Manual forecast checking does not scale. Once a decision matters enough that you check it several times a day, the next step is to let a system do the checking and only surface the moments that require a human. That is what programmatic WeatherNext 3 data access is for.
This guide covers the request shape you should design around, the operational details that cause the most trouble in production, and the patterns that work well for weather-driven automation.
What a forecast request is really made of
Almost every useful forecast request reduces to four parts, and leaving any one of them vague is what causes disappointing results:
- Location. Coordinates, or a place name resolved to coordinates. For a fixed asset, always use the asset coordinates.
- Time range. The hours that matter, not the whole week.
- Variables. Only the ones tied to a decision. Precipitation, temperature, humidity, wind, radiation, and cloud cover are the common set.
- Thresholds. The wind speed, rain rate, or temperature at which your plan changes. Thresholds are what turn a forecast into an alert.
Design your request format around these four fields and your integration will stay readable as you add sites.
Resolution and cadence in an automated pipeline
WeatherNext 3 provides targeted temperature and humidity at 5 km and other surface variables at 10 km, with forecasts generated on an hourly cadence.
For a pipeline, that means two design decisions:
- Poll on the hourly boundary, not continuously. Refreshing every few minutes returns the same run and burns quota for no new information.
- Store the run identity. Keep a timestamp with each fetched forecast so you can tell later whether two systems disagreed because of geography or because they were reading different runs.
Handling uncertainty instead of hiding it
The most common production mistake with weather data is collapsing a probability distribution into a single number too early. A pipeline that passes only a mean wind speed downstream has thrown away the part of the signal that matters.
Useful patterns:
- Keep the range. Store the spread alongside the central value.
- Track run-to-run movement. If the timing of a rain band shifts between hourly runs, that instability is itself an alert-worthy signal.
- Use tiered thresholds. Notify at a watch level, act at a limit level, and escalate only when the distribution is confidently beyond the limit.
- Preserve the raw payload. Keeping the original response makes it possible to re-derive signals later without re-fetching history.
Caching, quota, and cost control
Forecast credits renew monthly, so a well-behaved pipeline is also a cheaper one.
- Cache by location and run time. Many processes usually need the same grid cell; fetch once and share.
- Batch sites. Requesting twenty locations in one pass is far more efficient than twenty independent calls.
- Decouple ingestion from evaluation. Fetch on the hourly cadence and evaluate as often as you like against the cached payload.
- Back off on failure. A retry storm against a slow upstream helps nobody, least of all your own rate budget.
Alerting and downstream systems
Once forecasts are in a pipeline, decide who consumes them and when:
| Consumer | What it needs |
|---|---|
| Scheduling or dispatch system | Threshold flags per window, with the run timestamp |
| Dashboards | Trend across runs, so operators can see conditions evolving |
| Automated control | Conservative thresholds and explicit fallback behaviour |
| Human reviewers | The location, the variables, and why the threshold was crossed |
Give each consumer the smallest payload that lets it act, and keep the audit trail separate from the alert itself.
What to verify before going live
- Time zone handling. Store and transmit in UTC, then convert at the display layer. This is the single most common source of off-by-hours incidents.
- Units. Confirm the units you receive and the units your downstream systems assume. Wind in particular is a frequent mismatch.
- Missing data. Define behaviour for a gap: fail closed for safety-critical actions, and alert rather than silently substituting a default.
- Backtesting. Score the pipeline against historical windows at your own locations before trusting it in an operating procedure.
Getting started
You can explore forecasts in the browser first to understand which variables drive your decisions, then move the same request shape into a pipeline. Account-based access and credits are described on the pricing page, and the documentation covers variables and resolution in more detail.
For the analytics side of the same problem, see How to use WeatherNext 3 for the manual workflow these integrations are replacing.
Frequently asked questions
Do I need a paid plan for programmatic access?
Forecast access consumes the forecast credits included in your plan, and heavier automation consumes more. The pricing page lists what each tier includes and how credits renew monthly.
How often should a pipeline fetch new forecasts?
Match the hourly generation cadence. Fetching more often returns the same run, wastes credits, and adds no new information.
Can I use forecasts in a safety-critical control loop?
WeatherNext 3 is a decision-support input, not a replacement for official warnings. For safety-critical actions, use conservative thresholds, fail-closed behaviour, and keep national weather service alerts in the loop.
