Weather forecast models and apps
Practical Boat Owner|March 2021
Rupert Holmes unpicks the tech behind the data that underpins all weather apps and highlights his favourite software
Rupert Holmes

To understand the forecasts provided by different weather apps and websites we first need to have a basic appreciation of the data they present. Obviously they can only be as good as their data inputs, but where does that come from?

The answer lies in the raw data output of numerical weather prediction (NWP) models. We therefore also need to understand the inherent strengths and weaknesses of these forecasts.

As there aren’t that many different models that provide free or relatively low-cost data, often different apps or websites are simply presenting the same data in different ways.

Understanding numerical weather prediction

The key premise behind weather modelling is that the mathematical equations governing motion, fluid dynamics and thermodynamics can be used to predict the future state of the atmosphere.

Key variables within each model include wind strength and direction in three dimensions, air density, temperature, pressure and humidity.

This makes it a hugely data-intensive process, which necessitates dedicated use of some of the world’s most powerful supercomputers, which are often replaced on two- or three- yearly timescales. Even then, a single model run can take up to six hours to process.

The accuracy of model output depends on many factors including the size of the horizontal grid and the number of vertical atmospheric ‘slices’ considered.

A feature, whether meteorological or geographical, needs to cover at least three grid points in order to register in the model. Therefore, a model with a 22km grid will ignore weather features that are less than 65km across.

However, there’s a trade-off as fine-grained models can’t be used for longer range forecasts – the smaller the grid the sooner the output becomes unreliable. Therefore medium-term forecasts from global models tend to use a large grid size, while a small grid is used only for short-term predictions.

Still, it’s worth noting that in general grid sizes are becoming smaller, without loss of long-term accuracy.

Weather models

The Global Forecast System (GFS) model currently uses a 28km grid for the first seven days, expanding to 70km for days 8 to 16. This data is freely available, which means it’s the most commonly used, especially in free apps.

The ECMWF (European Centre for Medium-term Weather Forecasting) global model has a 9km grid and is a common option for paid-for apps.

The German ICON model grid is 13km, reducing to 6km for European coverage.

The MetOffice’s Unified Model has a 16km grid, but is a lot more expensive and is, therefore, seems to be rarely, if ever, used by consumer-facing third party operations. This is disappointing as, along with ECMWF, it is also one of the most accurate.

In addition there are many more models that cover smaller areas in more detail, but for shorter timelines.

The MetOffice short term UKV model, for instance, has a horizontal resolution varying from 1.5km (over the UK) to a maximum of 4km and is therefore capable of resolving features down to 5km across. This model is run more frequently than the global one – once every three hours – but only to 36 hours into the future. Pricing means it’s not viable for most app developers to access this model.

The French AROME model (2km grid size, six-hour refresh rate) covers most of the UK, other than the far north of Scotland, and consistently produces exceptionally good fine-grained forecasts.

However, to put all that in perspective: neither the MetOffice nor AROME yet offers a sufficiently small grid size to be able to predict local effects in places such as the Needles Channel, Portland Bill or the Menai Straits.

Atmosphere in 3D

As sailors we can easily fall into the trap of thinking of the atmosphere in a two-dimensional ‘plan’ view. Reality is very different, of course, and it’s critical to have a three-dimensional appreciation of the atmosphere.

Weather models consider up to 70 vertical slices, representing altitudes of up to 80,000m for medium-term forecasts, and 40,000m for shorter range predictions. In addition, as landmasses play a big part in determining weather outcomes, terrain maps are incorporated into the models.

The initial parameters for the start of each model run are enormously important in achieving a reliable forecast, and requires a complete set of up to date observations from around the globe, and at different altitudes.

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