Migrate off the Open-Meteo weather API (Phases 0-4)

Steady-state usage of the Open-Meteo API is removed; it remains only as a dormant fallback. Geocoding -> local GeoNames + Nominatim; wind gusts -> Meteostat; history -> NASA POWER (curated cells seeded with keyless ERA5); recent+forecast -> NASA range + MET Norway. Plus a drift-check tool comparing NASA vs Open-Meteo. All keyless, no new infra. Backend suite green in-image (gate).

backend/core/metrics.py

@@ -44,9 +44,15 @@ _PHASE_SOURCE = {
     "history_fetch": "open-meteo-archive",
     "history_topup": "open-meteo-archive",
     "recent_forecast_fetch": "open-meteo-forecast",
-    "geocode": "open-meteo-geocoding",
+    # Forward geocoding moved off Open-Meteo to Nominatim (same host as reverse) —
+    # /geocode now backstops the local GeoNames index on a miss.
+    "geocode": "nominatim",
     "history_nasa": "nasa-power",
     "forecast_metno": "met-norway",
+    # Wind gusts for the gust-less backup sources (NASA POWER / MET Norway), read
+    # from Meteostat's keyless bulk endpoints (station list + per-station daily).
+    "history_gust": "meteostat",
+    "meteostat_stations": "meteostat",
     "reverse_geocode": "nominatim",
 }
 

backend/data/climate.py

@@ -21,6 +21,7 @@ from core import audit
 from core import metrics
 import paths
 from data import climate_store
+from data import meteostat
 from data import store
 
 # Reused across every fetch (archive/forecast/geocode/revgeo) instead of a bare
@@ -59,7 +60,8 @@ MIN_ARCHIVE_DAYS = 3650            # ~10 yrs: far above any sync window, far bel
 # cached indefinitely (refetched only to add new metric columns). Only the recent
 # tail is refreshed — a small incremental fetch, at most hourly.
 HISTORY_TOPUP_INTERVAL = 3600      # seconds between recent-tail refresh attempts
-FORECAST_TTL_HOURS = 1             # refetch the forward forecast hourly to track updates
+FORECAST_TTL_HOURS = 4             # refetch the recent+forecast bundle every ~4h (lower IO;
+                                   # daily-resolution forecast normals move slowly)
 
 # The archive (historical) endpoint. Overridable so it can point at a self-hosted
 # Open-Meteo instance (ERA5 in object storage) instead of the rate-limited public
@@ -79,12 +81,13 @@ NASA_POWER_URL = "https://power.larc.nasa.gov/api/temporal/daily/point"
 NASA_START = "19810101"
 NASA_FILL = -900.0   # POWER's missing-value sentinel is ~-999
 
-# Backup forward forecast when Open-Meteo's forecast API is unavailable. MET Norway
-# (yr.no) is free + keyless + global, mirroring NASA POWER's role for history. It
-# returns a sub-daily timeseries in metric units with no gusts or apparent temp, so
-# we aggregate to daily, convert units, and derive feels-like like the NASA path.
-# It is forecast-only — no recent past days — so it's a degraded-but-working fallback.
-METNO_URL = "https://api.met.no/weatherapi/locationforecast/2.0/complete"
+# Forward-forecast source (primary for the forward days). MET Norway (yr.no) is free
+# + keyless + global. It returns a sub-daily timeseries in metric units with no gusts
+# or apparent temp, so we aggregate to daily, convert units, derive feels-like like
+# the NASA path, and fill gusts from Meteostat. It is forecast-only (no recent past),
+# so the recent observed days come from a NASA POWER range instead. The /compact
+# endpoint carries every field we use at a smaller payload than /complete.
+METNO_URL = "https://api.met.no/weatherapi/locationforecast/2.0/compact"
 # MET Norway's ToS requires an identifying User-Agent (a missing/generic one is
 # 403'd); include the app and a contact URL so they can reach us if usage misbehaves.
 METNO_UA = "Thermograph/0.2 (+https://thermograph.org)"
@@ -584,20 +587,29 @@ def _nasa_to_frame(param: dict) -> pl.DataFrame:
     return _finalize_approximated(df)
 
 
-def _fetch_history_nasa(cell: dict) -> pl.DataFrame:
-    """Backup history fetch from NASA POWER (used when Open-Meteo is unavailable)."""
-    end = (datetime.date.today() - datetime.timedelta(days=ARCHIVE_LATENCY_DAYS)).strftime("%Y%m%d")
+def _fetch_history_nasa(cell: dict, start: str = NASA_START,
+                        end: "str | None" = None) -> pl.DataFrame:
+    """Primary history fetch from NASA POWER (keyless, independent of Open-Meteo).
+
+    `start`/`end` accept NASA's YYYYMMDD or ISO YYYY-MM-DD; `end` defaults to the
+    archive-latency cutoff. Serves both the full multi-decade pull and the tail
+    top-up range. NASA carries no gusts, so they're filled from Meteostat below."""
+    if end is None:
+        end = (datetime.date.today() - datetime.timedelta(days=ARCHIVE_LATENCY_DAYS)).strftime("%Y%m%d")
     params = {
         "parameters": "T2M_MAX,T2M_MIN,PRECTOTCORR,WS10M_MAX,RH2M",
         "community": "RE",
         "latitude": cell["center_lat"],
         "longitude": cell["center_lon"],
-        "start": NASA_START,
-        "end": end,
+        "start": start.replace("-", ""),
+        "end": end.replace("-", ""),
         "format": "JSON",
     }
     r = _request(NASA_POWER_URL, params, ARCHIVE_FETCH_TIMEOUTS, phase="history_nasa")
-    return _nasa_to_frame(r.json()["properties"]["parameter"])
+    df = _nasa_to_frame(r.json()["properties"]["parameter"])
+    # NASA POWER carries no gusts; fill from the nearest Meteostat station, falling
+    # back to an estimate from sustained wind where no station is in range.
+    return meteostat.fill_gusts(cell["center_lat"], cell["center_lon"], df)
 
 
 def _metno_to_frame(props: dict) -> pl.DataFrame:
@@ -684,10 +696,21 @@ def _read_history_cache(path):
     return df, time.time() - os.path.getmtime(path)
 
 
+def _fetch_history_tail(cell: dict, start_date: str, end_date: str) -> pl.DataFrame:
+    """Fetch a recent history range to top up the cached tail — NASA POWER primary,
+    Open-Meteo archive fallback (mirroring the full-history source order). The
+    Open-Meteo fallback is skipped while it's in a rate-limit cooldown."""
+    try:
+        return _fetch_history_nasa(cell, start=start_date, end=end_date)
+    except Exception:  # noqa: BLE001 - NASA unavailable; try Open-Meteo unless it's cooling down
+        if time.time() < _archive_cooldown_until:
+            raise
+        return _fetch_history_range(cell, start_date, end_date)
+
+
 def _topup_tail(cell: dict, df: pl.DataFrame) -> pl.DataFrame:
     """Append newly-available archive days to a cached record. Best-effort and
     serialized per cell; a small incremental fetch, not the full multi-decade pull."""
-    global _archive_cooldown_until
     cell_id = cell["id"]
     with _cell_lock(cell_id):
         fresh = _read_history_backed(cell_id)   # another thread may have just refreshed it
@@ -695,15 +718,13 @@ def _topup_tail(cell: dict, df: pl.DataFrame) -> pl.DataFrame:
             df, age_s = fresh
             if age_s < HISTORY_TOPUP_INTERVAL:
                 return df
-        if time.time() < _archive_cooldown_until:
-            return df
         expected = datetime.date.today() - datetime.timedelta(days=ARCHIVE_LATENCY_DAYS)
         cached_max = df["date"].max()
         if cached_max >= expected:
             _touch_history_backed(cell_id)   # tail already current — reset the hourly timer
             return df
         try:
-            recent = _fetch_history_range(
+            recent = _fetch_history_tail(
                 cell, (cached_max + datetime.timedelta(days=1)).isoformat(), expected.isoformat())
         except Exception as e:  # noqa: BLE001 - keep the cached record on failure
             if is_rate_limit(e):
@@ -768,30 +789,30 @@ def _load_history(cell: dict) -> tuple[pl.DataFrame, dict]:
         def _serve_stale():
             return _with_doy(stale), {"cached": True, "stale": True, "cache_age_days": None}
 
-        # Fetch. Open-Meteo is primary (richer: gusts + apparent temp); NASA POWER is
-        # the backup when Open-Meteo is unavailable (network error or its daily limit).
-        # Skip Open-Meteo entirely while it's in a rate-limit cooldown.
+        # Fetch. NASA POWER is the primary source (keyless, independent of Open-Meteo);
+        # the gusts it lacks are filled from Meteostat inside _fetch_history_nasa.
+        # Open-Meteo is the fallback when NASA is unavailable — still guarded by its
+        # rate-limit cooldown. Both sources must return a plausibly-full span before
+        # being accepted and cached as a complete record (a short/partial response is
+        # rejected rather than held indefinitely).
         df = None
         source = None
-        if time.time() >= _archive_cooldown_until:
+        try:
+            fetched = _fetch_history_nasa(cell)
+            if fetched.height >= MIN_ARCHIVE_DAYS:
+                df = fetched
+                source = "nasa-power"
+        except Exception:  # noqa: BLE001 - primary unavailable; fall through to Open-Meteo
+            pass
+        if df is None and time.time() >= _archive_cooldown_until:
             try:
                 fetched = _fetch_history(cell)
-                # Guard a self-hosted archive that isn't backfilled yet: an all-null or
-                # recent-only response reduces to a near-empty frame, which would
-                # otherwise be cached indefinitely as a complete record and never
-                # fall to NASA. Require a plausibly-full span before accepting it.
                 if fetched.height >= MIN_ARCHIVE_DAYS:
                     df = fetched
                     source = "open-meteo"
             except Exception as e:  # noqa: BLE001
                 if is_rate_limit(e):
                     _note_rate_limit(e)
-        if df is None:
-            try:
-                df = _fetch_history_nasa(cell)
-                source = "nasa-power"
-            except Exception:  # noqa: BLE001 - backup unavailable too
-                df = None
         if df is None:
             if stale is not None:
                 return _serve_stale()
@@ -805,6 +826,10 @@ def _load_history(cell: dict) -> tuple[pl.DataFrame, dict]:
 
 RECENT_PAST_DAYS = 25   # recent observations window (covers the ~2-week graded view)
 FORECAST_DAYS = 8       # today + 7 days ahead
+# NASA POWER near-real-time lags a couple of days, so the recent observed window ends
+# here; the small today-1/today-2 gap before the MET Norway forecast begins grades as
+# missing (drop_nulls), bracketed by history behind and forecast ahead.
+RECENT_END_LAG_DAYS = 2
 
 
 def _rf_cache_path(cell_id: str) -> str:
@@ -846,20 +871,27 @@ def load_cached_recent_forecast(cell: dict) -> "pl.DataFrame | None":
         return None
 
 
-def _load_recent_forecast(cell: dict) -> pl.DataFrame:
-    """Recent observations AND the forward forecast in ONE forecast-API call.
+def _fetch_recent_forecast(cell: dict) -> pl.DataFrame:
+    """Recent observations + forward forecast WITHOUT Open-Meteo: the recent observed
+    window from a NASA POWER range (measured), the forward days from MET Norway,
+    merged by date. Gusts — which neither source carries — are filled from Meteostat:
+    measured for the observed days, estimated from wind for the forecast days."""
+    today = datetime.date.today()
+    start = (today - datetime.timedelta(days=RECENT_PAST_DAYS)).isoformat()
+    end = (today - datetime.timedelta(days=RECENT_END_LAG_DAYS)).isoformat()
+    past = _fetch_history_nasa(cell, start=start, end=end)   # measured + Meteostat gusts
+    fwd = meteostat.fill_gusts(
+        cell["center_lat"], cell["center_lon"], _fetch_forecast_metno(cell))
+    # Forecast wins on any overlapping day (freshest model value for today); the NASA
+    # observed days fill the recent past MET Norway lacks.
+    return (pl.concat([past, fwd], how="diagonal_relaxed")
+            .unique(subset="date", keep="last", maintain_order=True)
+            .sort("date"))
 
-    Both the recent (past) view and the forecast (future) view slice from this, so
-    a cell needs just one upstream forecast request per hour (plus the ~monthly
-    archive fetch). Cached per cell for one hour so it still follows model updates.
-    """
-    cell_id = cell["id"]
-    hit = _read_recent_backed(cell_id)
-    if hit is not None:
-        cached, age_s = hit
-        if age_s / 3600.0 < FORECAST_TTL_HOURS:
-            return _with_doy(cached)
 
+def _fetch_recent_forecast_om(cell: dict) -> pl.DataFrame:
+    """Fallback recent+forecast bundle from the Open-Meteo forecast API (the former
+    primary): recent past + forward days in one call."""
     params = {
         "latitude": cell["center_lat"],
         "longitude": cell["center_lon"],
@@ -871,49 +903,59 @@ def _load_recent_forecast(cell: dict) -> pl.DataFrame:
         "past_days": RECENT_PAST_DAYS,
         "forecast_days": FORECAST_DAYS,
     }
-    # Skip Open-Meteo's forecast endpoint entirely while it's in its own
-    # rate-limit cooldown (see _note_forecast_rate_limit) -- mirrors
-    # _load_history's archive-cooldown check, so a forecast brownout doesn't
-    # retry-storm the endpoint from every subscribed cell and every live request
-    # independently; it falls straight through to the MET Norway backup instead.
+    r = _request(FORECAST_URL, params, 60, phase="recent_forecast_fetch")
+    return _to_frame(r.json()["daily"])
+
+
+def _load_recent_forecast(cell: dict) -> pl.DataFrame:
+    """Recent observations AND the forward forecast for a cell.
+
+    The primary source is keyless and Open-Meteo-free: a NASA POWER recent-past range
+    plus the MET Norway forward forecast (see _fetch_recent_forecast). The Open-Meteo
+    forecast API is the fallback (skipped while it's in its own rate-limit cooldown,
+    see _note_forecast_rate_limit), then a stale cache. Cached per cell for
+    FORECAST_TTL_HOURS so it follows model updates without per-hour upstream IO.
+    """
+    cell_id = cell["id"]
+    hit = _read_recent_backed(cell_id)
+    if hit is not None:
+        cached, age_s = hit
+        if age_s / 3600.0 < FORECAST_TTL_HOURS:
+            return _with_doy(cached)
+
     df = None
-    primary_error = None
-    if time.time() >= _forecast_cooldown_until:
+    try:
+        df = _fetch_recent_forecast(cell)          # NASA recent + MET forward (primary)
+    except Exception:  # noqa: BLE001 - keyless primary unavailable; try Open-Meteo
+        pass
+
+    forecast_error = None
+    if df is None and time.time() >= _forecast_cooldown_until:
+        # Fall back to the Open-Meteo forecast API, skipped while it's in its own
+        # rate-limit cooldown so a brownout doesn't retry-storm the endpoint.
         try:
-            r = _request(FORECAST_URL, params, 60, phase="recent_forecast_fetch")
-            df = _to_frame(r.json()["daily"])
+            df = _fetch_recent_forecast_om(cell)
         except Exception as e:  # noqa: BLE001
             if is_rate_limit(e):
                 _note_forecast_rate_limit(e)
-            primary_error = e
+            forecast_error = e
 
     if df is None:
-        # Open-Meteo forecast unavailable (rate limit, cooldown, or outage). Fall
-        # back to MET Norway (yr.no) — global + keyless — mirroring the archive's
-        # NASA POWER backup. MET Norway is forecast-only (no recent past days),
-        # so this keeps the forecast / day-ahead views working in a degraded form.
-        try:
-            df = _fetch_forecast_metno(cell)
-        except Exception:  # noqa: BLE001 - backup unavailable too
-            # Last resort: serve the stale cache if we have one. An hours-old bundle
-            # (which still carries the recent observed days MET Norway lacks) beats a
-            # hard failure, mirroring _load_history's stale-serve. Return WITHOUT
-            # rewriting it, so recent_stamp stays old and the next request still
-            # retries upstream first rather than serving this as if it were fresh.
-            if hit is not None:
-                return _with_doy(hit[0])
-            # No cache either: surface the original error, classifying an Open-Meteo
-            # rate limit as the typed, daily-aware WeatherUnavailable (the archive
-            # path classifies its own inside _load_history). primary_error is None
-            # when the primary fetch was skipped outright (cooldown active) --
-            # report the cooldown itself in that case.
-            if primary_error is not None and is_rate_limit(primary_error):
-                daily = "daily" in _rate_limit_reason(primary_error).lower()
-                raise WeatherUnavailable(limit_message(daily), daily=daily) from primary_error
-            if primary_error is not None:
-                raise primary_error
-            raise WeatherUnavailable(limit_message(_forecast_limit_daily),
-                                     daily=_forecast_limit_daily)
+        # Last resort: serve the stale cache if we have one, WITHOUT rewriting it, so
+        # recent_stamp stays old and the next request retries upstream first (mirrors
+        # _load_history's stale-serve).
+        if hit is not None:
+            return _with_doy(hit[0])
+        # No cache either: surface the error, classifying an Open-Meteo rate limit as
+        # the typed, daily-aware WeatherUnavailable. forecast_error is None when the
+        # fallback was skipped outright (cooldown active) — report the cooldown then.
+        if forecast_error is not None and is_rate_limit(forecast_error):
+            daily = "daily" in _rate_limit_reason(forecast_error).lower()
+            raise WeatherUnavailable(limit_message(daily), daily=daily) from forecast_error
+        if forecast_error is not None:
+            raise forecast_error
+        raise WeatherUnavailable(limit_message(_forecast_limit_daily),
+                                 daily=_forecast_limit_daily)
     _write_recent_backed(cell_id, df)
     return df
 
@@ -1058,24 +1100,54 @@ def reverse_geocode(lat: float, lon: float) -> str | None:
         return None
 
 
-def geocode(name: str, count: int = 5) -> list[dict]:
-    """Look up places by name worldwide via Open-Meteo's geocoder."""
-    r = _request(
-        "https://geocoding-api.open-meteo.com/v1/search",
-        {"name": name, "count": count, "language": "en", "format": "json"},
-        30,
-        phase="geocode",
-    )
-    results = r.json().get("results", []) or []
-    return [
-        {
-            "name": g.get("name"),
-            "admin1": g.get("admin1"),
-            "country": g.get("country"),
-            "country_code": g.get("country_code"),
-            "lat": g.get("latitude"),
-            "lon": g.get("longitude"),
-            "population": g.get("population"),
-        }
-        for g in results
-    ]
+def geocode_nominatim(name: str, count: int = 5) -> list[dict]:
+    """Forward place-name lookup via OpenStreetMap Nominatim (keyless).
+
+    Replaces the former Open-Meteo geocoder. Nominatim covers the long tail the
+    local GeoNames index can't — neighbourhoods, postcodes, sub-1000-population
+    villages, and alternate/native-language spellings — so /geocode falls back to
+    it on a local miss. It carries no population, so results keep Nominatim's own
+    relevance order; name/admin/country map straight across.
+
+    Shares the reverse geocoder's lock and ~1/sec pacing: both hit the same host,
+    so serializing them together keeps total Nominatim traffic under the usage
+    policy. Only the low-volume /geocode miss path reaches here — autocomplete
+    (/suggest) is served purely from the local index and never calls out.
+    """
+    global _revgeo_last
+    with _REVGEO_LOCK:
+        wait = _REVGEO_MIN_INTERVAL - (time.monotonic() - _revgeo_last)
+        if wait > 0:
+            time.sleep(wait)
+        try:
+            r = _request(
+                "https://nominatim.openstreetmap.org/search",
+                {"q": name, "format": "jsonv2", "addressdetails": 1,
+                 "limit": count, "accept-language": "en"},
+                30,
+                phase="geocode",
+                headers={"User-Agent": "Thermograph/0.1 (local weather grading app)"},
+            )
+            rows = r.json() or []
+        finally:
+            _revgeo_last = time.monotonic()
+    out = []
+    for g in rows:
+        a = g.get("address", {}) or {}
+        # jsonv2 gives a `name` for named features; for addresses/areas fall back to
+        # the most specific place component, then the head of display_name.
+        label = (g.get("name")
+                 or a.get("city") or a.get("town") or a.get("village")
+                 or a.get("hamlet") or a.get("suburb") or a.get("neighbourhood")
+                 or (g.get("display_name") or "").split(",")[0].strip() or None)
+        cc = (a.get("country_code") or "").upper() or None
+        out.append({
+            "name": label,
+            "admin1": a.get("state") or a.get("province") or a.get("region"),
+            "country": a.get("country"),
+            "country_code": cc,
+            "lat": float(g["lat"]) if g.get("lat") is not None else None,
+            "lon": float(g["lon"]) if g.get("lon") is not None else None,
+            "population": None,   # Nominatim has no population; order is relevance-based
+        })
+    return out

backend/data/meteostat.py

@@ -0,0 +1,193 @@
+"""Wind-gust supplier via Meteostat's free, keyless bulk data.
+
+The archive sources that back Thermograph when Open-Meteo is unavailable — NASA
+POWER (history) and MET Norway (forecast) — carry no wind gusts, yet gust is a
+graded metric. This module fills that gap: it finds the nearest Meteostat weather
+station to a grid cell and reads its daily peak-gust record (``wpgt``) straight
+from Meteostat's gzipped bulk endpoints (no API key, no per-query quota).
+
+Where no station is within range — oceans, remote cells — it falls back to an
+*estimate* from that day's sustained wind (``wind * GUST_FACTOR``). Note that a
+constant factor makes an estimated gust move in lockstep with wind, so the gust
+grade carries independent signal only where a real station backs it; estimation
+just keeps the metric populated rather than leaving a hole.
+
+Everything degrades safely: any station-lookup or fetch failure falls through to
+estimation, so a Meteostat outage can never break a history fetch. Bulk files are
+downloaded once and cached on disk (like the GeoNames dump), so steady-state
+network IO is near zero.
+
+Wired in at the NASA-POWER history path (data/climate.py); it activates for real
+once NASA POWER becomes the primary history source. Open-Meteo already carries its
+own gusts, so nothing here runs while Open-Meteo is primary.
+"""
+import datetime
+import gzip
+import json
+import math
+import os
+import threading
+
+import httpx
+import numpy as np
+import polars as pl
+
+from core import audit
+from core import metrics
+import paths
+
+META_DIR = os.path.join(paths.DATA_DIR, "meteostat")
+STATIONS_URL = "https://bulk.meteostat.net/v2/stations/lite.json.gz"
+DAILY_URL = "https://bulk.meteostat.net/v2/daily/{}.csv.gz"
+
+# How far a station may sit from a cell's center before we treat it as "no
+# station" and estimate. ~100 km keeps gusts representative of the same synoptic
+# conditions without demanding a station in every remote cell.
+MAX_STATION_KM = 100.0
+# Sustained-wind → gust ratio for the estimate used where no station is in range.
+# ~1.4 is a typical over-land daily-max gust factor; see the module note on why an
+# estimated gust is redundant with wind (only measured gusts add real signal).
+GUST_FACTOR = 1.4
+KMH_TO_MPH = 0.6213711922
+
+# Meteostat daily bulk CSV is headerless; columns are positional. wpgt (peak gust,
+# km/h) is column 8; the date (YYYY-MM-DD) is column 0.
+_CSV_DATE = 0
+_CSV_WPGT = 8
+
+_STATIONS: tuple[list[str], np.ndarray, np.ndarray] | None = None
+_STATIONS_LOCK = threading.Lock()
+
+
+def _fetch_bytes(url: str, phase: str, timeout: float = 60.0) -> bytes:
+    """GET raw bytes, gunzipping a gzip payload (bulk files are ``.gz``; httpx only
+    auto-decodes Content-Encoding, not a gzip *file*). Records an outbound metric."""
+    r = httpx.get(url, timeout=timeout, follow_redirects=True)
+    r.raise_for_status()
+    metrics.record_outbound(phase, "ok")
+    data = r.content
+    if data[:2] == b"\x1f\x8b":            # gzip magic
+        return gzip.decompress(data)
+    return data
+
+
+def _parse_stations(raw: bytes) -> tuple[list[str], np.ndarray, np.ndarray]:
+    """Parse the bulk stations JSON into (ids, lat[], lon[]). Entries without an id
+    or coordinates are skipped."""
+    ids: list[str] = []
+    lats: list[float] = []
+    lons: list[float] = []
+    for e in json.loads(raw):
+        loc = e.get("location") or {}
+        sid = e.get("id")
+        lat = loc.get("latitude")
+        lon = loc.get("longitude")
+        if sid is None or lat is None or lon is None:
+            continue
+        ids.append(str(sid))
+        lats.append(float(lat))
+        lons.append(float(lon))
+    return ids, np.asarray(lats), np.asarray(lons)
+
+
+def _load_stations() -> tuple[list[str], np.ndarray, np.ndarray]:
+    """Load the station index (downloaded once into META_DIR, cached forever)."""
+    global _STATIONS
+    if _STATIONS is not None:
+        return _STATIONS
+    with _STATIONS_LOCK:
+        if _STATIONS is not None:
+            return _STATIONS
+        os.makedirs(META_DIR, exist_ok=True)
+        cache = os.path.join(META_DIR, "stations.json")
+        if os.path.exists(cache):
+            with open(cache, "rb") as fh:
+                raw = fh.read()
+        else:
+            raw = _fetch_bytes(STATIONS_URL, phase="meteostat_stations")
+            with open(cache, "wb") as fh:
+                fh.write(raw)
+        _STATIONS = _parse_stations(raw)
+        return _STATIONS
+
+
+def nearest_station(lat: float, lon: float) -> str | None:
+    """Meteostat station id nearest to (lat, lon) within MAX_STATION_KM, or None."""
+    ids, lats, lons = _load_stations()
+    if not ids:
+        return None
+    # Great-circle distance, vectorized over all stations.
+    rlat = math.radians(lat)
+    dlat = np.radians(lats - lat)
+    dlon = np.radians(lons - lon)
+    a = (np.sin(dlat / 2) ** 2
+         + math.cos(rlat) * np.cos(np.radians(lats)) * np.sin(dlon / 2) ** 2)
+    km = 6371.0 * 2 * np.arcsin(np.sqrt(a))
+    i = int(np.argmin(km))
+    return ids[i] if km[i] <= MAX_STATION_KM else None
+
+
+def _parse_daily_csv(text: str) -> dict[datetime.date, float]:
+    """Map a station's daily bulk CSV to {date: peak_gust_mph}, skipping days with
+    no recorded gust."""
+    out: dict[datetime.date, float] = {}
+    for line in text.splitlines():
+        cols = line.split(",")
+        if len(cols) <= _CSV_WPGT:
+            continue
+        raw = cols[_CSV_WPGT].strip()
+        if not raw:
+            continue
+        try:
+            out[datetime.date.fromisoformat(cols[_CSV_DATE])] = float(raw) * KMH_TO_MPH
+        except ValueError:
+            continue
+    return out
+
+
+def daily_gusts(station_id: str) -> dict[datetime.date, float]:
+    """Measured daily peak gusts (mph) for a station, keyed by date. Cached on disk
+    per station (bulk history barely changes; the recent tail staleness is immaterial
+    against a cache that is itself refetched only on a cold history pull)."""
+    os.makedirs(META_DIR, exist_ok=True)
+    cache = os.path.join(META_DIR, f"daily_{station_id}.csv")
+    if os.path.exists(cache):
+        with open(cache, "r", encoding="utf-8") as fh:
+            return _parse_daily_csv(fh.read())
+    text = _fetch_bytes(DAILY_URL.format(station_id), phase="history_gust").decode("utf-8")
+    with open(cache, "w", encoding="utf-8") as fh:
+        fh.write(text)
+    return _parse_daily_csv(text)
+
+
+def fill_gusts(lat: float, lon: float, df: pl.DataFrame) -> pl.DataFrame:
+    """Populate an all-null ``gust`` column from the nearest Meteostat station,
+    estimating from ``wind`` where the station has no value (or no station is in
+    range). A no-op if the frame already carries gusts or lacks the needed columns.
+
+    Never raises: any station-lookup / fetch failure degrades to pure estimation,
+    so the caller's history fetch always succeeds."""
+    if df.is_empty() or "gust" not in df.columns or "wind" not in df.columns:
+        return df
+    if df["gust"].drop_nulls().len() > 0:      # source already provided gusts
+        return df
+    measured: dict[datetime.date, float] = {}
+    try:
+        sid = nearest_station(lat, lon)
+        if sid:
+            measured = daily_gusts(sid)
+    except Exception as e:  # noqa: BLE001 - gusts are a nicety; never fail history
+        audit.log_event("error", {"phase": "history_gust", "error": repr(e)})
+        measured = {}
+    dates = df["date"].to_list()
+    wind = df["wind"].to_list()
+    gust = []
+    for d, w in zip(dates, wind):
+        m = measured.get(d)
+        if m is not None:
+            gust.append(m)
+        elif w is not None:
+            gust.append(w * GUST_FACTOR)
+        else:
+            gust.append(None)
+    return df.with_columns(pl.Series("gust", gust, dtype=pl.Float64))

backend/data/places.py

@@ -1,22 +1,25 @@
 """Typo-tolerant place-name index for search suggestions.
 
-Open-Meteo's geocoder (climate.geocode) only matches exact spellings — one
-mistyped letter and the query returns nothing. This module keeps a local index
-of world places (a GeoNames cities dump, downloaded once into data/geonames/
-and reused across restarts) so /suggest can answer instantly and tolerate a
-single-letter typo — a substituted, missing, or extra letter, or two adjacent
-letters swapped — anywhere in the query, including the first character.
+This module keeps a local index of world places (a GeoNames cities dump,
+downloaded once into data/geonames/ and reused across restarts) so /suggest can
+answer instantly, offline, and tolerate a single-letter typo — a substituted,
+missing, or extra letter, or two adjacent letters swapped — anywhere in the
+query, including the first character.
 
 It also builds a vocabulary of place-name tokens so a typo'd word in a
 multi-word query can be respelled against words the index knows ("pest
-seattle" → "west seattle"); the endpoint verifies those candidates against the
-upstream geocoder, which covers neighbourhood-level places the cities dump
-lacks.
+seattle" → "west seattle").
+
+`suggest()` still takes an injected ``upstream`` callable so the module stays
+free of the fetch layer, but /suggest now passes a no-op: autocomplete is served
+purely from this index and makes no per-keystroke network call. (/geocode, the
+explicit search, is the path that falls back to Nominatim on a local miss for
+neighbourhoods/postcodes/tiny places the cities dump lacks — see web/app.py.)
 
 The index loads in a background thread (start_loading()). Until it's ready —
 or forever, if the download fails — search()/corrections() return None/empty
-and /suggest degrades to the plain upstream geocoder, so the app never needs
-this data to boot or serve.
+and /suggest simply returns no results, so the app never needs this data to
+boot or serve.
 """
 import bisect
 import heapq

backend/drift_check.py

@@ -0,0 +1,136 @@
+"""Value-drift check: quantify how much the migration moved the data, per variable
+and by grade band, by comparing the two live history sources head-to-head.
+
+Open-Meteo is kept as a dormant fallback, so both sources are still fetchable: this
+pulls NASA POWER (the new primary) and the Open-Meteo archive (ERA5) for a sample of
+cells and reports, per variable:
+  - mean-absolute-difference / bias / max — the RAW value drift, and
+  - grade-band divergence — the share of (day, metric) whose graded band actually
+    changes between sources.
+Since Open-Meteo IS ERA5, this doubles as a fidelity check for the ERA5 seed.
+
+    python drift_check.py [--limit N] [--days N]   # on a networked box
+
+Note a real property of the grading model worth reading the two numbers together:
+grades are percentiles within each source's OWN distribution, so a uniform bias
+between sources shifts MAD but leaves grades unchanged. High MAD with low grade
+divergence means "different absolute values, same story" — usually fine. High grade
+divergence is the signal that actually matters.
+
+The comparison logic (compare_values / grade_band_divergence) is unit-tested; the
+fetch is not.
+"""
+import datetime
+import sys
+
+import polars as pl
+
+from data import cities
+from data import climate
+from data import grading
+from data import grid
+
+# Variables compared for raw drift (feels is derived; precip/wind/gust/humid optional).
+_METRICS = ("tmax", "tmin", "precip", "wind", "gust", "humid", "feels")
+
+
+def compare_values(a: pl.DataFrame, b: pl.DataFrame, metrics=_METRICS) -> dict:
+    """Per-variable raw-drift stats over the dates the two frames share: n aligned
+    days, mean-absolute-difference, mean bias (a - b), and max absolute difference.
+    Days where either source is null for a metric are dropped from that metric."""
+    j = a.join(b, on="date", how="inner", suffix="_b")
+    out = {}
+    for m in metrics:
+        if m not in a.columns or m not in b.columns:
+            continue
+        d = j.select((pl.col(m) - pl.col(f"{m}_b")).alias("d")).drop_nulls()
+        if d.height == 0:
+            out[m] = {"n": 0, "mad": None, "bias": None, "max_abs": None}
+            continue
+        diff = d["d"]
+        out[m] = {
+            "n": d.height,
+            "mad": round(float(diff.abs().mean()), 3),
+            "bias": round(float(diff.mean()), 3),
+            "max_abs": round(float(diff.abs().max()), 3),
+        }
+    return out
+
+
+def grade_band_divergence(a: pl.DataFrame, b: pl.DataFrame, start, end) -> dict:
+    """Per-metric share of days whose graded band differs between the two sources over
+    [start, end]. Each source's days are graded against its OWN ±7-day climatology
+    (grading.grade_range), so this measures whether the migration changes the story a
+    day tells, not just its raw value."""
+    ga = {d["date"]: d for d in grading.grade_range(a, start, end)}
+    gb = {d["date"]: d for d in grading.grade_range(b, start, end)}
+    per: dict[str, list[int]] = {}
+    for date in ga.keys() & gb.keys():
+        da, db = ga[date], gb[date]
+        for m in grading.CLIMO_METRICS:
+            va, vb = da.get(m), db.get(m)
+            if va and vb:
+                slot = per.setdefault(m, [0, 0])   # [compared, differ]
+                slot[0] += 1
+                slot[1] += int(va["g"] != vb["g"])
+    return {m: {"compared": c, "differ": d, "pct": round(100.0 * d / c, 1) if c else None}
+            for m, (c, d) in sorted(per.items())}
+
+
+def check_cell(cell: dict, days: int) -> dict:
+    """Fetch both sources for a cell and compare them over the most recent `days`."""
+    nasa = climate._fetch_history_nasa(cell)      # new primary (+ Meteostat gusts)
+    om = climate._fetch_history(cell)             # Open-Meteo archive (ERA5)
+    end = min(nasa["date"].max(), om["date"].max())
+    start = end - datetime.timedelta(days=days)
+    return {"values": compare_values(nasa, om),
+            "grades": grade_band_divergence(nasa, om, start, end)}
+
+
+def _mean(xs: list) -> "float | None":
+    xs = [x for x in xs if x is not None]
+    return round(sum(xs) / len(xs), 3) if xs else None
+
+
+def main(limit: "int | None" = None, days: int = 365) -> None:
+    todo = cities.all_cities()
+    if limit:
+        todo = todo[:limit]
+    mad_by_metric: dict[str, list] = {}
+    grade_by_metric: dict[str, list] = {}
+    checked = failed = 0
+    for i, c in enumerate(todo, 1):
+        cell = grid.snap(c["lat"], c["lon"])
+        try:
+            rep = check_cell(cell, days)
+        except Exception as e:  # noqa: BLE001 - one cell's outage shouldn't abort the sweep
+            failed += 1
+            print(f"[{i}/{len(todo)}] FAILED {c['slug']}: {e}")
+            continue
+        checked += 1
+        vals = ", ".join(f"{m} mad={s['mad']}" for m, s in rep["values"].items()
+                         if s.get("mad") is not None)
+        grds = ", ".join(f"{m} {s['pct']}%" for m, s in rep["grades"].items())
+        print(f"[{i}/{len(todo)}] {c['slug']} ({cell['id']})")
+        print(f"    values: {vals}")
+        print(f"    grade drift: {grds}")
+        for m, s in rep["values"].items():
+            mad_by_metric.setdefault(m, []).append(s.get("mad"))
+        for m, s in rep["grades"].items():
+            grade_by_metric.setdefault(m, []).append(s.get("pct"))
+
+    print(f"\n=== summary over {checked} cells (failed={failed}) ===")
+    print("mean MAD (raw drift):")
+    for m in _METRICS:
+        if m in mad_by_metric:
+            print(f"  {m:7s} {_mean(mad_by_metric[m])}")
+    print("mean grade-band divergence (share of days a grade changes):")
+    for m in sorted(grade_by_metric):
+        print(f"  {m:7s} {_mean(grade_by_metric[m])}%")
+
+
+if __name__ == "__main__":
+    argv = sys.argv[1:]
+    lim = int(argv[argv.index("--limit") + 1]) if "--limit" in argv else None
+    dys = int(argv[argv.index("--days") + 1]) if "--days" in argv else 365
+    main(limit=lim, days=dys)

backend/requirements-seed.txt

@@ -0,0 +1,9 @@
+# Seed-only dependencies for seed_era5.py (the one-time ERA5 backfill). NOT installed
+# in the app image or CI — kept out of requirements.txt so the runtime stays lean.
+# Install on the machine that runs the seed:  pip install -r requirements-seed.txt
+# Versions intentionally unpinned: the icechunk/xarray/zarr stack moves fast; install
+# the current compatible set on the seed box and verify with `python seed_era5.py --dry-run`.
+-r requirements.txt
+icechunk
+xarray
+zarr

backend/seed_era5.py

@@ -0,0 +1,174 @@
+"""One-time ERA5 seed for the curated-city cells (keeps ERA5 fidelity without Open-Meteo).
+
+Now that the live history primary is NASA POWER, this backfills the high-traffic
+curated-city cells (backend/cities.json) with true ERA5 data pulled from a public,
+KEYLESS source — the Earthmover Icechunk ERA5 archive on AWS Open Data — so the
+pages people actually visit keep ERA5-quality history. It writes straight into the
+history store (bypassing the live fetch), so a seeded cell is served from cache and
+never falls to NASA on first request.
+
+    # on a machine with network + the seed deps (see requirements-seed.txt):
+    pip install -r requirements-seed.txt
+    python seed_era5.py --dry-run            # fetch+transform one cell, print, no write
+    python seed_era5.py [--limit N] [--overwrite]
+
+Idempotent by default: a cell that already has a cached history is skipped (pass
+--overwrite to reseed). Not run in CI or the app image — the icechunk/xarray/zarr
+stack lives only in requirements-seed.txt.
+
+IMPORTANT — the S3/Icechunk *access* layer (_open_store / fetch_point) targets a
+young, fast-moving API and the store's exact variable/coordinate names; it is NOT
+exercised by the test suite. VERIFY it with `--dry-run` on the seed box before a
+full run. The hourly→daily transform (hourly_to_daily) IS unit-tested and is the
+part that must be numerically correct.
+
+Source: https://registry.opendata.aws/earthmover-era5/  (group="temporal", anonymous)
+"""
+import os
+import sys
+
+import polars as pl
+
+from data import cities
+from data import climate
+from data import grid
+
+# Match the live archive span (climate.START_DATE / ARCHIVE_LATENCY_DAYS).
+START_DATE = climate.START_DATE            # "1980-01-01"
+
+# Anonymous AWS Open Data Icechunk store; overridable in case the registry moves.
+ERA5_BUCKET = os.environ.get("THERMOGRAPH_ERA5_BUCKET", "earthmover-icechunk-era5")
+ERA5_PREFIX = os.environ.get("THERMOGRAPH_ERA5_PREFIX", "")   # verify on the seed box
+ERA5_REGION = os.environ.get("THERMOGRAPH_ERA5_REGION", "us-east-1")
+ERA5_GROUP = os.environ.get("THERMOGRAPH_ERA5_GROUP", "temporal")  # time-contiguous layout
+
+# ERA5 store variable name -> our short name. ERA5 gusts (i10fg) are real here, so the
+# seed keeps genuine measured gusts rather than the Meteostat/estimate fallback.
+ERA5_VARS = {
+    "2m_temperature": "t2m",                 # K
+    "2m_dewpoint_temperature": "d2m",        # K (→ RH with t2m)
+    "total_precipitation": "tp",             # m, hourly accumulation
+    "10m_u_component_of_wind": "u10",        # m/s
+    "10m_v_component_of_wind": "v10",        # m/s
+    "instantaneous_10m_wind_gust": "i10fg",  # m/s
+}
+
+_M_TO_IN = 39.37007874
+_MS_TO_MPH = 2.2369362920544
+
+
+def hourly_to_daily(hourly: pl.DataFrame) -> pl.DataFrame:
+    """Aggregate an hourly ERA5 point frame to the daily schema climate.py stores,
+    in the app's units (°F, inches, mph, %RH). Input columns: time (Datetime, UTC),
+    t2m/d2m (K), tp (m, per-hour accumulation), u10/v10/i10fg (m/s).
+
+    Days are UTC calendar days — the reanalysis is UTC, so this is a small offset
+    from a local-timezone daily boundary, immaterial for percentile climatology.
+    Relative humidity is the Magnus-formula RH from temperature and dewpoint."""
+    tc = pl.col("t2m") - 273.15
+    tdc = pl.col("d2m") - 273.15
+    es = (17.625 * tc / (243.04 + tc)).exp()
+    e = (17.625 * tdc / (243.04 + tdc)).exp()
+    df = hourly.with_columns(
+        pl.col("time").dt.date().alias("date"),
+        ((pl.col("t2m") - 273.15) * 9.0 / 5.0 + 32.0).alias("t_f"),
+        ((pl.col("u10") ** 2 + pl.col("v10") ** 2).sqrt() * _MS_TO_MPH).alias("wind_mph"),
+        (pl.col("i10fg") * _MS_TO_MPH).alias("gust_mph"),
+        (pl.col("tp") * _M_TO_IN).alias("precip_in"),
+        (100.0 * e / es).clip(0.0, 100.0).alias("rh"),
+    )
+    return (
+        df.group_by("date")
+        .agg(
+            pl.col("t_f").max().alias("tmax"),
+            pl.col("t_f").min().alias("tmin"),
+            pl.col("precip_in").sum().alias("precip"),
+            pl.col("wind_mph").max().alias("wind"),
+            pl.col("gust_mph").max().alias("gust"),
+            pl.col("rh").mean().alias("humid"),
+        )
+        .sort("date")
+    )
+
+
+def _default_end() -> str:
+    import datetime
+    return (datetime.date.today()
+            - datetime.timedelta(days=climate.ARCHIVE_LATENCY_DAYS)).isoformat()
+
+
+def _open_store():
+    """Open the anonymous Icechunk ERA5 store (temporal group). Lazy-imports the
+    seed-only deps. VERIFY against the current icechunk/xarray API on the seed box —
+    this young API and the store's exact paths are not covered by tests."""
+    import icechunk           # noqa: PLC0415 - seed-only dep (requirements-seed.txt)
+    import xarray as xr       # noqa: PLC0415
+
+    storage = icechunk.s3_storage(
+        bucket=ERA5_BUCKET, prefix=ERA5_PREFIX, region=ERA5_REGION, anonymous=True)
+    repo = icechunk.Repository.open(storage)
+    session = repo.readonly_session("main")
+    return xr.open_zarr(session.store, group=ERA5_GROUP, consolidated=False)
+
+
+def fetch_point(ds, lat: float, lon: float, start: str, end: str) -> pl.DataFrame:
+    """Pull the hourly ERA5 series for the nearest grid point over [start, end] into a
+    polars frame (no pandas). ERA5 longitudes are 0..360. Coordinate/variable names
+    may differ per store revision — verify with --dry-run."""
+    sub = (ds[list(ERA5_VARS)]
+           .sel(latitude=lat, longitude=lon % 360.0, method="nearest")
+           .sel(time=slice(start, end))
+           .load())
+    data = {"time": sub["time"].values}
+    for src, dst in ERA5_VARS.items():
+        data[dst] = sub[src].values
+    return pl.DataFrame(data)
+
+
+def seed_cell(ds, cell: dict, start: str, end: str) -> int:
+    """Fetch → daily → finalize → write one cell's ERA5 history. Returns row count."""
+    daily = hourly_to_daily(fetch_point(ds, cell["center_lat"], cell["center_lon"], start, end))
+    # ERA5 has no apparent-temperature field; approximate fmax/fmin from the NWS heat
+    # index / wind chill (same as the NASA/MET paths). _finalize_approximated adds
+    # feels + doy and folds NaN→null; the write path strips doy.
+    frame = climate._finalize_approximated(daily)
+    climate._write_history_backed(cell["id"], frame)
+    return frame.height
+
+
+def main(limit: "int | None" = None, overwrite: bool = False,
+         dry_run: bool = False, start: str = START_DATE, end: "str | None" = None) -> None:
+    end = end or _default_end()
+    ds = _open_store()
+    todo = cities.all_cities()
+    if limit:
+        todo = todo[:limit]
+    seeded = skipped = failed = 0
+    for i, c in enumerate(todo, 1):
+        cell = grid.snap(c["lat"], c["lon"])
+        if not overwrite:
+            cached = climate.load_cached_history(cell)
+            if cached is not None and not cached.is_empty():
+                skipped += 1
+                continue
+        try:
+            if dry_run:
+                daily = hourly_to_daily(
+                    fetch_point(ds, cell["center_lat"], cell["center_lon"], start, end))
+                print(daily.head())
+                print(f"[dry-run] {c['slug']} ({cell['id']}): {daily.height} days "
+                      f"({start}..{end}); nothing written")
+                return
+            n = seed_cell(ds, cell, start, end)
+            seeded += 1
+            print(f"[{i}/{len(todo)}] seeded {c['slug']} ({cell['id']}): {n} days")
+        except Exception as e:  # noqa: BLE001 - keep going; a failed cell self-heals via NASA
+            failed += 1
+            print(f"[{i}/{len(todo)}] FAILED {c['slug']}: {e}")
+    print(f"done: seeded={seeded} skipped(cached)={skipped} failed={failed}")
+
+
+if __name__ == "__main__":
+    argv = sys.argv[1:]
+    lim = int(argv[argv.index("--limit") + 1]) if "--limit" in argv else None
+    main(limit=lim, overwrite="--overwrite" in argv, dry_run="--dry-run" in argv)

backend/tests/core/test_metrics.py

@@ -14,9 +14,11 @@ def test_phase_source_map_covers_every_source():
     assert m.source_for_phase("history_fetch") == "open-meteo-archive"
     assert m.source_for_phase("history_topup") == "open-meteo-archive"
     assert m.source_for_phase("recent_forecast_fetch") == "open-meteo-forecast"
-    assert m.source_for_phase("geocode") == "open-meteo-geocoding"
+    assert m.source_for_phase("geocode") == "nominatim"   # forward geocoding moved off Open-Meteo
     assert m.source_for_phase("history_nasa") == "nasa-power"
     assert m.source_for_phase("forecast_metno") == "met-norway"
+    assert m.source_for_phase("history_gust") == "meteostat"
+    assert m.source_for_phase("meteostat_stations") == "meteostat"
     assert m.source_for_phase("reverse_geocode") == "nominatim"
     assert m.source_for_phase("unknown-phase") == "other"
 

backend/tests/data/test_climate.py

@@ -137,34 +137,53 @@ def test_metno_to_frame_aggregates_daily_and_converts_units():
     assert round(d2["precip"], 4) == round(6.0 / 25.4, 4)     # only the 6-hour block
 
 
-def test_recent_forecast_falls_back_to_metno(monkeypatch, tmp_path):
-    """When Open-Meteo's forecast API fails, the MET Norway backup serves the frame."""
+def test_recent_forecast_merges_nasa_past_and_metno_forward(monkeypatch, tmp_path):
+    """The recent/forecast bundle is built from a NASA POWER recent-past range plus the
+    MET Norway forward forecast, merged by date (Open-Meteo not consulted)."""
     monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
-    cell = {"id": "fallback_cell", "center_lat": 47.6062, "center_lon": -122.3321}
-    met = {"timeseries": [
-        _metno_step("2026-07-16T00:00:00Z", 18.0, 55.0, 3.0, p1=0.0),
-        _metno_step("2026-07-17T00:00:00Z", 22.0, 45.0, 5.0, p6=1.0),
-    ]}
+    cell = {"id": "rf_primary", "center_lat": 47.6, "center_lon": -122.3}
 
-    class Resp:
-        def json(self): return {"properties": met}
+    past = climate._finalize_approximated(pl.DataFrame({
+        "date": [datetime.date(2026, 7, 10), datetime.date(2026, 7, 11)],
+        "tmax": [70.0, 72.0], "tmin": [50.0, 51.0], "precip": [0.0, 0.1],
+        "wind": [5.0, 6.0], "humid": [60.0, 55.0],
+    }))
+    fwd = climate._finalize_approximated(pl.DataFrame({
+        "date": [datetime.date(2026, 7, 16), datetime.date(2026, 7, 17)],
+        "tmax": [80.0, 82.0], "tmin": [60.0, 61.0], "precip": [0.0, 0.0],
+        "wind": [3.0, 4.0], "humid": [40.0, 45.0],
+    }))
+    monkeypatch.setattr(climate, "_fetch_history_nasa", lambda c, start, end: past)
+    monkeypatch.setattr(climate, "_fetch_forecast_metno", lambda c: fwd)
+    monkeypatch.setattr(climate.meteostat, "fill_gusts", lambda lat, lon, df: df)
+    def _om_should_not_run(c): raise AssertionError("Open-Meteo forecast should not run")
+    monkeypatch.setattr(climate, "_fetch_recent_forecast_om", _om_should_not_run)
 
-    def fake_request(url, params, timeout, *, phase, headers=None, attempts=climate.MAX_ATTEMPTS):
-        if url == climate.FORECAST_URL:
-            raise RuntimeError("open-meteo forecast outage")
-        assert url == climate.METNO_URL
-        assert headers and headers.get("User-Agent"), "MET Norway needs a User-Agent"
-        return Resp()
-
-    monkeypatch.setattr(climate, "_request", fake_request)
     df = climate._load_recent_forecast(cell)
-    assert len(df) == 2 and df["date"].max() == datetime.date(2026, 7, 17)
+    assert df["date"].to_list() == [
+        datetime.date(2026, 7, 10), datetime.date(2026, 7, 11),
+        datetime.date(2026, 7, 16), datetime.date(2026, 7, 17)]
+
+
+def test_recent_forecast_falls_back_to_open_meteo(monkeypatch, tmp_path):
+    """When the NASA + MET Norway primary fails, the Open-Meteo forecast API serves."""
+    monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
+    monkeypatch.setattr(climate, "_forecast_cooldown_until", 0.0)
+    cell = {"id": "rf_fallback", "center_lat": 47.6, "center_lon": -122.3}
+
+    def _primary_down(c): raise RuntimeError("NASA + MET Norway down")
+    monkeypatch.setattr(climate, "_fetch_recent_forecast", _primary_down)
+    om = climate._to_frame(_om_daily(3))
+    monkeypatch.setattr(climate, "_fetch_recent_forecast_om", lambda c: om)
+
+    df = climate._load_recent_forecast(cell)
+    assert df.height == om.height
 
 
 def test_recent_forecast_serves_stale_cache_when_all_sources_fail(monkeypatch, tmp_path):
-    """With Open-Meteo AND MET Norway both down, an existing (stale) cache is served
-    rather than failing — and it is NOT rewritten, so its mtime stays old and the
-    next request still retries upstream first."""
+    """With every source down (the NASA + MET Norway primary and the Open-Meteo
+    fallback), an existing (stale) cache is served rather than failing — and it is NOT
+    rewritten, so its mtime stays old and the next request still retries upstream first."""
     import os
     import time
     monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
@@ -292,64 +311,101 @@ def _full_history_frame(n=4000):
     }))
 
 
-def test_short_archive_is_rejected_and_falls_back_to_nasa(monkeypatch, tmp_path):
-    """A self-hosted archive that isn't backfilled yet returns too few days; the load
-    must reject it (not cache a near-empty history as complete) and use NASA instead."""
+def test_nasa_is_primary_history_source(monkeypatch, tmp_path):
+    """NASA POWER is the primary history source: a full NASA frame is accepted and
+    Open-Meteo is never consulted."""
     monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
     monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
-    cell = {"id": "short_cell", "center_lat": 47.6, "center_lon": -122.3}
+    cell = {"id": "nasa_primary", "center_lat": 47.6, "center_lon": -122.3}
 
-    short = climate._to_frame(_om_daily(3))                     # 2 usable days « threshold
-    full = _full_history_frame()                                # >= MIN_ARCHIVE_DAYS
-    assert short.height < climate.MIN_ARCHIVE_DAYS <= full.height
-    monkeypatch.setattr(climate, "_fetch_history", lambda c: short)
+    full = _full_history_frame()
     monkeypatch.setattr(climate, "_fetch_history_nasa", lambda c: full)
+    def _om_should_not_run(c): raise AssertionError("Open-Meteo should not be called")
+    monkeypatch.setattr(climate, "_fetch_history", _om_should_not_run)
 
     df, meta = climate._load_history(cell)
-    assert meta["source"] == "nasa-power"      # rejected the short archive
+    assert meta["source"] == "nasa-power"
     assert df.height == full.height
 
 
-def test_full_archive_is_accepted(monkeypatch, tmp_path):
-    """A backfilled archive (enough days) is accepted and served as open-meteo."""
+def test_falls_back_to_open_meteo_when_nasa_unavailable(monkeypatch, tmp_path):
+    """When NASA POWER fails, the load falls back to the Open-Meteo archive."""
     monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
     monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
-    cell = {"id": "full_cell", "center_lat": 47.6, "center_lon": -122.3}
+    cell = {"id": "om_fallback", "center_lat": 47.6, "center_lon": -122.3}
 
     full = _full_history_frame()
+    def _nasa_down(c): raise RuntimeError("NASA POWER outage")
+    monkeypatch.setattr(climate, "_fetch_history_nasa", _nasa_down)
     monkeypatch.setattr(climate, "_fetch_history", lambda c: full)
-    def _nasa_should_not_run(c): raise AssertionError("NASA should not be called")
-    monkeypatch.setattr(climate, "_fetch_history_nasa", _nasa_should_not_run)
 
     df, meta = climate._load_history(cell)
     assert meta["source"] == "open-meteo"
     assert df.height == full.height
 
 
+def test_short_nasa_is_rejected_and_falls_back_to_open_meteo(monkeypatch, tmp_path):
+    """A too-short NASA response (a partial/unavailable point) is rejected rather than
+    cached as a complete record, and Open-Meteo serves instead."""
+    monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
+    monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
+    cell = {"id": "short_nasa", "center_lat": 47.6, "center_lon": -122.3}
+
+    short = climate._to_frame(_om_daily(3))                     # 2 usable days « threshold
+    full = _full_history_frame()                                # >= MIN_ARCHIVE_DAYS
+    assert short.height < climate.MIN_ARCHIVE_DAYS <= full.height
+    monkeypatch.setattr(climate, "_fetch_history_nasa", lambda c: short)
+    monkeypatch.setattr(climate, "_fetch_history", lambda c: full)
+
+    df, meta = climate._load_history(cell)
+    assert meta["source"] == "open-meteo"      # rejected the short NASA response
+    assert df.height == full.height
+
+
+def test_history_tail_prefers_nasa(monkeypatch):
+    """The tail top-up fetches NASA POWER first; Open-Meteo's range fetch is not
+    touched when NASA succeeds."""
+    cell = {"center_lat": 1.0, "center_lon": 2.0}
+    sentinel = object()
+    monkeypatch.setattr(climate, "_fetch_history_nasa",
+                        lambda c, start=None, end=None: sentinel)
+    def _om_should_not_run(c, s, e): raise AssertionError("Open-Meteo tail should not run")
+    monkeypatch.setattr(climate, "_fetch_history_range", _om_should_not_run)
+
+    assert climate._fetch_history_tail(cell, "2026-06-01", "2026-06-10") is sentinel
+
+
+def test_history_tail_falls_back_to_open_meteo(monkeypatch):
+    """When NASA fails and Open-Meteo isn't cooling down, the tail falls back to the
+    Open-Meteo archive range."""
+    monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
+    cell = {"center_lat": 1.0, "center_lon": 2.0}
+    def _nasa_down(c, start=None, end=None): raise RuntimeError("NASA down")
+    monkeypatch.setattr(climate, "_fetch_history_nasa", _nasa_down)
+    sentinel = object()
+    monkeypatch.setattr(climate, "_fetch_history_range", lambda c, s, e: sentinel)
+
+    assert climate._fetch_history_tail(cell, "2026-06-01", "2026-06-10") is sentinel
+
 # --- forecast cooldown (mirrors the archive path's, tracked separately) -----
 
-def test_forecast_cooldown_skips_open_meteo_and_falls_to_metno(monkeypatch, tmp_path):
-    """While the forecast cooldown is active, the forecast fetch must not call
-    Open-Meteo at all -- straight to the MET Norway backup, mirroring
-    _load_history's archive-cooldown skip."""
+def test_forecast_cooldown_skips_the_open_meteo_fallback(monkeypatch, tmp_path):
+    """While the forecast cooldown is active, the Open-Meteo FALLBACK is skipped: with
+    the keyless primary (NASA + MET Norway) also down and no cache, the load surfaces
+    WeatherUnavailable without ever calling Open-Meteo."""
     import time as time_mod
     monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
     monkeypatch.setattr(climate, "_forecast_cooldown_until", time_mod.time() + 60)
     cell = {"id": "fc_cooldown_cell", "center_lat": 47.6, "center_lon": -122.3}
 
-    met = {"timeseries": [_metno_step("2026-07-16T00:00:00Z", 18.0, 55.0, 3.0, p1=0.0)]}
+    def _primary_down(c): raise RuntimeError("NASA + MET Norway down")
+    monkeypatch.setattr(climate, "_fetch_recent_forecast", _primary_down)
+    def _om_should_not_run(c):
+        raise AssertionError("Open-Meteo fallback must not run during cooldown")
+    monkeypatch.setattr(climate, "_fetch_recent_forecast_om", _om_should_not_run)
 
-    class Resp:
-        def json(self): return {"properties": met}
-
-    def fake_request(url, params, timeout, *, phase, headers=None, attempts=climate.MAX_ATTEMPTS):
-        assert url != climate.FORECAST_URL, "must not call Open-Meteo during cooldown"
-        assert url == climate.METNO_URL
-        return Resp()
-
-    monkeypatch.setattr(climate, "_request", fake_request)
-    df = climate._load_recent_forecast(cell)
-    assert len(df) == 1
+    with pytest.raises(climate.WeatherUnavailable):
+        climate._load_recent_forecast(cell)
 
 
 def test_forecast_429_sets_its_own_cooldown(monkeypatch, tmp_path):

backend/tests/data/test_meteostat.py

@@ -0,0 +1,96 @@
+"""Unit tests for the Meteostat gust supplier — all hermetic (no network): the
+station index and per-station daily fetch are monkeypatched, and the pure parse
+helpers are fed literal payloads."""
+import datetime
+import json
+
+import numpy as np
+import polars as pl
+import pytest
+
+from data import meteostat
+
+
+def _frame(gust=(None, None, None), wind=(10.0, 20.0, None)):
+    return pl.DataFrame(
+        {
+            "date": [datetime.date(2020, 1, 1), datetime.date(2020, 1, 2),
+                     datetime.date(2020, 1, 3)],
+            "wind": list(wind),
+            "gust": pl.Series(list(gust), dtype=pl.Float64),
+        }
+    )
+
+
+def test_parse_daily_csv_converts_kmh_to_mph_and_skips_blanks():
+    csv = "\n".join([
+        "2020-01-01,,,,,,,,50.0,,",   # wpgt (col 8) = 50 km/h
+        "2020-01-02,,,,,,,,,,",       # no gust -> skipped
+        "2020-01-03,,,,,,,,80.0,,",   # wpgt = 80 km/h
+        "bad-date,,,,,,,,50.0,,",     # unparseable date -> skipped
+        "x,y",                        # too few columns -> skipped
+    ])
+    out = meteostat._parse_daily_csv(csv)
+    assert set(out) == {datetime.date(2020, 1, 1), datetime.date(2020, 1, 3)}
+    assert out[datetime.date(2020, 1, 1)] == pytest.approx(50.0 * meteostat.KMH_TO_MPH)
+    assert out[datetime.date(2020, 1, 3)] == pytest.approx(80.0 * meteostat.KMH_TO_MPH)
+
+
+def test_parse_stations_skips_entries_without_id_or_coords():
+    raw = json.dumps([
+        {"id": "A", "location": {"latitude": 47.6, "longitude": -122.3}},
+        {"id": "B", "location": {"latitude": 51.5, "longitude": -0.1}},
+        {"id": "C", "location": {}},                      # no coords -> skip
+        {"location": {"latitude": 1, "longitude": 2}},    # no id -> skip
+    ]).encode()
+    ids, lats, lons = meteostat._parse_stations(raw)
+    assert ids == ["A", "B"]
+    assert lats.tolist() == [47.6, 51.5]
+    assert lons.tolist() == [-122.3, -0.1]
+
+
+def test_nearest_station_picks_closest_within_range(monkeypatch):
+    monkeypatch.setattr(meteostat, "_STATIONS",
+                        (["seattle", "london"],
+                         np.array([47.6, 51.5]), np.array([-122.3, -0.1])))
+    assert meteostat.nearest_station(47.61, -122.31) == "seattle"
+    # Middle of the Pacific — both stations are far beyond MAX_STATION_KM.
+    assert meteostat.nearest_station(0.0, -160.0) is None
+
+
+def test_fill_gusts_prefers_measured_then_estimates(monkeypatch):
+    monkeypatch.setattr(meteostat, "nearest_station", lambda lat, lon: "s1")
+    monkeypatch.setattr(meteostat, "daily_gusts",
+                        lambda sid: {datetime.date(2020, 1, 1): 33.0})
+    out = meteostat.fill_gusts(47.6, -122.3, _frame())
+    gust = out["gust"].to_list()
+    assert gust[0] == pytest.approx(33.0)                 # measured wins
+    assert gust[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR)  # estimated from wind
+    assert gust[2] is None                                # no wind -> no estimate
+
+
+def test_fill_gusts_estimates_everywhere_with_no_station(monkeypatch):
+    monkeypatch.setattr(meteostat, "nearest_station", lambda lat, lon: None)
+    out = meteostat.fill_gusts(0.0, 0.0, _frame())
+    gust = out["gust"].to_list()
+    assert gust[0] == pytest.approx(10.0 * meteostat.GUST_FACTOR)
+    assert gust[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR)
+    assert gust[2] is None
+
+
+def test_fill_gusts_degrades_on_fetch_error(monkeypatch):
+    def _boom(lat, lon):
+        raise RuntimeError("meteostat down")
+    monkeypatch.setattr(meteostat, "nearest_station", _boom)
+    # Must not raise — falls back to pure estimation.
+    out = meteostat.fill_gusts(47.6, -122.3, _frame())
+    assert out["gust"].to_list()[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR)
+
+
+def test_fill_gusts_noop_when_source_has_gusts(monkeypatch):
+    def _fail(*a, **k):
+        raise AssertionError("must not consult Meteostat when gusts already present")
+    monkeypatch.setattr(meteostat, "nearest_station", _fail)
+    df = _frame(gust=(40.0, 41.0, 42.0))
+    out = meteostat.fill_gusts(47.6, -122.3, df)
+    assert out["gust"].to_list() == [40.0, 41.0, 42.0]

backend/tests/test_drift_check.py

@@ -0,0 +1,55 @@
+"""Unit tests for the drift-check comparison logic (hermetic — no network). The
+source fetch (check_cell) is not tested here; it runs on a networked box."""
+import datetime
+import math
+
+import polars as pl
+import pytest
+
+import drift_check
+from data import climate
+
+_D = [datetime.date(2024, 1, d) for d in (1, 2, 3)]
+
+
+def test_compare_values_stats():
+    a = pl.DataFrame({"date": _D, "tmax": [70.0, 80.0, 90.0], "wind": [5.0, 6.0, 7.0]})
+    b = pl.DataFrame({"date": _D, "tmax": [72.0, 78.0, 90.0], "wind": [5.0, 6.0, 7.0]})
+    out = drift_check.compare_values(a, b, metrics=("tmax", "wind"))
+    assert out["tmax"] == {"n": 3, "mad": pytest.approx(4 / 3, abs=1e-3),
+                           "bias": pytest.approx(0.0), "max_abs": pytest.approx(2.0)}
+    assert out["wind"] == {"n": 3, "mad": 0.0, "bias": 0.0, "max_abs": 0.0}
+
+
+def test_compare_values_skips_nulls_and_missing_metric():
+    a = pl.DataFrame({"date": _D, "tmax": [70.0, 80.0, 90.0]})
+    b = pl.DataFrame({"date": _D, "tmax": [71.0, None, 90.0]})
+    out = drift_check.compare_values(a, b, metrics=("tmax", "gust"))
+    assert out["tmax"]["n"] == 2                     # the null day is dropped
+    assert out["tmax"]["mad"] == pytest.approx(0.5)  # |70-71| and |90-90|
+    assert "gust" not in out                         # metric absent from both frames
+
+
+def _gradeable(n=800):
+    """A finalized, gradeable multi-year frame (seasonal temp swing so windows have a
+    real distribution)."""
+    start = datetime.date(2019, 1, 1)
+    dates = [start + datetime.timedelta(days=i) for i in range(n)]
+    tmax = [70.0 + 15.0 * math.sin(i / 58.0) for i in range(n)]
+    tmin = [t - 15.0 for t in tmax]
+    return climate._finalize_frame(pl.DataFrame({
+        "date": dates, "tmax": tmax, "tmin": tmin, "precip": [0.0] * n,
+        "wind": [5.0] * n, "gust": [8.0] * n, "humid": [60.0] * n,
+        "fmax": tmax, "fmin": tmin,
+    }))
+
+
+def test_grade_band_divergence_identical_is_zero():
+    f = _gradeable()
+    end = f["date"].max()
+    start = end - datetime.timedelta(days=60)
+    div = drift_check.grade_band_divergence(f, f, start, end)
+    assert div                                        # some metrics were compared
+    assert any(s["compared"] > 0 for s in div.values())
+    assert all(s["differ"] == 0 for s in div.values())   # identical frames never diverge
+    assert all(s["pct"] == 0.0 for s in div.values())

backend/tests/test_seed_era5.py

@@ -0,0 +1,72 @@
+"""Unit tests for the ERA5 seed's hourly→daily transform (hermetic — no network, no
+icechunk/xarray). The S3 access layer (_open_store/fetch_point) is deliberately not
+tested here; it must be verified with `seed_era5.py --dry-run` on the seed box."""
+import datetime
+import math
+
+import polars as pl
+import pytest
+
+import seed_era5
+
+_MS_TO_MPH = 2.2369362920544
+_M_TO_IN = 39.37007874
+
+
+def _rh(t_c, td_c):
+    es = math.exp(17.625 * t_c / (243.04 + t_c))
+    e = math.exp(17.625 * td_c / (243.04 + td_c))
+    return min(100.0, 100.0 * e / es)
+
+
+def _hourly(rows):
+    """rows: list of (time, t2m_K, d2m_K, tp_m, u10, v10, i10fg)."""
+    cols = ["time", "t2m", "d2m", "tp", "u10", "v10", "i10fg"]
+    return pl.DataFrame({c: [r[i] for r in rows] for i, c in enumerate(cols)})
+
+
+def test_hourly_to_daily_aggregates_and_converts():
+    d1 = datetime.datetime(2020, 1, 1)
+    d2 = datetime.datetime(2020, 1, 2)
+    hourly = _hourly([
+        # day 1: 10°C sat, then 20°C / 10°C dew; winds 5 m/s then calm; gusts 10/12
+        (d1.replace(hour=0), 283.15, 283.15, 0.001, 3.0, 4.0, 10.0),
+        (d1.replace(hour=1), 293.15, 283.15, 0.002, 0.0, 0.0, 12.0),
+        # day 2: 0°C sat; wind 10 m/s; gust 20
+        (d2.replace(hour=0), 273.15, 273.15, 0.0, 6.0, 8.0, 20.0),
+    ])
+    daily = seed_era5.hourly_to_daily(hourly).sort("date")
+    assert daily["date"].to_list() == [datetime.date(2020, 1, 1), datetime.date(2020, 1, 2)]
+
+    r0 = daily.row(0, named=True)
+    assert r0["tmax"] == pytest.approx(68.0)      # 20°C
+    assert r0["tmin"] == pytest.approx(50.0)      # 10°C
+    assert r0["precip"] == pytest.approx(0.003 * _M_TO_IN)
+    assert r0["wind"] == pytest.approx(5.0 * _MS_TO_MPH)     # max(5, 0)
+    assert r0["gust"] == pytest.approx(12.0 * _MS_TO_MPH)    # max(10, 12)
+    assert r0["humid"] == pytest.approx((_rh(10, 10) + _rh(20, 10)) / 2)
+
+    r1 = daily.row(1, named=True)
+    assert r1["tmin"] == pytest.approx(32.0)      # 0°C
+    assert r1["wind"] == pytest.approx(10.0 * _MS_TO_MPH)    # sqrt(6²+8²)
+
+
+def test_saturated_air_reads_full_humidity():
+    hourly = _hourly([(datetime.datetime(2021, 6, 1), 293.15, 293.15, 0.0, 1.0, 1.0, 2.0)])
+    daily = seed_era5.hourly_to_daily(hourly)
+    assert daily.row(0, named=True)["humid"] == pytest.approx(100.0)
+
+
+def test_finalize_produces_the_store_schema():
+    """The daily frame, run through climate._finalize_approximated (as seed_cell does),
+    yields the persisted columns incl. derived feels."""
+    from data import climate
+    hourly = _hourly([
+        (datetime.datetime(2019, 7, 1, 0), 300.0, 290.0, 0.0, 2.0, 2.0, 5.0),
+        (datetime.datetime(2019, 7, 1, 1), 305.0, 291.0, 0.001, 3.0, 1.0, 7.0),
+    ])
+    frame = climate._finalize_approximated(seed_era5.hourly_to_daily(hourly))
+    for col in ("date", "tmax", "tmin", "precip", "wind", "gust", "humid",
+                "fmax", "fmin", "feels"):
+        assert col in frame.columns
+    assert frame.height == 1

backend/tests/web/test_api.py

@@ -6,6 +6,7 @@ from fastapi.testclient import TestClient
 
 from web import app as appmod
 from data import climate
+from data import places
 
 
 @pytest.fixture
@@ -182,11 +183,53 @@ def test_cell_bundle_matches_per_view_payloads(client):
     assert r304.status_code == 304
 
 
-def test_suggest_falls_back_to_upstream_geocoder(client, monkeypatch):
-    upstream = [{"name": "Seattle", "admin1": "Washington", "country": "United States",
-                 "country_code": "US", "lat": 47.6, "lon": -122.33, "population": 737015}]
-    monkeypatch.setattr(climate, "geocode", lambda q, count=5: list(upstream))
-    r = client.get("/thermograph/api/v2/suggest", params={"q": "seattle-fallback-probe"})
+_LOCAL_HIT = [{"name": "Seattle", "admin1": "Washington", "country": "United States",
+               "country_code": "US", "lat": 47.6, "lon": -122.33, "population": 737015,
+               "match": "prefix"}]
+
+
+def test_geocode_local_first_skips_network(client, monkeypatch):
+    """A local GeoNames hit answers /geocode with no outbound Nominatim call, and
+    the internal prefix/fuzzy tag is stripped from the wire shape."""
+    monkeypatch.setattr(places, "search", lambda q, limit=5: list(_LOCAL_HIT))
+
+    def _boom(*a, **k):
+        raise AssertionError("Nominatim must not be called when the local index hits")
+    monkeypatch.setattr(climate, "geocode_nominatim", _boom)
+
+    r = client.get("/thermograph/api/v2/geocode", params={"q": "seattle"})
+    assert r.status_code == 200
+    res = r.json()["results"]
+    assert res[0]["name"] == "Seattle"
+    assert "match" not in res[0]
+
+
+def test_geocode_falls_back_to_nominatim_on_local_miss(client, monkeypatch):
+    """A local miss (or a still-loading index) falls back to Nominatim /search —
+    the path that covers neighbourhoods/postcodes the cities dump lacks."""
+    monkeypatch.setattr(places, "search", lambda q, limit=5: [])   # genuine miss
+    nom = [{"name": "West Seattle", "admin1": "Washington", "country": "United States",
+            "country_code": "US", "lat": 47.57, "lon": -122.38, "population": None}]
+    called = []
+    monkeypatch.setattr(climate, "geocode_nominatim",
+                        lambda q, count=5: (called.append(q), nom)[1])
+
+    r = client.get("/thermograph/api/v2/geocode", params={"q": "west seattle"})
+    assert r.status_code == 200
+    assert r.json()["results"][0]["name"] == "West Seattle"
+    assert called == ["west seattle"]
+
+
+def test_suggest_is_local_only_no_network(client, monkeypatch):
+    """Autocomplete is served purely from the local index — even a reachable
+    Nominatim must never be hit per keystroke."""
+    monkeypatch.setattr(places, "search", lambda q, limit=5: list(_LOCAL_HIT))
+
+    def _boom(*a, **k):
+        raise AssertionError("/suggest must not make an outbound geocoder call")
+    monkeypatch.setattr(climate, "geocode_nominatim", _boom)
+
+    r = client.get("/thermograph/api/v2/suggest", params={"q": "seattle"})
     assert r.status_code == 200
     body = r.json()
     assert body["results"][0]["name"] == "Seattle"

backend/web/app.py

@@ -2,7 +2,6 @@
 import asyncio
 import contextlib
 import datetime
-import functools
 import hashlib
 import hmac
 import json
@@ -414,9 +413,22 @@ def _cached_response(request, run, kind, cell, key, token, build, cache_placeles
 
 # --- endpoints ----------------------------------------------------------------
 
+_GEOCODE_LIMIT = 5
+
+
 def api_geocode(q: str = Query(..., min_length=1)):
+    """Forward place search. The local GeoNames index answers first (no network);
+    only a genuine miss — or a query the index can't hold (neighbourhoods,
+    postcodes, tiny villages, native-language names) — falls back to Nominatim.
+    `search` returns None while the index is still loading, which also falls
+    through to the network so search keeps working during warmup."""
+    local = places.search(q, _GEOCODE_LIMIT)
+    if local:
+        # Strip the internal prefix/fuzzy tag so the wire shape matches the
+        # Nominatim branch (and the former Open-Meteo contract) exactly.
+        return {"results": [{k: v for k, v in r.items() if k != "match"} for r in local]}
     try:
-        return {"results": climate.geocode(q)}
+        return {"results": climate.geocode_nominatim(q, _GEOCODE_LIMIT)}
     except Exception as e:  # noqa: BLE001 - surface upstream failures to the client
         raise HTTPException(status_code=502, detail=f"geocoding failed: {e}")
 
@@ -424,21 +436,20 @@ def api_geocode(q: str = Query(..., min_length=1)):
 _SUGGEST_LIMIT = 5
 
 
-@functools.lru_cache(maxsize=1024)
-def _suggest_upstream(q: str) -> tuple:
-    """Open-Meteo lookup for /suggest, memoized per query string — type-ahead
-    re-asks the same prefixes constantly (backspacing, retyping). Failures
-    raise and are not cached, so a transient upstream error doesn't stick."""
-    return tuple(climate.geocode(q, count=_SUGGEST_LIMIT))
+def _no_upstream(q: str) -> tuple:
+    """Autocomplete is served purely from the local GeoNames index — no per-keystroke
+    call to any external geocoder. Typo-correction still runs against the local
+    index, so respellings ("pest seattle" → "west seattle") keep working offline."""
+    return ()
 
 
 def api_suggest(q: str = Query(..., min_length=1)):
     """Type-ahead location suggestions: the top 5 places for a (possibly
     typo'd) query prefix; `corrected` reports the respelling that produced the
     results ("pest seattle" → "west seattle"), or null. The ranking/typo policy
-    lives in places.suggest."""
+    lives in places.suggest, now driven entirely by the local index."""
     try:
-        results, corrected = places.suggest(q, _suggest_upstream, _SUGGEST_LIMIT)
+        results, corrected = places.suggest(q, _no_upstream, _SUGGEST_LIMIT)
     except Exception as e:  # noqa: BLE001 - nothing at all to serve
         raise HTTPException(status_code=502, detail=f"geocoding failed: {e}")
     return {"results": results, "corrected": corrected}