Weather Forecast & Operational Intelligence

Monitor weather-driven operational risk across one site or an entire portfolio. Track changes run-to-run, rank locations, detect threshold crossings, surface the sites that need attention, and follow a feed of what changed — all powered by deterministic weather intelligence. Underneath it is a full forecast (current conditions, multi-day, hourly), but the output is built for decisions: a 0-100 risk score, recommended actions, exceptions, escalations, and site health. No API key required for the default data source.

Weather Forecast Search is an Apify actor that turns a place name or a pair of coordinates into a complete weather picture plus the decisions that come with it. By default it draws from the MET Norway weather API (the Norwegian Meteorological Institute), which is free and licensed for commercial use. Type a city like "London" or "Tokyo" and the actor resolves it to coordinates through OpenStreetMap, fetches the forecast, and returns temperature, feels-like, humidity, wind, precipitation, UV index, cloud cover, sunrise/sunset, threshold-based weather alerts, a deterministic risk score, operational-impact tags (travel-delay, frost, heat-stress, ideal-conditions…), forecast trend, and per-day confidence — ready for dashboards, automations, agents, or alerting pipelines.

Monitoring a fleet of sites? Pass a locations list and the actor returns one forecast record per location plus a ranking summary — best location, highest-risk location, and a portfolio risk-band breakdown — turning it into a weather operations center, not just a forecast API. Add a watchlistName to track what got worse since the last run, or watchFor thresholds to catch the day a limit is crossed. Have your own Open-Meteo commercial API key? Drop it in for full native fidelity — your subscription, your bill.

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Why use Weather Forecast Search?

• No API key needed to start — the default MET Norway data source is free and commercially licensed (CC BY 4.0 / NLOD 2.0). Run it immediately, no signup.
• Worldwide coverage — any city name or any latitude/longitude on the planet, from capital cities to remote farms, airports, and offshore sites.
• Weather alerts built in — every run computes deterministic advisories for severe weather, extreme heat/cold, high wind, heavy precipitation, and high UV, so downstream automations can branch on conditions without parsing raw numbers.
• Agent- and automation-ready — a one-line summary string plus stable enum fields (recordType, alerts[].severity) drop straight into Dify, Zapier, Make, n8n, Slack rules, and AI agent tool calls.
• Bring your own provider — supply an Open-Meteo commercial key for full native fidelity and up to 16 forecast days; otherwise the free MET Norway source covers ~10 days.
• Lightweight and cheap — a run completes in seconds with minimal memory and one or two HTTP calls.

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Key features

• Current conditions — temperature, feels-like (apparent) temperature, humidity, precipitation, weather description, wind speed, wind direction, wind gusts (where available), cloud cover, and a day/night indicator.
• Daily forecast — high/low temperatures, feels-like range, sunrise, sunset, max UV index, precipitation totals, max wind speed and gusts, and dominant wind direction per day.
• Weather intelligence layer — a deterministic 0-100 riskScore and riskLevel (severe/high/moderate/low/minimal) on current and every day, plus weatherImpacts[] tags (travel-delay-risk, frost-risk, heat-stress-risk, high-uv-exposure, outdoor-event-risk, ideal-conditions…) and a top-level overallRisk you can branch on at a glance.
• Recommended actions — a recommendations[] array of deterministic action enums (avoid-outdoor-events, delay-high-wind-operations, uv-protection-recommended, frost-protection-recommended, conditions-favourable…).
• Forecast outlook and stability — a run-level forecastOutlook (0-100 + rating: is the week good or bad?) and a stability rating (stable/variable/volatile from day-to-day swings).
• Prioritization layer — topRisks decomposes each score into its biggest drivers (so you know why), recovery tells you the first day an elevated-risk forecast eases, and the multi-location summary surfaces an exceptions list (only the sites that need attention) plus a comparison explaining why the best site won.
• Multi-location ranking + portfolio — pass a locations list and get a summary record that ranks sites best-first, names the bestLocation and highestRiskLocation, and counts how many fall in each risk band. Monitor depots, stores, sites, or farms as one portfolio.
• Persistent site intelligence — set a watchlistName and the actor keeps a rolling history per location: each record gains a change block (deteriorating / improving / stable vs the prior run) and a siteHealth block (multi-run trend, riskAverage, daysInHighRisk). The multi-location summary adds an operations feed — a changelog of risk increases and threshold crossings across your sites. Answers "what has been happening?", not just "what's next?"
• Threshold watch — pass watchFor thresholds (e.g. temperature above 30, wind above 50) and the actor reports the first day each is crossed in watchEvents.
• Industry lens (opt-in) — set industryMode to travel, agriculture, construction, events, drone, or logistics for a deterministic sector assessment (flight-delay risk, frost/spraying/irrigation, crane risk, outdoor suitability, drone flight conditions, delivery disruption).
• Trend and confidence — a trend block (warming/cooling, wetter/drier across the window) and a lead-time confidence per day.
• Best-time windows — when hourly data is on, the actor surfaces favourable daytime windows (dry, light wind, comfortable) for outdoor work, events, and photography.
• Multi-location batch mode — pass a locations list and get one forecast record per place in a single run.
• Optional hourly breakdown — temperature, humidity, precipitation, weather code, wind speed, and wind direction for each hour.
• Deterministic weather alerts — threshold-based advisories and warnings for thunderstorms, extreme heat/cold, freezing, high/strong wind, heavy precipitation, snow, and high/very-high/extreme UV.
• One-line summary — a plain-English summary an LLM, agent, or dashboard can quote verbatim.
• City geocoding or direct coordinates — resolve any place name via OpenStreetMap Nominatim, or pass exact latitude (-90 to 90) and longitude (-180 to 180).
• Celsius or Fahrenheit, plus a configurable IANA timezone (or auto, derived from the location).
• Stable, versioned output — every record carries a schemaVersion and a recordType discriminator for clean downstream filtering.

All intelligence is deterministic — computed from documented thresholds, no AI and no fabrication. The same forecast always produces the same scores and tags.

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How to use Weather Forecast Search

Using the Apify Console

1. Open the Weather Forecast Search actor on the Apify Store.
2. Click Try for free to open it in Apify Console.
3. Enter a Location (a city such as "London", "Tokyo", or "San Francisco") or provide Latitude and Longitude directly.
4. Set the number of Forecast Days (1 to 16; default 7).
5. Optionally enable Include Hourly Forecast.
6. Choose your Temperature Unit (Celsius or Fahrenheit) and a Timezone (or leave auto).
7. (Optional) Under Advanced, paste an Open-Meteo API key to use your own commercial subscription.
8. Click Start, then read or download the result from the Dataset tab as JSON, CSV, or Excel.

Using the Apify API

curl -X POST "https://api.apify.com/v2/acts/TpfQHy4R1EZT3ZqOp/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "location": "Tokyo",
    "forecastDays": 5,
    "includeHourly": true,
    "temperatureUnit": "celsius",
    "timezone": "auto"
  }'

Using the Apify CLI

apify call ryanclinton/weather-forecast-search -i '{
  "location": "Berlin",
  "forecastDays": 3,
  "temperatureUnit": "celsius"
}'

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Input parameters

Parameter	Type	Required	Default	Description
location	String	No*	"New York"	City name to search for. Geocoded to coordinates via OpenStreetMap. Ignored if locations is set or if latitude/longitude are provided.
locations	String[]	No*	—	Batch mode: a list of city names (up to 25). Returns one forecast record per location. Overrides location when set.
latitude	Number	No*	—	Direct latitude (-90 to 90). Overrides location when both lat and lon are set.
longitude	Number	No*	—	Direct longitude (-180 to 180). Overrides location when both lat and lon are set.
forecastDays	Integer	No	7	Number of forecast days (1 to 16). MET Norway provides ~10 days; an Open-Meteo key reaches 16.
includeHourly	Boolean	No	false	Include an hourly weather breakdown.
temperatureUnit	String	No	"celsius"	"celsius" or "fahrenheit".
timezone	String	No	"auto"	IANA timezone (e.g. "America/New_York"). "auto" derives it from the location's coordinates.
industryMode	String	No	"none"	Optional sector lens: travel, agriculture, construction, events, drone, or logistics. Adds an industry assessment block.
watchFor	Object	No	—	Threshold watch. Keys: temperatureAbove, temperatureBelow, windSpeedAbove, precipitationAbove, uvIndexAbove. Reports the first day each is crossed in watchEvents.
watchlistName	String	No	—	Turns on cross-run change detection. Each location record gains a change block vs the prior run on this watchlist.
openMeteoApiKey	String (secret)	No	—	Your own Open-Meteo commercial API key. When set, forecasts come from Open-Meteo at full native fidelity. When blank, the free MET Norway source is used.

*You must provide either location or both latitude and longitude.

Input example

{
  "location": "Paris",
  "forecastDays": 10,
  "includeHourly": true,
  "temperatureUnit": "celsius",
  "timezone": "Europe/Paris"
}

Batch input example (multiple locations)

{
  "locations": ["London", "Paris", "Berlin", "Madrid"],
  "forecastDays": 5,
  "temperatureUnit": "celsius"
}

Industry-lens example (agriculture)

{
  "location": "Fresno",
  "forecastDays": 7,
  "industryMode": "agriculture"
}

Operations-monitoring example (fleet of sites, with change detection)

{
  "locations": ["Liverpool", "Manchester", "London", "Leeds"],
  "forecastDays": 3,
  "watchlistName": "uk-depots",
  "watchFor": { "windSpeedAbove": 50, "temperatureBelow": 0 }
}

A multi-location run also emits one recordType: "summary" record:

{
  "recordType": "summary",
  "actionRequired": true,
  "criticalSites": 1,
  "topOperationalRisk": "wind",
  "locationCount": 4,
  "bestLocation": "London",
  "highestRiskLocation": "Liverpool",
  "portfolioSummary": { "severe": 0, "high": 1, "moderate": 1, "low": 1, "minimal": 1 },
  "exceptions": [
    { "location": "Liverpool", "severity": "high", "reason": "wind-risk" }
  ],
  "comparison": {
    "winner": "London",
    "reasons": ["best operational score (88 vs portfolio average 66)", "lowest risk band (minimal)"]
  },
  "escalations": [
    { "location": "Liverpool", "reason": "entered-high-risk-band" }
  ],
  "feed": [
    { "type": "risk-increase", "location": "Liverpool", "delta": 18 },
    { "type": "threshold-crossed", "location": "Leeds", "threshold": "wind-above" }
  ],
  "watchlistHealth": { "status": "deteriorating", "riskDelta": 18 },
  "portfolioMovement": { "critical": 1, "deteriorating": 2, "improving": 1, "stable": 1, "safe": 2 },
  "rankings": [
    { "rank": 1, "location": "London", "operationalScore": 88, "overallRisk": "minimal", "riskScore": 8, "topRisk": null },
    { "rank": 4, "location": "Liverpool", "operationalScore": 34, "overallRisk": "high", "riskScore": 58, "topRisk": "wind" }
  ]
}

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Output

The actor outputs a single structured JSON record per run to the default dataset.

Output example

{
  "schemaVersion": "2.0",
  "recordType": "forecast",
  "location": {
    "name": "London",
    "country": "United Kingdom",
    "latitude": 51.5074,
    "longitude": -0.1278,
    "elevation": null,
    "timezone": "Europe/London"
  },
  "overallRisk": "low",
  "forecastOutlook": { "score": 82, "rating": "excellent" },
  "stability": { "score": 75, "rating": "stable" },
  "recommendations": ["uv-protection-recommended"],
  "trend": { "temperature": "warming", "precipitation": "stable" },
  "current": {
    "temperature": 14.2,
    "feelsLike": 12.8,
    "humidity": 72,
    "precipitation": 0,
    "weatherCode": 2,
    "weatherDescription": "Partly cloudy",
    "windSpeed": 15.3,
    "windDirection": 230,
    "windGusts": null,
    "cloudCover": 45,
    "isDay": true,
    "riskScore": 0,
    "riskLevel": "minimal",
    "weatherImpacts": ["ideal-conditions"]
  },
  "daily": [
    {
      "date": "2026-06-04",
      "weatherCode": 3,
      "weatherDescription": "Overcast",
      "tempMax": 16.4,
      "tempMin": 9.1,
      "feelsLikeMax": 14.8,
      "feelsLikeMin": 6.7,
      "sunrise": "2026-06-04T04:42",
      "sunset": "2026-06-04T21:11",
      "uvIndexMax": 4.3,
      "precipitationSum": 1.2,
      "rainSum": 1.2,
      "windSpeedMax": 22.6,
      "windGustsMax": null,
      "windDirection": 215,
      "riskScore": 8,
      "riskLevel": "minimal",
      "weatherImpacts": ["ideal-conditions"],
      "confidence": 96
    }
  ],
  "alerts": [
    { "type": "high-uv", "severity": "advisory", "message": "High UV on 2026-06-06: index 7.", "day": "2026-06-06" }
  ],
  "summary": "London, United Kingdom — currently 14.2°C, Partly cloudy. Next 7 days: 9.1°C to 24.5°C. 1 weather alert.",
  "dataSource": "MET Norway",
  "attribution": "Weather data: MET Norway (api.met.no), licensed CC BY 4.0 / NLOD 2.0. Geocoding: © OpenStreetMap contributors (ODbL).",
  "extractedAt": "2026-06-04T13:39:46.903Z"
}

Output fields

Field	Type	Description
schemaVersion	String	Output contract version. Branch on this to detect shape changes.
recordType	String	forecast for results, notice or error for diagnostics.
summary	String	One-line plain-English summary of conditions, range, and alert count.
overallRisk	String	Worst risk level across current + daily: severe/high/moderate/low/minimal. The at-a-glance status.
forecastOutlook	Object	{ score: 0-100, rating: excellent/good/fair/poor } — is the window good or bad?
stability	Object	{ score: 0-100, rating: stable/variable/volatile } from day-to-day temperature, wind, and precipitation swings.
recommendations	Array	Deterministic recommended-action enums (e.g. avoid-outdoor-events, uv-protection-recommended, conditions-favourable).
industry	Object	Present only when industryMode is set. Sector assessment with a mode discriminator (travel/agriculture/construction/events/drone/logistics).
watchEvents	Array	Present only when watchFor is set. First forecast day each threshold is crossed: { type, threshold, day, value }.
change	Object	Present only when watchlistName is set. Cross-run change vs the prior run: { status, changeType, riskDelta, previousRiskScore, previousSeenAt }.
siteHealth	Object	Present only when watchlistName is set. Longitudinal: { runsTracked, riskAverage, daysInHighRisk, trend }.
recovery	Object	Present only when overallRisk is high/severe: { expectedDay, riskAfterRecovery }.
timeline	Array	Present only when hourly is on: daytime good/fair/poor segments { day, from, to, status }.
current.topRisks / daily[].topRisks	Array	The biggest drivers of each risk score: [{ type, contribution, reason }].
riskTrajectory	Array	Day-by-day risk series [{ day, riskScore }].
riskTrend	String	Risk direction over the window: rapidly-deteriorating / deteriorating / stable / improving / rapidly-improving.
trend	Object	temperature (warming/cooling/stable) and precipitation (increasing/decreasing/stable) over the window. Null if too few days.
Summary record (recordType: "summary", multi-location only)	—	actionRequired, criticalSites, topOperationalRisk, bestLocation, highestRiskLocation, portfolioSummary, rankings[], exceptions[], comparison, and — on watchlist runs — escalations[] (band transitions), feed[] (operations changelog), watchlistHealth, portfolioMovement.
bestWindows[]	Array	Favourable daytime windows (dry, light wind, comfortable), present only when includeHourly is true. Each has day, start, end, reason.
alerts[]	Array	Deterministic threshold advisories. Each has type, severity (warning/advisory), message, and day.
location	Object	Resolved location: name, country, latitude, longitude, elevation, timezone.
current.*	Object	Current conditions plus riskScore (0-100), riskLevel, and weatherImpacts[].
daily[]	Array	Per-day forecast plus riskScore, riskLevel, weatherImpacts[], and confidence (lead-time, 0-100).
hourly[]	Array	Per-hour forecast (optional, when includeHourly is true).
dataSource	String	Upstream provider: MET Norway or Open-Meteo (commercial).
attribution	String	Required data-provider attribution (CC BY 4.0 / ODbL).
extractedAt	String	ISO 8601 timestamp of when the data was collected.
failureType	String	Present only on notice/error records: no-data, invalid-input, upstream-unreachable, or unknown.

Weather alerts

The actor scans the forecast and emits deterministic alerts using documented meteorological thresholds — no AI, no fabrication:

Alert type	Severity	Fires when
thunderstorm	warning	Current or forecast thunderstorm conditions
extreme-heat / heat	warning / advisory	Daily high ≥ 35°C / ≥ 30°C (95°F / 86°F)
extreme-cold / freezing	warning / advisory	Daily low ≤ -10°C / ≤ 0°C (14°F / 32°F)
high-wind / strong-wind	warning / advisory	Max wind ≥ 75 / ≥ 50 km/h
heavy-precipitation / precipitation	warning / advisory	Daily precipitation ≥ 50 / ≥ 20 mm
extreme-uv / very-high-uv / high-uv	warning / advisory	Max UV index ≥ 11 / ≥ 8 / ≥ 6
snow	advisory	Snow in the daily forecast

---

Weather intelligence

Beyond raw numbers, every record carries a deterministic decision layer so your automation does not have to interpret the forecast itself.

• riskScore (0-100) and riskLevel — a composite of wind, precipitation, temperature extremes, UV, and severe-weather codes, on current and every daily entry. Bands: severe ≥ 70, high ≥ 50, moderate ≥ 30, low ≥ 12, else minimal.
• overallRisk — the worst riskLevel across the whole forecast: one field to branch on for "is this location a problem?"
• weatherImpacts[] — stable enum tags describing what the weather means: travel-delay-risk, outdoor-event-risk, frost-risk, heat-stress-risk, extreme-heat-risk, high-wind-risk, heavy-rain-risk, snow-risk, thunderstorm-risk, high-uv-exposure, or ideal-conditions.
• topRisks — the score's biggest drivers, each with a plain-English reason, e.g. [{ "type": "wind", "contribution": 40, "reason": "forecast winds up to 72 km/h" }]. Answers "why is this risky?"
• recovery — when overallRisk is high or severe, the first forecast day risk eases to low/minimal and the level then. Answers "when does it improve?"
• riskTrajectory + riskTrend — the day-by-day risk score as a series, plus a direction (rapidly-deteriorating … rapidly-improving). See risk evolve, not just a snapshot.
• timeline — when hourly is on, the day split into good/fair/poor segments for scheduling operations.
• escalations (watchlist summary) — band-level state transitions across the portfolio (entered-high-risk-band / left-high-risk-band). Operations think in transitions, not values.
• recommendations[] — recommended-action enums derived from those impacts: avoid-outdoor-events, delay-high-wind-operations, prepare-for-wet-conditions, allow-extra-travel-time, frost-protection-recommended, heat-precautions-recommended, uv-protection-recommended, monitor-severe-weather, or conditions-favourable.
• forecastOutlook — a run-level 0-100 score + rating answering "is the next few days good or bad?" — this is the operational score (100 = perfect operating conditions, 0 = impossible). The dashboard headline metric.
• stability — 0-100 + stable/variable/volatile from how much temperature, wind, and precipitation swing day to day. Operations teams care about predictability.
• industry (opt-in via industryMode) — a sector assessment from the same engine. travel: flightDelayRisk + drivingConditions. agriculture: frostRisk + heatStressRisk + sprayingConditions + irrigationNeed. construction: workability + concretePourRisk + craneRisk. events: outdoorSuitability + weatherRisk.
• trend — whether temperature is warming/cooling and precipitation increasing/decreasing across the window.
• confidence — a per-day lead-time confidence (near-term days score higher than long-range; a documented heuristic, not an ensemble spread).
• bestWindows[] — when hourly is on, the favourable daytime windows for outdoor activity.

All of it is computed from documented thresholds — deterministic, reproducible, and free of AI.

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Data sources and attribution

This actor uses openly licensed data. When you republish or display the output, keep the attribution string the actor provides.

• Weather (default): MET Norway — the Norwegian Meteorological Institute, licensed under NLOD 2.0 and CC BY 4.0. Commercial use is permitted with credit to "MET Norway".
• Weather (optional): Open-Meteo commercial API, used only when you supply your own API key. Billed to your Open-Meteo subscription.
• Geocoding: OpenStreetMap Nominatim, © OpenStreetMap contributors, licensed under the ODbL.

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Use in Dify

Weather Forecast Search returns decisions, not just raw numbers — a top-level overallRisk enum, a recordType discriminator, and an alerts[] array whose entries carry a stable severity (warning / advisory) and type. Dify's if/else branching nodes route on equality matches, so a weather-monitoring agent plugs in cleanly: pull the forecast, branch on overallRisk, and act. Competitors pointed at the same source return raw numbers; this returns the risk call your downstream node branches on.

• Actor ID: TpfQHy4R1EZT3ZqOp

Sample input for a scheduled multi-location watch:

{
  "locations": ["Miami", "Houston", "New Orleans"],
  "forecastDays": 3,
  "temperatureUnit": "celsius"
}

Branching example (Dify if/else node):

• If recordType equals error → notify the operator that the forecast run failed (read failureType).
• Else if overallRisk equals severe or high → send a severe-weather alert to Slack with the summary and any alerts[].message.
• Else if overallRisk equals moderate → log a heads-up for review.
• Else → store the summary and continue.

The summary, weatherImpacts[], and alerts[].message values are usable verbatim — no LLM rewriting needed.

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Use cases

• Daily weather briefings — schedule a morning run and push the summary and any alerts to Slack, email, or a dashboard.
• Severe-weather monitoring — branch automations on alerts[].severity to notify teams about heat, cold, wind, or storms.
• Travel planning — check destination conditions 1–16 days ahead before booking or packing.
• Agriculture and field operations — track temperature extremes, precipitation totals, UV, and wind for planting, irrigation, and harvest decisions.
• Event and construction planning — watch the forecast leading up to outdoor work and flag weather risk early.
• Energy and demand forecasting — feed temperature forecasts into heating/cooling demand models.
• AI agents and pipelines — give an agent a single tool that turns a place name into structured weather plus a quotable summary.

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API and integrations

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run_input = {
    "location": "New York",
    "forecastDays": 7,
    "temperatureUnit": "fahrenheit",
    "timezone": "America/New_York",
}

run = client.actor("TpfQHy4R1EZT3ZqOp").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["summary"])
    for day in item.get("daily", []):
        print(f"  {day['date']}: {day['tempMin']} - {day['tempMax']}, {day['weatherDescription']}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("TpfQHy4R1EZT3ZqOp").call({
  location: "Sydney",
  forecastDays: 5,
  includeHourly: true,
  temperatureUnit: "celsius",
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
  console.log(item.summary);
  for (const alert of item.alerts ?? []) console.log(`  [${alert.severity}] ${alert.message}`);
}

cURL

# Start a run
curl -X POST "https://api.apify.com/v2/acts/TpfQHy4R1EZT3ZqOp/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"location": "Berlin", "forecastDays": 3}'

# Fetch the result (replace DATASET_ID from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN"

Integrations

Works with all native Apify integrations: Google Sheets, Slack, Zapier, Make (Integromat), n8n, and webhooks/REST API. Schedule recurring runs to build weather-monitoring pipelines on autopilot.

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How it works

1. Input validation — the actor checks for a city name or latitude/longitude. At least one is required.
2. Geocoding (if needed) — a city name is resolved to coordinates via OpenStreetMap Nominatim; the timezone is derived from those coordinates (or taken from your timezone input).
3. Forecast fetch — with no API key, the actor calls MET Norway (api.met.no); with an Open-Meteo key, it calls customer-api.open-meteo.com. Both calls retry transient network errors with exponential backoff.
4. Normalisation — the provider response is mapped to a single, consistent output shape (units converted, weather codes resolved, daily values aggregated, sunrise/sunset computed).
5. Alerts and summary — deterministic threshold alerts and a one-line summary are computed from the forecast.
6. Output — the structured record is written to the Apify dataset with attribution.

Input --> Geocoding (OpenStreetMap, if city name) --> Coordinates + timezone
              |
              v
       MET Norway (default) or Open-Meteo (your key) --> Normalise --> Alerts + Summary --> Dataset

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Performance and cost

Metric	Value
Average run time	3 to 8 seconds
Memory	256 MB default
External API cost	Free (MET Norway), or billed to your own Open-Meteo subscription
Max forecast range	~10 days (MET Norway) / 16 days (Open-Meteo key)

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What this actor does NOT do

Honest scope so you know what you are getting:

• Not real-time severe-weather warnings. The alerts are computed from forecast thresholds, not official government warnings. For life-safety alerts, use your national meteorological agency.
• Not historical weather. It returns current conditions and forecasts only, not past observations.
• Up to 25 locations per run. Pass a locations list to monitor a portfolio in one run; for larger fleets, split across multiple runs (via API or scheduled tasks).
• Wind gusts and UV can be null on the free source. MET Norway does not provide wind gusts for every location, and clear-sky UV is populated mainly for the near-term days. On the free source, feels-like temperature is computed (Bureau of Meteorology apparent-temperature formula) rather than supplied. For complete native fields, use an Open-Meteo commercial key.
• Geocoding returns the top match. Ambiguous names (e.g. "Springfield") may resolve to an unintended place — pass coordinates directly for precision.
• Change detection needs at least two runs. The first run on a new watchlistName marks every location change.status: "new"; deltas appear from the second run onward. Schedule the actor for it to be useful.

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Responsible use

• Keep the attribution. When you republish or display this data, credit MET Norway and OpenStreetMap as the actor's attribution field states (a licence requirement).
• Do not rely on it alone for safety-critical decisions. Supplement with official warnings for severe weather, evacuations, and emergencies.
• Rate-limit your runs. Once per hour or a few times per day is plenty for most monitoring; avoid excessive polling.

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FAQ

Does this actor require an API key? No. The default MET Norway data source is free and needs no key. You can optionally supply your own Open-Meteo commercial key for full native fidelity and up to 16 days.

Where does the weather data come from? By default, MET Norway (the Norwegian Meteorological Institute), which is free and licensed for commercial use. If you supply an Open-Meteo commercial key, data comes from Open-Meteo instead.

How many forecast days can I get? Up to ~10 days on the free MET Norway source, or up to 16 days with your own Open-Meteo key.

What are the weather alerts? Deterministic advisories computed from documented thresholds (severe weather, extreme heat/cold, high wind, heavy precipitation, high UV). They are signals for automation, not official government warnings.

Why is windGusts or uvIndexMax sometimes null? MET Norway does not provide wind gusts for every location, and clear-sky UV is mainly available for the near-term days. Use an Open-Meteo key for fuller native coverage.

Can I get weather for exact coordinates? Yes. Provide latitude and longitude and the actor skips geocoding.

Celsius or Fahrenheit? Both. Set temperatureUnit to "celsius" or "fahrenheit"; all temperatures use your choice.

Can I set a custom timezone? Yes. Pass any IANA timezone (e.g. "Asia/Tokyo"), or use "auto" to derive it from the location.

Is this good for AI agents and automations? Yes. The summary string, recordType, and alerts[].severity are designed for tool calls and if/else routing in Dify, Zapier, Make, and n8n.

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Related actors

Actor	Description
Nominatim Geocoder	Convert place names to coordinates, or reverse-geocode, using OpenStreetMap data.
NOAA Weather Alert Monitor	Monitor official severe-weather alerts and warnings for United States locations.
Sunrise Sunset Search	Detailed sunrise, sunset, and twilight phase data for any location worldwide.
OpenStreetMap POI Search	Search points of interest, businesses, and landmarks with geographic filtering.