Barometric Pressure Alone Tells You Almost Nothing
Most developers pull pressure_mb from a weather API response, display it somewhere in the UI, and move on. The raw number — say, 1013 hPa — is largely meaningless without context. A reading of 1008 hPa could mean a warm, stable day with slightly below-normal pressure, or it could mean a deep low is three hours away and you’re watching it arrive in slow motion. The value itself doesn’t tell you which.
What actually predicts incoming rough weather is the rate of change: how fast pressure is falling, over what interval, and whether that rate is itself accelerating. That’s the signal. Building a simple derivative over hourly forecast data turns a static field into something actionable.
What the Meteorology Says
The WMO and national services like the UK Met Office have published pressure-tendency thresholds for decades. A fall of more than 1 hPa per hour over three consecutive hours is generally considered rapid, reliably associated with approaching fronts and surface low development. A fall exceeding 1.75 hPa/hour sustained over a few hours crosses into what forecasters call “explosive cyclogenesis” territory — rare outside open ocean and high-latitude environments, but worth knowing the term when you’re reading operational guidance.
For practical app-building purposes, three thresholds cover most scenarios:
- Steady or rising: <0.5 hPa/hour sustained — conditions likely stable for the near term
- Moderate fall: 0.5–1.0 hPa/hour — worth watching, possible frontal approach
- Rapid fall: >1.0 hPa/hour sustained — active weather likely within 6–12 hours
These are guidelines, not rules. The same pressure fall that signals a convective build-up in a continental interior in summer almost certainly means a synoptic-scale system in the North Atlantic. The thresholds are a reasonable generic starting point; the context is yours to add.
Getting the Data from WeatherAPI
The hourly forecast endpoint gives you pressure_mb for each hour in the forecast window. A three-day forecast is 72 hourly data points — enough resolution to compute a meaningful pressure tendency.
The request looks like this:
GET https://api.weatherapi.com/v1/forecast.json
?key=YOUR_KEY
&q=55.8642,-4.2518
&days=3
&aqi=no
&alerts=no
Each hour object inside forecast.forecastday[].hour[] contains a pressure_mb field. Pull every hourly reading across all days and flatten them into a time-ordered array — that’s your raw input.
Computing Pressure Tendency in Python
Here’s a minimal implementation. It takes the hourly array, computes rolling one-hour deltas, then evaluates a three-hour rolling window for the average rate of change:
import requests
from datetime import datetime
API_KEY = "YOUR_KEY"
LOCATION = "55.8642,-4.2518" # Glasgow
def fetch_hourly_pressure():
url = "https://api.weatherapi.com/v1/forecast.json"
params = {"key": API_KEY, "q": LOCATION, "days": 3, "aqi": "no", "alerts": "no"}
r = requests.get(url, params=params)
r.raise_for_status()
data = r.json()
readings = []
for day in data["forecast"]["forecastday"]:
for hour in day["hour"]:
readings.append({
"time": datetime.fromisoformat(hour["time"]),
"pressure_mb": hour["pressure_mb"]
})
return readings
def pressure_tendency(readings, window_hours=3):
"""
Returns a list of dicts with time, pressure_mb, and rate_per_hour
computed over the trailing window_hours.
"""
results = []
for i in range(window_hours, len(readings)):
current = readings[i]
prior = readings[i - window_hours]
delta = current["pressure_mb"] - prior["pressure_mb"]
rate = delta / window_hours # hPa per hour
results.append({
"time": current["time"],
"pressure_mb": current["pressure_mb"],
"rate_per_hour": round(rate, 3)
})
return results
def classify(rate):
if rate > -0.5:
return "stable"
elif rate > -1.0:
return "moderate-fall"
else:
return "rapid-fall"
if __name__ == "__main__":
readings = fetch_hourly_pressure()
tendency = pressure_tendency(readings)
for t in tendency[:12]: # first 12 hours
label = classify(t["rate_per_hour"])
print(f"{t['time'].strftime('%H:%M')} {t['pressure_mb']} hPa "
f"{t['rate_per_hour']:+.2f} hPa/hr [{label}]")
Output for a quiet day in Glasgow might look like:
01:00 1015.2 hPa -0.07 hPa/hr [stable]
02:00 1014.9 hPa -0.10 hPa/hr [stable]
...
09:00 1012.1 hPa -1.03 hPa/hr [rapid-fall]
That transition at 09:00 is what’s worth surfacing to your user, or triggering a webhook, or flagging in your advisory endpoint — not the raw 1012 hPa reading on its own.
A Practical Wrinkle: Forecast Pressure vs. Observed Pressure
This approach runs on forecast pressure, not observed station data. GFS and ECMWF — the two models behind most API providers’ extended outlooks — handle synoptic-scale pressure patterns well. They’re good at predicting where a low tracks and roughly how deep it gets. Where they’re weaker is mesoscale timing precision, particularly inside 6 hours and for convective events not driven by frontal dynamics.
For the next 24 hours over CONUS, HRRR resolves convective pressure signals better than GFS because its 3 km grid actually captures individual storm cells — GFS at 13 km smooths over them. Beyond that window, GFS and ECMWF both handle the synoptic pressure trend reasonably well. The 3-day tendency curve from this code is useful for flagging that something significant is coming, not for pinning timing down to the hour.
Turning Tendency Into an Advisory
The classification function above is the skeleton. To make it useful, find the first hour in the next 24 where the rate crosses into rapid-fall and compute how many hours away that is. That’s the number to show your user.
from datetime import datetime, timezone
def next_rapid_fall(tendency):
now = datetime.now(tz=timezone.utc)
for t in tendency:
if t["time"].replace(tzinfo=timezone.utc) > now:
if t["rate_per_hour"] < -1.0:
delta = (t["time"].replace(tzinfo=timezone.utc) - now).total_seconds() / 3600
return round(delta, 1), t["pressure_mb"]
return None, None
hours_away, pressure_at_onset = next_rapid_fall(tendency)
if hours_away:
print(f"Pressure rapid-fall onset in approx {hours_away}h (at {pressure_at_onset} hPa)")
Wire that output to a push notification, a dashboard badge, or a JSON field in your own API response layer and you have something users will find useful before they're caught in rain. The raw pressure_mb value never would have gotten you there.
Where This Breaks Down
A few edge cases worth knowing. First, pressure-tendency thresholds are calibrated for mid-latitudes. In tropical environments, pressure varies much less across synoptic systems — severe convective weather can develop with barely any surface pressure signal until you're already inside it. Don't apply these thresholds for equatorial locations without rethinking the numbers entirely.
Second, if your application is caching forecast responses — which you should be, both for cost and rate-limit reasons — watch your cache TTL during fast-moving situations. A 2-hour cache is fine for a stable regime. GFS updates every six hours, HRRR every hour; serving a 4-hour-old forecast during active storm development can miss meaningful shifts in the pressure trace. That TTL decision is worth revisiting if you're building for severe-weather-prone regions.
Third, this method assumes the model's pressure forecast is reasonably calibrated for your location. In complex terrain it often isn't — valley pressure readings behave differently from ridgeline readings even 10 km apart horizontally. For mountainous locations, treat the tendency signal as directional rather than precise.
If you extend the code above, the most useful next step is a short hysteresis buffer: require the rate to stay below -1.0 hPa/hour for two consecutive readings before firing the alert, rather than triggering on a single crossing. Single-sample thresholds produce noisy alerts; two-sample confirmation cuts false positives without meaningfully delaying the warning. That one change will make the difference between an alert users trust and one they start ignoring.