# METAR & NWS Weather Station Observations API (`bigdavidson/metar-nws-weather-station-observations`) Actor Raw METAR strings and decoded NWS ASOS hourly observations by ICAO station or lat/lon: temp, wind, ceiling, visibility, flight category. The official station data Kalshi weather markets settle on. No login, no API keys. From $0.50 per 1,000 records. - **URL**: https://apify.com/bigdavidson/metar-nws-weather-station-observations.md - **Developed by:** [Jack Sheward](https://apify.com/bigdavidson) (community) - **Categories:** Other, Travel, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 1,000 observation records This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 & NWS Weather Station Observations API Pull **raw METAR strings** and **hourly ASOS/NWS weather station observations** for any ICAO station (KORD, KJFK, KLAX…) or any lat/lon point — decoded into clean JSON with temperature, dewpoint, wind, ceiling, visibility, and **flight category** (VFR/MVFR/IFR/LIFR). Up to **30 days of hourly history** per station. This is the observation data that aviation tools, weather dashboards, and **prediction markets (Kalshi/Polymarket weather contracts)** actually resolve on — straight from official sources (aviationweather.gov + the ASOS archive), not scraped weather websites that break. ### What you get One record per station per observation: | Field | Example | |---|---| | `station_id` | `KORD` | | `station_name` | `Chicago O'Hare Intl` | | `lat`, `lon` | `41.97`, `-87.9` | | `utc_time` | `2026-06-09T18:52:00Z` | | `temp_f`, `dewpoint_f` | `78.8`, `60.1` | | `wind_dir`, `wind_speed_kt` | `240`, `12` | | `ceiling_ft` | `5500` | | `visibility_sm` | `10` | | `flight_category` | `VFR` | | `metar_raw` | `KORD 091852Z 24012KT 10SM BKN055 26/16 A3002 …` | | `source` | `aviationweather.gov` | Export as JSON, CSV, or Excel directly from the dataset. ### Input ```json { "stationIds": ["KORD", "KMDW"], "latLon": [{ "lat": 40.78, "lon": -73.97 }], "hoursBack": 24 } ```` - `stationIds` — ICAO codes. US ASOS stations fully supported; international stations supported up to 72h lookback. - `latLon` — optional points, resolved to the nearest official observation station automatically. - `hoursBack` — 1 to 720. Up to 72h is served from aviationweather.gov (freshest); longer lookbacks from the ASOS archive. - `fields` — optional list to slim the output (e.g. just `["temp_f"]`). ### Pricing Pay per observation record — no subscription. A 24h pull for one station is ~24 records. ### Why this instead of scraping a weather website? - **Primary sources.** aviationweather.gov + ASOS archive — the same observations NWS publishes, with raw METAR included for auditability. - **No scrape fragility.** Public JSON/CSV APIs, not HTML scraping that breaks on redesigns. - **Resolution-grade.** If you settle weather derivatives or prediction-market positions, you need the observation of record, not a weather app's interpolation. ### FAQ #### Which stations are supported? Any ICAO station reporting METAR. Deep history (beyond 72h) covers US ASOS stations (K-prefix). #### How fresh is the data? aviationweather.gov observations typically appear within minutes of the station report. #### Can I get the exact raw METAR string? Yes — every record carries `metar_raw` exactly as published. #### How do I get daily max/min temperature for Kalshi weather markets? Pull `hoursBack: 24` for the station (e.g. `KNYC` markets resolve on Central Park-area stations) and take the max/min of `temp_f` — or watch this developer's other actors for a dedicated Kalshi weather-markets feed. #### Does it work for a lat/lon with no airport nearby? Yes — `latLon` resolves through the official NWS station registry to the nearest observing station. ### Use with AI agents (MCP) Connect your agent to the Apify MCP server (mcp.apify.com) and it can call this actor as a tool: ask for "the latest METAR observations at KNYC" or "hourly temperatures at KMDW for the last 24 hours" and the agent fills the input schema (stationIds, hoursBack) itself. Useful as a ground-truth weather lookup for trading agents, aviation assistants, and research pipelines. ### More prediction-market data actors - [Kalshi Weather Markets Data (KXHIGH & KXLOW)](https://apify.com/bigdavidson/kalshi-weather-markets) - live bracket prices and settlement history for the markets these observations resolve. - [Prediction Market Resolution Evidence Checker](https://apify.com/bigdavidson/prediction-market-resolution-evidence) - paste a Kalshi weather market URL, get the live observation that resolves it, graded. # Actor input Schema ## `stationIds` (type: `array`): Weather station ICAO codes, e.g. KORD, KJFK, KLAX. US ASOS stations have full support; international ICAO stations are served for lookbacks up to 72 hours. ## `latLon` (type: `array`): Lat/lon points to resolve to the nearest observation station, e.g. \[{"lat": 41.97, "lon": -87.9}]. Each resolves via the NWS station registry and is added to the station list. ## `hoursBack` (type: `integer`): How many hours of hourly observations to pull per station. Up to 72h is served from aviationweather.gov (freshest, sub-hour latency); 73-720h is served from the Iowa State Mesonet ASOS archive. ## `fields` (type: `array`): Subset of output fields to keep (station\_id and utc\_time always included). Leave empty for all fields. ## Actor input object example ```json { "stationIds": [ "KORD" ], "latLon": [], "hoursBack": 24, "fields": [] } ``` # 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 = { "stationIds": [ "KORD" ] }; // Run the Actor and wait for it to finish const run = await client.actor("bigdavidson/metar-nws-weather-station-observations").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 = { "stationIds": ["KORD"] } # Run the Actor and wait for it to finish run = client.actor("bigdavidson/metar-nws-weather-station-observations").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 '{ "stationIds": [ "KORD" ] }' | apify call bigdavidson/metar-nws-weather-station-observations --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bigdavidson/metar-nws-weather-station-observations", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "METAR & NWS Weather Station Observations API", "description": "Raw METAR strings and decoded NWS ASOS hourly observations by ICAO station or lat/lon: temp, wind, ceiling, visibility, flight category. The official station data Kalshi weather markets settle on. No login, no API keys. From $0.50 per 1,000 records.", "version": "0.1", "x-build-id": "uV9cLzlhpv8uAbaDx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bigdavidson~metar-nws-weather-station-observations/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bigdavidson-metar-nws-weather-station-observations", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/bigdavidson~metar-nws-weather-station-observations/runs": { "post": { "operationId": "runs-sync-bigdavidson-metar-nws-weather-station-observations", "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/bigdavidson~metar-nws-weather-station-observations/run-sync": { "post": { "operationId": "run-sync-bigdavidson-metar-nws-weather-station-observations", "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": { "stationIds": { "title": "Station IDs (ICAO)", "type": "array", "description": "Weather station ICAO codes, e.g. KORD, KJFK, KLAX. US ASOS stations have full support; international ICAO stations are served for lookbacks up to 72 hours.", "default": [ "KORD" ], "items": { "type": "string" } }, "latLon": { "title": "Coordinates (optional)", "type": "array", "description": "Lat/lon points to resolve to the nearest observation station, e.g. [{\"lat\": 41.97, \"lon\": -87.9}]. Each resolves via the NWS station registry and is added to the station list.", "default": [] }, "hoursBack": { "title": "Hours of history", "minimum": 1, "maximum": 720, "type": "integer", "description": "How many hours of hourly observations to pull per station. Up to 72h is served from aviationweather.gov (freshest, sub-hour latency); 73-720h is served from the Iowa State Mesonet ASOS archive.", "default": 24 }, "fields": { "title": "Fields filter (optional)", "type": "array", "description": "Subset of output fields to keep (station_id and utc_time always included). Leave empty for all fields.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```