Christian L. Keppenne

Data Assimilation into a Primitive-Equation Model with a Parallel Ensemble Kalman Filter

Data assimilation experiments are performed using an ensemble Kalman filter (EnKF) implemented for a twolayer spectral shallow water model at triangular truncation T100 representing an abstract planet covered by a strongly stratified fluid. Advantage is taken of the inherent parallelism in the EnKF by running each ensemble member on a different processor of a parallel computer. The Kalman filter update step is parallelized by letting each processor handle the observations from a limited region. The algorithm is applied to the assimilation of synthetic altimetry data in the context of an imperfect model and known representation-error statistics. The effect of finite ensemble size on the residual errors is investigated and the error estimates obtained with the EnKF are compared to the actual errors.

Initial Testing of a Massively Parallel Ensemble Kalman Filter with the Poseidon Isopycnal Ocean General Circulation Model

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been developed for the Poseidon ocean circulation model and tested with a Pacific basin model configuration. There are about 2 million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase‐space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF implementation are discussed. To verify the proper functioning of the algorithms, results from an initial experiment with in situ temperature data are presented. Furthermore, it is shown that the regionalization of the background covariances has a negligible impact on the quality of the analyses. Even though the parallel algorithm is very efficient for large numbers of observations, individual PE memory, rather than speed, dictates how large an ensemble can be used in practice on a platform with distributed memory.

An ENSO Signal in Soybean Futures Prices

Abstract An example of socioeconomic repercussions of the El Nino-Southern Oscillation (ENSO) is examined. Multichannel singular spectrum analysis, a variant of principal component analysis useful in isolating the spatial and temporal variability associated with anharmonic oscillations, is applied to normalized monthly mean time series of soybean futures prices and the Southern Oscillation index. The method isolates the variability common to the two time series from the remaining variability and noise. It identifies the low- and high-frequency, quasi-biennial modes of ENSO as part of this variability.

Error Covariance Modeling in the GMAO Ocean Ensemble Kalman Filter

Abstract In practical applications of the ensemble Kalman filter (EnKF) for ocean data assimilation, the computational burden and memory limitations usually require a trade-off between ensemble size and model resolution. This is certainly true for the NASA Global Modeling and Assimilation Office (GMAO) ocean EnKF used for ocean climate analyses. The importance of resolution for the adequate representation of the dominant current systems means that small ensembles, with their concomitant sampling biases, have to be used. Hence, strategies have been sought to address sampling problems and to improve the performance of the EnKF for a given ensemble size. Approaches assessed herein consist of spatiotemporal filtering of background-error covariances, improving the system-noise representation, imposing a steady-state error covariance model, and speeding up the analysis by performing the most expensive operation of the analysis on a coarser computational grid. A judicious combination of these approaches leads to...

Comparison and Sensitivity of ODASI Ocean Analyses in the Tropical Pacific

Abstract Two global ocean analyses from 1993 to 2001 have been generated by the Global Modeling and Assimilation Office (GMAO) and Geophysical Fluid Dynamics Laboratory (GFDL), as part of the Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI) consortium efforts. The ocean general circulation models (OGCM) and assimilation methods in the analyses are different, but the forcing and observations are the same as designed for ODASI experiments. Global expendable bathythermograph and Tropical Atmosphere Ocean (TAO) temperature profile observations are assimilated. The GMAO analysis also assimilates synthetic salinity profiles based on climatological T–S relationships from observations (denoted “TS scheme”). The quality of the two ocean analyses in the tropical Pacific is examined here. Questions such as the following are addressed: How do different assimilation methods impact the analyses, including ancillary fields such as salinity and currents? Is there a significant difference in interpre...

Application of Coupled Bred Vectors to Seasonal-to-Interannual Forecasting and Ocean Data Assimilation

Abstract Coupled bred vectors (BVs) generated from the NASA Global Modeling and Assimilation Office (GMAO) coupled general circulation model are designed to capture the uncertainties related to slowly varying coupled instabilities. Two applications of the BVs are investigated in this study. First, the coupled BVs are used as initial perturbations for ensemble-forecasting purposes. Results show that the seasonal-to-interannual variability forecast skill can be improved when the oceanic and atmospheric perturbations are initialized with coupled BVs. The impact is particularly significant when the forecasts are initialized from the cold phase of tropical Pacific SST (e.g., August and November), because at these times the early coupled model errors, not accounted for in the BVs, are small. Second, the structure of the BVs is applied to construct hybrid background error covariances carrying flow-dependent information for the ocean data assimilation. Results show that the accuracy of the ocean analyses is impro...

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

Abstract This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vector (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This bet...

The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system

Ocean salinity and temperature differences drive thermohaline circulation. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius along-track retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with in situ salinity measurements from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in situ (Argo) and space-borne SSS retrievals.

Improving Short-term Climate Forecasts with Satellite Observations

Understanding and predicting seasonal-to-interannual climate variations is a central goal within U.S. climate research. At NASAs Global Modeling and Assimilation Office, we are developing a coupled model forecast system to optimize the use of existing and planned satellite data, together with in situ observations, for experimental predictions of short-term climate variations. Our focus is on using satellite data to initialize the ocean and land surface, the slower components of the climate system that have the potential memory to enhance climate prediction. The longer temporal scales of the ocean and land surface are the key sources of memory in the coupled climate system that promise skill in predicting short-term climate variability. The focus within the GMAO is on optimizing the use of satellite altimeter data and evaluating the impact of planned satellite observations such as surface salinity data from Aquarius. Predictability experiments and hindcast tests that use observed precipitation to precondition the soil moisture distribution have also been conducted. The results indicate that the key to summertime precipitation forecasts over transition zones between dry and humid areas in tropical and mid-latitude regions (such as the central U.S.) lies in the initialization of soil moisture (1). Given these results, the GMAO system also focuses on the initialization of the land surface model, with developments undertaken for assimilation of soil moisture estimates from AMSR-E. III. THE FORECAST SYSTEM