SIParCS 2023 - Ameya Patil
Ameya Patil, University of Washington
Interactive Visualization of Uncertainty in High-Resolution Ensembles
Quantifying uncertainty through analysis of ensemble forecasts in numerical weather prediction or ocean modeling remains a challenge. The massive number of observations in addition to the large dimension of atmospheric and ocean models makes it difficult to properly assess the quality and uncertainty of the prediction. Consequently, deriving risk measures and informative solutions may become strenuous and borderline impossible. Ensemble data assimilation (DA) provides a flexible ensemble framework to estimate the state of an earth system. The main goal of this project is to interface the ensemble visualization package OVIS with the Data Assimilation Research Testbed (DART) at NCAR. OVIS is an interactive visualization framework that allows for an efficient and easy analysis of ocean forecasts and their uncertainties. By utilizing data on the fly, OVIS can help users dive into the data, change parameters, select subsets of the ensemble, and instantly visualize the results. Various risk measures can be also computed based on the statistics of the ensemble. While DART is written in Fortran, OVIS is implemented in Objective C and OpenGL. The project also entails extending the scope of OVIS to support atmospheric and other Earth system models and potentially exploring CISL’s own VAPOR for depicting uncertainty. Overall, the objective is to make it possible for DART’s large user base to have access to a state-of-the-art diagnostic package that is modern, highly informative, and easy to use.
Mentors: Helen Kershaw, Moha Gharamti, Marlee Smith
Slides and poster