Numeric weather prediction is undergoing a revolution resulting from the
continuous advances in scientific knowledge and technologies. With
dozens of weather models emerging that all generate different
predictions from each other, forecasts have been gradually shifting from
a deterministic form to a probabilistic form which shows the increasing
concerns of, not just the absolute prediction values, but the confidence
of predictions and the uncertainty of models. As a computational
problem, generating uncertainty information can be an expensive task.
Conventionally, prediction models are initiated with slightly perturbed
parameters and then the diversion of model results can be a measure of
model uncertainty. However, the multi-simulation approach drastically
increases the computational requirement so that it can potentially
exceed the ability of the state-of-art high-performance computing
platforms. Meanwhile, if spatial and temporal resolutions are of
concern, this approach is far from being efficient and viable. The
Parallel Ensemble Forecast system is designed to generate probabilistic
weather forecasts by using the revolutionary numerical weather
prediction technique, Analog Ensemble. It is a data-driven method that
derives probability information of a deterministic prediction model
using past forecasts and observations without multiple simulation runs.
Integrated with high-performance platforms, the system distributes
computational tasks among nodes and therefore further boosts the data
simulation process.