Open AccessUsing the Linearized Observation Operator to Calculate Observation Space Ensemble Perturbations in Ensemble Filters
Author(s) -
Shlyaeva Anna,
Whitaker Jeffrey S.
Publication year - 2018
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2018ms001309
Subject(s) - data assimilation , computation , operator (biology) , ensemble forecasting , shift operator , computer science , ensemble kalman filter , algorithm , meteorology , linear interpolation , mathematics , physics , kalman filter , compact operator , artificial intelligence , repressor , pattern recognition (psychology) , extension (predicate logic) , gene , biochemistry , chemistry , transcription factor , extended kalman filter , programming language
Within the National Oceanic and Atmospheric Administration National Weather Service, the hybrid ensemble‐variational system (Gridpoint Statistical Interpolation, GSI) is run together with the 80‐member ensemble square root filter (EnSRF) operationally for the global forecast data assimilation system. EnSRF uses observation operator from GSI: current operational configuration requires 81 runs of GSI in the observation operator mode to run EnSRF (for each of the 80 ensemble members and for the ensemble mean). To reduce data assimilation cycle computation time, a GSI‐EnSRF configuration that requires a single run of the GSI system in the observation operator mode was developed. In this configuration EnSRF uses full observation operator for the ensemble mean and linearized observation operator for the ensemble perturbations. Comparison of the two approaches shows that using linearized observation operator for ensemble perturbations compared to using full observation operator does not change the analysis results significantly and allows to reduce overall data assimilation cycle computation time.