Lagrangian Data Assimilation of Surface Drifters to Support Ocean and Coupled Model Initialization

2020 ◽  
Author(s):  
Luyu Sun
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Coupled Model ◽  
Ocean Model ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Observation Error ◽  
Localization Radius ◽  
Model Initialization ◽  
Lagrangian Data ◽  
Surface Drifters

<p>The air-sea interface is one of the most physically active interfaces of the Earth's environments and significantly impacts the dynamics in both the atmosphere and ocean. In this study, we discuss the data assimilation of surface drifters, of which the dynamic motions are highly relevant to the instant change of both surface wind field and underlying ocean flow fields. We intend to take advantage of this relationship and improve the estimation of the model initialization in both ocean and coupled atmosphere-ocean systems.</p><p>The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we first propose an augemented-state Lagrangian data assimilation (LaDA) method that is based on the Local Ensemble Transform Kalman Filter (LETKF). The algorithm is tested with “identical twin” approach of Observing System Simulation Experiments (OSSEs) using the ocean model. Examinations on both of the eddy-permitting and the eddy-resolving Modular Ocean Model of the Geophysical Fluid Dynamics Laboratory (GFDL) are tested, which is intended to update the ocean states (T/S/U/V) at both the surface and at depth by directly assimilating the drifter locations. Results show that with a proper choice of localization radius, the LaDA can outperform conventional assimilation of surface in situ temperature and salinity measurements. The improvements are seen not only in the surface state estimate, but also throughout the ocean column to deep layer. The impacts of localization radius and model error in estimating accuracy of both fluid and drifter states are further investigated. In the second section, we investigate the LaDA within a Strongly Coupled Data Assimilation (SCDA) system using the simplified Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), a three-layer truncated quasi-geostrophic model. Results show that assimilating the surface drifter locations directly is capable of improving not only the ocean states but also the atmosphere states as well. We then compare it to the conventional approach to assimilate the approximated velocities instead of the direct drifter locations and it shows that the assimilating drifter locations outperforms the other approach.</p>

scholarly journals Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter

2019 ◽  
Vol 147 (12) ◽  
pp. 4533-4551
Author(s):  
Luyu Sun ◽  
Stephen G. Penny
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ocean Model ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Observation Error ◽  
Localization Radius ◽  
Lagrangian Data ◽  
Ensemble Transform Kalman Filter ◽  
Double Gyre ◽  
Ensemble Transform

Abstract The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we propose a localized Lagrangian data assimilation (LaDA) method that is based on the local ensemble transform Kalman filter (LETKF). The algorithm is tested with an “identical twin” approach in observing system simulation experiments (OSSEs) using a simple double-gyre configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model. Results from the OSSEs show that with a proper choice of localization radius, the LaDA can outperform conventional assimilation of surface in situ temperature and salinity measurements. The improvements are seen not only in the surface state estimate, but also throughout the ocean column to 1000 m depth. The impacts of localization radius and model error in estimating accuracy of both fluid and drifter states are further investigated.

Improving Ocean Circulations Using Lagrangian Data Assimilation of Surface Drifters During Grand Lagrangian Deployments

2021 ◽  
Author(s):  
Luyu Sun ◽  
Stephen Penny ◽  
Matthew Harrison
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
World Ocean ◽  
Global Ocean ◽  
Observation System ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Near Surface ◽  
Lagrangian Data ◽  
Surface Drifters ◽  
The Impact

<p>Accurate forecast of ocean circulation is important in many aspects. A lack of direct ocean velocity observations has been one of the overarching issues in nowadays operational ocean data assimilation (DA) system. Satellite-tracked surface drifters, providing measurement of near-surface ocean currents, have been of increasing importance in global ocean observation system. In this work, the impact of an augmented-state Lagrangian data assimilation (LaDA) method using Local Ensemble Transform Filter (LETKF) is investigated within a realistic ocean DA system. We use direct location data from 300 surface drifters released in the Gulf of Mexico (GoM) by the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment. These drifter observations are directly assimilated into a realistic eddy-resolving GoM configuration of the Modular Ocean Model version 6 (MOM6) of the Geophysical Fluid Dynamics Laboratory (GFDL). Ocean states (T/S/U/V) are updated at both the surface and at depth by utilizing dynamic forecast error covariance statistics. Four experiments are conducted: (1) a free run generated by MOM6; 2) a DA experiment assimilating temperature and salinity profile observations from World Ocean Database 2018 (WOD18); and 3) a DA experiment assimilating both drifter and the profile observations. The LaDA results are then compared with the traditional assimilation using the drifter-derived velocity field from the same GLAD database. In addition, we evaluate the impact of the LaDA algorithm on different eddy-permitting and eddy-resolving model resolutions to determine the most effective horizontal resolutions for assimilating drifter position data using LaDA.</p>

scholarly journals Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM

2019 ◽  
Author(s):  
Lars Nerger ◽  
Qi Tang ◽  
Longjiang Mu
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Computing Time ◽  
Coupled Model ◽  
Ocean Model ◽  
Coupled Models ◽  
Parallel Data ◽  
Efficient Data ◽  
Abstract Data ◽  
Assimilation Process

Abstract. Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g. the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation are ensemble-based methods which use an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the filter reading and writing and also model restarts during the data assimilation process. The study explains the required modifications of the programs on the example of the coupled atmosphere-sea ice-ocean model AWI-CM. Using the case of the assimilation of oceanic observations shows that the data assimilation leads only small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in that the development of data assimilation methods and be separated from the model application.

scholarly journals Simulation of ENSO in the New NCEP Coupled Forecast System Model (CFS03)

2005 ◽  
Vol 133 (6) ◽  
pp. 1574-1593 ◽  
Author(s):  
Wanqiu Wang ◽  
Suranjana Saha ◽  
Hua-Lu Pan ◽  
Sudhir Nadiga ◽  
Glenn White
Keyword(s):  
Southern Oscillation ◽  
Coupled Model ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
Ocean Model ◽  
System Model ◽  
Enso Cycle ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Forecast System ◽  
Enso Events

Abstract A new global coupled atmosphere–ocean forecast system model (CFS03) has recently been developed at the National Centers for Environmental Prediction (NCEP). The new coupled model consists of a T62L64 version of the operational NCEP Atmospheric Global Forecast System model and the Geophysical Fluid Dynamics Laboratory Modular Ocean Model version 3, and is expected to replace the current NCEP operational coupled seasonal forecast model. This study assesses the performance of the new coupled model in simulating El Niño–Southern Oscillation (ENSO), which is considered to be a desirable feature for models used for seasonal prediction. The diagnoses indicate that the new coupled model simulates ENSO variability with realistic frequency. The amplitude of the simulated ENSO is similar to that of the observed strong events, but the ENSO events in the simulation occur more regularly than in observations. The model correctly simulates the observed ENSO seasonal phase locking with the peak amplitude near the end of the year. On average, however, simulated warm events tend to start about 3 months earlier and persist longer than observed. The simulated ENSO is consistent with the delayed oscillator, recharge oscillator, and advective–reflective oscillator theories, suggesting that each of these mechanisms may operate at the same time during the ENSO cycle. The diagnoses of the simulation indicate that the model may be suitable for real-time prediction of ENSO.

Evaluations of BDA Scheme Using the Advanced Research WRF (ARW) Model

2009 ◽  
Vol 48 (3) ◽  
pp. 680-689 ◽  
Author(s):  
Qingnong Xiao ◽  
Liqiang Chen ◽  
Xiaoyan Zhang
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Three Dimensional ◽  
Ocean Basin ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Level Pressure ◽  
Noticeable Improvement ◽  
Arw Model ◽  
Definition Of

Abstract A tropical cyclone bogus data assimilation (BDA) scheme is built in the Weather Research and Forecasting three-dimensional variational data assimilation system (WRF 3D-VAR). Experiments were conducted (21 experiments with BDA in parallel with another 21 without BDA) to assess its impacts on the predictions of seven Atlantic Ocean basin hurricanes observed in 2004 (Charley, Frances, Ivan, and Jeanne) and in 2005 (Katrina, Rita, and Wilma). In addition, its performance was compared with the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane initialization scheme in a case study of Hurricane Humberto (2007). It is indicated that hurricane initialization with the BDA technique can improve the forecast skills of track and intensity in the Advanced Research WRF (ARW). Among the three hurricane verification parameters [track, central sea level pressure (CSLP), and maximum surface wind (MSW)], BDA improves CSLP the most. The improvement of MSW is also considerable. The track has the smallest, but still noticeable, improvement. With WRF 3D-VAR, the initial vortex produced by BDA is balanced with the dynamical and statistical balance in the 3D-VAR system. It has great potential for improving the hurricane intensity forecast. The case study on Hurricane Humberto (2007) shows that BDA performs better than the GFDL bogus scheme in the ARW forecast for the case. Better definition of the initial vortex is the main reason for the advanced skill in hurricane track and intensity forecasting in this case.

scholarly journals Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

2020 ◽  
Vol 13 (9) ◽  
pp. 4305-4321
Author(s):  
Lars Nerger ◽  
Qi Tang ◽  
Longjiang Mu
Keyword(s):  
Data Assimilation ◽  
Climate Model ◽  
Numerical Models ◽  
Computing Time ◽  
Coupled Model ◽  
Ensemble Methods ◽  
Ocean Model ◽  
Coupled Models ◽  
Parallel Data ◽  
Efficient Data

Abstract. Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application.

scholarly journals Coupled Climate Simulation by Constraining Ocean Fields in a Coupled Model with Ocean Data

2009 ◽  
Vol 22 (20) ◽  
pp. 5541-5557 ◽  
Author(s):  
Yosuke Fujii ◽  
Toshiyuki Nakaegawa ◽  
Satoshi Matsumoto ◽  
Tamaki Yasuda ◽  
Goro Yamanaka ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
The Philippines ◽  
Ocean Model ◽  
Climate Simulation ◽  
Philippine Sea ◽  
Estimation System

Abstract The authors developed a system for simulating climate variation by constraining the ocean component of a coupled atmosphere–ocean general circulation model (CGCM) through ocean data assimilation and conducted a climate simulation [Multivariate Ocean Variational Estimation System–Coupled Version Reanalysis (MOVE-C RA)]. The monthly variation of sea surface temperature (SST) is reasonably recovered in MOVE-C RA. Furthermore, MOVE-C RA has improved precipitation fields over the Atmospheric Model Intercomparison Project (AMIP) run (a simulation of the atmosphere model forced by observed daily SST) and the CGCM free simulation run. In particular, precipitation in the Philippine Sea in summer is improved over the AMIP run. This improvement is assumed to stem from the reproduction of the interaction between SST and precipitation, indicated by the lag of the precipitation change behind SST. Enhanced (suppressed) convection tends to induce an SST drop (rise) because of cloud cover and ocean mixing in the real world. A lack of this interaction in the AMIP run leads to overestimating the precipitation in the Bay of Bengal in summer. Because it is recovered in MOVE-C RA, the overestimate is suppressed. This intensifies the zonal Walker circulation and the monsoon trough, resulting in enhanced convection in the Philippine Sea. The spurious positive correlation between SST and precipitation around the Philippines in the AMIP run in summer is also removed in MOVE-C RA. These improvements demonstrate the effectiveness of simulating ocean interior processes with the ocean model and data assimilation for reproducing the climate variability.

Weakly and strongly coupled data assimilation with the coupled ocean-atmosphere model AWI-CM

2020 ◽  
Author(s):  
Qi Tang ◽  
Longjiang Mu ◽  
Dmitry Sidorenko ◽  
Lars Nerger
Keyword(s):  
Data Assimilation ◽  
Climate Model ◽  
Current System ◽  
Coupled Model ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Strongly Coupled ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
Ocean Observations

<p>In this study we compare the results of strongly coupled data assimilation (SCDA) and weakly coupled data assimilation (WCDA), and among the different WCDAs by analyzing the assimilation effect on the prediction of the ocean as well as the atmosphere variables. We have implemented the parallel data assimilation framework (PDAF, http://pdaf.awi.de) with the AWI climate model (AWI-CM), which couples the ocean model FESOM and the atmospheric model ECHAM. In the WCDA, the assimilation acts separately on each component in the coupled model and observations of one component only directly influence its own component. The other components can benefit from the DA through the model dynamics. The alternative to WCDA is SCDA, in which the atmosphere as well as the ocean variables are updated jointly using cross-covariances between the two components. Our current system allows both the SCDA and the WCDA. For the SCDA configuration, either the ocean observations (e.g., satellite sea surface temperature, profiles of temperature and salinity) or the atmosphere observations (e.g., air temperature, surface pressure) or both of them can be assimilated to update the ocean as well as the atmosphere variables. For the WCDA, it allows 1) assimilating only the ocean observations into the ocean state; 2) assimilating only the atmosphere observations into the atmosphere state; 3) assimilating both types of observations into the corresponding component models. The results are evaluated by comparing the estimated ocean and atmosphere variables with the observational data.</p>

Initialization of an ENSO Forecast System Using a Parallelized Ensemble Filter

2005 ◽  
Vol 133 (11) ◽  
pp. 3176-3201 ◽  
Author(s):  
S. Zhang ◽  
M. J. Harrison ◽  
A. T. Wittenberg ◽  
A. Rosati ◽  
J. L. Anderson ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid Model ◽  
Sequential Analysis ◽  
Surface Wind ◽  
Ocean Model ◽  
Consistent Estimate ◽  
State Variables ◽  
Test Bed ◽  
Background Error ◽  
Temperature State

Abstract As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing. A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.

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