Paul Dorian

Surface forecast map for next Monday, April 1st, made by the 00Z “Artificial intelligence” version of the Euro model; Map courtesy ECMWF, tropicaltidbits.com

Overview

It was just a matter of time…artificial intelligence (AI) has hit the numerical weather prediction world with a strong emphasis on “pattern recognition” and there is no telling where this will lead in the world of weather forecasting. Numerical weather prediction is well suited for AI as – in its current form – it requires a tremendous amount of data crunching and super computing power to resolve the physical laws of fluid dynamics to produce weather conditions in the future. One of the most notable AI advances in recent years has come with the European Centre for Medium-Range Weather Forecasts which is generating experimental AI forecasts that are made available to the public.

Surface forecast map for next Monday, April 1st, made by the 00Z “conventional” version of the Euro model; Map courtesy ECMWF, tropicaltidbits.com

Details

Weather forecasts have improved in accuracy over the years with today’s 6-day forecasts about as good as the 3-day forecast from 30 years ago. This improvement in overall accuracy has come about for numerous reasons one of which has to do with the much better computing power in today’s world compared to three decades ago. Artificial intelligence is now spurring a new revolution in numerical weather prediction that many believe will produce model-based weather forecasts as good or even better than the best traditional models.

The European Centre for Medium-Range Weather Forecasts (ECMWF) is known for generating what is considered to be one of the top “traditional” computer forecast models in the world known to most as the “Euro”. In the fall of 2023, this agency began to generate its own experimental AI model-based forecasts known officially as the “ECMWF-AIFS” where AIFS is an acronym for “Artificial Intelligence Forecasting System”. This experimental forecast model, based on ECMWF initial conditions, has been made available in an alpha version to the general public for free and can be found at their own web site here. The resolution of the ECMWF-AIFS model is approximately one degree (111 km) with plans for this to be regularly increased in the future. 

850 mb temperature anomaly forecast map for next Monday, April 1st, made by the 00Z “Artificial intelligence” version of the Euro model; Map courtesy ECMWF, tropicaltidbits.com

Traditional weather models start off by feeding a snapshot of current conditions, based on observations from satellites, weather stations and buoys, into a grid-like computer model that divides the atmosphere into millions of boxes. This snapshot is then run forward in time for each box by applying equations that are based on the physical laws of fluid dynamics and this requires great computational power. Indeed, this kind of data crunching requires supercomputers with 1 million processors and can take several hours to run…usually four times a day.

850 mb temperature anomaly forecast map for next Monday, April 1st, made by the 00Z “Artificial intelligence” version of the Euro model; Map courtesy ECMWF, tropicaltidbits.com

The new AI models play a role in weather prediction by simulating and analyzing past weather events, learning from historical data, and recognizing recurring weather patterns which enhances AI’s ability to predict future weather conditions. In other words, AI skips the expense of solving the equations in favor of “deep learning” after training on 40 years of ECMWF “reanalysis” data (a combination of observations and short-term model forecasts that best represents past weather) (source).

The European Agency is not alone in producing AI forecast models as numerous tech giants are getting involved. In a paper published recently in Science, Google introduced GraphCast and claims it can make weather predictions more accurately (and faster) than the ECMWF High-Resolution Forecast (HRES) on 90% of its verification targets up to 10 days in advance.

The advance in AI forecasting has been rapid during the past few years and one of the important next steps will be to produce ensemble results, which helps to capture uncertainty by running a model multiple times with slightly differing input parameters to create a range of outcomes. While few expect traditional model forecasts to disappear anytime soon, AI will likely approach the point in the near-term where it can be a very useful complement. And when it comes to artificial intelligence, the bottom line is that there is really no telling where this will lead us over the next five or ten years; therefore, as is usually the case when it comes to weather forecasting, stay tuned. 

Meteorologist Paul Dorian
Arcfield
arcfieldweather.com

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From NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

https://www.telegraph.co.uk/business/2024/03/23/china-solar-panels-europe-net-zero-energy/

The naive journalist makes several references to the “massive” amounts of solar capacity on offer, for instance:

The clueless reporter evidently does not understand the difference between CAPACITY and GENERATION.

You simply cannot compare solar power, typically producing at about 10 to 15%, with nuclear, which runs at close to 100%

Worse still, that 10% is not available all year round. During December last year, it averaged only 297 MW, which is just 2% of capacity:

https://www.solar.sheffield.ac.uk/pvlive/#

In summer months, solar power can peak at ten times as much, which will clearly destabilise the grid, if capacity is increased as much as the government wants.