Automate Daily Weather Data Collection with n8n and Airtable

Opening Problem Statement

Meet Sarah, a local environmental researcher who needs to track weather changes daily for her climate study in Ahmedabad, India. Every morning, she manually checks the weather from various websites, notes down temperature, humidity, wind speed, and time zone data in a spreadsheet. This process is time-consuming, taking her about 15 minutes daily, and mistakes creep in due to copy-pasting errors and inconsistent data formats. Sarah often finds herself rechecking data because she lacks an automated, reliable way to log weather information systematically.

Sarah’s problem is not uncommon among researchers, meteorologists, and hobbyists who need consistent historical weather data. Without automation, the task wastes time, introduces human error, and delays insights crucial for decision-making.

What This Automation Does

This Automated Daily Weather Data Fetcher and Storage workflow built with n8n solves Sarah’s problem by:

• Running automatically every day at 10 AM India Standard Time without manual intervention.
• Fetching live weather data such as temperature, humidity, wind speed, and timezone using the OpenWeatherMap API for a fixed location (lat 23.0059, lon 72.5547).
• Storing this information directly into an Airtable base for organized, accessible, and historical data logging.
• Ensuring data consistency and reducing manual errors by automating API interaction and data entry.
• Making the weather history easy to visualize or export for further analysis.

By automating these steps, Sarah saves about 1.75 hours weekly and gains confidence in her data quality and availability for her study.

Prerequisites ⚙️

Before you start building this workflow, ensure you have the following ready:

• n8n account with access to create workflows (cloud or self-hosted). Self-hosting is available for advanced users who want more control; see Hostinger guide.
• OpenWeatherMap API account to get your API key (used in the HTTP Request node for authentication). 📡
• Airtable account with a base and table ready to store weather data (with columns like Temp, Humidity, Wind Speed, Location, and Timezone). 📊
• API credentials set up in n8n for OpenWeatherMap (HTTP Query Authentication) and Airtable (Airtable API token). 🔑

Step-by-Step Guide to Building This Workflow

1. Set Up the Schedule Trigger Node
   Navigate to Nodes → Add Node → Trigger → Schedule Trigger.
   Configure it: set the trigger to run daily at 10:00 AM (triggerAtHour: 10).
   You should see the cron job scheduling reflecting the daily trigger.
   Outcome: This node fires every day at the specified hour automatically.
   Common mistake: forgetting to set the correct time zone or interval, causing no trigger or multiple triggers per day.
2. Add the HTTP Request Node to Fetch Weather Data
   Add a new node: Nodes → Add Node → Core → HTTP Request.
   Set the URL to https://api.openweathermap.org/data/2.5/weather?lat=23.0059&lon=72.5547.
   Use the HTTP Query Authentication credentials for your OpenWeatherMap API key.
   Add URL query parameter units=metric to get temperature in Celsius.
   You’ll see JSON output with weather details when you test this node.
   Expected: Node outputs current weather data including main.temp (temperature), main.humidity, wind.speed, name (location), and timezone.
   Common mistake: incorrect API key or missing query parameter causing authorization error or wrong data format.
3. Link HTTP Request Output to Airtable Node
   Add an Airtable node from Nodes → Add Node → Integrations → Airtable.
   Choose your Airtable base and table where weather data will be stored.
   Map the incoming JSON data fields to Airtable columns:
   – Temp = {{ $json.main.temp }}
   – Humidity = {{ $json.main.humidity }}
   – Wind Speed = {{ $json.wind.speed }}
   – Location = {{ $json.name }}
   – Timezone = {{ $json.timezone }}
   Save the node and connect the HTTP Request node’s output to this Airtable node.
   Expected: On execution, the weather entry is created in Airtable.
   Common mistake: incorrect mapping of fields or using fields that don’t exist in Airtable table/schema.
4. Add Informative Sticky Notes for Documentation
   Add sticky notes from Nodes → Add Node → Core → Sticky Note.
   Use one note for the workflow’s title and one for summarizing what the workflow does.
   This helps future you and collaborators understand the workflow purpose quickly.
   Common mistake: skipping documentation making future troubleshooting harder.
5. Test the Entire Workflow
   Manually trigger the Schedule node or use the ‘Execute Workflow’ button.
   Ensure the workflow runs without errors and you see a new weather record in Airtable.
   Expected: Check Airtable to confirm new rows with correct weather data.
   If errors occur, check the error messages in each node.
   Common mistake: skipping test runs before automation.
6. Activate Your Workflow
   Turn the workflow toggle from inactive to active.
   This enables the daily schedule to trigger automatically.
   Confirm the time zone settings in n8n matches your local time to avoid scheduling issues.
   Common mistake: forgetting to activate the workflow leaving it idle.

Customizations ✏️

• Change Location Coordinates: In the HTTP Request node, update the lat and lon parameters to fetch weather data for your city or region.
• Add More Weather Metrics: Modify the Airtable node’s column mappings to include other weather details like pressure (main.pressure) or visibility (visibility).
• Adjust Schedule Time: Change the trigger hour in the Schedule Trigger node to collect data at any time suitable for your requirements.
• Use Multiple Locations: Clone the HTTP Request and Airtable nodes and set different coordinates to log weather data for several cities in one workflow.

Troubleshooting 🔧

• Problem: “HTTP Request node returns 401 Unauthorized error”
  Cause: Incorrect or expired OpenWeatherMap API key.
  Solution: Verify the API key in your HTTP Query Authentication credentials. Update if expired.
• Problem: “No data appearing in Airtable after workflow runs”
  Cause: Incorrect field mapping or missing Airtable API token.
  Solution: Double-check the column mapping in the Airtable node and ensure credentials are correctly added.
• Problem: “Scheduled trigger isn’t firing at the set time”
  Cause: Time zone mismatch or inactive workflow.
  Solution: Confirm the workflow is active and check if n8n is set to the correct time zone.

Pre-Production Checklist ✅

• Ensure OpenWeatherMap API credentials are correct and test the HTTP Request node independently.
• Verify Airtable base schema matches the mapped fields in the workflow.
• Run manual test triggers to confirm proper execution before going live.
• Back up your Airtable data regularly to avoid accidental loss.

Deployment Guide

Activate the workflow by toggling the active switch in n8n. The Schedule Trigger will now run it every day at 10 AM IST.
Monitor execution logs in n8n’s executions page to ensure smooth performance. Set up alerting in n8n or via your own monitoring tools if you want to be notified on errors.
This workflow is lightweight and suitable for running on n8n cloud or self-hosted instances.

FAQs

• Can I use a different weather API?
  Yes, just update the HTTP Request node URL and authentication accordingly.
• Does this workflow consume API credits?
  Yes, depending on your OpenWeatherMap plan, API calls count towards your quota.
• Is my weather data safe in Airtable?
  Data is as safe as Airtable’s security policies. Use secure API tokens and restrict access.
• Can I add alerts based on weather conditions?
  Yes, integrate messaging or notification nodes after Airtable to send alerts based on data.

Conclusion

By following this guide, you’ve built a robust automation that fetches daily weather data accurately and stores it in Airtable without manual effort. This saves you hours every week and gives you a reliable historical weather dataset to analyze or report on.
Next, you might want to enhance this workflow by adding multiple locations, integrating weather alerts, or visualizing data using dashboards or Google Sheets.
Keep automating and enjoy stress-free data collection!