Aardvark Weather: end-to-end data-driven weather forecasting
Anna Vaughan Stratis Markou Will Tebbutt James Requeima Wessel P. Bruinsma Tom R. Andersson Michael Herzog Nicholas D. Lane J. Scott Hosking Richard E. Turner
Machine learning is revolutionising medium-range weather prediction. However
it has only been applied to specific and individual components of the weather
prediction pipeline. Consequently these data-driven approaches are unable to be
deployed without input from conventional operational numerical weather
prediction (NWP) systems, which is computationally costly and does not support
end-to-end optimisation. In this work, we take a radically different approach
and replace the entire NWP pipeline with a machine learning model. We present
Aardvark Weather, the first end-to-end data-driven forecasting system which
takes raw observations as input and provides both global and local forecasts.
These global forecasts are produced for 24 variables at multiple pressure
levels at one-degree spatial resolution and 24 hour temporal resolution, and
are skillful with respect to hourly climatology at five to seven day lead
times. Local forecasts are produced for temperature, mean sea level pressure,
and wind speed at a geographically diverse set of weather stations, and are
skillful with respect to an IFS-HRES interpolation baseline at multiple
lead-times. Aardvark, by virtue of its simplicity and scalability, opens the
door to a new paradigm for performing accurate and efficient data-driven
medium-range weather forecasting.