How to Develop a Power BI Dashboard with WeatherAPI

Power BI is a fantastic tool for visualizing data — and you can easily integrate live weather data into your reports too. In this blog, we’ll walk you through the process of building a real-time weather dashboard powered by WeatherAPI, and then we’ll add some Air Quality Index (AQI) indicators for a richer dashboard experience.

🎯 Why WeatherAPI?

WeatherAPI.com is a simple and powerful service that returns live, historical, and forecast weather data — perfect for Power BI. The data is available in JSON format, making it easy to process and transform.

🛠️ Prerequisites

  • ✅ A free or paid account on WeatherAPI.com

  • ✅ Power BI Desktop installed

  • ✅ Basic Power BI data model knowledge

🔑 Step 1: Get Your WeatherAPI Key

  1. Sign up at WeatherAPI.com, then copy your API key.

  2. You’ll use this key to authenticate API calls.

🌐 Step 2: Build the API URL

For current weather data, use:

bash

https://api.weatherapi.com/v1/current.json?key=YOUR_API_KEY&q=CITY_NAME

🧠 Step 3: Connect Power BI to WeatherAPI

  1. Open Power BI Desktop.

  2. Click Get Data → Web.

  3. Enter your WeatherAPI URL.

  4. Click OK.

🧹 Step 4: Transform the Data

Power BI will show a preview in the Power Query Editor:

  • Expand the current record.

  • Expand sub-records like condition or air_quality.

  • Rename columns for clarity.

  • Click Close & Apply.

📊 Step 5: Build Your Dashboard

Add:

  • ✅ Cards for temperature, humidity, etc.

  • ✅ Gauges for wind speed.

  • ✅ Charts for daily variations.

And you can also set up filters/slicers for different cities.

🎨 Step 6: Styling & Interactivity

  • Insert icons representing the current weather.

  • Plot the map visual with city locations.

  • Allow your users to select cities dynamically.

⚡ Step 7: Adding AQI Indicators with Reusable Measures

You can easily incorporate Air Quality Index (AQI) data into your report using the current.air_quality part of the WeatherAPI response.

Here’s a quick Power BI DAX pattern you can copy-paste and adapt to your needs:

🎨 Generic Template for AQI Color:

DAX

AQI Color TEMPLATE =
VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.COLUMN_NAME]),0)
RETURN
SWITCH(
TRUE(),
AQI <= 50, "#43d946",
AQI <= 100, "#fff570",
AQI <= 150, "#ff9800",
AQI <= 200, "#d99343",
AQI <= 300, "#ff5b0f",
"#d95243"
)

💡 Simply copy this measure and replace COLUMN_NAME with the air quality column you want to check — for example:

  • pm2_5

  • co

  • no2

🎨 Generic Template for AQI Suggestion:

DAX
AQI Suggestion TEMPLATE =
VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.COLUMN_NAME]),0)
RETURN
SWITCH(
TRUE(),
AQI <= 50, "Air is clean and healthy",
AQI <= 100, "Acceptable air quality, stay active",
AQI <= 150, "Sensitive groups should reduce outdoor time",
AQI <= 200, "Limit prolonged outdoor exertion",
AQI <= 300, "Avoid outdoor activity if possible",
"Stay indoors, wear a mask if outside"
)

🎨 Generic Template for AQI Status:

DAX
AQI Status TEMPLATE =
VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.COLUMN_NAME]),0)
RETURN
SWITCH(
TRUE(),
AQI <= 50, "Good",
AQI <= 100, "Moderate",
AQI <= 150, "Unhealthy for Sensitive",
AQI <= 200, "Unhealthy",
AQI <= 300, "Very Unhealthy",
"Hazardous"
)

✅ Again, just replace COLUMN_NAME with the pollutant of interest.

All Power BI Measures:

Curr_Temp_C = SUM('Current'[current.temp_c]) & " °C"

Curr_Temp_f = SUM('Current'[current.temp_f]) & " °F"

last_update = "Last Updated, " & FORMAT(FIRSTNONBLANK('Current'[current.last_updated], ""), "dd mmm")

For_Temp_C = AVERAGE(Forcast_Day[forecast.forecastday.day.avgtemp_c]) & " °C"

AQI_Status_Text = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.pm10]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "Good",
        AQI <= 100, "Moderate",
        AQI <= 150, "Unhealthy for Sensitive",
        AQI <= 200, "Unhealthy",
        AQI <= 300, "Very Unhealthy",
        "Hazardous"
    )

AQI_Color_PM10 = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.pm10]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",   -- Good (Green)
        AQI <= 100, "#fff570",  -- Moderate (Yellow)
        AQI <= 150, "#ff9800",  -- Poor (Orange)
        AQI <= 200, "#d99343",  -- Unhealthy (Red)
        AQI <= 300, "#ff5b0f",  -- Severe (Purple)
        "#d95243"               -- Hazardous (Dark Maroon)
    )

AQI_Suggestion = 
    VAR AQI = SELECTEDVALUE('Current'[current.air_quality.pm10])
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "Air is clean and healthy",
        AQI <= 100, "Acceptable air quality, stay active",
        AQI <= 150, "Sensitive groups should reduce outdoor time",
        AQI <= 200, "Limit prolonged outdoor exertion",
        AQI <= 300, "Avoid outdoor activity if possible",
        "Stay indoors, wear mask if outside"
    )

Left_Rain_Chance = 100 - SUM(Forcast_Day[forecast.forecastday.day.daily_chance_of_rain])

avg_day_Temp_c = SUM(Forcast_Day[forecast.forecastday.day.avgtemp_c])

avg_hour_Temp_c = SUM(Forcast_Hour[forecast.forecastday.hour.temp_c])

PM10_Left = 300 - SUM('Current'[current.air_quality.pm10])

AQI_Color_CO = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.co]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",
        AQI <= 100, "#fff570",
        AQI <= 150, "#ff9800",
        AQI <= 200, "#d99343",
        AQI <= 300, "#ff5b0f",
        "#d95243"
    )

AQI_Color_SO2 = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.so2]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",
        AQI <= 100, "#fff570",
        AQI <= 150, "#ff9800",
        AQI <= 200, "#d99343",
        AQI <= 300, "#ff5b0f",
        "#d95243"
    )

AQI_Color_O3 = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.o3]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",
        AQI <= 100, "#fff570",
        AQI <= 150, "#ff9800",
        AQI <= 200, "#d99343",
        AQI <= 300, "#ff5b0f",
        "#d95243"
    )

AQI_Color_NO2 = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.no2]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",
        AQI <= 100, "#fff570",
        AQI <= 150, "#ff9800",
        AQI <= 200, "#d99343",
        AQI <= 300, "#ff5b0f",
        "#d95243"
    )

AQI_Color_PM2_5 = 
    VAR AQI = ROUND(SELECTEDVALUE('Current'[current.air_quality.pm2_5]),0)
    RETURN SWITCH(
        TRUE(),
        AQI <= 50, "#43d946",
        AQI <= 100, "#fff570",
        AQI <= 150, "#ff9800",
        AQI <= 200, "#d99343",
        AQI <= 300, "#ff5b0f",
        "#d95243"
    )

Humidity = SUM('Current'[current.humidity]) & " %"

Wind_Speed = SUM('Current'[current.wind_kph]) & " Kph"

Visibility = SUM('Current'[current.vis_km]) & " KM"

Pressure = SUM('Current'[current.pressure_mb]) & " mm"

UV_Index = SUM('Current'[current.uv])

Precipitation = SUM('Current'[current.precip_mm]) & " mm"

💡 Quick Tip:
Keep these generic DAX measures as templates — so when you want to add AQI visualizations for new pollutants like so2, no2, or o3, you only need to copy-paste and tweak one column name.

🎉 Conclusion

By integrating WeatherAPI into Power BI, you can create a dynamic weather dashboard with live data, then enrich it with custom AQI visualizations — all in a few easy steps. With these reusable DAX measures, your dashboard stays scalable, maintainable, and easy to enhance as your requirements grow.

Happy dashboarding! 🎨

Leave a Reply

Type to search

Shopping cart

0
image/svg+xml

No products in the cart.

Continue Shopping