METAR vs TAF: Which Aviation Weather Endpoint Does Your App Actually Need?

Most applications that need aviation weather call both endpoints because both exist, then discover that one of them returns 404 at half the airports they care about and that the other one never changes between polls.

METAR and TAF are not two formats for the same information. One is a measurement of what is happening; the other is a meteorologist's forecast of what will happen. That difference determines which you need, how often to call it, how long to cache it, and what to do when it isn't there.

If you need to read the reports themselves — decoding 24017G23KT and the rest — METAR and TAF explained covers the format field by field. This post is the decision that comes before that one.

The difference that matters

A METAR is an observation. Someone or something at the airfield measured the wind, visibility, cloud, temperature and pressure, and published what was there. It is a statement about the past — recent past, but past.

A TAF is a forecast. A meteorologist assessed what conditions are expected in the airport's vicinity, typically about five nautical miles around the field, and published a prediction covering the next 24 or 30 hours. It is a professional judgement about the future, and it can be wrong.

Everything else follows from that.

Which one your app needs

You want METAR if the question is "what is it like there now."

Live airport boards. A travel app showing conditions at the destination. A dashboard that flags airports currently below minima. Anything colouring a map by current flight category. A delay-explanation feature that wants to say why things are backing up at the moment. If your user is looking at the present tense, you want the observation.

You want TAF if the question is "what will it be like when I get there."

Flight planning, where departure is in three hours and arrival in seven. Alternate selection, which is entirely a forecast problem. Crew and dispatch tools. Anything that needs to warn a user in advance rather than tell them what already happened.

You want both if you're building a briefing.

A preflight briefing shows current conditions and the forecast together, because a pilot needs to know both what's there now and whether it's about to deteriorate. That's the classic case, and it's the one where calling both is genuinely right rather than reflexive.

The question worth asking about your own product is whether anything acts on the future. A flight tracker showing destination weather does not need a TAF — by the time the user looks, the METAR is the answer. A tool that helps someone decide whether to depart absolutely does.

import requests

BASE = "https://skylink-api.p.rapidapi.com"
H = {"X-RapidAPI-Key": KEY, "X-RapidAPI-Host": "skylink-api.p.rapidapi.com"}

# Observation — what it is like now
metar = requests.get(f"{BASE}/weather/metar/KJFK", headers=H).json()
print(metar["raw"])

# Forecast — what it is expected to be
taf = requests.get(f"{BASE}/weather/taf/KJFK", headers=H)
print(taf.json()["raw"] if taf.status_code == 200 else "no TAF published here")

Note the status check on the TAF call. That isn't defensive style for its own sake — see below.

A TAF 404 is usually not an error

This is the single most common surprise, and it generates more confused support questions than anything else in the weather group.

Many airports publish METARs and no TAF at all. Producing a terminal forecast requires a meteorologist to assess that specific airport four times a day, and that effort is spent on airports where it's operationally justified. A regional field with an automated weather station will report observations continuously and never have a forecast issued for it.

So a 404 from the TAF endpoint at a small airport means "no TAF is published here", not "this airport doesn't exist" and not "something went wrong". Your code needs to treat it as a normal outcome:

def get_weather(icao):
    out = {"icao": icao, "metar": None, "taf": None, "taf_available": False}

    m = requests.get(f"{BASE}/weather/metar/{icao}", headers=H)
    if m.status_code == 200:
        out["metar"] = m.json()

    t = requests.get(f"{BASE}/weather/taf/{icao}", headers=H)
    if t.status_code == 200:
        out["taf"] = t.json()
        out["taf_available"] = True
    elif t.status_code != 404:
        t.raise_for_status()          # a real failure, not an absent forecast

    return out

The interface consequence matters as much as the code. "No forecast published for this airport" is useful and honest. An error state, an empty panel, or a spinner that never resolves all tell the user something is broken when nothing is.

Polling and caching: match the update rate

The most common waste in weather integrations is polling far faster than the data changes.

METARs are issued hourly or half-hourly depending on the station. A SPECI — a special report — is issued when conditions change significantly at a staffed site, which is exactly when you'd want to know. Polling every five minutes is a reasonable upper bound; polling every thirty seconds returns the identical string fifty-nine times out of sixty and spends your quota doing it.

TAFs are issued four times a day at 00Z, 06Z, 12Z and 18Z, with amendments in between when the forecast changes materially. Caching a TAF for an hour is conservative. Caching for fifteen minutes is generous. Polling every minute is pure waste.

A simple rule that works: cache METARs for five minutes, TAFs for thirty, and if you're serving many users looking at the same airports, cache centrally rather than per user. Airport weather is the same for everybody, which makes it ideal to share.

CACHE_TTL = {"metar": 300, "taf": 1800}   # seconds

If you need the decoded fields rather than the raw string, both endpoints take parsed=true, which adds structured output under a parsed key. Request it when you're going to render individual values, and skip it when you're displaying the raw text — there's no point decoding something you're about to print verbatim.

Where the other endpoints fit

METAR and TAF cover the airport. They say nothing about what happens between airports, which catches people building anything route-shaped.

SIGMETs and AIRMETs are en-route advisories — severe turbulence, icing, convective activity, volcanic ash. A flight can depart and arrive in clear conditions and cross something significant in the middle, and neither the departure METAR nor the arrival TAF will mention it.

Winds aloft give cruise-altitude wind and temperature, which is what actually determines how long the flight takes. US only.

PIREPs are voluntary pilot reports of what's actually being encountered — the only source that tells you turbulence was real rather than forecast.

If you find yourself calling four endpoints for one flight, the flight briefing endpoint assembles the package in one call, and the aviation weather API overview covers how the pieces relate.

A decision you can make in one line

If your user is looking at the present, call METAR. If your user is deciding about the future, call TAF. If they're doing both, call both — and handle the 404.

Most apps that think they need both actually need METAR and a clear label saying when it was observed. Forecasts carry an obligation: if you show one, you should also show its validity period, because a TAF issued at 06Z and read at 20Z is a different proposition from a fresh one, and nothing in the raw string shouts that at the reader.

Getting started

Both METAR and TAF are available on every plan including the free trial, and with sensible caching a weather feature is one of the cheapest things you can build on an aviation API — a few dozen airports refreshed every five minutes fits comfortably inside a small quota.

The weather group overview lists the rest of the surface when you outgrow the two.