  Artificial intelligence (AI)-based data-driven weather forecasting models
have experienced rapid progress over the last years. Recent studies, with
models trained on reanalysis data, achieve impressive results and demonstrate
substantial improvements over state-of-the-art physics-based numerical weather
prediction models across a range of variables and evaluation metrics. Beyond
improved predictions, the main advantages of data-driven weather models are
their substantially lower computational costs and the faster generation of
forecasts, once a model has been trained. However, most efforts in data-driven
weather forecasting have been limited to deterministic, point-valued
predictions, making it impossible to quantify forecast uncertainties, which is
crucial in research and for optimal decision making in applications. Our
overarching aim is to systematically study and compare uncertainty
quantification methods to generate probabilistic weather forecasts from a
state-of-the-art deterministic data-driven weather model, Pangu-Weather.
Specifically, we compare approaches for quantifying forecast uncertainty based
on generating ensemble forecasts via perturbations to the initial conditions,
with the use of statistical and machine learning methods for post-hoc
uncertainty quantification. In a case study on medium-range forecasts of
selected weather variables over Europe, the probabilistic forecasts obtained by
using the Pangu-Weather model in concert with uncertainty quantification
methods show promising results and provide improvements over ensemble forecasts
from the physics-based ensemble weather model of the European Centre for
Medium-Range Weather Forecasts for lead times of up to 5 days.
