Abstract
With the improvement in numerical weather prediction models and
high-performance computing technology, ensemble modeling and
probabilistic forecasts have taken on some of the most challenging
tasks, such as weather model uncertainty estimation and the global
climate projection. High-resolution model simulations that were deemed
impossible to complete within a reasonable amount of time in the old
days are now running as an ensemble to better characterize the model
uncertainty. However, with advances in computation which make large
parallel computing widely accessible, important questions are being
increasingly addressed on how to interpret each forecast ensemble
member, instead of relying on a summarization of all ensemble members.
The analysis of individual ensemble members allows for an in-depth
analysis of specific possible future outcomes. Thus, it is desirable to
have the ability to generate a large forecast ensemble in order to help
researchers understand the forecast uncertainty. But it is also crucial
to determine which ensemble members are the better ones and to identify
metrics to assess the uncertainty captured by each ensemble member. This
work proposes the Empirical Inverse Transform (EITrans) function to
address these questions. EITrans is a technique for ensemble
transformation and member selection based on knowledge from historical
forecasts and the corresponding observations. This technique is applied
to a particular ensemble forecast to select ensemble members that would
offer a sharper and more reliable distribution without compromising the
accuracy of the prediction.