A Comparison of Hybrid‐Gain Versus Hybrid‐Covariance Data Assimilation for Global NWP
Abstract
Two methods for incorporating a time‐invariant, high‐rank covariance estimate in an ensemble‐based data assimilation system for global weather prediction are compared. The hybrid‐covariance approach linearly combines the static and ensemble‐based covariance estimate in a four‐dimensional variational solver, whereas the hybrid‐gain approach blends analysis increments computed separately using a three‐dimensional variational solution and an ensemble Kalman filter solution. Results show that the simpler and less expensive hybrid‐gain approach performs similarly if the incremental normal‐mode balance constraint applied to the ensemble‐part of the hybrid‐covariance update is turned off.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2022
- Source ID
- 10.1029/2022ms003036
Entities
People
- Anna Shlyaeva
- Jeffrey S. Whitaker
- Stephen G Penny
Organizations
- NOAA Research
- Office of Naval Research Global
- University Corporation for Atmospheric Research
- University of Colorado Boulder