# Weather Forecast API by City or Coordinates (MET Norway) (`lyfe_tools/weather-forecast-by-place-met-norway`) Actor

Weather forecast API for a list of place names or coordinates: daily temperature, rain, wind, cloud and an outdoor verdict, from MET Norway. No API key needed.

- **URL**: https://apify.com/lyfe_tools/weather-forecast-by-place-met-norway.md
- **Developed by:** [LYFE Offshore](https://apify.com/lyfe_tools) (community)
- **Categories:** Travel, Developer tools, MCP servers
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.70 / 1,000 place forecasts

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Weather Forecast API by City or Coordinates (MET Norway)

Weather forecast API for a list of cities, place names or coordinates: hourly or daily forecast per
place, from MET Norway, with no API key needed.

Give it a list of place names or coordinates. It returns, per place, a daily forecast in the
**place's own local days**: min/max temperature, rain, wind, cloud, a plain-words condition and a
simple outdoor verdict with the reason. Data comes from the Norwegian Meteorological Institute
(MET Norway); no API key needed. Hourly rows are available too.

### One run, real output

Four Caribbean port towns, run on 4 October 2026 with `days: 3` (a fifth entry, plain coordinates, gave the same result as Oranjestad):

| Place | Day (local) | Temp °C | Rain mm | Wind avg / max km/h | Condition | Outdoor |
|---|---|---|---|---|---|---|
| Oranjestad, Aruba | 2026-10-04 (partial) | 28.7 – 29.8 | 0.6 | 30.7 / 32.8 | light rain showers | **fair** – wind up to 33 km/h |
| Oranjestad, Aruba | 2026-10-05 | 26.3 – 29.9 | 0.8 | 33.3 / 44.6 | fair | **poor** – wind up to 45 km/h |
| Oranjestad, Aruba | 2026-10-06 | 27.9 – 29.7 | 0 | 27.3 / 31.7 | partly cloudy | **fair** – wind up to 32 km/h |
| Kralendijk, Bonaire | 2026-10-04 (partial) | 28.3 – 29.5 | 0.3 | 29.5 / 31.7 | partly cloudy | **fair** – wind up to 32 km/h |
| Kralendijk, Bonaire | 2026-10-05 | 27.9 – 29.5 | 0.2 | 28.3 / 29.5 | fair | **fair** – wind up to 30 km/h |
| Kralendijk, Bonaire | 2026-10-06 | 27.4 – 29.7 | 0.1 | 25.4 / 31.3 | fair | **fair** – wind up to 31 km/h |
| Cozumel, Mexico | 2026-10-04 (partial) | 27.4 – 30.4 | 1.1 | 19 / 24.8 | partly cloudy | **fair** – 1.1 mm of rain |
| Cozumel, Mexico | 2026-10-05 | 25.5 – 29.7 | 2.4 | 18.6 / 24.8 | fair | **fair** – 2.4 mm of rain |
| Cozumel, Mexico | 2026-10-06 | 24.9 – 30 | 1.6 | 16.1 / 20.2 | partly cloudy | **fair** – 1.6 mm of rain |
| Bridgetown, Barbados | 2026-10-04 (partial) | 25.8 – 30.6 | 0.5 | 26.4 / 29.2 | partly cloudy | **fair** – wind up to 29 km/h |
| Bridgetown, Barbados | 2026-10-05 | 25.7 – 29.1 | 4.9 | 26.1 / 29.5 | light rain showers | **fair** – 4.9 mm of rain; wind up to 30 km/h |
| Bridgetown, Barbados | 2026-10-06 | 26.2 – 29.9 | 2.8 | 25.7 / 28.1 | light rain showers | **fair** – 2.8 mm of rain; wind up to 28 km/h |

Every place is in its own timezone, rain is counted over the daytime window (08:00-20:00 local) so a
wet afternoon is not hidden by a dry night, and each verdict names its cause. Trade-wind places like
Aruba rarely score "good": wind of 28 km/h or more already counts as "fair" (thresholds below).

### Input example

The run above (`INPUT.json`):

```json
{ "places": ["Oranjestad, Aruba", "Kralendijk, Bonaire", "Cozumel, Mexico", "Bridgetown, Barbados", "12.5186,-70.0358"],
  "days": 3, "outputMode": "daily" }
```

`places` takes names, `"lat,lon"` strings or `{name, lat, lon}` objects; or use `inputDatasetId`
(+ `placeField`) to read another Actor's dataset. `days` 1-10 (default 5), `outputMode` `daily` | `hourly`,
`daytimeFromHour` (default 8), `daytimeToHour` (default 20).

### Output example

Each place is one dataset row in daily mode. The first row of the run above, shortened to one of its
three days (`attribution` and `billable` left out):

```json
{ "input": "Oranjestad, Aruba", "place": "Oranjestad, Aruba, Netherlands", "latitude": 12.5201, "longitude": -70.0371,
  "timezone": "America/Aruba", "status": "ok", "forecastUpdatedAt": "2026-10-04T13:18:14Z", "daysCount": 3,
  "days": [ { "date": "2026-10-05", "tempMinC": 26.3, "tempMaxC": 29.9, "rainMm": 0.8, "windAvgKmh": 33.3,
             "windMaxKmh": 44.6, "cloudAvgPercent": 44, "condition": "fair", "thunder": false, "partial": false,
             "outdoorRating": "poor", "outdoorReason": "wind up to 45 km/h" } ] }
```

#### Output fields (daily mode)

| Field | Meaning |
|---|---|
| `input` | the entry as you gave it |
| `place`, `latitude`, `longitude` | the place it was taken to mean, and the coordinates used |
| `timezone` | IANA timezone worked out from the coordinates |
| `status`, `error` | `ok`, or the reason there is no forecast (see below) |
| `forecastUpdatedAt` | when MET Norway last updated this forecast (UTC) |
| `daysCount`, `days` | number of local days, and one summary per day |
| `days[].date`, `partial` | local date; `true` when the forecast covers only part of it |
| `days[].tempMinC`, `tempMaxC` | min/max temperature over the whole local day |
| `days[].rainMm`, `windAvgKmh`, `windMaxKmh`, `cloudAvgPercent`, `condition`, `thunder` | over the daytime window |
| `days[].outdoorRating`, `outdoorReason` | `good` / `fair` / `poor` and the cause |
| `attribution` | data credit for MET Norway and OpenStreetMap |

In hourly mode each row has `time` (local), `timeUtc`, `temperatureC`, `windKmh`, `windFromDeg`,
`cloudPercent`, `humidityPercent`, `pressureHpa`, `precipitationMm`, `periodHours`, `symbol` and `condition`.

### What it does

1. **Resolves names** with OpenStreetMap's Nominatim (one request per second, as its usage policy requires). The matched place is returned in `place`, so you can see what a name was taken to mean. Add the country to avoid the wrong namesake. Coordinates (`"12.5186,-70.0358"` or `{name, lat, lon}`) skip this step.
2. **Fetches the forecast** from MET Norway Locationforecast 2.0, with an identifying User-Agent, coordinates cut to 4 decimals, requests spaced out, and the `Expires` / `If-Modified-Since` rules honoured.
3. **Works out the local timezone offline** from the coordinates (the `tz-lookup` package, CC0) and cuts the forecast into local days, not UTC days.
4. **Summarises** temperature over the whole local day, and rain, wind, cloud, condition and verdict over the daytime window you choose (default 08-20).

`outputMode: "hourly"` returns one row per place and hour instead: local time, temperature, wind, wind direction, cloud, humidity, pressure, rain over the period and condition. The feed is hourly for about 2.5 days and 6-hourly after that; a row says how many hours its rain amount covers (`periodHours`).

#### The outdoor verdict, exactly

A simple rule, not a forecast model. Per day, over the daytime window:

| | fair | poor |
|---|---|---|
| Rain | 1 mm or more | 5 mm or more |
| Wind (max of the forecast steps) | 28 km/h or more (about Beaufort 4-5) | 40 km/h or more (about Beaufort 6) |
| Max temperature | 34 °C or more, or 0 °C or less | 38 °C or more, or -10 °C or less |
| Thunder in any daytime step | | poor |

The worst applicable level wins and every cause is listed in `outdoorReason`. Use the raw numbers if
you have your own rules.

### What it does not do

- **No gusts.** MET Norway's compact format has wind speed but no gust field, so `windMaxKmh` is the highest wind speed of the forecast steps. Gusts will be higher.
- **Partial days.** Today (the forecast starts at the current hour) and the last day of the feed are often incomplete; those days carry `"partial": true`.
- **Later days are coarser and less certain.** After roughly 2.5 days the feed has 6-hourly steps, so min/max and wind maximum are read from fewer points and tend to be a little too mild.
- **Timezone labels near borders.** The offline lookup gives the right UTC offset, but its name can be a neighbour with the same offset (Kralendijk, Bonaire came back as `America/Caracas`, which is UTC-4 like `America/Kralendijk`).
- **Not a warning service.** Do not use it for safety-critical decisions; check your national weather service for warnings.
- Place names depend on OpenStreetMap: an unknown or misspelt name returns a `place_not_found` row, not a guess.

### Rows that are not a forecast

A place that cannot be forecast still gets a row with a `status` and an `error`, and **is not charged**:
`place_not_found`, `geocoding_failed`, `forecast_unavailable` (the source failed after retries) or `invalid_input`.
If no place at all produced a forecast the run fails with a message instead of finishing empty.

### Price

Price per 1,000 by your Apify plan (Apify applies the right column automatically):

| | Free | Starter | Scale | Business and up |
|---|---|---|---|---|
| Place forecast, per 1,000 | $1.00 | $1.00 | $0.85 | $0.70 |

No start fee. The figures below use the Free-plan price; on a paid plan the same run costs less.

**$0.001 per place with a forecast** (event `place-forecast`), in daily or hourly mode alike. Platform usage
is included. Places without a result are free.

- 1,000 places ≈ $1.
- The run above: 5 places ≈ $0.005.
- 100 places ≈ $0.10, whether you ask for 1 day or 10, daily or hourly.

A run's maximum-charge limit caps the number of places processed. Names take about 1.1 seconds each to
resolve (Nominatim's limit); 100 names take about two minutes, 100 coordinates seconds.

### Use it from code or an AI agent

```bash
curl -X POST "https://api.apify.com/v2/acts/lyfe_tools~weather-forecast-by-place-met-norway/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"places": ["Cozumel, Mexico", "52.3676,4.9041"], "days": 3}'
```

AI agents (Claude, Cursor and other MCP clients) can call it through the Apify MCP server at
https://mcp.apify.com: the agent sends `places` and `days` (optionally `outputMode`), and reads `place`,
`timezone` and per day `date`, `tempMinC`, `tempMaxC`, `rainMm`, `windMaxKmh`, `condition` and `outdoorRating`.

### Port days

It pairs with [Caribbean Cruise Ship Schedules](https://apify.com/lyfe_tools/caribbean-cruise-ship-schedules)
for a port-day forecast: the weather for the port on the day a ship calls.

### FAQ

**Do I need an API key for the weather data?**
No. MET Norway and Nominatim need no key; you only need an Apify account to run the Actor.

**Hourly or daily forecast?**
Both. `outputMode: "daily"` gives one row per place with a summary per local day; `"hourly"` gives one row
per place and forecast step (hourly for about 2.5 days, then 6-hourly). The price per place is the same.

**How many days ahead?**
`days` goes up to 10; the feed reaches about 9 days.

**Can I give coordinates instead of city names?**
Yes, as `"lat,lon"` or `{name, lat, lon}`. That skips the name lookup and is much faster.

**Does it work outside Norway?**
Yes. The real run above is for four Caribbean places.

### Sources and attribution

- Weather data: **MET Norway** (api.met.no), [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and the Norwegian licence for open government data. Every row carries the attribution; if you republish the data, keep it. See the [terms of service](https://api.met.no/doc/TermsOfService).
- Place names and coordinates: (c) OpenStreetMap contributors, [ODbL](https://www.openstreetmap.org/copyright), via Nominatim.
- Timezones: `tz-lookup` (CC0), offline.

Issues and questions: tools@lyfeoffshore.com.

# Actor input Schema

## `places` (type: `array`):

Place names ("Oranjestad, Aruba"), coordinates as "lat,lon", or objects {"name", "lat", "lon"}. Names are resolved with OpenStreetMap's Nominatim at 1 per second, so a list of 100 names takes about two minutes; coordinates skip that step. Add the country to a name to avoid the wrong namesake.

## `inputDatasetId` (type: `string`):

Read the places from an existing dataset instead (each row is read like one entry above).

## `placeField` (type: `string`):

Column holding the place name or "lat,lon". Leave empty if the rows already have name/lat/lon.

## `days` (type: `integer`):

Number of local days to summarise, today included. The forecast reaches about 9 days; the last days are less certain and the first and last day may be partial (see the `partial` field).

## `outputMode` (type: `string`):

daily: one row per place, with a summary per local day (min/max temperature, daytime rain, wind, cloud, condition and an outdoor verdict). hourly: one row per place and forecast step in local time (hourly for about 2.5 days, then 6-hourly), with temperature, wind, wind direction, cloud, humidity, pressure and rain. The price is per place either way.

## `daytimeFromHour` (type: `integer`):

Rain, wind, cloud, condition and the outdoor verdict use this window; min/max temperature use the whole local day.

## `daytimeToHour` (type: `integer`):

End of the daytime window (local hour, exclusive).

## Actor input object example

```json
{
  "places": [
    "Oranjestad, Aruba",
    "Kralendijk, Bonaire",
    "Cozumel, Mexico",
    "Bridgetown, Barbados",
    "52.3676,4.9041"
  ],
  "days": 5,
  "outputMode": "daily",
  "daytimeFromHour": 8,
  "daytimeToHour": 20
}
```

# Actor output Schema

## `results` (type: `string`):

Per place: coordinates, timezone, and daily summaries (or hourly rows) in local time.

## `summary` (type: `string`):

Places requested, forecast, not found, failed. Attribution.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "places": [
        "Oranjestad, Aruba",
        "Kralendijk, Bonaire",
        "Cozumel, Mexico",
        "Bridgetown, Barbados",
        "52.3676,4.9041"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("lyfe_tools/weather-forecast-by-place-met-norway").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "places": [
        "Oranjestad, Aruba",
        "Kralendijk, Bonaire",
        "Cozumel, Mexico",
        "Bridgetown, Barbados",
        "52.3676,4.9041",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("lyfe_tools/weather-forecast-by-place-met-norway").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "places": [
    "Oranjestad, Aruba",
    "Kralendijk, Bonaire",
    "Cozumel, Mexico",
    "Bridgetown, Barbados",
    "52.3676,4.9041"
  ]
}' |
apify call lyfe_tools/weather-forecast-by-place-met-norway --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,lyfe_tools/weather-forecast-by-place-met-norway"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/wlsMEqtsQc8VOmcQc/builds/PnMVgxinUEZvV8I8X/openapi.json