A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

Abstract

A coupled ensemble square root filter-three-dimensional ensemble-variational hybrid (EnSRF-En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at 1 /3 of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

GSI‐based ensemble‐variational hybrid data assimilation for HWRF for hurricane initialization and prediction: impact of various error covariances for airborne radar observation assimilation

“…For all the Hybrid related experiments in this study, horizontal and vertical localization cut‐off distances (Gaspari and Cohn, ) are 450 km and 1 scale height respectively. The details about converting between the localization length‐scale in the e‐folding distance used in the recursive filter and that in the cut‐off distance used in the method by Gaspari and Cohn () can be found in equation (4) of Pan et al (). These values are selected after tests with a range of values.…”

Impact of hybrid GSI analysis using ETR ensembles

“…In two-way coupling, additionally ensemble analysis is re-centered to the control hybrid analysis. The two-way coupling implicitly assumes that the hybrid control analysis is better than the ensemble mean analysis, and the re-centering should help to prevent the divergence between the ensemble and hybrid analyses so that the ensemble perturbations can sample the control forecast uncertainty well (Pan et al 2014). In the hybrid configuration we have chosen, ensemble system is not an independent full data assimilation system and ensemble analyses are generated by perturbing control analysis (hybrid analysis).…”

Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

“…All observation processing was performed by GSI, and for the EnSRF, GSI generated prior modelsimulated observations for each ensemble member, assigned observation errors, and performed observation thinning and quality control decisions [as in Hamill et al (2011a), SL14, Pan et al (2014), Wang and Lei (2014), Zhu et al (2013), and Kleist and Ide (2015a,b)]. …”

“…Ide (2015a), several studies (e.g., Clayton et al 2013;Wang et al 2013;Pan et al 2014;Schwartz et al 2015a) revealed little sensitivity to whether this recentering was performed, which suggests that a single ensemble can safely provide flow-dependent BECs for multiple hybrid experiments (Clayton et al 2013). Thus, based on these collective previous results, EnSRF analyses were not recentered about hybrid analyses, and unless otherwise stated, the same 20-km ensembles provided BECs for all hybrid configurations, which permitted a large number of hybrid experiments to be performed that would have otherwise been impossible (given finite computing resources) had it been necessary to produce new EnSRF analyses for each hybrid experiment.…”

“…Hybrid DA systems traditionally produce deterministic analyses within variational frameworks, like 3DVAR, but ingest ensembles to derive flow-dependent BECs, as in EnKFs, and typically initialize better forecasts than DA methods using purely static BECs (e.g., Wang et al 2008bWang et al , 2013Buehner et al 2010;Hamill et al 2011b;Zhang and Zhang 2012;Zhang et al 2013;Pan et al 2014;Schwartz and Liu 2014), including when the analysis has convectionallowing resolution (Li et al 2012;Gao et al 2013;Li et al 2015).…”