Flight Rules Classification Isn’t a Lookup Table
If you’ve ever tried to build a flight condition dashboard — the kind that tells a pilot at a glance whether a route is VFR, MVFR, IFR, or LIFR — you’ve probably discovered that the logic is messier than the FAA’s clean threshold table suggests. Ceiling and visibility interact. Cloud layers matter, not just the lowest cloud. And the data you get from a weather API rarely arrives pre-labeled with a flight category.
Here’s how to do it properly using WeatherAPI.com’s forecast and current conditions endpoints, with actual working thresholds and a few sharp corners you should know about before you ship this in production.
The Thresholds, and Why They’re Not Enough on Their Own
The FAA defines these four categories by ceiling (lowest broken or overcast layer, in feet AGL) and visibility (statute miles):
- VFR: Ceiling > 3,000 ft and visibility > 5 sm
- MVFR (Marginal VFR): Ceiling 1,000–3,000 ft or visibility 3–5 sm
- IFR: Ceiling 500–999 ft or visibility 1–3 sm
- LIFR (Low IFR): Ceiling < 500 ft or visibility < 1 sm
The key word is or. A 5,000 ft ceiling with 0.5 sm visibility in fog is LIFR. That asymmetry trips up a lot of implementations that only check one dimension. The final category is always driven by whichever parameter is worse.
What WeatherAPI Actually Gives You
The current conditions endpoint (/v1/current.json) returns vis_miles and vis_km — straightforward visibility figures. The forecast endpoint (/v1/forecast.json) gives you vis_miles per hour block inside forecastday[].hour[].
Ceiling is trickier. WeatherAPI doesn’t return a dedicated ceiling field labeled as such. What you get is cloud cover percentage (cloud) and, on the current conditions response, a condition text and code. For a proper ceiling determination at a certified aerodrome, you’d be pulling METAR data — which WeatherAPI does surface via the aviation endpoints if your plan includes that access. For general route planning across arbitrary coordinates (think: an app plotting a VFR cross-country from KBDR to KACK), you’re working with cloud cover percentage and visibility together.
Mapping Cloud Cover to a Ceiling Estimate
Cloud cover percentage isn’t ceiling, but you can derive a rough ceiling tier from it. The standard sky condition thresholds used in METARs map roughly like this:
- 0–12% → SKC/CLR (clear)
- 13–25% → FEW (few clouds, not a ceiling layer)
- 26–50% → SCT (scattered, not a ceiling layer)
- 51–87% → BKN (broken — this is a ceiling)
- 88–100% → OVC (overcast — this is a ceiling)
So cloud cover ≥ 51% means you have a ceiling. Below that threshold — even at 50% — it technically isn’t a ceiling by METAR convention. A lot of naive implementations treat any significant cloud cover as a ceiling and end up classifying scattered-layer days as IFR.
The missing piece is the height of that ceiling layer. WeatherAPI’s standard forecast data doesn’t return cloud layer altitudes. For any application where actual AGL ceiling height matters — low-altitude VFR planning, helicopter operations, drone ops under Part 107 — you need to either supplement with METAR data from nearby ICAO stations or accept that your ceiling classification is coarse.
A Practical Implementation
Here’s a Python function that classifies flight conditions from WeatherAPI data, treating cloud cover ≥ 51% as indicating a ceiling exists and using visibility as the primary discriminator when cloud data is ambiguous:
import requests
API_KEY = "your_key_here"
def get_flight_category(lat: float, lon: float) -> dict:
url = f"https://api.weatherapi.com/v1/current.json"
resp = requests.get(url, params={"key": API_KEY, "q": f"{lat},{lon}"})
resp.raise_for_status()
data = resp.json()["current"]
vis_sm = data["vis_miles"]
cloud_pct = data["cloud"]
# Determine if a ceiling layer is present (BKN or OVC equivalent)
has_ceiling = cloud_pct >= 51
# Without actual ceiling height, we treat presence/absence conservatively.
# If ceiling exists, we cap the category at MVFR unless visibility pushes it lower.
if vis_sm < 1:
category = "LIFR"
elif vis_sm < 3:
category = "IFR"
elif vis_sm < 5:
category = "MVFR"
elif has_ceiling:
# Ceiling present but visibility OK — conservatively MVFR
category = "MVFR"
else:
category = "VFR"
return {
"category": category,
"vis_sm": vis_sm,
"cloud_pct": cloud_pct,
"has_ceiling_layer": has_ceiling,
}
This is intentionally conservative. When cloud cover says BKN or OVC but we don't know the ceiling altitude, we don't assume it's high enough for VFR. Better to show MVFR and let the pilot check a METAR than to false-clear an IFR situation.
Using the Forecast Endpoint for Route Planning
For a route that takes 2–3 hours to fly, you want conditions at departure, at intermediate waypoints, and at the destination across time, not just right now. The forecast endpoint at /v1/forecast.json?days=1 gives hourly data you can walk through:
def get_route_forecast(lat: float, lon: float, hours: int = 4) -> list:
url = "https://api.weatherapi.com/v1/forecast.json"
resp = requests.get(url, params={
"key": API_KEY,
"q": f"{lat},{lon}",
"days": 1
})
resp.raise_for_status()
hours_data = resp.json()["forecast"]["forecastday"][0]["hour"]
from datetime import datetime, timezone
now_hour = datetime.now().hour
results = []
count = 0
for h in hours_data:
if datetime.strptime(h["time"], "%Y-%m-%d %H:%M").hour >= now_hour and count < hours:
vis_sm = h["vis_miles"]
cloud_pct = h["cloud"]
has_ceiling = cloud_pct >= 51
if vis_sm < 1:
cat = "LIFR"
elif vis_sm < 3:
cat = "IFR"
elif vis_sm < 5 or has_ceiling:
cat = "MVFR"
else:
cat = "VFR"
results.append({"time": h["time"], "category": cat, "vis_sm": vis_sm})
count += 1
return results
Run this for each waypoint on the route, collect the worst category across all time windows, and surface that as the route's overall flight conditions status. Pilots call this thinking in terms of the "weakest link."
Where This Approach Falls Short
Three limitations worth stating directly rather than burying in fine print:
Mountain obscuration. In terrain like the Rockies or the Pacific ranges, cloud-in-terrain obscuration creates IFR conditions even when a flat-land visibility reading looks fine. WeatherAPI data represents conditions at the query point, not terrain clearance. Don't use this for mountain VFR planning without supplemental terrain-aware checks.
Rapidly developing convection. The hourly forecast model won't capture a thunderstorm that develops in 20 minutes. If your app serves flight planning in the central US during summer afternoons, you need to layer in convective outlook data — a separate pipeline entirely.
Fog onset. Temperature/dewpoint spread convergence is the classic fog predictor. WeatherAPI returns dewpoint_f and temp_f on current conditions. Compute the spread and add a fog-risk flag when it's ≤ 4°F, especially overnight and early morning. That's a cheap signal that adds real value.
The Fog Risk Supplement
def fog_risk(current: dict) -> bool:
spread = abs(current["temp_f"] - current["dewpoint_f"])
return spread <= 4.0
Combine this with a cloud cover check and you've got a reasonable radiation fog detector. Not perfect — sea fog, advection fog, and upslope fog have different triggers — but useful as a first-pass flag in a UI.
One Design Choice I'd Defend
Some developers want to show a "VFR" label whenever the model says visibility is good, regardless of cloud cover. Push back on that. If you're building something pilots actually use — even just for situational awareness, not official flight planning — the conservative approach where any ceiling layer degrades to MVFR is the right call. A MVFR label that leads someone to check a METAR before flying is a good outcome. A false VFR label that doesn't is not.
If your app serves purely non-aviation use cases (drone delivery dispatch, outdoor event planning, construction scheduling), relax the ceiling conservatism. The distinction between BKN at 2,000 ft and BKN at 4,000 ft matters a lot less than whether it's raining.
One last thing: for any route that crosses multiple ARTCC boundaries or includes a Class B approach, pull the actual METARs for the 3–4 nearest ICAO stations and let those override your modeled estimates. The WeatherAPI-derived classification is useful for interpolating between stations. It should never completely replace them for real flight planning decisions.