The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: 1st Workshop on Enabling Predictive Science with Optimization and Uncertainty Quantification in HPC
Authors: Alessandro Fanfarillo and Nicholas Malaya (Advanced Micro Devices (AMD) Inc), Guido Cervone (Pennsylvania State University), and Luca Delle Monache (Scripps Research Institute)
Abstract: Ensemble forecasting techniques are gaining popularity in the weather and renewable energy communities, thanks to their ability to produce accurate predictions and at the same time to provide a measure of the uncertainty in the forecast. Analog Ensemble techniques are a class of computationally efficient ensemble forecasting methods that predict future weather events based on historical similar cases (i.e., analogs). The definition of "similar" is dependent on the type of predictors used for searching in the historical dataset, and on how relevant they are to identify a similar weather event happened in the past. For a given geographical location, the relevancy of a predictor in identifying good analogs requires a long tuning process usually performed via brute-force. In this work, we provide several probabilistic alternatives to the tuning process, based on the dataset size, computational cost of a single evaluation, and number of predictors.