How data science can enhance weather forecasting

As anyone who has carried an umbrella under the sunniest sky possible knows, weather forecasts are far from being infallible. They can’t offer 100% accuracy about climate conditions and they are infamous for being incorrect even when dealing with the short term. Yet, even though most people see them more like a possibility rather than an informed prediction, forecasts have become increasingly precise in the last years.

There’s a powerful reason for that – the use of data science to enhance the current weather forecasting models. It’s true that these models have always relied on data collection and analysis. But the technological advancements brought by Python, R, and Java development companies in data science can usher in a new era in weather forecasting. Let’s see that in further detail – but first, let’s see a little more about weather forecasting.


How modern weather forecasting works

Though we all like to blame the weather anchors that share the forecast on the news for how wrong they can be, the process of predicting the weather is extremely complicated. This process is based on two main pillars. On one hand, there’s a vast amount of data coming from a lot of sources. On the other, there are weather models and analyses that try to make sense of all the incoming information.

There are a wide variety of devices and technologies gathering information about the weather. From basic instruments like thermometers, barometers, anemometers to sophisticated equipment ranging from weather balloons and radar systems to environmental and deep-space satellites, there’s a lot of data going around.

It’s fairly evident that data availability isn’t a problem when it comes to forecasts. So, we must look into the weather models used to identify patterns and insights from those that data sets. Given that they produce the best results when working in real-time but also considering that the atmosphere is constantly changing, estimations for long periods are hard to come by.

In such a context, where a lot of data has to be on continuous analysis for better predictions, it’s pretty commendable that these models can produce seven-day weather forecasts with an estimated 80% of reliability. Is it possible to get it closer to 100%, though? That’s the question that Python, R, and Java developers are trying to answer with new tech approaches.


The future of weather forecasting

If data science is already an established process in weather forecasting, then it’s up to the new approaches in it that can drive that 80% up. And there’s no better ally for data science that artificial intelligence (AI) and one of its most powerful subsets, machine learning (ML). Weather models can greatly benefit from the introduction of ML-based algorithms since this tech can process large amounts of weather data and refine itself for more accurate predictions the more it’s used.

The best thing about using machine learning for weather forecasting is that it can draw immediate comparisons and identify patterns on the fly. Python, R, and Java development services are coming up with new platforms and solutions capable of taking data from weather stations, radars, and satellites and comparing it to past weather reports. By doing that, the software is trained to filter errors and inaccuracies based on the already-known data and the current conditions.

There’s more. Going beyond machine learning and into deep learning, weather forecasting could produce even more accurate predictions over time. That’s because deep learning algorithms work like a human brain in processing data and analyzing it. The only (hugely advantageous) difference is that these algorithms do so much faster than humans could ever.

There are examples that are already showing the potential data science coupled with machine and deep learning can bring to the table. For instance, IBM bought The Weather Company and used its data to feed its famous AI machine, Watson. From there, Deep Thunder was born and, with it, hyper-localized forecasts with a high degree of accuracy.

Another example involves Bayer’s Climate Corporation, which is heavily investing in AI for weather forecasting applied to the agricultural field. By using machine learning algorithms, their Fieldview solution provides with estimates about the weather that greatly benefit the decision-making process of farmers.

There’s one final technology that has made its way into weather forecasting – smartphones. Yes, mobile devices are also boosting the data science in the field. How so? By collecting data from their users’ specific locations, weather services can tailor-made forecasts with high-levels of accuracy. In this way, users that access weather apps are tied into a feedback loop that provides them with accurate information about the weather which, in turn, gets better thanks to them using the app in the first place.


What to expect next

Over the last few decades, weather forecasting has been through deep transformations. However, there’s more in store for the field, especially with AI and ML being thrown in the mix. The goal is to increase the forecasts’ accuracy, so there’s a lot of work to be done.

The expansion of hyper-localized forecasts and nowcasting is set to grow on the coming years as well. This will come thanks to the increasing use of machine learning models as well as deep learning solutions but also with the ever-growing presence of smartphones. All of that will be combined to offer more accurate data that will feed more sophisticated platforms.

It’s probable that we’ll never get to predictions with 100% of reliability. However, improving current estimates, even by 1%, is a major step forward, not only for everyday weather prediction but also for anticipating potential climate disasters. In that way, data science certainly feels like the perfect ally to enhance current forecasts and bring better estimations for everyone.


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