The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

Abstract

Abstract. The most widely used methods of data assimilation in large-scale oceanography, such as the Simple Ocean Data Assimilation (SODA) algorithm, specify the background error covariances and thus are unable to refine the weights in the assimilation as the circulation changes. In contrast, the more computationally expensive Ensemble Kalman Filters (EnKF) such as the Local Ensemble Transform Kalman Filter (LETKF) use an ensemble of model forecasts to predict changes in the background error covariances and thus should produce more accurate analyses. The EnKFs are based on the approximation that ensemble members reflect a Gaussian probability distribution that is transformed linearly during the forecast and analysis cycle. In the presence of nonlinearity, EnKFs can gain from replacing each analysis increment by a sequence of smaller increments obtained by recursively applying the forecast model and data assimilation procedure over a single analysis cycle. This has led to the development of the "running in place" (RIP) algorithm by Kalnay and Yang (2010) and Yang et al. (2012a,b) in which the weights computed at the end of each analysis cycle are used recursively to refine the ensemble at the beginning of the analysis cycle. To date, no studies have been carried out with RIP in a global domain with real observations. This paper provides a comparison of the aforementioned assimilation methods in a set of experiments spanning seven years (1997)(1998)(1999)(2000)(2001)(2002)(2003) using identical forecast models, initial conditions, and observation data. While the emphasis is on understanding the similarities and differences between the assimilation methods, comparisons are also made to independent ocean station temperature, salinity, and velocity time series, as well as ocean transports, providing information about the absolute error of each. Comparisons to independent observations are similar for the assimilation methods but the observation-minus-background temperature differences are distinctly lower for LETKF and RIP. The results support the potential for LETKF to improve the quality of ocean analyses on the space and timescales of interest for seasonal prediction and for RIP to accelerate the spin up of the system.

GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

“…The main components of the GEOS AOGCM are the GEOS AGCM (Molod et al, 2015;Rienecker et al, 2008), the catchment land surface model (Koster et al, 2000), the GOCART aerosol model (Chin et al, 2002;Colarco et al, 2010), the MOM5 ocean general circulation model (Griffies et al, 2005;Griffies, 2012), and the Community Ice CodE-4 sea ice model (Hunke, 2008). The atmospheric data assimilation component is the preexisting "Forward Processing for Instrument Teams" near-real-time assimilation (FPIT;FPIT, 2016), and the ocean data assimilation follows the LETKF (Penny et al, 2013). All components are coupled together using the Earth System Modeling Framework (Hill et al, 2004) and the Modeling Analysis and Prediction Layer interface layer (Suarez et al, 2007).…”

“…The Ocean Analysis used by the GEOS-S2S-2 system follows the LETKF developed by Penny et al (2013) for ocean applications. Unlike Penny et al (2013), GEOS-S2S-2 ensemble members are derived from an existing trajectory of a free-running coupled model. Thus, in this regard, the GEOS-S2S-2 version of the analysis procedure more closely resembles the Ensemble Optimal Interpolation (EnOI; e.g., Keppenne et al, 2008;Karspeck et al, 2013;Vernieres et al, 2012).…”

“…GEOS‐S2S‐2 also benefited from model developments that addressed short‐term climate variability linked to coupling with the land surface, the stratosphere, and the ocean. Those developments are combined with major new infrastructure and capabilities in GEOS‐S2S‐2 that include the implementation of a local ensemble transform Kalman filter (LETKF; Penny et al, ) ocean analysis facilitating the assimilation of satellite altimetry data, an upgrade from the Modular Ocean Model version 4 (MOM4) to version 5 (MOM5) (Griffies et al, ; Griffies, ), the implementation of the Goddard Chemistry Aerosol Radiation and Transport (GOCART; Chin et al, ; Colarco et al, ) interactive aerosol model allowing experimental predictions of aerosol loadings, and the implementation of a two‐moment cloud microphysics scheme (Barahona et al, ) which allows the inclusion of the aerosol indirect effect on clouds.…”

Mathematical foundations of hybrid data assimilation from a synchronization perspective

“…We must include as much dynamical information as possible from the model to ensure that all information from the observations is utilized, while still maintaining a state that is consistent with the numerical model. Such was the motivation for iterative techniques such as the "Running in Place" method (Yang et al, 2012 andPenny et al, 2013).…”

Using Observations near the Poles in the AFES-LETKF Data Assimilation System

“…The LETKF can be implemented independently of the model, is suitable for ensemble forecasting and is efficient for parallel computing. Recently, the LETKF has been implemented with various models such as the global and regional atmosphere (e.g., Miyoshi and Aranami 2006;Miyoshi et al 2010;Miyoshi and Kunii 2012;Terasaki et al 2015), global and coastal ocean (Hoffman et al 2008;Penny et al 2013) and Martian atmosphere (Hoffman et al 2010;Greybush et al 2012).…”