Understanding and Improving the Predictability of Arctic Meso- and Synoptic-scale Cyclones through Multi-scale Ensemble Based Data Assimilation and Ensemble Forecast
Abstract
Funds are provided to conduct research to understand the inherent predictability and factors that limit the practical predictability of arctic weather. Past studies suggest that ensemble based data assimilation (DA) can effectively extrapolate observation information in dynamically and thermodynamically consistent manners and therefore more suitable for data sparse regions such as Arctic. This study will further extend the ensemble based data assimilation with multi-scale capabilities. In addition to initial condition (IC) errors, two sources of errors that limit the predictability are coarser model resolution and imperfectness of physics parameterizations. The primary objectives of the proposal are to understand and improve the predictability of meso- and synoptic scale cyclones over the arctic, specifically, arctic cyclone and polar lows, through ensemble based multiscale DA and ensemble forecast system, high resolution regional models and specially collected observations over the arctic. The proposed work will be in collaboration with NRL scientists Drs. James Doyle and Craig Bishop and will provide basic understanding needed to advance Navy~s operational prediction capability.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 10, 2018
- Source ID
- N000141812226
Entities
People
- Xuguang Wang
Organizations
- Office of Naval Research
- United States Navy
- University of Oklahoma