# ML Storm Forecast by Address, 24 Hours to 10 Days (`truefixr_atlasunited/storm-forecast-by-address`) Actor

Per-peril machine-learning models identify addresses in the forecast footprint of storms over the next 24 hours to 10 days, across 10 perils. Each address row carries structure replacement value, occupants, storm type, forecast time, a risk label and prior storm-hit count. Could happen, not will.

- **URL**: https://apify.com/truefixr_atlasunited/storm-forecast-by-address.md
- **Developed by:** [Valentin Yslas](https://apify.com/truefixr_atlasunited) (community)
- **Categories:** Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 85.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 forecast addresses

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Storm Forecast by Address: Value and People at Risk

**What it is:** a family of per-peril machine-learning models. They identify the addresses inside the forecast footprint of a storm over the next 24 hours to 10 days, across 10 perils: `TORNADO`, `HAIL`, `WIND`, `FLASH_FLOOD`, `HEAVY_RAIN`, `WINTER`, `ICE`, `COASTAL_SURGE`, `HURRICANE`, `WILDFIRE`. Could happen, not will.

**What you send:** one or more US counties as `County, ST`, a forecast window, and optionally one peril.

**What you get per row:** one address with the structure replacement value and estimated occupants attached to it, the storm type, forecast time, a risk label, the count of prior storm hits at the address, the window, a `reference_id` and the forecast generation time.

**Cost:** pricing is not enabled yet. The planned rate is $0.05 per delivered address. A preview of the address count and estimated cost is logged per county before delivery, and `maxAddresses` and `maxChargeUsd` bound the run.

Models are refreshed 4 times per day.

### What you get

One flat record per forecast address. These are exactly the fields delivered in the dataset.

| Field | Meaning |
|---|---|
| `structure_value` | Estimated structure replacement value in USD (rebuild cost, not market value) |
| `people` | Estimated number of occupants |
| `full_address`, `city`, `state`, `zip`, `county` | Street address and its locality |
| `lat`, `lon` | Address coordinates |
| `storm_type` | The peril (one of the 10 above) the record refers to |
| `forecast_time` | Time the forecast event is expected to affect the address |
| `risk_label` | The model's risk label for this address and peril (for example Extreme) |
| `storm_hits` | Count of past storm hits at the address |
| `facility_name`, `facility_type` | Facility name and type when the address is a recognized facility, otherwise empty |
| `window` | The forecast window requested (24h to 10d) |
| `reference_id` | Identifier of the forecast pull, for citation and reproducibility |
| `forecast_generated` | Timestamp at which the forecast was generated |

### Methodology

Each peril has a dedicated machine-learning model that identifies, address by address, which addresses fall inside the forecast footprint of that peril within the window. Models are trained and validated chronologically: they are fit on earlier periods and evaluated on later, held-out reported events, so evaluation reflects forecasting forward in time. Models are versioned and the pipeline is reproducible, and every pull carries a `reference_id` and a `forecast_generated` timestamp so any record can be traced to a specific forecast run. Output is at the level of the individual address, not a county or polygon summary, and is refreshed four times per day.

### Interpretation

A record means a storm could affect that address within the window, not that it will. A record is a forecast record, not a damage assessment, an insurance determination or a claim of loss. `risk_label` is a category expressing the model output; `structure_value` and `people` are exposure context, not a loss estimate. Use `forecast_generated` to judge freshness and `reference_id` to cite the run.

### Input

```json
{ "counties": ["Dallas, TX"], "window": "24h", "peril": "", "maxAddresses": 100 }
```

- `counties` (required): list of `County, ST` strings.
- `window`: `24h`, `48h`, `3d`, `5d`, `7d` or `10d`. Default `24h`.
- `peril`: optional, one of the 10 perils. Empty means all 10.
- `maxAddresses`: cap on delivered records, 1 to 5000. Default 100.
- `maxChargeUsd`: optional spend cap.

### Example record

```json
{ "full_address": "405 Evergreen Trail, Cedar Hill, TX 75104", "county": "Dallas", "state": "TX",
  "structure_value": 300882, "people": 3.5, "storm_type": "HEAVY_RAIN", "risk_label": "Extreme",
  "storm_hits": 2, "window": "24h", "reference_id": "TFX-202610-3A48FA8C",
  "forecast_generated": "2026-10-04T03:32:44.962420Z" }
```

### Compliance

Forecasts describe what could happen, not what will happen. This is data, not insurance, investment or safety advice, and no guarantee of accuracy or results is made. Not affiliated with NOAA or the National Weather Service.

# Actor input Schema

## `counties` (type: `array`):

Counties to forecast, each given as 'County, ST' (for example 'Dallas, TX'). A county and state are both required; state-only input is not supported.

## `window` (type: `string`):

Forecast horizon the model output refers to, from 24 hours to 10 days. Each record reports the window it was produced for.

## `peril` (type: `string`):

Restrict output to one of the 10 perils. Leave empty to include all 10 perils.

## `maxAddresses` (type: `integer`):

Upper bound on the number of address records delivered across all counties in this run.

## `maxChargeUsd` (type: `number`):

Upper bound on the amount this run may charge once pricing is enabled. The planned rate is $0.05 per address, so this also bounds the address count. Leave empty to rely on your Apify run budget.

## Actor input object example

```json
{
  "counties": [
    "Dallas, TX"
  ],
  "window": "24h",
  "maxAddresses": 100
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `overview` (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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "counties": [
        "Dallas, TX"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("truefixr_atlasunited/storm-forecast-by-address").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "counties": ["Dallas, TX"] }

# Run the Actor and wait for it to finish
run = client.actor("truefixr_atlasunited/storm-forecast-by-address").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "counties": [
    "Dallas, TX"
  ]
}' |
apify call truefixr_atlasunited/storm-forecast-by-address --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,truefixr_atlasunited/storm-forecast-by-address"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/YIXPV8gfeYLdQIueg/builds/MzrqsCNGCQJIfM8fY/openapi.json