feat: add wind rose (10m) using wind direction var detected by long_name; ensure snow level fetch prefers snowlvl_0m; include wind rose plot in UI

app.py

@@ -23,6 +23,7 @@ from plot_utils import (
     make_cloud_layers_fig,
     make_precip_type_fig,
     make_snow_level_fig,
+    make_wind_rose_fig,
 )
 
 
@@ -45,6 +46,7 @@ def run_forecast(lat, lon, hours=24):
     cloud_layers_fig = None
     precip_type_fig = None
     snow_level_fig = None
+    wind_rose_fig = None
 
     def y(msg):
         print(msg, flush=True)
@@ -61,6 +63,7 @@ def run_forecast(lat, lon, hours=24):
             cloud_layers_fig,
             precip_type_fig,
             snow_level_fig,
+            wind_rose_fig,
         )
 
     if lat is None or lon is None:
@@ -162,6 +165,17 @@ def run_forecast(lat, lon, hours=24):
     except Exception as e:
         print(f"Snow level plot error: {e}")
 
+    # Wind rose (if direction present)
+    try:
+        import pandas as _pd
+        if "wdir_deg" in df.columns and "wind_mph" in df.columns:
+            x = _pd.to_datetime(df["time_utc"], utc=True, errors="coerce")
+            wdir = _pd.Series(df["wdir_deg"].astype(float).values, index=x)
+            wspd = _pd.Series(df["wind_mph"].astype(float).values, index=x)
+            wind_rose_fig = make_wind_rose_fig(wdir, wspd)
+    except Exception as e:
+        print(f"Wind rose plot error: {e}")
+
     # Deterministic snowfall derivations if available
     snow6_fig = None
     snow24_fig = None
@@ -217,6 +231,7 @@ def run_forecast(lat, lon, hours=24):
         cloud_layers_fig,
         precip_type_fig,
         snow_level_fig,
+        wind_rose_fig,
     )
     return
 
@@ -305,22 +320,23 @@ with gr.Blocks(title="NBM Point Forecast (NOAA NOMADS)") as demo:
             cloud_layers_plot = gr.Plot(label="Cloud Layers (%)")
             precip_type_plot = gr.Plot(label="Precip Type Probabilities")
             snow_level_plot = gr.Plot(label="Snow Level + Precip")
+            wind_rose_plot = gr.Plot(label="Wind Rose (10 m)")
 
     # Trigger when clicking Fetch, or when lat/lon are edited (e.g., via map)
     btn.click(
         run_forecast,
         inputs=[lat_in, lon_in, hours],
-        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot],
+        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot, wind_rose_plot],
     )
     lat_in.change(
         run_forecast,
         inputs=[lat_in, lon_in, hours],
-        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot],
+        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot, wind_rose_plot],
     )
     lon_in.change(
         run_forecast,
         inputs=[lat_in, lon_in, hours],
-        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot],
+        outputs=[status, table, temp_wind_plot, cloud_precip_plot, snow_prob_plot, snow6_plot, snow24_plot, snow48_plot, cloud_layers_plot, precip_type_plot, snow_level_plot, wind_rose_plot],
     )
 
 

nbm_client.py

@@ -218,6 +218,15 @@ def fetch_point_forecast_df(
     )
     t_fetch = time.perf_counter()
     subset = subset.isel(time=slice(0, n)).load()
+    # Attempt to locate 10m wind direction variable by metadata
+    wdir_deg = None
+    try:
+        wdir_name = _find_10m_wind_dir_var(ds)
+        if wdir_name:
+            arr = ds[wdir_name].isel(lat=ilat, lon=ilon, time=slice(0, n)).load().values
+            wdir_deg = _mask_fill(arr)
+    except Exception:
+        wdir_deg = None
     logger.info(f"Fetched subset data in {time.perf_counter()-t_fetch:.2f}s")
     t_index = t_index[:n]
 
@@ -273,6 +282,8 @@ def fetch_point_forecast_df(
         data["snow_in"] = np.round(snow_in, 2)
     if snow_est_in is not None and (snow_in is None or np.nanmax(snow_in) <= 0):
         data["snow_est_in"] = np.round(snow_est_in, 2)
+    if wdir_deg is not None:
+        data["wdir_deg"] = np.round(wdir_deg.astype(float), 1)
     df = pd.DataFrame(data)
 
     meta = {
@@ -455,13 +466,17 @@ def fetch_snow_level_kft(
     lon_vals = ds["lon"].values
     ilat = _nearest_index(lat_vals, lat)
     ilon = _nearest_index(lon_vals, lon)
-    # find first var whose long_name mentions 'snow level'
+    # Prefer canonical name if present
     candidate: Optional[str] = None
-    for v in ds.variables:
-        ln = str(ds[v].attrs.get("long_name", "")).lower()
-        if "snow level" in ln:
-            candidate = v
-            break
+    if "snowlvl_0m" in ds.variables:
+        candidate = "snowlvl_0m"
+    else:
+        # find first var whose long_name mentions 'snow level'
+        for v in ds.variables:
+            ln = str(ds[v].attrs.get("long_name", "")).lower()
+            if "snow level" in ln:
+                candidate = v
+                break
     if candidate is None:
         return pd.DatetimeIndex([]), None
     t_full = _to_datetime_index(ds["time"])  # assume aligned
@@ -473,3 +488,15 @@ def fetch_snow_level_kft(
     # convert meters to kft
     kft = (vals_m / 0.3048) / 1000.0
     return t, pd.Series(np.round(kft.astype(float), 2), index=t)
+
+
+def _find_10m_wind_dir_var(ds: xr.Dataset) -> Optional[str]:
+    for v in ds.variables:
+        ln = str(ds[v].attrs.get("long_name", "")).lower()
+        if "10 m" in ln and "wind direction" in ln and "blowing" in ln:
+            return v
+    # heuristic fallback
+    for name in ("wdir10m", "wd10m", "winddir10m"):
+        if name in ds.variables:
+            return name
+    return None

plot_utils.py

@@ -288,3 +288,53 @@ def make_snow_level_fig(
         legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
     )
     return fig
+
+
+def make_wind_rose_fig(dir_deg: pd.Series, spd_mph: pd.Series) -> go.Figure:
+    """Aggregate a wind rose with 16 direction bins and 4 speed classes.
+
+    Speed bins: 0-5, 5-10, 10-20, >20 mph
+    """
+    import numpy as np
+
+    valid = (~dir_deg.isna()) & (~spd_mph.isna())
+    d = dir_deg[valid].astype(float)
+    s = spd_mph[valid].astype(float)
+    if len(d) == 0:
+        return go.Figure()
+
+    dir_bins = np.arange(-11.25, 360 + 22.5, 22.5)
+    dir_labels = (dir_bins[:-1] + dir_bins[1:]) / 2.0
+
+    speed_edges = [0, 5, 10, 20, 1e6]
+    speed_labels = ["0-5", "5-10", "10-20", ">20"]
+    colors = ["#d0f0fd", "#86c5da", "#2ca02c", "#d62728"]
+
+    traces = []
+    for i in range(len(speed_edges) - 1):
+        mask = (s >= speed_edges[i]) & (s < speed_edges[i + 1])
+        if mask.sum() == 0:
+            counts = np.zeros(len(dir_labels))
+        else:
+            hist, _ = np.histogram(d[mask] % 360.0, bins=dir_bins)
+            counts = hist
+        traces.append(
+            go.Barpolar(
+                r=counts,
+                theta=dir_labels,
+                name=speed_labels[i],
+                marker_color=colors[i],
+                opacity=0.85,
+            )
+        )
+
+    fig = go.Figure(traces)
+    fig.update_layout(
+        margin=dict(l=40, r=40, t=30, b=40),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        polar=dict(
+            angularaxis=dict(direction="clockwise"),
+            radialaxis=dict(visible=True, ticks=""),
+        ),
+    )
+    return fig