Online Uncertainty Quantification for Novel Atmospheric Measurements

University Of Maryland, College Park, College Park MD

Investigators

Abstract

In numerical weather modeling, current observations of the state of the atmosphere are assimilated as an initial condition into the model. For the observations to be as useful as possible, the range of potential uncertainty in the measurements needs to be determined. For completely new types of measurements, such as remote sensing from satellites, it is difficult to independently validate the observations and determine the uncertainty characteristics. This work in this project will assess a variety of numerical techniques to determine the uncertainty of these new observations. The main impact of the project will be on weather forecasting, with the potential for the information to be used in various other fields. A graduate student will be involved in the project, ensuring the training of the next generation of data assimilation experts. This project will address the topic of uncertainty quantification (UQ) for atmospheric observations. More specifically, the project will target “novel” measurements, where new observations are unable to be validated against independent observations. The research team will perform an examination of current methodology and develop new methods for advancing the practice of online observation UQ. The first step of the project will be the development of experiments using the two-scale Lorenz (L96) model and the subsequent evaluation of various existing strategies for uncertainty quantification. A new theoretical development based on Kernel density estimates (KDE) will also be tested and matured. The research team will then expand the analysis to general circulation model (GCM) use cases. The result of the project will be: 1) An exhaustive evaluation of assumptions made by leading observation UQ techniques suggested for geoscience, and 2) A new UQ technique for non-Gaussian error estimation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.