# Weather Forecast & Historical Data (`moving_beacon-owner1/weather-forecast-historical-data`) Actor Scrapes weather forecasts, historical weather, and air quality data from the Open-Meteo API. Supports multi-location batch queries, 50+ weather variables, city name geocoding, and data going back to 1940. No API key required. - **URL**: https://apify.com/moving\_beacon-owner1/weather-forecast-historical-data.md - **Developed by:** [Jamshaid Arif](https://apify.com/moving_beacon-owner1) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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/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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## 🌤️ Open-Meteo — Weather Forecast & Historical Data — Apify Actor Scrapes **weather forecasts, historical weather, and air quality data** from the [Open-Meteo API](https://open-meteo.com/). Supports multi-location batch queries, 50+ weather variables, city name geocoding, and data going back to 1940. **No API key required.** ### Features - **3 data modes** — forecast (up to 16 days), historical (1940–present), and air quality. - **Multi-location** — batch query any number of cities or coordinates in one run. - **Auto-geocoding** — type city names like `Lahore, PK` — coordinates are resolved automatically. - **50+ weather variables** — temperature, precipitation, wind, humidity, UV, cloud cover, soil, solar radiation, and more. - **21 air quality variables** — PM2.5, PM10, ozone, NO₂, CO, pollen, AQI indices. - **Hourly + Daily** — fetch either or both granularities. - **Unit selection** — °C/°F, km/h/mph/m·s⁻¹/knots, mm/inches. - **WMO weather codes** decoded into human-readable descriptions. - **4 output formats** — one-per-day, one-per-hour, one-per-location, raw. ### Input Examples #### 7-Day Forecast for Multiple Cities ```json { "mode": "forecast", "locations": "New York, US\nLondon, UK\nTokyo, JP\nLahore, PK", "dailyVariables": [ "temperature_2m_max", "temperature_2m_min", "precipitation_sum", "windspeed_10m_max", "weathercode", "sunrise", "sunset", "uv_index_max" ], "forecastDays": 7, "temperatureUnit": "celsius", "outputFormat": "one_per_day" } ```` #### Hourly Forecast with Wind & Humidity ```json { "mode": "forecast", "locations": "48.8566,2.3522,Paris\n40.7128,-74.0060,New York", "hourlyVariables": [ "temperature_2m", "apparent_temperature", "relative_humidity_2m", "windspeed_10m", "precipitation_probability", "weathercode" ], "forecastDays": 3, "outputFormat": "one_per_hour" } ``` #### Historical Weather for 2024 ```json { "mode": "historical", "locations": "Islamabad, PK\nDelhi, IN", "dailyVariables": [ "temperature_2m_max", "temperature_2m_min", "precipitation_sum", "windspeed_10m_max" ], "startDate": "2024-01-01", "endDate": "2024-12-31", "outputFormat": "one_per_day" } ``` #### Air Quality Monitoring ```json { "mode": "air_quality", "locations": "Lahore, PK\nDelhi, IN\nBeijing, CN", "airQualityVariables": [ "pm2_5", "pm10", "us_aqi", "nitrogen_dioxide", "ozone", "carbon_monoxide" ], "forecastDays": 5, "outputFormat": "one_per_hour" } ``` ### Location Input Formats Each line in the locations field can be: | Format | Example | Notes | |---|---|---| | City, Country | `Lahore, PK` | Auto-geocoded via Open-Meteo | | City name | `London` | Works but add country for precision | | Coordinates | `32.68,73.96` | Direct lat,lon | | Coords + Label | `32.68,73.96,Lala Musa` | Lat,lon with custom name | ### Output Formats #### One Per Day (default) Best for daily data. Each row = one location × one date. | Field | Example | |---|---| | `id` | `1` | | `location` | `London, United Kingdom` | | `latitude` | `51.5085` | | `longitude` | `-0.1257` | | `country` | `United Kingdom` | | `timezone` | `Europe/London` | | `date` | `2025-04-06` | | `temperature_2m_max` | `14.2` | | `temperature_2m_max_unit` | `°C` | | `temperature_2m_min` | `7.8` | | `precipitation_sum` | `0.3` | | `weathercode` | `3` | | `weather_description` | `Overcast` | | `sunrise` | `2025-04-06T06:23` | | `sunset` | `2025-04-06T19:42` | #### One Per Hour Best for hourly data. Each row = one location × one hour. #### One Per Location All daily/hourly data nested under each location. Best for JSON export. #### Raw Original API response with `_location_label` added. ### Daily Variables (21 available) Temperature (max/min, actual/apparent), precipitation (total/rain/snow/hours/probability), weather code, sunrise/sunset, sunshine/daylight duration, wind (speed/gusts/direction), solar radiation, evapotranspiration, UV index. ### Hourly Variables (29 available) Temperature, apparent temperature, humidity, dewpoint, precipitation, rain, snowfall, snow depth, weather code, pressure (MSL/surface), cloud cover (total/low/mid/high), visibility, wind (speed/direction/gusts at 10m and 80m), UV index, is\_day flag, sunshine duration, radiation (shortwave/direct/diffuse), soil temperature, soil moisture. ### Air Quality Variables (21 available) PM10, PM2.5, CO, NO₂, SO₂, O₃, dust, UV, ammonia, pollen (6 types), European AQI, US AQI (total and per-pollutant). ### WMO Weather Codes The actor automatically decodes WMO numeric codes into descriptions: | Code | Description | Code | Description | |---|---|---|---| | 0 | Clear sky | 61 | Slight rain | | 1 | Mainly clear | 63 | Moderate rain | | 2 | Partly cloudy | 65 | Heavy rain | | 3 | Overcast | 71 | Slight snowfall | | 45 | Fog | 80 | Slight rain showers | | 51 | Light drizzle | 95 | Thunderstorm | # Actor input Schema ## `mode` (type: `string`): What type of weather data to fetch. ## `locations` (type: `string`): One location per line. Use either 'City, Country' names (auto-geocoded) or 'lat,lon' coordinates. Examples: Los Angeles, US London, UK 48.8566,2.3522 Lahore, PK ## `dailyVariables` (type: `array`): Select daily weather variables to fetch. ## `hourlyVariables` (type: `array`): Select hourly weather variables. Leave empty to skip hourly data. ## `airQualityVariables` (type: `array`): Select air quality variables (used in Air Quality mode). ## `forecastDays` (type: `integer`): Number of forecast days (1–16). Used in Forecast mode. ## `startDate` (type: `string`): Start date for historical mode (YYYY-MM-DD). E.g. '2024-01-01'. ## `endDate` (type: `string`): End date for historical mode (YYYY-MM-DD). E.g. '2024-12-31'. ## `temperatureUnit` (type: `string`): Temperature unit for output. ## `windspeedUnit` (type: `string`): Wind speed unit for output. ## `precipitationUnit` (type: `string`): Precipitation unit for output. ## `timezone` (type: `string`): Timezone for time values. Use 'auto' to detect from coordinates, or specify (e.g. 'America/New\_York', 'Asia/Karachi'). ## `outputFormat` (type: `string`): How to structure each record in the dataset. ## `addSequentialId` (type: `boolean`): Add auto-incrementing ID to each record. ## Actor input object example ```json { "mode": "forecast", "locations": "New York, US\nLondon, UK\nTokyo, JP", "dailyVariables": [ "temperature_2m_max", "temperature_2m_min", "precipitation_sum" ], "hourlyVariables": [], "airQualityVariables": [ "pm10", "pm2_5", "us_aqi" ], "forecastDays": 7, "temperatureUnit": "celsius", "windspeedUnit": "kmh", "precipitationUnit": "mm", "timezone": "auto", "outputFormat": "one_per_day", "addSequentialId": true } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("moving_beacon-owner1/weather-forecast-historical-data").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 = {} # Run the Actor and wait for it to finish run = client.actor("moving_beacon-owner1/weather-forecast-historical-data").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 '{}' | apify call moving_beacon-owner1/weather-forecast-historical-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moving_beacon-owner1/weather-forecast-historical-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Weather Forecast & Historical Data", "description": "Scrapes weather forecasts, historical weather, and air quality data from the Open-Meteo API. Supports multi-location batch queries, 50+ weather variables, city name geocoding, and data going back to 1940. No API key required.", "version": "0.0", "x-build-id": "EwtPIBOjbCtM3XdVu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moving_beacon-owner1~weather-forecast-historical-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moving_beacon-owner1-weather-forecast-historical-data", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moving_beacon-owner1~weather-forecast-historical-data/runs": { "post": { "operationId": "runs-sync-moving_beacon-owner1-weather-forecast-historical-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moving_beacon-owner1~weather-forecast-historical-data/run-sync": { "post": { "operationId": "run-sync-moving_beacon-owner1-weather-forecast-historical-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode", "locations" ], "properties": { "mode": { "title": "Data Mode", "enum": [ "forecast", "historical", "air_quality" ], "type": "string", "description": "What type of weather data to fetch.", "default": "forecast" }, "locations": { "title": "Locations", "type": "string", "description": "One location per line. Use either 'City, Country' names (auto-geocoded) or 'lat,lon' coordinates. Examples:\nLos Angeles, US\nLondon, UK\n48.8566,2.3522\nLahore, PK", "default": "New York, US\nLondon, UK\nTokyo, JP" }, "dailyVariables": { "title": "Daily Variables", "type": "array", "description": "Select daily weather variables to fetch.", "items": { "type": "string", "enum": [ "temperature_2m_max", "temperature_2m_min", "apparent_temperature_max", "apparent_temperature_min", "precipitation_sum", "rain_sum", "snowfall_sum", "precipitation_hours", "precipitation_probability_max", "weathercode", "sunrise", "sunset", "sunshine_duration", "daylight_duration", "windspeed_10m_max", "windgusts_10m_max", "winddirection_10m_dominant", "shortwave_radiation_sum", "et0_fao_evapotranspiration", "uv_index_max", "uv_index_clear_sky_max" ], "enumTitles": [ "Max Temperature (2m)", "Min Temperature (2m)", "Max Apparent Temperature (feels like)", "Min Apparent Temperature (feels like)", "Total Precipitation", "Total Rain", "Total Snowfall", "Precipitation Hours", "Max Precipitation Probability", "Weather Code (WMO)", "Sunrise Time", "Sunset Time", "Sunshine Duration", "Daylight Duration", "Max Wind Speed (10m)", "Max Wind Gusts (10m)", "Dominant Wind Direction (10m)", "Solar Radiation Sum", "Evapotranspiration (FAO ET₀)", "Max UV Index", "Max UV Index (Clear Sky)" ] }, "default": [ "temperature_2m_max", "temperature_2m_min", "precipitation_sum" ] }, "hourlyVariables": { "title": "Hourly Variables", "type": "array", "description": "Select hourly weather variables. Leave empty to skip hourly data.", "items": { "type": "string", "enum": [ "temperature_2m", "apparent_temperature", "relative_humidity_2m", "dewpoint_2m", "precipitation", "precipitation_probability", "rain", "snowfall", "snow_depth", "weathercode", "pressure_msl", "surface_pressure", "cloudcover", "cloudcover_low", "cloudcover_mid", "cloudcover_high", "visibility", "windspeed_10m", "windspeed_80m", "winddirection_10m", "windgusts_10m", "uv_index", "is_day", "sunshine_duration", "shortwave_radiation", "direct_radiation", "diffuse_radiation", "soil_temperature_0cm", "soil_moisture_0_to_1cm" ], "enumTitles": [ "Temperature (2m)", "Apparent Temperature (feels like)", "Relative Humidity (2m)", "Dewpoint (2m)", "Precipitation", "Precipitation Probability", "Rain", "Snowfall", "Snow Depth", "Weather Code (WMO)", "Pressure (mean sea level)", "Surface Pressure", "Cloud Cover (total)", "Cloud Cover (low)", "Cloud Cover (mid)", "Cloud Cover (high)", "Visibility", "Wind Speed (10m)", "Wind Speed (80m)", "Wind Direction (10m)", "Wind Gusts (10m)", "UV Index", "Is Daytime", "Sunshine Duration", "Shortwave Radiation", "Direct Radiation", "Diffuse Radiation", "Soil Temperature (0cm)", "Soil Moisture (0–1cm)" ] }, "default": [] }, "airQualityVariables": { "title": "Air Quality Variables", "type": "array", "description": "Select air quality variables (used in Air Quality mode).", "items": { "type": "string", "enum": [ "pm10", "pm2_5", "carbon_monoxide", "nitrogen_dioxide", "sulphur_dioxide", "ozone", "dust", "uv_index", "ammonia", "alder_pollen", "birch_pollen", "grass_pollen", "mugwort_pollen", "olive_pollen", "ragweed_pollen", "european_aqi", "us_aqi", "us_aqi_pm2_5", "us_aqi_pm10", "us_aqi_nitrogen_dioxide", "us_aqi_ozone" ], "enumTitles": [ "PM10", "PM2.5", "Carbon Monoxide (CO)", "Nitrogen Dioxide (NO₂)", "Sulphur Dioxide (SO₂)", "Ozone (O₃)", "Dust", "UV Index", "Ammonia (NH₃)", "Alder Pollen", "Birch Pollen", "Grass Pollen", "Mugwort Pollen", "Olive Pollen", "Ragweed Pollen", "European AQI", "US AQI", "US AQI (PM2.5)", "US AQI (PM10)", "US AQI (NO₂)", "US AQI (Ozone)" ] }, "default": [ "pm10", "pm2_5", "us_aqi" ] }, "forecastDays": { "title": "Forecast Days", "minimum": 1, "maximum": 16, "type": "integer", "description": "Number of forecast days (1–16). Used in Forecast mode.", "default": 7 }, "startDate": { "title": "Start Date", "type": "string", "description": "Start date for historical mode (YYYY-MM-DD). E.g. '2024-01-01'." }, "endDate": { "title": "End Date", "type": "string", "description": "End date for historical mode (YYYY-MM-DD). E.g. '2024-12-31'." }, "temperatureUnit": { "title": "Temperature Unit", "enum": [ "celsius", "fahrenheit" ], "type": "string", "description": "Temperature unit for output.", "default": "celsius" }, "windspeedUnit": { "title": "Wind Speed Unit", "enum": [ "kmh", "ms", "mph", "kn" ], "type": "string", "description": "Wind speed unit for output.", "default": "kmh" }, "precipitationUnit": { "title": "Precipitation Unit", "enum": [ "mm", "inch" ], "type": "string", "description": "Precipitation unit for output.", "default": "mm" }, "timezone": { "title": "Timezone", "type": "string", "description": "Timezone for time values. Use 'auto' to detect from coordinates, or specify (e.g. 'America/New_York', 'Asia/Karachi').", "default": "auto" }, "outputFormat": { "title": "Output Format", "enum": [ "one_per_day", "one_per_hour", "one_per_location", "raw" ], "type": "string", "description": "How to structure each record in the dataset.", "default": "one_per_day" }, "addSequentialId": { "title": "Add Sequential ID", "type": "boolean", "description": "Add auto-incrementing ID to each record.", "default": true } } }, "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 } } } } } } } } } } ```