Why microclimate matters for wine
Your county forecast says 38°F overnight. Your vineyard sits in a drainage valley where cold air pools and the real low hits 30°F. You lose 40% of your primary buds. This is the gap between weather data and winemaking decisions — and it's exactly what modern AI weather models, combined with open-source downscaling, are built to close.
In this guide, you'll build a practical vineyard frost forecaster that: (1) fetches hyperlocal temperature forecasts from GraphCast, (2) downscales them to kilometer resolution with Prithvi WxC, (3) adds your own weather station observations for calibration, (4) triggers a webhook when frost risk exceeds your threshold. No GPU infrastructure. No licensing gotchas.
The three signals that actually matter
Frost damage in viticulture hinges on three interacting variables, not just air temperature:
- Air temperature (T). The one everyone checks. But forecasts are point estimates — reality depends on microtopography.
- Dew point (Td). When T approaches Td at night under calm, clear skies, evaporative cooling accelerates and frost forms fast.
- Wind speed. Below 3 mph, cold air settles into low spots. Above 5 mph, the boundary layer mixes and frost risk drops sharply.
A forecast that reports only air temperature is missing 2 of the 3 signals. The API calls below pull all three in a single request.
Step 1: Baseline forecast from Open-Meteo
Open-Meteo aggregates multiple government sources (GFS, ECMWF, DWD) into a single free API. It's the fastest way to get a reasonable baseline. Zero credits cost on SciRouter — we pass the free tier through.
import httpx
# Your vineyard lat/lng
vineyard = {"latitude": 38.297, "longitude": -122.286} # Napa Valley
resp = httpx.post(
"https://scirouter-gateway-production.up.railway.app/v1/weather/forecast",
headers={"Authorization": f"Bearer {YOUR_API_KEY}"},
json={
"location": vineyard,
"model": "open_meteo",
"lead_time_hours": 72,
"variables": ["temperature_2m", "dew_point_2m", "wind_speed_10m"],
},
)
forecast = resp.json()
# forecast['hourly'] is a list of variable timeseries.
# forecast['credits_charged'] will be 0 for open_meteo.Step 2: Refine with GraphCast for 5+ day lead
GraphCast excels on days 3–10. If your vineyard is budding and you need to plan wind-machine staffing a week out, switch the model argument. The request shape is identical.
# Same request, but model=graphcast — 5 credits per run.
resp = httpx.post(
"https://scirouter-gateway-production.up.railway.app/v1/weather/forecast",
headers={"Authorization": f"Bearer {YOUR_API_KEY}"},
json={"location": vineyard, "model": "graphcast", "lead_time_hours": 168},
)Step 3: Downscale to your fields with Prithvi WxC
Prithvi WxC 2.1 is a foundation model jointly released by IBM and NASA under Apache 2.0. It takes a coarse forecast and produces a high-resolution regional grid. For our vineyard, we can downscale from 0.25° (~25 km) to ~3 km, which resolves cold-air drainage along our valley.
resp = httpx.post(
"https://scirouter-gateway-production.up.railway.app/v1/weather/downscale",
headers={"Authorization": f"Bearer {YOUR_API_KEY}"},
json={
"location": vineyard,
"region_km": 50, # 50 km radius around the vineyard
"source_resolution_deg": 0.25,
"target_resolution_km": 3,
"lead_time_hours": 24,
},
)
downscaled = resp.json()
# downscaled['download_url'] → GeoTIFF of the regional grid
# downscaled['grid_summary'] → min/max/mean over the region
# 10 credits per downscale run.Step 4: Add your weather station for calibration
If you have a Tempest or Ambient Weather station, SciRouter ingests its readings via webhook. Over a few weeks, the system learns your local bias — maybe your valley runs 2°C colder than the model at 4 AM under clear skies — and corrects future forecasts.
Register a webhook for your station:
curl -X POST https://scirouter-gateway-production.up.railway.app/v1/weather/webhooks \
-H "Authorization: Bearer $SCIROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"name": "North vineyard Tempest", "source_type": "tempest"}'
# Response includes a unique webhook_url + secret.
# Point your Tempest app at the webhook_url. Done.Step 5: Automate the alert
Finally, subscribe to an alert that fires when the forecast dew point over your vineyard dips below 1°C within any 24-hour window. The webhook lands in Slack, email, or your phone.
httpx.post(
"https://scirouter-gateway-production.up.railway.app/v1/weather/alerts/subscribe",
headers={"Authorization": f"Bearer {YOUR_API_KEY}"},
json={
"name": "Vineyard frost — north block",
"location": vineyard,
"metric": "temperature",
"threshold": 1.0, # 1°C
"comparator": "<",
"window_hours": 24,
"notify_channel": "webhook",
"notify_target": "https://hooks.slack.com/services/...",
},
)What this costs to run at harvest scale
A reasonable monitoring pattern for a single vineyard — hourly Open-Meteo refresh + daily GraphCast + weekly Prithvi WxC downscale — runs roughly:
- Open-Meteo refreshes: 24 × 30 = 720/month at 0 credits = free
- GraphCast daily: 30 × 5 = 150 credits/month
- Prithvi WxC weekly: 4 × 10 = 40 credits/month
- Total: ~190 credits/month. Free tier covers this with 310 credits to spare.
When open models are NOT the right answer
We'd be lying if we said AI forecasts are better than a local meteorologist's judgment during rapidly evolving events. For the 12–24 hour frost watch window, NWS point forecasts and your own station's real-time readings beat any model. For 2–10 day planning, the combination above — model + downscale + calibration — is very hard to beat at any price point.
Try it
Open WeatherLab in the dashboard — pick your vineyard on the map, pick a model, run a forecast. No GPU. No credit card. 500 free credits a month while you build.