Global Weather Scraper API — Structured Weather Extraction for Pipelines & Integrations

Extract structured daily weather datasets for any country, region, city, or town with 50+ fields per day item, covering forecast windows up to 16 days and historical date ranges up to one year. Each dataset item contains daily summaries, 24-hour hourly breakdowns, current conditions, and air quality signals — all serialized as enriched JSON for downstream pipelines, monitoring systems, and AI agent workflows.

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Why Choose This Weather Extraction API

Structured Output Designed for Integration Pipelines

📡 Single-Location, Multi-Dimension Extraction One API call resolves a place name via geocoding and returns the full weather dataset: daily summaries, hourly rows per day, current conditions, and air quality — structured for direct ingestion into data pipelines, event-driven workflows, or AI agents without additional preprocessing.

📊 50+ Fields Per Day Item Each day record exposes complete weather intelligence including temperature ranges (max/min/mean), apparent temperature, wind speed and gust vectors with compass labels, precipitation totals, UV index, sunrise/sunset with UTC offset, daylight hours, and current real-time readings for all variables.

🌬️ Integrated Air Quality Signals Every result includes European AQI, US AQI, PM2.5, PM10, carbon monoxide, carbon dioxide, nitrogen dioxide, sulphur dioxide, ozone, ammonia, methane, dust, UV index, and five pollen types (alder, birch, grass, mugwort, ragweed). Historical runs return hourly air quality rows aligned to the requested date range.

🗓️ Forecast & Historical Modes Target upcoming weather with the days parameter (1–16 day forecast window) or extract historical records using start_date / end_date with support for ranges up to 366 days. Both modes share the same output schema, enabling consistent downstream processing.

🌍 Global Location Resolution Accepts any country, state, province, region, city, town, district, or village as a free-text string. Geocoding resolves casual inputs like Munich, Ontario, Canada, or Chiang Mai, Thailand into precise coordinates and returns enriched location metadata: resolved name, country, region, city, latitude, longitude, and timezone.

🔗 Schema-Consistent Enriched Records Every dataset item carries processor metadata, ISO 8601 timestamps, mode indicator, and full location context fields — enabling deterministic joins with upstream datasets, audit trails, and multi-run deduplication in data warehouses.

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Quick Start Guide

Extract Weather Data in 3 Steps

Step 1: Configure Your Target Location

Open Actor Input

Enter a location string in the Location field. For forecast mode, set Days (1–16). For historical mode, set both Start Date and End Date.

Step 2: Run the Extraction

Click ▷ Start. The actor geocodes the input, retrieves weather and air quality data, and outputs one structured dataset item per calendar day.

Step 3: Access Structured Output

Export results as JSON or access programmatically via the Apify Dataset API. Each item contains the full day's weather schema including hourly and air quality arrays.

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Input Parameters

Parameter	Type	Required	Description	Example
location	string	✅ Yes	Target country, region, city, town, or district for extraction	"Tokyo, Japan"
start_date	string	❌ No	Historical start date — ISO date (YYYY-MM-DD) or relative period (30 days, 2 weeks)	"2025-04-01"
end_date	string	❌ No	Historical end date — ISO date or relative period (1 day, 1 week)	"2025-04-21"
days	integer	❌ No	Forecast window in days (1–16) when start_date/end_date are not configured	3

Date Format Options

Absolute dates (YYYY-MM-DD):

• "2025-04-01" — Specific calendar date

Relative periods (resolved relative to extraction date):

• "30 days" — 30 calendar days back from today
• "2 weeks" — 14 days back
• "1 month" — 30 days back

Historical date range must not exceed 366 days. Both start_date and end_date must be provided together.

Example Input — Forecast Mode

{
  "location": "Tokyo, Japan",
  "days": 3
}

Example Input — Historical Mode

{
  "location": "Tokyo, Japan",
  "start_date": "2025-04-01",
  "end_date": "2025-04-07"
}

---

Output Data Schema

Each dataset item represents one calendar day. The schema is consistent across forecast and historical modes.

Core Fields

Field	Type	Description
processor	string	Apify actor URL that processed this record
processed_at	string	ISO 8601 UTC timestamp when this record was created
status	string	Extraction status (success)
mode	string	forecast or historical
start_date	string	Historical start date when date-range mode is active, otherwise null
end_date	string	Historical end date when date-range mode is active, otherwise null

Location Fields

Field	Type	Description
location	string	Resolved full place name
country	string	Country name of the resolved location
region	string	Resolved state, province, or county
city	string	Resolved city, town, or municipality
latitude	number	Latitude coordinate (6 decimal places)
longitude	number	Longitude coordinate (6 decimal places)
timezone	string	IANA timezone identifier (e.g. Asia/Tokyo)

Daily Summary Fields

Field	Type	Description
date	string	Calendar day (YYYY-MM-DD)
weather	string	Human-readable weather label (e.g. Partly cloudy)
temperature_max	number	Maximum temperature (°C)
temperature_min	number	Minimum temperature (°C)
temperature_mean	number	Mean temperature (°C)
apparent_temperature_max	number	Maximum feels-like temperature (°C)
apparent_temperature_min	number	Minimum feels-like temperature (°C)
wind_speed_max	number	Maximum wind speed (km/h)
wind_gusts_max	number	Maximum wind gust speed (km/h)
wind_direction_dominant	number	Dominant wind direction in degrees
wind_direction_label	string	Compass label (N, NE, S, SW, etc.)
precipitation_sum	number	Total precipitation (mm)
precipitation_probability_max	number	Maximum precipitation probability (%)
rain_sum	number	Total rain (mm)
snowfall_sum	number	Total snowfall (cm)
precipitation_hours	number	Hours with precipitation
uv_index_max	number	Maximum UV index
sunrise	string	Sunrise time with UTC offset (e.g. 2026-04-22T04:59+09:00)
sunset	string	Sunset time with UTC offset
daylight_hours	number	Total daylight hours
sunshine_hours	number	Total sunshine hours

Current Conditions Fields (forecast mode only)

Field	Type	Description
current_temperature	number	Current air temperature (°C)
current_apparent_temperature	number	Current feels-like temperature (°C)
current_humidity	number	Current relative humidity (%)
current_weather	string	Current weather label
current_cloud_cover	number	Current cloud cover (%)
current_pressure	number	Current atmospheric pressure (hPa)
current_visibility_km	number	Current visibility (km)
current_uv_index	number	Current UV index
current_wind_speed	number	Current wind speed (km/h)
current_wind_direction	number	Current wind direction (degrees)
current_wind_direction_label	string	Compass label for current wind direction
current_wind_gusts	number	Current wind gust speed (km/h)
current_is_day	boolean	Whether the observation is daytime
current_precipitation	number	Current precipitation amount (mm)
current_rain	number	Current rain amount (mm)
current_showers	number	Current shower amount (mm)
current_snowfall	number	Current snowfall (cm)

Air Quality & Array Fields

Field	Type	Description
air_quality_status	string	forecast, historical, or unavailable
air_quality_note	string	Plain-language note about air quality data availability
hourly_forecast	array	24 hourly weather rows for this day (time, temperature, wind, precipitation, etc.)
air_quality_current	object	Current AQI, pollutant concentrations, pollen, and UV values
air_quality_hourly	array	Hourly air quality rows for this day

Example Output

[
  {
    "processor": "https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi",
    "processed_at": "2026-04-22T04:08:06+00:00",
    "status": "success",
    "mode": "forecast",
    "start_date": null,
    "end_date": null,
    "location": "Tokyo, Japan",
    "country": "Japan",
    "region": "Tokyo",
    "city": null,
    "latitude": 35.67686,
    "longitude": 139.763895,
    "timezone": "Asia/Tokyo",
    "temperature_unit": "celsius",
    "wind_speed_unit": "kmh",
    "date": "2026-04-22",
    "weather": "Partly cloudy",
    "temperature_max": 21.5,
    "temperature_min": 8.2,
    "temperature_mean": 15.5,
    "apparent_temperature_max": 21.9,
    "apparent_temperature_min": 5.7,
    "wind_speed_max": 12.2,
    "wind_gusts_max": 52.2,
    "wind_direction_dominant": 200,
    "wind_direction_label": "S",
    "precipitation_sum": 0.0,
    "precipitation_probability_max": 3,
    "rain_sum": 0.0,
    "snowfall_sum": 0.0,
    "precipitation_hours": 0.0,
    "uv_index_max": 7.5,
    "sunrise": "2026-04-22T04:59+09:00",
    "sunset": "2026-04-22T18:19+09:00",
    "daylight_hours": 13.3,
    "sunshine_hours": 10.0,
    "current_temperature": 20.9,
    "current_apparent_temperature": 20.2,
    "current_humidity": 54,
    "current_weather": "Mainly clear",
    "current_cloud_cover": 45,
    "current_pressure": 1011.0,
    "current_visibility_km": 24.1,
    "current_uv_index": 6.5,
    "current_wind_speed": 11.5,
    "current_wind_direction": 178,
    "current_wind_direction_label": "S",
    "current_wind_gusts": 51.8,
    "current_is_day": true,
    "current_precipitation": 0.0,
    "current_rain": 0.0,
    "current_showers": 0.0,
    "current_snowfall": 0.0,
    "air_quality_status": "forecast",
    "air_quality_note": null,
    "hourly_forecast": [
      {
        "time": "2026-04-22T00:00+09:00",
        "weather": "Clear sky",
        "temperature": 11.9,
        "apparent_temperature": 9.1,
        "humidity": 44,
        "wind_speed": 4.4,
        "wind_gusts": 6.1,
        "wind_direction": 325,
        "wind_direction_label": "NW",
        "cloud_cover": 2,
        "precipitation": 0.0,
        "rain": 0.0,
        "pressure": 1016.7,
        "uv_index": 0.0,
        "is_day": false
      }
    ],
    "air_quality_current": {
      "time": "2026-04-22T13:00+09:00",
      "european_aqi": 60,
      "us_aqi": 69,
      "pm2_5": 12.8,
      "pm10": 17.7,
      "carbon_monoxide": 173.0,
      "carbon_dioxide": 454,
      "nitrogen_dioxide": 6.6,
      "sulphur_dioxide": 6.6,
      "ozone": 131.0,
      "uv_index": 4.2
    },
    "air_quality_hourly": [
      {
        "time": "2026-04-22T13:00+09:00",
        "european_aqi": 60,
        "us_aqi": 69,
        "pm2_5": 12.8,
        "pm10": 17.7
      }
    ]
  }
]

---

Integration Examples

Actor ID

Yn1ksjorQhCtBcLN6

🐍 Python Integration

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run_input = {
    "location": "Tokyo, Japan",
    "days": 3,
}

run = client.actor("Yn1ksjorQhCtBcLN6").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["date"], item["weather"], item["temperature_max"])

🟨 JavaScript Integration

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor("Yn1ksjorQhCtBcLN6").call({
  location: "Tokyo, Japan",
  days: 3,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(item.date, item.weather, item.temperature_max));

Ⓜ️ Make.com Integration

1. Login to Make.com
2. Add module "Run an Actor"
3. Enable Map toggle → paste Actor ID Yn1ksjorQhCtBcLN6
4. Click ⟳ Refresh to load the input schema
5. Configure location, days (or start_date / end_date)
6. Set "Run synchronously" to YES
7. Add module "Get Dataset Items" → select defaultDatasetId

🎱 N8N Integration

1. Add Apify → Run Actor and get dataset node
2. Actor → By ID → paste Yn1ksjorQhCtBcLN6
3. Configure input JSON with location and days parameters
4. Route dataset items to downstream nodes (Google Sheets, webhooks, AI agents)

🧾 JSON-LD Metadata

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Global Weather Scraper",
  "applicationCategory": "DeveloperApplication",
  "operatingSystem": "Web",
  "description": "Extracts structured daily weather datasets for any country, region, city, or town — forecast and historical date ranges with hourly breakdowns, air quality signals, and enriched location metadata.",
  "url": "https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi",
  "dateModified": "2026-04-22"
}

---

Pricing & Cost Calculator

Event	Price	Description
Actor Start	$0.02	Charged when the Actor starts. Billed per GB of memory allocated (minimum 1 event).
Result	$0.01	Charged per completed location weather extraction (one event per run).

Example Cost Calculation

Scenario	Result Items	Estimated Cost
Single location, 3-day forecast	3 day items	~$0.03
Single location, 7-day historical	7 day items	~$0.03
Single location, 30-day historical	30 day items	~$0.03

Pricing is per completed location lookup, not per day item. All day items for a single run count as one charged Weather Result event. The Actor Start event is billed once per run based on memory allocation.

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Use Cases & Applications

Geospatial Intelligence & Location Enrichment

Enrich address or property datasets with weather baseline signals — precipitation seasonality, temperature ranges, UV exposure, and air quality scores — for real estate valuation models, insurance underwriting, or site suitability analysis.

Climate Monitoring & Event Detection

Integrate the weather extraction API into scheduled pipelines to monitor climate conditions for specific regions over time. Correlate weather signals with supply chain events, agricultural yield data, or demand forecasting models.

AI Agent & LLM Workflows

Provide structured weather context to AI agents processing location-based queries, travel planning prompts, or environmental risk assessments. The consistent JSON schema integrates directly with LangChain tools, function-calling APIs, and MCP server pipelines.

Logistics & Operations Intelligence

Extract weather conditions for origin and destination nodes in routing systems. Integrate into SLA monitoring pipelines to flag weather risk flags — high wind, precipitation, visibility — aligned to specific operational dates.

Research & Academic Data Collection

Bulk-extract historical weather records for climate research, economic correlation studies, or social science datasets requiring localized weather variables at daily or hourly resolution.

Market Research & Competitive Intelligence

Correlate weather conditions with retail performance, foot traffic, and consumer behaviour signals. Extract seasonal weather baselines for geographic market segmentation and demand forecasting.

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FAQ

How do I extract weather for a small town not on major maps?

Enter the town name and country as a free-text string (e.g. Hakuba, Japan). The geocoder resolves the location from the full address hierarchy — if the place exists in OSM, it will return coordinates.

What is the output schema for historical vs. forecast mode?

Both modes return the same top-level daily schema. Forecast mode additionally populates all current_* fields. Historical mode omits current_* fields and returns hourly air quality rows in air_quality_hourly rather than a air_quality_current object.

Can I extract weather for entire regions or countries?

Yes. You can specify Bavaria, Germany or Ontario, Canada as the target location. The geocoder resolves to the centroid coordinate of the region, returning representative weather for that area.

How do I integrate this API into a scheduled monitoring pipeline?

Use the Apify API or SDK to trigger runs on a schedule with a fixed location and days: 1 for daily monitoring. Each run outputs a single day item containing all current-day weather and air quality signals.

What air quality variables are included in the output?

Each air_quality_current object contains: european_aqi, us_aqi, pm2_5, pm10, carbon_monoxide, carbon_dioxide, nitrogen_dioxide, sulphur_dioxide, ozone, ammonia, methane, dust, uv_index, alder_pollen, birch_pollen, grass_pollen, mugwort_pollen, ragweed_pollen.

What is the maximum historical date range per extraction?

Up to 366 days per run. For longer ranges, split into multiple runs with adjacent date windows.

How does this compare to commercial weather APIs like Bright Data or Meteomatics?

This actor delivers structured, per-day enriched records via Apify's dataset API with zero authentication setup, direct JSON output, and a consistent schema across both forecast and historical modes. No API key management or rate limit negotiation is required.

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SEO Keywords & Search Terms

Primary Keywords

• global weather scraper API
• weather extraction pipeline
• structured weather dataset API
• weather data extraction JSON

Long-Tail Keywords

• extract daily weather data by city name API
• historical weather dataset by location API
• weather forecast API integration pipeline
• structured weather signals for AI agents

Technical Keywords

• weather API Apify actor
• daily weather JSON extraction
• open-meteo structured dataset
• weather data enrichment pipeline
• air quality signals extraction API

Industry Keywords

• weather data for logistics routing
• climate signals real estate enrichment
• weather API for AI agent workflows
• location weather extraction developer tool

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Trust & Certifications

• Data Source: Open-Meteo — open-source weather API with 99.9% uptime SLA and global coverage
• Geocoding: Nominatim / OpenStreetMap — authoritative open geodata with global address resolution
• Output Format: Validated structured JSON schema with consistent field types across all extraction modes
• Infrastructure: Runs on Apify's managed cloud platform with automatic scaling and monitoring

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Legal & Compliance

Data Rights: This actor extracts publicly available weather and air quality data from open APIs. The extracted data represents environmental observations and is not subject to copyright restrictions.

Privacy Compliance (GDPR/CCPA): No personal data is collected or processed. Location inputs are geocoded to geographic coordinates only. No user identification or tracking is performed.

Platform Terms of Service: Data is sourced from Open-Meteo (CC BY 4.0 license) and Nominatim/OpenStreetMap (ODbL license). Commercial use is permitted under these licenses. Always verify compliance with the terms of your downstream use case.

Accuracy Disclaimer: Weather forecast data is provided for informational and integration purposes. Do not use extracted data as the sole input for safety-critical, aviation, medical, or emergency decisions. Verify conditions locally for high-stakes operations.

Enterprise Support: Contact t.me/AiAgentApi for enterprise SLA, custom extraction configurations, or bulk data delivery arrangements.

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Support & Community

• 💬 Actor Support: t.me/AiAgentApi
• 👥 Join Group: t.me/Apify_Actor
• 🔌 All AgentX APIs: apify.com/agentx
• 📖 Actor Page: apify.com/agentx/global-weather-scraper

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Last Updated: April 22, 2026