How hyperlocal forecast differs from generic forecasts

When the digital camera came along, pictures were about 320×240 pixels. Nowadays, we take 4k photos with our mobile phone, which is equivalent to 3,840 x 2,160 pixels!

The low quality of the photos is observed when we can easily see those squares in the image. But over time, these little squares got smaller and smaller. That's what the increase in image resolution means.

Without a doubt, it was a great evolution and is a great parallel for the spatial resolution of numerical weather forecast models.

In this text we will talk about hyperlocal sea and weather forecasts. And as you might guess, the term hyperlocal refers to the spatial resolution of a forecast.

How does forecast resolution work?

Climate models divide a region, be it a state, a country or even the entire globe, into a set of cells – as if they were the pixels of a photo. The size of these cells reflects the resolution of the model and influences prediction usage and accuracy.

Large cells mean low resolution or an inability to tell what is happening in small areas, but a comprehensive view of broader climate trends.

For example, this forecast is useful to know if there is a storm approaching the coast of Brazil, for example, but it is insufficient if you need to know what will happen on a specific stretch of coast.

On the other hand, the smaller the cells, the higher the resolution. Higher resolution models can predict phenomena in more detail at specific locations.

Depending on the territory in which the model is implemented, it can be classified as:

GLOBAL: with cells from 8 km to 40 km, REGIONAL: with cells from 3 km to 6 km, LOCAL: with cells between 500 m and 3 km, and HYPERLOCAL: reaching just a few tens of meters!

It's these ultra-high resolution predictions that we're going to talk more about now.

What are hyperlocal forecasts?

As we have seen, global forecasts are not adequate to represent specific stretches of coastal areas. Similar to a photo with very large pixels, image detail is lost.

Global forecasts have a resolution of around 25 km, while hyperlocal forecasts can reach resolutions of a few meters, offering much more detailed and accurate information.

A interesting example of the power of hyperlocal forecasts is the wave forecast for the Port of Sines in Portugal.

The i4cast® wave models are capable of predicting, with details, the impact of waves at a berth level. Therefore it has the ability to support decision making on the efficiency and safety of Port Terminals’ operations.

How is hyperlocal forecasting done?

To generate hyperlocal forecasts it is necessary to refine the global forecast to smaller and smaller scales, through new specific numerical models and data of local conditions.

This means that the information of a storm approaching the coast of Brazil provided by a global forecast can be refined to know how the waves of that storm will impact your port.

This is the magic of the technology we develop at i4sea!

Why are hyperlocal predictions better?

At i4sea, we analyze all publicly available global climate models and compare them to obtain the most likely to come true result of all these predictions.

We then apply our own high-resolution model to tailor the forecast to our customers' needs. In this process, we increase forecasting assertiveness and are able to predict the impact and risk of sea and weather conditions for your business.

In the i4cast® hyperlocal model in operation in the Porto do Açu region, for example, the distance between the forecast points can reach 10 m, while the global forecast points closest to the ship maneuvering area are around 40 km away – corresponding to 400 football fields.

The large number of points in the hyperlocal model allows an adequate representation of the Port features, navigation channels and small bathymetric variations – which the global model is not able to represent.

The consequence of this higher resolution is a significant improvement in forecasts.

It's amazing the difference between a global forecast and a hyperlocal forecast. And the result gets even better when we apply artificial intelligence techniques, machine learning, to further increase the accuracy of forecasts.