np.random.normal(0, 1.0)
+    
+    return base_tendency + intensity_noise
 
+def calculate_dynamic_confidence(hour, lat, lon, use_real_data, sst_success, slp_success):
+    """Calculate dynamic confidence based on data availability and conditions"""
+    base_confidence = 0.92
+    
+    # Time penalty
+    time_penalty = (hour / 120) * 0.35
+    
+    # Data quality bonus
+    data_bonus = 0.0
+    if use_real_data:
+        if sst_success:
+            data_bonus += 0.08
+        if slp_success:
+            data_bonus += 0.05
+    
+    # Environmental uncertainty
+    environment_penalty = 0.0
+    if lat > 30 or lon < 115:  # Challenging forecast regions
+        environment_penalty = 0.12
+    elif lat > 25:
+        environment_penalty = 0.06
+    
+    final_confidence = base_confidence + data_bonus - time_penalty - environment_penalty
+    return max(0.25, min(0.95, final_confidence))
+
+def get_environmental_development_stage(hour, intensity, environmental_limit):
+    """Determine development stage based on time and environmental context"""
+    intensity_fraction = intensity / max(environmental_limit, 50)
+    
+    if hour <= 24:
+        return 'Genesis'
+    elif hour <= 72:
+        if intensity_fraction < 0.3:
+            return 'Early Development'
+        elif intensity_fraction < 0.6:
+            return 'Active Development'
+        else:
+            return 'Rapid Development'
+    elif hour <= 120:
+        if intensity_fraction > 0.8:
+            return 'Peak Intensity'
+        else:
+            return 'Mature Stage'
+    else:
+        return 'Extended Forecast'
 def extract_storm_features(typhoon_data):
     """Extract comprehensive features for clustering analysis - FIXED VERSION"""
     try: