# Open-Meteo Weather Scraper - Forecast & Historical Data (`cloud9_ai/open-meteo-scraper`) Actor

Fetch weather forecasts, historical weather data, and air quality data from Open-Meteo API. Supports multiple locations, hourly/daily granularity, temperature units. Free, no API key needed.

- **URL**: https://apify.com/cloud9_ai/open-meteo-scraper.md
- **Developed by:** [cloud9](https://apify.com/cloud9_ai) (community)
- **Categories:** Automation, Lead generation
- **Features:** Limited permissions
- **Stats:** 48 total users, 8 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## Open-Meteo Weather Scraper - Forecast & Historical Data

Fetch weather forecasts, historical weather data, and air quality data from Open-Meteo API. Supports multiple locations, hourly/daily granularity, temperature units. Free, no API key needed.

### Use cases

- Add a weather-driven feature to an app without an API key
- Correlate daily sales or footfall with temperature and rain
- Plan logistics, events, or field work around forecasts
- Build agricultural or energy-demand models
- Backfill historical weather for a location

### Input

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| `mode` | string | Yes | `"forecast"` | Data type to fetch Allowed: `forecast`, `historical`, `airQuality`. |
| `locations` | array | Yes | `[{"name":"Tokyo","latitude":35.6762,"longitude":139.6503}]` | List of locations to fetch weather for. Each item must be an object with name, latitude, and longitude. Example: \[{"name": "Tokyo", "latitude": 35.6762, "longitude": 139.6503}] |
| `startDate` | string | No | — | Start date for historical mode (YYYY-MM-DD) |
| `endDate` | string | No | — | End date for historical mode (YYYY-MM-DD) |
| `forecastDays` | integer | No | `7` | Number of forecast days (1-16) |
| `granularity` | string | No | `"daily"` | Data granularity Allowed: `hourly`, `daily`. |
| `temperatureUnit` | string | No | `"celsius"` | Temperature unit Allowed: `celsius`, `fahrenheit`. |

#### Example input

```json
{
  "mode": "forecast",
  "locations": [
    {
      "name": "Tokyo",
      "latitude": 35.6762,
      "longitude": 139.6503
    }
  ],
  "forecastDays": 7,
  "granularity": "daily",
  "temperatureUnit": "celsius"
}
```

### Output

The exact fields depend on the mode you run. This is real output from an actual run of this Actor:

```json
{
  "locationName": "Tokyo",
  "latitude": 35.6762,
  "longitude": 139.6503,
  "date": "2026-09-07",
  "temperatureMax": 26.8,
  "temperatureMin": 20.9,
  "precipitation": 195.3,
  "windSpeedMax": 11.7,
  "weatherCode": 65,
  "weatherDescription": "Heavy rain"
}
```

| Field | Type |
|---|---|
| `locationName` | string |
| `latitude` | number |
| `longitude` | number |
| `date` | string |
| `temperatureMax` | number |
| `temperatureMin` | number |
| `precipitation` | number |
| `windSpeedMax` | number |
| `weatherCode` | number |
| `weatherDescription` | string |

The dataset also ships a preset table view (**Overview**), so the key columns are readable straight away in Apify Console, and exportable to JSON, CSV, Excel, or XML.

### How to run it

**In Apify Console** — open the Actor, fill in the input form, click **Start**, then download the results from the **Dataset** tab.

**With the JavaScript client**

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

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('cloud9_ai/open-meteo-scraper').call({
  "mode": "forecast",
  "locations": [
    {
      "name": "Tokyo",
      "latitude": 35.6762,
      "longitude": 139.6503
    }
  ],
  "forecastDays": 7,
  "granularity": "daily",
  "temperatureUnit": "celsius"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

**With the Python client**

```python
from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')

run = client.actor('cloud9_ai/open-meteo-scraper').call(run_input={
  "mode": "forecast",
  "locations": [
    {
      "name": "Tokyo",
      "latitude": 35.6762,
      "longitude": 139.6503
    }
  ],
  "forecastDays": 7,
  "granularity": "daily",
  "temperatureUnit": "celsius"
})

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)
```

**With the API** — `POST https://api.apify.com/v2/acts/cloud9_ai~open-meteo-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN` with the input JSON as the body.

### Notes and limits

- No API key, account, or login is needed — just the input above.
- Requests to the source are paced to stay inside its rate limits.
- Only publicly available data is collected. How you use the output is your responsibility, including the source's terms of use and any applicable data-protection law.

### Support

Found a bug, or need a field that isn't in the output? Open an issue on the **Issues** tab of this Actor in Apify Console. Issues there are read and answered.

### License

Apache-2.0

# Actor input Schema

## `mode` (type: `string`):

Data type to fetch

## `locations` (type: `array`):

List of locations to fetch weather for. Each item must be an object with name, latitude, and longitude. Example: \[{"name": "Tokyo", "latitude": 35.6762, "longitude": 139.6503}]

## `startDate` (type: `string`):

Start date for historical mode (YYYY-MM-DD)

## `endDate` (type: `string`):

End date for historical mode (YYYY-MM-DD)

## `forecastDays` (type: `integer`):

Number of forecast days (1-16)

## `granularity` (type: `string`):

Data granularity

## `temperatureUnit` (type: `string`):

Temperature unit

## Actor input object example

```json
{
  "mode": "forecast",
  "locations": [
    {
      "name": "Tokyo",
      "latitude": 35.6762,
      "longitude": 139.6503
    }
  ],
  "forecastDays": 7,
  "granularity": "daily",
  "temperatureUnit": "celsius"
}
```

# 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 = {
    "locations": [
        {
            "name": "Tokyo",
            "latitude": 35.6762,
            "longitude": 139.6503
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("cloud9_ai/open-meteo-scraper").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 = { "locations": [{
            "name": "Tokyo",
            "latitude": 35.6762,
            "longitude": 139.6503,
        }] }

# Run the Actor and wait for it to finish
run = client.actor("cloud9_ai/open-meteo-scraper").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 '{
  "locations": [
    {
      "name": "Tokyo",
      "latitude": 35.6762,
      "longitude": 139.6503
    }
  ]
}' |
apify call cloud9_ai/open-meteo-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,cloud9_ai/open-meteo-scraper"
        }
    }
}
```

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/wFVCsbrsR5gfgExeJ/builds/YLtR4b5T57Fobkafv/openapi.json