Weather affects nearly every organization in some way. A well-designed weather dashboard turns raw weather data into information that people can understand quickly and use to make better decisions.
A weather dashboard might show the latest conditions at a job site, the forecast for upcoming operations, historical weather alongside business results, or even the forecasts that customers and employees were seeing when past decisions were made.
The most effective dashboards don’t try to display every available weather measurement. Instead, they combine the weather data that matters most with clear visualizations such as KPIs, charts, maps, tables, and alerts.
In this article, we’ll look at the main types of weather data available for a dashboard, how to retrieve that data, and some practical ways to present it.
Why build a weather dashboard?
Weather dashboards provide a fast way to understand how weather has affected past activities, what is happening now, and what conditions are expected in the future.
Different organizations will naturally focus on different weather variables. For example:
- Agriculture may track temperature, precipitation, soil conditions, growing degree days, and frost risk.
- Construction teams may focus on precipitation, wind, heat, cold, and severe weather.
- Energy organizations may monitor wind speed, solar radiation, temperature, and demand-related weather conditions.
- Retailers may compare historical sales with temperature, rain, snow, or forecasts.
- Transportation and logistics teams may monitor wind, precipitation, visibility, snow, and hazardous conditions.
- Outdoor venues and travel businesses may use both forecasts and historical weather to support staffing, scheduling, and customer planning.
Some dashboards focus entirely on weather. Others combine weather with business data such as sales, traffic, energy consumption, staffing levels, claims, equipment performance, or customer demand.
The key is to start with the decisions the dashboard is intended to support and then choose the weather information that helps users make those decisions.
Types of weather data
A weather dashboard can include several different types of weather data. Four of the most useful are:
- Weather forecasts
- Historical weather observations
- Climate statistics and weather normals
- Historical weather forecasts
Each answers a different question.
Weather forecast data
Weather forecasts describe the conditions expected over the coming hours and days. They form the basis of many operational weather dashboards because they help users plan upcoming activities.
A forecast dashboard might help answer questions such as:
Will rain affect tomorrow’s construction schedule?
Will temperatures become cold enough to create a frost risk?
Will wind speeds exceed safe operating limits?
Should staffing or inventory be adjusted for the expected weather?
Forecast dashboards can display daily or hourly data depending on the use case. Common forecast elements include temperature, precipitation, probability of precipitation, snow, wind speed and direction, humidity, cloud cover, visibility, solar radiation, and weather conditions.
Forecast information becomes even more useful when combined with historical or climate data. For example, showing an expected high temperature of 95°F is useful. Showing that the normal high is 82°F immediately gives the user additional context.
Historical weather observation data
Historical weather data describes conditions that actually occurred in the past.
Historical observations are useful when you need to understand how weather influenced previous activities or outcomes. For example:
How did unusually cold weather affect attendance at an outdoor venue?
Did rainfall reduce construction productivity?
How closely does product demand correlate with temperature?
How much solar radiation was available during periods of low energy production?
Historical weather dashboards are especially powerful when weather data is combined with internal business data. This makes it possible to identify relationships between weather and sales, demand, equipment performance, staffing, customer activity, or other operational metrics.
Visual Crossing Weather provides historical weather data for locations around the world, allowing weather history to be analyzed for cities, addresses, ZIP or postal codes, and latitude/longitude coordinates.
Climate statistics and weather normals
Sometimes knowing what happened on a particular day isn’t enough. You also need to understand what is typical for that location and time of year.
Climate statistics and historical weather summaries can help answer questions such as:
What is the typical high temperature for this date?
How frequently does it rain during this time of year?
What range of temperatures has occurred historically?
How unusual is the current forecast?
Historical averages, ranges, and extremes can provide useful context for both forecasts and recent observations.
For example, a dashboard could display the forecast high temperature alongside the historical average and observed historical range. Users can then immediately see whether the predicted weather is normal or unusual.
Climate statistics are also useful for longer-term planning when a conventional short-range forecast is not yet available, such as evaluating likely conditions for a future event or estimating seasonal operating conditions.
Historical weather forecast data
Historical weather forecasts answer a different question from traditional historical observations:
What did the weather forecast say at a particular point in the past?
This distinction can be extremely important.
People and organizations make decisions based on the weather they expect, not simply on the weather that eventually occurs. A forecast for heavy rain may cause customers to cancel reservations, crews to reschedule work, or an organization to increase staffing even if the rain never arrives.
This is sometimes called the forecast effect.
For example, imagine an outdoor attraction where attendance was unusually low on a Saturday that ultimately turned out to be sunny. Historical observations alone may suggest that weather had nothing to do with the poor attendance.
But suppose the forecast issued on Tuesday predicted an 80% chance of rain for Saturday. Customers may have made other plans several days in advance. By the time the forecast improved, those decisions had already been made.
A historical forecast dashboard can help reveal that relationship.
Historical forecast data is useful for:
- Comparing predicted weather with actual observed conditions
- Understanding the weather information available when past decisions were made
- Analyzing customer behavior influenced by forecasts
- Reviewing operational planning and staffing decisions
- Studying forecast accuracy and how forecasts changed as an event approached
- Recreating weather inputs used by historical business models or automated systems
Visual Crossing provides access to historical forecasts through the Historical Forecast API and the Timeline Weather API.
For more information, see Historical Forecast Data and How to query weather forecasts from the past.
Choosing weather data elements
Within each category of weather data are many individual weather elements, including:
- Temperature and feels-like temperature
- Rainfall and snowfall
- Probability of precipitation
- Wind speed, gust, and direction
- Humidity and dew point
- Atmospheric pressure
- Cloud cover
- Visibility
- Solar radiation and solar energy
- UV index
- Severe weather alerts
- Agricultural and energy-related measurements
See the Weather Data Documentation for the available fields and definitions.
A good dashboard generally should not display every available element.
Instead, identify the measurements that directly affect the user’s decisions. A logistics dashboard and an agriculture dashboard may both use the same underlying Weather API while presenting very different information.
Limiting the dashboard to meaningful measurements makes important conditions and trends easier to identify.
Retrieving weather data
Once you know what information your dashboard needs, the next step is retrieving the weather data.
There are two common approaches: downloading weather data files and retrieving data dynamically through a Weather API.
Downloading weather data files
CSV is one of the simplest formats for moving weather data into spreadsheets, databases, and business intelligence systems.
A daily weather data file might look like this:
datetime,tempmax,tempmin,precip
2021-09-28,78.8,58,0
2021-09-29,71,56,0
2021-09-30,70.1,52.1,0
2021-10-01,69,49.9,0
2021-10-02,72.9,53,0.2
2021-10-03,74,58,0.23CSV data can be imported into applications such as Microsoft Excel and many database, analytics, and business intelligence platforms.
Downloading weather data is often the easiest approach for historical analysis or one-time projects.
The main limitation is that a downloaded file represents a snapshot of the data at the time it was created. If your dashboard needs continually updated current conditions or forecasts, you will normally want to retrieve the weather automatically.
Using a Weather API for dynamic weather data
A Weather API allows a dashboard or application to request updated weather data automatically.
For example, a dashboard can retrieve a new forecast periodically, update current conditions, or request historical data dynamically when the user selects a different location or date range.
The Visual Crossing Weather API provides current conditions, weather forecasts, historical weather, and other weather datasets through a consistent interface.
A simple Timeline Weather API request for London might look like:
https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/London,UK?&key=YOUR_API_KEY&contentType=csv&include=days&elements=datetime,tempmax,tempmin,precipAdditional parameters can control the unit system, dates, weather elements, output format, and other query options.
Weather data can be returned in formats including CSV and JSON.
CSV or JSON?
CSV is ideal for applications that work naturally with rows and columns, including spreadsheets, databases, and many analytics tools.
JSON is particularly useful for software applications because it can represent more complex and hierarchical data.
For example, a single JSON response can contain:
- Information about the requested location
- Current conditions
- Daily weather data
- Hourly weather data
- Weather alerts
- Additional metadata
This makes JSON a convenient choice for web applications and other software that needs multiple types of information from the same weather request.
If your dashboard platform supports JSON directly, it is often the most flexible format for a dynamic weather dashboard.
Get started with the Weather API
You can sign up for a free Visual Crossing Weather account to receive an API key and begin making Weather API requests.
You can also use the Weather Data Query Builder to explore weather data interactively and create queries without writing code.
Weather data aggregation
Weather dashboards often summarize hourly data into daily, weekly, or other reporting periods.
The correct aggregation depends on the weather element.
For example:
Totals and sums are commonly appropriate for:
- Rainfall
- Snowfall
- Solar energy
- Growing degree days and similar accumulated measurements
Minimums, maximums, or averages are more appropriate for:
- Temperature
- Feels-like temperature
- Humidity
- Pressure
- Wind speed
Other measurements require additional thought. For example, maximum wind gust may be more useful for operational risk than average wind speed.
The aggregation method should reflect the question the dashboard is intended to answer rather than simply applying the same calculation to every weather field.
Weather dashboard presentation elements
Once the data is available, the goal is to make it understandable at a glance.
The best visualization depends on the information being communicated, but several techniques work particularly well with weather data.
KPIs
A KPI, or Key Performance Indicator, presents a single important value prominently.
Weather KPIs might include:
- Current temperature
- Today’s expected high
- Chance of precipitation
- Rainfall total
- Maximum wind gust
- Snow accumulation
- Solar energy
- Number of hours above or below an operational threshold
KPIs are most useful when limited to the measurements users genuinely need to see immediately.
Context can make a KPI much more valuable. Instead of simply displaying:
Forecast high: 88°F
a dashboard could show:
Forecast high: 88°F
Normal high: 84°F
This allows users to immediately understand both the value and its significance.
KPIs can also show changes rather than absolute values. This can be useful to users whose activities are affected by changes in weather.
Line and bar charts
Charts are useful for showing how weather changes over time.
Line charts work particularly well for continuous values such as hourly temperature, humidity, or wind speed.
Bar charts can effectively communicate totals such as daily precipitation, snowfall, or solar energy.
Charts can also compare different types of weather information. For example, a daily temperature chart can show:
- Forecast or observed minimum and maximum temperatures
- Normal temperature range
- Historical extremes
The user can then see not only how temperatures are changing but also how unusual those temperatures are.
Tables and grids
Tables work well when exact values matter or when users need to compare many weather elements across several locations or time periods.
A grid might show locations as rows and upcoming forecast days as columns, with conditional formatting used to highlight potentially important conditions.
Tables are particularly useful in operational dashboards where users may need to review many sites quickly.
Maps
Maps are a natural visualization when weather conditions need to be understood across multiple geographic locations.
A dashboard can combine location markers with values such as temperature, precipitation, wind, alerts, or risk status.
Maps are especially useful for organizations managing distributed assets, stores, facilities, job sites, transportation routes, or other geographically dispersed operations.
Thresholds, colors, and alerts
Weather becomes operationally important when it crosses a meaningful threshold.
For example:
- Wind above 30 mph
- Temperature below 32°F
- Heat index above 100°F
- More than one inch of expected rainfall
- Thunderstorms during business hours
Rather than requiring users to interpret every weather value manually, a dashboard can visually highlight these conditions using icons, colors, status indicators, or alerts.
The thresholds should be based on the needs of the organization rather than arbitrary weather categories.
Combine weather with business data
One of the most valuable uses of a weather dashboard is combining weather with other data.
Historical weather can be compared with sales, energy demand, insurance claims, attendance, transportation volume, equipment performance, or almost any other time- and location-based dataset.
Current conditions and forecasts can then be incorporated into the same analysis to help predict what may happen next.
Historical forecasts add another dimension by showing what weather was expected when earlier decisions were made.
Together, these datasets can help answer three important questions:
What actually happened?
Use historical weather observations.
What did people expect to happen?
Use historical weather forecasts.
What is likely to happen next?
Use current conditions and weather forecasts.
That combination can turn a basic weather display into a much more useful decision-support tool.
Putting it all together
A great weather dashboard starts with the decisions users need to make.
Identify the weather conditions that affect those decisions, choose the appropriate current, forecast, historical, historical forecast, or climate data, and present the information using visualizations that make significant conditions easy to recognize.
Visual Crossing Weather provides current conditions, forecasts, historical observations, historical forecasts, climate statistics, and specialized weather data that can be incorporated into dashboards, applications, spreadsheets, databases, and business intelligence systems.
To start building your own weather dashboard, sign up for a free Weather API account or explore the data using the Weather Data Query Builder.
Questions or need help?
For immediate help, try our AI Support Assistant, which can answer questions about Visual Crossing Weather data, APIs, documentation, and common technical issues. You can also post questions on our actively monitored support forum or contact our Support Team directly.