# Open Meteo Weather Forecast Scraper (`parseforge/open-meteo-forecast-scraper`) Actor Pull a weather forecast for any coordinates through the Open Meteo service. Each row returns the time step with temperature, precipitation, and wind speed, plus the timezone and elevation for the point. Useful for planning, research, and adding weather context to location records. - **URL**: https://apify.com/parseforge/open-meteo-forecast-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Other, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.52 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## 🌤 Open-Meteo Weather Forecast Scraper > 🚀 **Get an hourly weather forecast for anywhere on Earth.** Enter latitude and longitude and pull temperature, precipitation and wind speed step by step, with units. Give it a pair of coordinates and get the upcoming hourly forecast as clean, structured rows - one per time step - ready for a spreadsheet, dashboard, automation or app. No account or API key required. Each row is mapped to a consistent schema - time, temperature, precipitation, wind speed, their units, and the location metadata - so you can chart, filter and trigger alerts without any glue code. | 🎯 Target Audience | 💡 Primary Use Cases | |---|---| | Dashboard and app developers, logistics/ops teams, agriculture and events planners, analysts | Weather in apps and automations, planning, datasets, threshold alerts | ### 📋 What the Open-Meteo Weather Forecast Scraper does - Returns the hourly forecast for any latitude/longitude - Captures temperature, precipitation and wind speed with their units - Includes location metadata (coordinates and timezone) on every row - Exports to CSV, Excel, JSON, or XML > 💡 **Why it matters:** weather APIs return nested arrays keyed by variable. This actor flattens them into one clean row per hour, so the data drops straight into a sheet or chart. ### 📊 Data fields Each record includes: `elevation`, `latitude`, `longitude`, `precipitation`, `precipitationUnit`, `scrapedAt`, `temperature`, `temperatureUnit`, `time`, `timezone`, `windSpeed`, `windSpeedUnit`. All 12 field names come from a real production run, so what you see here is what lands in your dataset. ### 🚀 How to use 1. [Create a free account w/ $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) 2. Open the Actor on Apify Console 3. Enter the `latitude` and `longitude` of your location 4. Click Start 5. Download the results as CSV, Excel, JSON, or XML ### 🆘 Need Help? If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our [contact form](https://tally.so/r/BzdKgA) or write to parseforge@protonmail.com. We also take on paid custom data projects. For faster answers, join our [Discord](https://parseforge.co/discord). It's the best place to get support and suggest new actors. # Actor input Schema ## `latitude` (type: `string`): Required. Latitude of the location in decimal degrees (for example 52.52 for Berlin). Range -90 to 90. ## `longitude` (type: `string`): Required. Longitude of the location in decimal degrees (for example 13.41 for Berlin). Range -180 to 180. ## `maxItems` (type: `integer`): Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000 ## Actor input object example ```json { "latitude": "52.52", "longitude": "13.41", "maxItems": 10 } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "latitude": "52.52", "longitude": "13.41", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/open-meteo-forecast-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "latitude": "52.52", "longitude": "13.41", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("parseforge/open-meteo-forecast-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "latitude": "52.52", "longitude": "13.41", "maxItems": 10 }' | apify call parseforge/open-meteo-forecast-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/open-meteo-forecast-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Open Meteo Weather Forecast Scraper", "description": "Pull a weather forecast for any coordinates through the Open Meteo service. Each row returns the time step with temperature, precipitation, and wind speed, plus the timezone and elevation for the point. Useful for planning, research, and adding weather context to location records.", "version": "0.1", "x-build-id": "95Cn0wprZO6hHuKns" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~open-meteo-forecast-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-open-meteo-forecast-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~open-meteo-forecast-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-open-meteo-forecast-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~open-meteo-forecast-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-open-meteo-forecast-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "latitude", "longitude" ], "properties": { "latitude": { "title": "Latitude", "type": "string", "description": "Required. Latitude of the location in decimal degrees (for example 52.52 for Berlin). Range -90 to 90." }, "longitude": { "title": "Longitude", "type": "string", "description": "Required. Longitude of the location in decimal degrees (for example 13.41 for Berlin). Range -180 to 180." }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```