# METAR & TAF Airport Weather Scraper — Aviation Weather Reports (`compute-edge/metar-taf-airport-weather-scraper`) Actor Fetches METAR current weather observations and TAF terminal area forecasts for any airport worldwide using the NOAA/NWS Aviation Weather Center API. No API key required. - **URL**: https://apify.com/compute-edge/metar-taf-airport-weather-scraper.md - **Developed by:** [Compute Edge](https://apify.com/compute-edge) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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 ## METAR & TAF Airport Weather Scraper — Aviation Weather Reports Extract real-time **METAR weather observations** and **TAF terminal area forecasts** for any airport worldwide using the official NOAA/NWS Aviation Weather Center API. No API key required. Perfect for flight operations, drone/UAV planning, logistics routing, and weather analytics pipelines. **METAR** (Meteorological Aerodrome Report) is the standard format for current weather observations at an airport — issued hourly or whenever conditions change significantly. **TAF** (Terminal Aerodrome Forecast) is the 24–30 hour weather forecast for the airspace around an airport, updated every 6 hours by the National Weather Service. ### Key Features - Fetch **METAR**, **TAF**, or **both** in a single run - Query any combination of ICAO station IDs (e.g. `KJFK`, `EGLL`, `OMDB`) - Use **state/region codes** like `@NY` (all New York stations) or `@CA` (all California stations) — the API supports these natively - Configure **lookback window** for METAR history (1–72 hours) - Structured JSON output with clean camelCase fields — ready for databases and analytics platforms - Raw METAR/TAF text included for aviation-grade use cases - Runs in under 30 seconds for typical workloads ### Output Data Fields | Field | Type | Description | |---|---|---| | `reportType` | string | `"METAR"` or `"TAF"` | | `icaoId` | string | ICAO airport identifier (e.g. `KJFK`) | | `stationName` | string | Airport full name | | `reportTime` | string | ISO 8601 observation or issue time | | `flightCategory` | string | VFR / MVFR / IFR / LIFR (METAR only) | | `temp` | number | Temperature in Celsius (METAR only) | | `dewpoint` | number | Dew point in Celsius (METAR only) | | `windDir` | number/string | Wind direction in degrees or "VRB" | | `windSpeed` | number | Wind speed in knots | | `windGust` | number | Wind gust speed in knots (if present) | | `visibility` | string | Visibility in statute miles | | `altimeter` | number | Altimeter setting in hPa | | `seaLevelPressure` | number | Sea level pressure in hPa (METAR only) | | `clouds` | array | Cloud layers: `[{cover, base}]` | | `lat` / `lon` | number | Station coordinates | | `elev` | number | Station elevation in meters | | `rawText` | string | Raw METAR or TAF text string | | `validFrom` / `validTo` | string | TAF validity window (ISO 8601) | | `forecasts` | array | TAF forecast periods with wind/vis/clouds | ### Who Needs This Data? - **Flight operations teams** — pre-flight weather briefings and go/no-go decisions - **Drone and UAV operators** — wind, visibility, and ceiling checks before launch - **Logistics and freight** — weather-aware routing for time-sensitive shipments - **Weather analytics platforms** — historical observation ingestion and model validation - **Airport management** — monitoring surface observations across multiple stations - **Research and academia** — building climatological datasets from raw NWS data --- ### How to scrape METAR and TAF aviation weather data 1. **Open the Actor** on Apify and click **Try for free**. 2. In the **Station IDs** field, enter one or more ICAO codes, e.g. `KJFK`, `KLAX`, `KORD`. You can also use state codes like `@NY` to pull all stations in New York. 3. Set **Data Type** to `metar`, `taf`, or `both` depending on your use case. 4. Set **METAR Hours Lookback** to control how many hours of observations to retrieve (default 2, max 72). 5. Optionally set **Max Results** to cap output size. 6. Click **Start** and wait for the run to complete (typically under 30 seconds). 7. Open the **Dataset** tab to browse results or download as JSON, CSV, or XLSX. For large-scale pulls (e.g. all US airports), use the `@XX` state codes and increase **Max Results** accordingly. --- ### Pricing This Actor costs approximately **$0.003 per result** plus standard Apify compute time. A typical run fetching 20 stations with both METAR and TAF (~40 records) costs under $0.15 including compute. | Run Size | Approx. Cost | |---|---| | 10 stations, METAR only (~10–20 records) | ~$0.06 | | 50 stations, both types (~100–150 records) | ~$0.45 | | All @CA stations, both types (~500+ records) | ~$1.50 | --- ### Input Example ```json { "stations": ["KJFK", "KLAX", "KORD", "@NY"], "dataType": "both", "hoursBack": 3, "maxResults": 500 } ```` ### Output Example ```json { "reportType": "METAR", "icaoId": "KJFK", "stationName": "New York/JF Kennedy Intl, NY, US", "reportTime": "2026-07-02T17:00:00.000Z", "rawText": "METAR KJFK 021651Z 20013KT 10SM FEW070 37/21 A2998 RMK SLP152 T03720206 $", "temp": 37.2, "dewpoint": 20.6, "windDir": 200, "windSpeed": 13, "windGust": null, "visibility": "10+", "altimeter": 1015.3, "seaLevelPressure": 1015.2, "flightCategory": "VFR", "clouds": [{ "cover": "FEW", "base": 7000 }], "lat": 40.6392, "lon": -73.7639, "elev": 3, "validFrom": null, "validTo": null, "forecasts": null } ``` *** ### Other Scrapers You might also find these related government weather and aviation data Actors useful: - [NWS Weather Alerts Scraper](https://apify.com/compute-edge/nws-weather-alerts-scraper) — Real-time active weather alerts and watches from National Weather Service - [FAA NAS Status Scraper](https://apify.com/compute-edge/faa-nas-status-scraper) — FAA National Airspace System status, delays, and NOTAMs - [NOAA SWPC Space Weather Scraper](https://apify.com/compute-edge/noaa-swpc-space-weather-scraper) — Solar flare alerts, geomagnetic storm data, and space weather forecasts - [NTSB Aviation Accident Scraper](https://apify.com/compute-edge/ntsb-aviation-scraper) — NTSB aviation accident and incident database *** ### FAQ **How often are METARs updated?** METARs are issued at the top of every hour and whenever conditions change significantly (a "special observation" or SPECI). Aviation Weather Center typically reflects updates within 5–10 minutes of issuance. **What is a flight category (VFR/MVFR/IFR/LIFR)?** Flight categories classify visibility and ceiling conditions: VFR (Visual Flight Rules) = clear flying conditions; MVFR (Marginal VFR) = reduced but usable; IFR (Instrument Flight Rules) = low ceiling or visibility; LIFR (Low IFR) = severe low-visibility conditions. They appear in METAR records only. **Can I scrape international airports?** Yes — the NOAA Aviation Weather Center API includes thousands of international stations. Use the standard ICAO code format (e.g. `EGLL` for London Heathrow, `RJTT` for Tokyo Haneda). State/region codes (`@XX`) are US-only. **How far back can I pull METAR history?** The `hoursBack` parameter supports up to 72 hours. For older historical data, consider NOAA's ASOS/AWOS archives or the IEM ASOS download service. **Why are some fields null in TAF records?** TAF records represent forecasts, not current conditions — so observed fields like `temp`, `dewpoint`, `altimeter`, `seaLevelPressure`, and `flightCategory` are not applicable and are returned as `null`. The forecast periods in the `forecasts` array contain wind, visibility, and cloud data. *** ### Legal Disclaimer This Actor fetches data from the NOAA/NWS Aviation Weather Center API (`aviationweather.gov`), which is a public United States government data service. Data is in the public domain under 17 U.S.C. § 105. Use of this data is subject to [NOAA's data disclaimer](https://www.weather.gov/disclaimer). This Actor is not affiliated with or endorsed by NOAA, NWS, or the FAA. Aviation weather data must **not** be used as the sole basis for flight planning decisions — always consult certified aviation weather services and a qualified meteorologist for safety-critical operations. For support, contact us through the Apify Actor page. # Actor input Schema ## `stations` (type: `array`): List of ICAO airport station IDs (e.g. KJFK, KLAX) or state/region codes prefixed with @ (e.g. @NY for all New York stations, @CA for California). Defaults to 10 major US airports. ## `dataType` (type: `string`): Choose which report type(s) to fetch. 'metar' = current weather observations only. 'taf' = terminal area forecasts only. 'both' = fetch METARs and TAFs. ## `hoursBack` (type: `integer`): How many hours back to retrieve METAR observations. 1 = most recent report only, 2 = last 2 hours, up to 72 hours. Does not affect TAF lookback. ## `maxResults` (type: `integer`): Maximum total number of records (METARs + TAFs combined) to return. Set to 0 for unlimited. ## Actor input object example ```json { "stations": [ "KJFK", "KLAX", "KORD", "KATL", "KDFW", "KDEN", "KSFO", "KSEA", "KMIA", "KBOS" ], "dataType": "both", "hoursBack": 2, "maxResults": 1000 } ``` # 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("compute-edge/metar-taf-airport-weather-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 = {} # Run the Actor and wait for it to finish run = client.actor("compute-edge/metar-taf-airport-weather-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 '{}' | apify call compute-edge/metar-taf-airport-weather-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=compute-edge/metar-taf-airport-weather-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "METAR & TAF Airport Weather Scraper — Aviation Weather Reports", "description": "Fetches METAR current weather observations and TAF terminal area forecasts for any airport worldwide using the NOAA/NWS Aviation Weather Center API. No API key required.", "version": "0.1", "x-build-id": "c357LfD0vW05Sroax" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/compute-edge~metar-taf-airport-weather-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-compute-edge-metar-taf-airport-weather-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/compute-edge~metar-taf-airport-weather-scraper/runs": { "post": { "operationId": "runs-sync-compute-edge-metar-taf-airport-weather-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/compute-edge~metar-taf-airport-weather-scraper/run-sync": { "post": { "operationId": "run-sync-compute-edge-metar-taf-airport-weather-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "stations": { "title": "Station IDs", "type": "array", "description": "List of ICAO airport station IDs (e.g. KJFK, KLAX) or state/region codes prefixed with @ (e.g. @NY for all New York stations, @CA for California). Defaults to 10 major US airports.", "default": [ "KJFK", "KLAX", "KORD", "KATL", "KDFW", "KDEN", "KSFO", "KSEA", "KMIA", "KBOS" ], "items": { "type": "string" } }, "dataType": { "title": "Data Type", "enum": [ "metar", "taf", "both" ], "type": "string", "description": "Choose which report type(s) to fetch. 'metar' = current weather observations only. 'taf' = terminal area forecasts only. 'both' = fetch METARs and TAFs.", "default": "both" }, "hoursBack": { "title": "METAR Hours Lookback", "minimum": 1, "maximum": 72, "type": "integer", "description": "How many hours back to retrieve METAR observations. 1 = most recent report only, 2 = last 2 hours, up to 72 hours. Does not affect TAF lookback.", "default": 2 }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum total number of records (METARs + TAFs combined) to return. Set to 0 for unlimited.", "default": 1000 } } }, "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 } } } } } } } } } } ```