# Multi-Model Weather Ensemble Forecast API (`constructive_wainscot/weather-ensemble-forecast`) Actor High-accuracy temperature and precipitation forecasts using 82-member multi-model ensemble (GFS 31 + ECMWF 51). Returns confidence intervals, percentiles, and NWS-corrected predictions. 1.8°F MAE vs ~3-4°F for single-model services. - **URL**: https://apify.com/constructive\_wainscot/weather-ensemble-forecast.md - **Developed by:** [Stew Williamson](https://apify.com/constructive_wainscot) (community) - **Categories:** AI, Integrations, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Probe: Weather Ensemble Forecast API ### Hypothesis Users want higher-accuracy weather forecasts than basic single-model APIs. Our multi-model ensemble (GFS 31 + ECMWF 51 members + NWS bias correction) produces 1.8°F MAE vs ~3-4°F for single-model services. Existing Apify weather actors are thin wrappers around Open-Meteo (single model). A multi-model ensemble with confidence intervals is a genuine technical moat. ### What Apify Actor that provides high-accuracy temperature forecasts for US cities with confidence intervals. Input: city/zip/lat-lng + date range. Output: ensemble mean, confidence bands (10th/25th/50th/75th/90th percentile), NWS-corrected prediction, ensemble spread (uncertainty indicator). ### Revenue Model Pay-per-event on Apify Store (80% rev share): - $0.01 per city-day forecast (includes ensemble run) - Estimated Apify platform cost: ~$0.001/call (lightweight compute, no browser) - Net margin: ~$0.007/call ### Target Users - Prediction market traders (Kalshi, Polymarket weather markets) - Event planners needing high-confidence forecasts - Agriculture/energy companies needing probabilistic weather - AI agents that need weather data with uncertainty quantification ### Technical Plan - Extract weather pipeline from agent-gamma (ensemble.py, calibrator.py, NWS correction) - Package as standalone Apify Actor (TypeScript wrapper or Python Actor) - Input schema: cities[], date_range, include_hourly - Output: structured JSON with ensemble statistics per city per day - ≤200 LOC new code (mostly adapter around existing pipeline) ### Build Cost - Time: ≤1 session (Builder hat) - Money: $0 (Apify free tier, all weather APIs are free) - Dependencies: Open-Meteo API (free, no key), NWS API (free, no key) ### Distribution - Apify Store (primary — searchable as "weather forecast ensemble") - Name: "Weather Ensemble Forecast API" or "Multi-Model Weather Forecast" - Keywords to rank for: "weather forecast", "weather API", "temperature forecast", "weather ensemble" - Secondary: mention in prediction market communities (Reddit r/kalshi, r/polymarket) ### Kill Criteria - 0 external Apify runs after 14 days → KILL - Actor consistently returns errors or slow responses → FIX or KILL ### Success Signal (graduation to full engine) - ≥5 external users making repeat calls within 30 days - OR ≥$1 cumulative revenue within 30 days - Graduation: becomes Agent Delta, gets dedicated directory, expanded city coverage ### Differentiation from Existing Weather Actors | Feature | Existing (Open-Meteo wrappers) | Ours | |---------|-------------------------------|------| | Models | 1 (Open-Meteo default) | 82 (GFS 31 + ECMWF 51) | | Bias correction | None | NWS-corrected (70% weight) | | Confidence intervals | None | 10th-90th percentile | | Accuracy (MAE) | ~3-4°F | ~1.8°F (backtested) | | Per-city calibration | No | Yes (sigma multipliers) | ### Risks - Free weather data (Open-Meteo, NWS) may be "good enough" for most users - Ensemble computation adds latency vs simple API passthrough - Apify Store discovery problem (same as Alpha) — mitigated by keyword-optimized naming # Actor input Schema ## `cities` (type: `array`): City codes to forecast. Available: NYC, CHI, MIA, AUS, LAX, DEN, BOS, DAL, PHX, SEA, SFO, ATL, HOU, MIN, DC, LV, NOLA, OKC, SATX. Leave empty for all 14 major cities. ## `days` (type: `integer`): Number of days to forecast (1-15). Default: 3. ## `variable` (type: `string`): What to forecast: temperature_max, temperature_min, or precipitation. ## Actor input object example ```json { "cities": [], "days": 3, "variable": "temperature_max" } ```` # 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("constructive_wainscot/weather-ensemble-forecast").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("constructive_wainscot/weather-ensemble-forecast").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 constructive_wainscot/weather-ensemble-forecast --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=constructive_wainscot/weather-ensemble-forecast", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Multi-Model Weather Ensemble Forecast API", "description": "High-accuracy temperature and precipitation forecasts using 82-member multi-model ensemble (GFS 31 + ECMWF 51). Returns confidence intervals, percentiles, and NWS-corrected predictions. 1.8°F MAE vs ~3-4°F for single-model services.", "version": "0.1", "x-build-id": "5oXgAoaGr2BWYWGqY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/constructive_wainscot~weather-ensemble-forecast/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-constructive_wainscot-weather-ensemble-forecast", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/constructive_wainscot~weather-ensemble-forecast/runs": { "post": { "operationId": "runs-sync-constructive_wainscot-weather-ensemble-forecast", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/constructive_wainscot~weather-ensemble-forecast/run-sync": { "post": { "operationId": "run-sync-constructive_wainscot-weather-ensemble-forecast", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "cities": { "title": "Cities", "type": "array", "description": "City codes to forecast. Available: NYC, CHI, MIA, AUS, LAX, DEN, BOS, DAL, PHX, SEA, SFO, ATL, HOU, MIN, DC, LV, NOLA, OKC, SATX. Leave empty for all 14 major cities.", "default": [], "items": { "type": "string" } }, "days": { "title": "Forecast Days", "minimum": 1, "maximum": 15, "type": "integer", "description": "Number of days to forecast (1-15). Default: 3.", "default": 3 }, "variable": { "title": "Variable", "enum": [ "temperature_max", "temperature_min", "precipitation" ], "type": "string", "description": "What to forecast: temperature_max, temperature_min, or precipitation.", "default": "temperature_max" } } }, "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 } } } } } } } } } } ```