# Model-Based Hourly Forecast by Address, 1 to 48 Hours (`truefixr_atlasunited/model-hourly-forecast-by-address`) Actor

Send a street address or coordinates, get a model-derived forecast 1 to 48 hours ahead as flat time-series rows for CSV or Excel. Forecasts are probabilistic, not guarantees.

- **URL**: https://apify.com/truefixr_atlasunited/model-hourly-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, 16.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 forecast rows

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

## Model-Based Point Forecast, 1-48 Hours

**Model-derived forecast for any US point from our weather modeling pipeline. One detailed multi-field row per location, summarizing the next 1 to 48 hours, with the sampled time-series path attached.**

Forecast hours go up to 48. The service provides model output for 0 to 48 hours ahead.

### What you get

| Field | Meaning |
|---|---|
| `status` | `ok`, or `error` with an explanation (error rows are not charged) |
| `location`, `lat`, `lon` | The location as given and its coordinates |
| `hours_ahead` | Horizon requested (1 to 48) |
| `temp_start_f`, `temp_end_f` | Modeled temperature at the start and end of the horizon (F) |
| `temp_min_f`, `temp_max_f`, `temp_change_f` | Minimum, maximum and net change over the horizon |
| `trend` | `warming`, `cooling` or `steady`, from the net change |
| `peak_gust_mph`, `peak_wind_mph` | Highest modeled gust and wind speed over the horizon |
| `peak_precip_in` | Highest modeled precipitation amount in a sampled step (inches) |
| `rain_possible` | Whether the model indicates rain could occur in the horizon |
| `summary` | One-line text summary of the row |
| `path_json` | JSON list of sampled time steps with `time`, `temp_f`, `gust_mph`, `precip_in` |

### Methodology

For each location, the actor samples our weather modeling pipeline at the coordinate across the requested forecast hours, then reduces the sampled path to summary statistics (start, end, extremes, change, peaks) and a trend flag. The full sampled path is kept in `path_json` for time-series use. Output is flat and typed for direct use in code.

### Interpretation

Values describe what could happen at the point, not what will happen. `rain_possible` indicates that the model output allows rain, not that rain is certain. Rows are model-derived forecasts, not instrument measurements, and are not safety or trading advice.

### Input

```json
{ "locations": ["Denver", "30.27,-97.74"], "hoursAhead": 24, "stepHours": 6 }
```

`locations` accepts about 100 built-in major US city names, `lat,lon`, or `Label=lat,lon`. `hoursAhead` is 1 to 48. `stepHours` is 1 to 12. `maxChargeUsd` optionally caps spend. US locations only.

### Example record

```json
{ "status": "ok", "location": "Denver", "hours_ahead": 24, "temp_start_f": 58.4, "temp_end_f": 79.3,
  "temp_change_f": 21, "trend": "warming", "peak_gust_mph": 6.35, "rain_possible": false }
```

### Cost

Pricing is not enabled yet. The planned rate is $0.0002 per call (one location). Rows with `"status": "error"` are not charged.

### Compliance

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

# Actor input Schema

## `locations` (type: `array`):

A full US street address (for example '1600 Pennsylvania Ave NW, Washington, DC'), a 'lat,lon' pair (for example '30.27,-97.74'), 'Label=lat,lon' to attach your own label, or a city name (for example 'Chicago', 'NYC'; about 100 major US cities are built in). Street addresses are geocoded for you and the geocoded address is returned in the row.

## `hoursAhead` (type: `integer`):

Forecast horizon in hours, from 1 to 48. The service provides model output for 0 to 48 hours ahead.

## `stepHours` (type: `integer`):

Spacing between sampled forecast hours in the path, from 1 to 12. Smaller steps give a finer path and take longer per location.

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

Upper bound on the amount this run may charge once pricing is enabled. The planned rate is $0.0002 per location. Leave empty to rely on your Apify run budget.

## Actor input object example

```json
{
  "locations": [
    "New York",
    "Chicago",
    "Austin"
  ],
  "hoursAhead": 24,
  "stepHours": 6
}
```

# 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 = {
    "locations": [
        "New York",
        "Chicago",
        "Austin"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("truefixr_atlasunited/model-hourly-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 = { "locations": [
        "New York",
        "Chicago",
        "Austin",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("truefixr_atlasunited/model-hourly-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 '{
  "locations": [
    "New York",
    "Chicago",
    "Austin"
  ]
}' |
apify call truefixr_atlasunited/model-hourly-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/model-hourly-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/gUsbfvdVe8u5cmbVh/builds/eygqyKtYhbgwlt0pO/openapi.json