*Artificial intelligence (AI) making strides in the world of weather forecasting…European Center for Forecasting makes its AI-model fully operational*
Paul Dorian
A side-by-side comparison of the conventional European forecast model (left) and the Euro-AI version (right) of “Total Snowfall Amounts” in the Northeast US for the period ending Friday AM, April 4th. Forecast maps courtesy ECMWF, Pivotal Weather
Overview
Artificial intelligence (AI) is a collection of technologies that allow computers to perform tasks that typically require human intelligence, and it is increasingly impacting the world of weather forecasting. The European Center for Medium-Range Forecasting (ECMWF) has made strides with its Artificial Intelligence Forecasting System (AIFS) as it has recently become fully operational and is now run side-by-side with its traditional physics-based Integrated Forecasting System (IFS). According to the ECMWF, the AIFS has outperformed the physics-based model for many measures including, for example, tropical cyclone tracks. In addition to the ECMWF AIFS, there are at least four other known “A.I. trained” weather models including NOAA/Google GraphCast, Microsoft’s Aurora, NVIDIA’s FourCast, and Huawei’s Pangu-Weather.
A side-by-side comparison of the conventional European forecast model (left) and the Euro-AI version (right) of “Total Snowfall Amounts” in the continental US for the period ending Friday AM, April 4th. Forecast maps courtesy ECMWF, Pivotal Weather
Discussion
The traditional approach to weather forecasting has been to make use of numerical weather prediction (NWP) which relies on current conditions, physics-based models, and the solving of complex equations on powerful supercomputers to output such parameters as temperature, pressure, winds, and precipitation at future times. Artificial intelligence (AI) models, particularly machine learning, are being increasingly used to improve weather forecasting by learning from large datasets of weather data to identify patterns and trends. AI models can process data faster and identify complex patterns, potentially leading to quicker and more accurate forecasts. The increasingly important role of AI in weather forecasting will be to complement and enhance traditional NWP models.
The European Center for Medium-Range Forecasting (ECMWF) has made its Artificial Intelligence Forecasting System (AIFS) the first such fully operational weather prediction model that uses machine learning and artificial intelligence. Making such a system operational means that it is openly available and has 24/7 support for the meteorological community. This AIFS can produce a wide range of output parameters including winds, temperatures, and details on precipitation types from snow to rain. The AIFS currently has a grid spacing of 28 km and, according to the ECMWF, it can outperform its physics-based counterpart by as much as 20% on certain measures.
A side-by-side comparison of the conventional European forecast model (left) and the Euro-AI version (right) of “500 millibar height anomalies” across the continental US for the validation time of 8AM, Sunday, March 30th. Forecast maps courtesy ECMWF, Pivotal Weather
The AIFS uses the same initial atmospheric conditions for its forecasts as the IFS. These are based on the combination of a previous short-term forecast with around 60 million quality-controlled observations from satellites as well as many other streams, including from planes, boats, sea buoys and many other Earth-based measurement stations. Every six hours, these initial conditions feed into the AIFS. The machine learning model, trained on how the weather has evolved in the past, assesses how the initial conditions will influence the weather for the coming days. By contrast, the IFS uses physics-based capabilities to arrive at a forecast with a grid-spacing of 9 km over the globe, integrating the laws of physics in its computer code.
Meteorologists rarely put total confidence into a single model but instead, look at a suite of model forecasts. Most global models compute 10-day outlooks every six hours and one way forecasters gauge a model’s reliability is by checking its consistency from run to run. The Euro-AI model did well with this test for Hurricane Francine during September 2024 as it predicted landfall in southern Louisiana every run from 96-hours out until its landfall near Morgan City, LA. Maps courtesy foxweather.com
The first operational version is called the AIFS “Single”. It runs a single forecast at a time, known as a deterministic forecast. However, ECMWF is pushing this model to create a collection of 50 different forecasts with slight variations at any given time to provide the full range of possible scenarios. This is known as ensemble modelling, a technique developed and implemented by ECMWF more than thirty years ago. According to ECMWF, the launch of AIFS “Single” as an operational service is the first step in upgrading its artificial intelligence forecasting capabilities. The next step will be to make ensemble forecasts available by using artificial intelligence and extended range (seasonal) forecasts as well and we’ll continue to monitor the progress here at Arcfield Weather.
Meteorologist Paul Dorian
Arcfield
arcfieldweather.com