  Data-driven modeling based on machine learning (ML) is showing enormous
potential for weather forecasting. Rapid progress has been made with impressive
results for some applications. The uptake of ML methods could be a game-changer
for the incremental progress in traditional numerical weather prediction (NWP)
known as the 'quiet revolution' of weather forecasting. The computational cost
of running a forecast with standard NWP systems greatly hinders the
improvements that can be made from increasing model resolution and ensemble
sizes. An emerging new generation of ML models, developed using high-quality
reanalysis datasets like ERA5 for training, allow forecasts that require much
lower computational costs and that are highly-competitive in terms of accuracy.
Here, we compare for the first time ML-generated forecasts with standard
NWP-based forecasts in an operational-like context, initialized from the same
initial conditions. Focusing on deterministic forecasts, we apply common
forecast verification tools to assess to what extent a data-driven forecast
produced with one of the recently developed ML models (PanguWeather) matches
the quality and attributes of a forecast from one of the leading global NWP
systems (the ECMWF IFS). The results are very promising, with comparable skill
for both global metrics and extreme events, when verified against both the
operational analysis and synoptic observations. Increasing forecast smoothness
and bias drift with forecast lead time are identified as current drawbacks of
ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML
models and state-of-the-art analysis and reanalysis datasets for forecast
initialization and model training.
