Historical Weather Data for Data Science Models Improvement

“Those who do not live in the past cannot live in the future.” 

— Lord Acton (19th-century historian)

This is quite relevant in the present context, as presently, AI models are predicting the future with the help of past data.

However, the quality of those forecasts is 100% influenced by the size and quality of the data fed to the model. 

The numerical data is crucial, but it just answers the “whats”, not “whys” or “hows”. Data scientists need to have answers to all the tangential questions around a variable to get the big picture and design accurate predictive models.

Weather is an all-encompassing variable that affects everything: from humans to businesses, from performances to behaviours, from spending to logistics. It truly defines our economy.

There’s a lot to cover for most data scientists considering AI models, as it’s still a considerably newer field. 

In this article, I’ll list some aspects to keep in mind regarding climate data integration in your prediction models. It doesn’t matter the application or field you might be working in as a data scientist.

KEY TAKEAWAYS

• Weather impacts almost every economic activity on earth, so it becomes really important to integrate its data into every business model.
• It allows differentiating between operational errors and environmental calamities when abrupt performance dips happen.
• The use case is integral to agriculture and yield forecasting.
• While integrating, be aware that business seasonality and weather seasonality are two different things.

Streamlined Feature Engineering for Retail Demand 

Both weather and retail have seasonality ingrained into them. Isn’t this a great thing for easy integration into the predictive models? No, because the seasonality of both is not the same. They might overlap or maybe not; they may also have delayed effects.

A model that estimates a jump in outdoor furniture sales in May just because ‘it’s spring’ can be fragile; it loses its hold in an unseasonably wet month. 

Instead of filling in a model with categorical months, you replace daily weather with continuous data features. This creates a model able to recognize the real power driver(s) of demand. 

With an integrated Visual Crossing weather data API, developers will be able to extract accurate historical records at their store coordinates. Rather than a random ‘summer’ term, your Python or R script can draw on real-time maximum daily temperatures and rainfall levels. 

This can help your model learn that there is a relationship between ‘heat index’ and consumer foot traffic. For a data scientist, this means going from a model that knows when people are buying to one that knows why they are buying.

Anomaly Detection in Supply Chain and Logistics 

Operational efficiency and performance can go down abruptly for any reason in logistics. And all of them are noted down as operational failures. If a shipment gets delayed, there’s no way to know whether it’s due to a bottleneck in the warehouse or a severe storm that hit only that region.

Without weather and environment data, a predictive model on logistics is bound to flag false positives.

Then overlay historical wind speeds, visibility details, and snowfall numbers onto your logistics timeline to train your model to identify environmental noise.

This helps you clean up external disturbances and concentrate on the process you can actually manage, through which you get more effective. 

Take, for example, a logistics company that has performed a comparative analysis against different hubs. A warehouse in Chicago that might experience longer transit times each month in January than a hub in Phoenix. Factoring in historical weather data, a data scientist will be able to ensure that Chicago delays are 15% more likely to be weather-associated lake-effect events rather than personnel inefficiencies.

This puts leadership in all the necessary contexts to ensure operational fairness on a state level.

SURPRISING STAT
As per research, AI models trained on historical data perform more accurately than traditional NWP models in 90% of the cases.

Energy Load Prediction and Utility Management 

Energy is as elemental as weather. Both are closely tied to each other. It’s also a significant part of the business economy, so designing energy prediction models around climate information seems to be a no-brainer.

Utility companies must now predict ‘Peak Load’ (a.k.a. Peak Demand), the moment when demand for electricity reaches its maximum, to avoid failure of the grid.

Data scientists apply ‘Degree Days’ – more precisely ‘Heating Degree Days’ (HDD) and ‘Cooling Degree Days’ (CDD) – to measure the energy needed to keep a building comfortable compared to outside temperature. 

The climate baseline can be built from high-resolution, historical weather details. 

This way, not only will you be able to make estimates based on whether energy use will rise or not, but you’re also able to predict precisely how many megawatts the demand will increase each step the temperature rises. 

This precision is important for modern smart-grid utilization and carbon reduction.

Precision Agriculture and Yield Forecasting 

Not many people know, but statistical modeling has its origins in agriculture. Today, it has become ‘Precision Ag’ where data scientists assist farmers to determine precisely when to plant or harvest. Average info isn’t enough to create a good crop yield model; you need specific ‘Growing Degree Units’ (GDU) over the seasons. 

Historical weather data is very helpful when you want to identify some particular weather trends that traditional models perhaps won’t (or can’t) catch (e.g., flash droughts, unseasonable frosts, etc.)

All these accurate details can actually create multi-dimensional models that track crop health by analyzing and predicting various conditions. This provides you with accurate preventative countermeasures before any damage is done.

More Time-Series Prediction Against Extreme Events 

Time-series forecasting forms the basics of data science curricula. However, too many models fail to capture ‘shocks’, spikes, or dips in the future that don’t map into the time-series trend. 

And frequently, those shocks are extreme weather events, such as hurricanes or heatwaves. When you add historical climate information as an ‘Exogenous Variable’ in an ARIMA or Prophet model, you give the algorithm the ability to ‘explain away’ those shocks. 

This really makes your long-term predictions a lot stronger. Whether it’s forecasted daily visitation to a theme park or an emergency services call, weather is not a footnote; it is the context in which your data operates. 

By training your model on historical weather anomalies, you enable it to tackle future volatility very well.

Conclusion

To become a competent data scientist, you have to get the “View of the Big Picture.” Raw, underprocessed, or wrongly taken details rarely solve complex problems. 

You’re the one who decides to enable your models to harness historical environmental data by leveraging high-end APIs. This way, you give them the context they require to convert raw, meaningless numbers into actionable recommendations that help you tremendously.

FAQs

How much has weather forecasting improved?

5-day weather forecasting is as accurate today as 1-day forecasting was in the 1980s.

How is AI improving weather forecasting?

AI predicts faster and more accurately by processing data sets in mintues which took hours earlier.

Do revolutionary AI tools rescue old weather info to improve climate models?

Yes. AI is improving climate models using age-old, even handwritten, weather records.