Fix smoke plume artifacts: realistic HMS thresholds, light smoothing/morphology for polygons, proper contour grid axes, and legend sync

app.py

@@ -185,7 +185,7 @@ class FoliumSmokeRenderer:
         <div style="margin: 5px 0;">
             <span style="display: inline-block; width: 20px; height: 15px; 
                          background-color: #E8E8E8; border: 1px solid #CCC; margin-right: 5px;"></span>
-            <span style="font-size: 12px;">Light (0.5-15 µg/m³)</span>
+            <span style="font-size: 12px;">Light (3–15 µg/m³)</span>
         </div>
         <div style="margin: 5px 0;">
             <span style="display: inline-block; width: 20px; height: 15px; 
@@ -272,12 +272,12 @@ class HMSSmokePolygonGenerator:
     """Generate HMS-style smoke plume polygons from HRRR-Smoke data"""
     
     def __init__(self):
-        # HMS operational smoke detection categories (post-2022 update)
-        # HMS analysts use visual detection from GOES 1km imagery, not direct PM2.5 thresholds
+        # HMS-style category thresholds expressed in µg/m³ (approximate, for visualization)
+        # These align with the legend and produce HMS-like plumes
         self.density_categories = {
-            'light': {'min': 1, 'max': 2, 'color': '#FFFF88', 'description': 'Light smoke (category 1)'},
-            'medium': {'min': 2, 'max': 3, 'color': '#FFB366', 'description': 'Medium smoke (category 2)'},
-            'heavy': {'min': 3, 'max': 999, 'color': '#FF6666', 'description': 'Heavy smoke (category 3)'}
+            'light': {'min': 3, 'max': 15, 'color': '#FFFF88', 'description': 'Light smoke (3–15 µg/m³)'},
+            'medium': {'min': 15, 'max': 35, 'color': '#FFB366', 'description': 'Medium smoke (15–35 µg/m³)'},
+            'heavy': {'min': 35, 'max': 9999, 'color': '#FF6666', 'description': 'Heavy smoke (35+ µg/m³)'}
         }
         
         # HMS visibility-based smoke detection thresholds (primary detection method)
@@ -496,7 +496,7 @@ class HMSSmokePolygonGenerator:
         return classifications
     
     def extract_smoke_polygons(self, lat2d, lon2d, smoke_values, min_area=0.01, is_hms_categorical=False):
-        """Extract smoke plume polygons from gridded data - optimized for speed"""
+        """Extract smoke plume polygons from gridded data with light smoothing for HMS-like shapes"""
         polygons = []
         
         if not HMS_LIBS_AVAILABLE:
@@ -510,8 +510,14 @@ class HMSSmokePolygonGenerator:
                 classifications = smoke_values.astype(int)
                 print(f"Using HMS categorical data directly: {np.unique(classifications)}")
             else:
-                # Classify smoke densities from concentration values
-                classifications = self.classify_smoke_density(smoke_values)
+                # Lightly smooth the continuous field before categorization to avoid speckle
+                try:
+                    from scipy import ndimage
+                    smoothed = ndimage.gaussian_filter(smoke_values.astype(float), sigma=1.0)
+                except Exception:
+                    smoothed = smoke_values
+                # Classify smoke densities from concentration values (µg/m³)
+                classifications = self.classify_smoke_density(smoothed)
             
             # Process each density category
             for category_name, category_info in self.density_categories.items():
@@ -528,9 +534,30 @@ class HMSSmokePolygonGenerator:
                 if not np.any(category_mask):
                     continue
                 
-                # Skip morphological operations for speed - use raw mask
-                # smoothed_mask = binary_dilation(category_mask, iterations=1)
-                smoothed_mask = category_mask
+                # Apply minimal morphology to reduce tiny blocky artifacts
+                try:
+                    from scipy import ndimage
+                    # Remove isolated noise, close small gaps
+                    smoothed_mask = ndimage.binary_opening(category_mask, iterations=1)
+                    smoothed_mask = ndimage.binary_closing(smoothed_mask, iterations=1)
+                    # Light dilation to smooth edges
+                    smoothed_mask = ndimage.binary_dilation(smoothed_mask, iterations=1)
+                except Exception:
+                    smoothed_mask = category_mask
+
+                # Remove very small components (size threshold by category)
+                try:
+                    from scipy import ndimage
+                    labeled, num = ndimage.label(smoothed_mask)
+                    sizes = np.bincount(labeled.ravel())
+                    # Heavier categories can retain smaller features; light should be larger
+                    min_pixels = {1: 80, 2: 50, 3: 25}.get(category_id, 40)
+                    remove = sizes < min_pixels
+                    remove_idx = np.nonzero(remove)[0]
+                    if len(remove_idx) > 0:
+                        smoothed_mask[np.isin(labeled, remove_idx)] = False
+                except Exception:
+                    pass
                 
                 # Find contours with simpler algorithm
                 try:
@@ -544,8 +571,8 @@ class HMSSmokePolygonGenerator:
                         if len(contour) < 4:  # Need at least 4 points for polygon
                             continue
                         
-                        # Simplify contour for speed - use smaller step for light smoke to preserve more detail
-                        step = 2 if category_name == 'light' else 2
+                        # Simplify contour for speed while retaining shape
+                        step = 2 if category_name == 'heavy' else 3
                         simplified_contour = contour[::step]
                         
                         # Convert pixel coordinates to lat/lon
@@ -1587,11 +1614,19 @@ class HRRRSmokeApp:
                     # Try contour plotting
                     try:
                         opacity = 0.6 if polygons else 1.0  # Reduce opacity if polygons are shown
+                        # Use 2D z with 1D axes for rectilinear grids (avoids jagged artifacts)
+                        if lat2d.ndim == 2 and lon2d.ndim == 2:
+                            x_axis = lon2d[0, :]
+                            y_axis = lat2d[:, 0]
+                        else:
+                            x_axis = lon2d
+                            y_axis = lat2d
                         fig.add_trace(go.Contour(
-                            x=lon2d.flatten(),
-                            y=lat2d.flatten(),
-                            z=z2d.flatten(),
+                            x=x_axis,
+                            y=y_axis,
+                            z=z2d,
                             colorscale=colorscale,
+                            contours=dict(coloring='heatmap'),
                             showscale=True,
                             opacity=opacity,
                             colorbar=dict(
@@ -2438,4 +2473,4 @@ with gr.Blocks(title="HRRR Smoke Forecast", theme=gr.themes.Soft()) as app:
     """)
 
 if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860)
+    app.launch(server_name="0.0.0.0", server_port=7860)