We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model.
- Authors
Shin, Seoleun; Kang, Ji‐Sun; Yang, Shu‐Chih; Kalnay, Eugenia
- Abstract
We test an ensemble data assimilation system using the four‐dimensional Local Ensemble Transform Kalman Filter (4D‐LETKF) for a global numerical weather prediction (NWP) model with unstructured grids on the cubed‐sphere. It is challenging to selectively represent structures of dynamically growing errors in background states under system uncertainties such as sampling and model errors. We compute Ensemble Singular Vectors (ESVs) in an attempt to capture fast‐growing errors on the subspace spanned by ensemble perturbations, and use them as additive inflation to enlarge the covariance in the area where errors are flow‐dependently growing. The performance of the 4D‐LETKF system with ESVs is evaluated in real data assimilation, as well as Observing System Simulation Experiments (OSSEs). We find that leading ESVs help to capture fast‐growing errors effectively, especially when model errors are present, and that the use of ESVs as additive inflation significantly improves the performance of the 4D‐LETKF. Background error (contours, solid: positive, dash: negative) and analysis increment (shading) of zonal wind U (m/s) at the model level 45, around 850 hPa at 0600 UTC 20 February 2014 from the tests (a) ImPerfModel‐Random, and (b) ImPerfModel‐ESV.
- Subjects
VECTORS (Calculus); KALMAN filtering; NUMERICAL weather forecasting; SAMPLING errors; PERTURBATION theory
- Publication
Quarterly Journal of the Royal Meteorological Society, 2019, Vol 145, Issue 718, p258
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.3429