GCCOM\_DART: Ensemble Data Assimilation Analysis System for Sub-mesoscale Processes
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Our research demonstrates how data assimilation can be used, with a non-hydrostatic coastal ocean model, to study sub-mesoscale processes and accurately estimate the state variables. The implementation is non trivial for physical ocean models which are highly nonlinear, sensitive to perturbations, and require a dense spatial discretization in order to correctly reproduce the dynamics. A major challenge of this approach is the high computational cost incurred by a high-resolution numerical model with a three-dimensional data assimilation scheme in a complicated stratified system. Interfacing the General Curvilinear Coastal Ocean Model (GCCOM) with the faster data assimilation framework, NCAR Data Assimilation Research Testbed (DART), allowed us to assimilate very high resolution observations into the system. Observing System Simulation Experiments (OSSEs) in very steep seamount test cases are presented. These were used to explore the proper initial ensemble members for the model, estimate the observation error variance needed to reproduce the dynamics in a turbulent flow experiment, and to analyze the impact of localization in such small processes. Our results demonstrate that the DART-GCCOM model can assimilate high resolution observations (tenths of meters) using as few as 30 ensemble members.
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GCCOM\_DART: Ensemble Data Assimilation Analysis System for Sub-mesoscale Processes
- 1. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Ensemble Data Assimilation Analysis System for Sub-Mesoscale Processes GCCOM DART: Sensitivity Analysis Mariangel Garcia mgarcia@sciences.sdsu.edu http://www.csrc.sdsu.edu/ Jose Castillo, SDSU-CSERC Tim Hoar, NCAR-DAReS Mary Thomas, Barbara Bailey, SDSU-CSERC Beijing, China SIAM-ICIAM 2015 Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 1 / 52
- 2. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Outline • Motivation • GCEM Project (New features) • Data Assimilation Frameworks • GCCOM-DART OSSE • 3D Perfect Model Experiment Seamount • Practical Implementation Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 2 / 52
- 3. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART The need of high resolution coastal ocean model To obtain a more realistic representation of the ocean, models will need to be developed that have higher resolution, improved precision, simultaneous representation of a number of processes. photo: Raincoast GeoResearch Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 3 / 52
- 4. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART The need of high resolution coastal ocean model Relationship between the spatial and temporal scales for different atmospheric and oceanic processes. The horizontal and vertical scale ranges are 10 to 105 km, and 1 hour to 10,000 years, respectively. Source: Modified after Dickey (2001). http://www.theseusproject.eu/ Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 4 / 52
- 5. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART UCOAM: Unified Curvilinear Ocean Atmosphere Model 1 Primitive 3D Navier-Stokes equations using Boussinesq approximation. 2 Nondimensionalization and scaling of the NavierStokes equations. 3 Large Eddie Simulation (LES) 4 Fully written in FORTRAN 90. 5 Uses General Curvilinear Coordinates. 6 Using Fully Non-Hydrostatic Pressure Equation. 7 Using UNESCO Equation of State for density. 1 1Mohammad Abouali and Jose E. Castillo (2013). “Unified Curvilinear Ocean Atmosphere Model (UCOAM): A vertical velocity case study”. In: Math. Comput. Model. 57.9-10, pp. 2158–2168. issn: 08957177. doi: 10.1016/j.mcm.2011.03.023. url: http://linkinghub.elsevier.com/retrieve/pii/S089571771100183X.Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 5 / 52
- 6. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART sigma Vs Curvilinear Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 6 / 52
- 7. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART UCOAM Framework With the goal to be more flexible and easier to use, and offer easy access to data analysis and visualization tools. 2 2Mary P. Thomas (2014). “Parallel Implementation of the Unified Curvilinear Ocean and Atmospheric (UCOAM) Model and Supporting Computational Environment”. PhD thesis. San Diego: Claremont Graduate University and San Diego State University, p. 110. url: Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 7 / 52
- 8. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART UCOAM Framework 1 General Curvilinear Environmental Model (GCEM) • General Curvilinear Coastal Ocean Model (GCCOM) • General Curvilinear Atmosphere Model (GCAM) 2 Distributed Coupling Tools (DCT) 3 Computational Environment (CE ) • Cyber-infrastructure Web Application Framework (CyberWeb) 4 Data Assimilation Unit (DAU) 34 3Dany De Cecchis (2012). “Development of a Parallel Coupler Library with Minimal Inter-process Synchronization for Large Scale Computer Simulations”. In: 4M. Abouali and J E Castillo (2010). General Curvilinear Ocean Model (GCOM) Next Generation. Tech. rep. CSRCR2010-02. Computational Sciences Research Center, San Diego State University, pp. 1–6. url: http://www.csrc.sdsu.edu/research_reports/CSRCR2010-02.pdf. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 8 / 52
- 9. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART GCCOM new features New features • Netcdf I/O integration • 19 points Stencil Laplacian Curvilinear Coordinates CSR format • Two Multigrid libraries implemented to solve non-hydrostatic Pressure • 50% clock time improvement respecting GS (SOR) • Matlab Visualization Tool Upgraded • Upgrading to 4th order in space • Test new multigrid libraries • Building an internal wave ideal experiment • Coupling GCCOM-ROMS • 3D Curvilinear mesh generator app. • Second version of the parallel model. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 9 / 52
- 10. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART MATLAB Visualization Toolbox Upgrade 3D Animation Velocity Speed cross-sections Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 10 / 52
- 11. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART MATLAB Visualization Toolbox Upgrade 3D Animation Velocity Speed cross-sections Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 11 / 52
- 12. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART GCCOM Test Cases Buoyancy Effect Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 12 / 52
- 13. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART GCCOM Test Cases Lock Exchange CUBE Experiment 1km x 1km x 1km Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 13 / 52
- 14. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART GCCOM Test Cases Lock Exchange Seamount Experiment 3.5km x 2.5km x 1km Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 14 / 52
- 15. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART GCCOM Application River meeting with the ocean Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 15 / 52
- 16. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Practical Implementation Stratification and mixing events associated with nearshore internal bores in southern Monterey Bay Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 16 / 52
- 17. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART FACT: Model errors are currently inevitable Uncertainty quantification (UQ) UQ is the process by which uncertainty is estimated in a system. Y − y = e (1) where e is an unknown error Uncertainty reduction (UR) UR which has the purpose of reducing the uncertainty in modeling and simulation. In weather and ocean modeling UR is called Data Assimilation Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 17 / 52
- 18. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Motivation Any data assimilation system consists of three components: 1 set of observations 2 a dynamical model 3 data assimilation scheme The Main goal Reduce the uncertainty in the entire system Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 18 / 52
- 19. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Data Assimilation Framework Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 19 / 52
- 20. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Problem Statement Estimating accurately the state variables in sub-mesoscale process is very difficult, particularly for physical ocean models, which are highly nonlinear and require a dense spatial discretization in order to correctly reproduce the dynamics. 1 High computational cost incurred by a high-resolution numerical model. 2 The efficiency of Kalman Filter in sub-mesoscale processes is unknown. 3 Sensitivity of the model to perturbation. 4 Resolution and Instrument error can affect the forecast. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 20 / 52
- 21. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Challenge to be addressed SOURCE: Hotteit, TAMOS workshop NCAR 2015. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 21 / 52
- 22. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART The Plan Main Objective Develop a very high resolution forecast system by coupling to the General Curvilinear Environmental Model a data assimilation and parametrization schemes based on ensemble filters. Design Thinking 1 Work on the development of GCCOM model. 2 Interfaced with a Data Assimilation framework. 3 Prototype 4 Do Sensitivity Analysis 5 Test and get feedback Repeat 3-5 as long as need it. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 22 / 52
- 23. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Data Assimilation Scheme Question to be addressed • What models do we use? • What assimilation algorithms do we use? • What type of observations do we assimilate? • What are the observation errors? • What are the model and analysis errors? Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 23 / 52
- 24. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Assimilation Approaches Variational Approach • Optimal Interpolation • 3D Var • 4D Var Sequential Approach • Kalman Filter Kalman, 1960 • EnsKF Evensen, 1994 • ELTKF Bishoop Hunt, 2001 • EAKF Anderson, 2001 • Particular Filter Non Gaussian • ESRKF Tippett, 2003 • Hybrid: OI EnsKF, SSEnsKF 56 5E. Kalnay (2003). Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press. isbn: 9780521791793. url: http://books.google.com/books?id=zx_BakP2I5gC. 6Geir Evensen (2006). Data Assimilation: The Ensemble Kalman Filter. Secaucus, NJ, USA: Springer-Verlag New York, Inc. isbn: 354038300X. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 24 / 52
- 25. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART DA Frameworks 789 7National Center for Atmospheric Research (NCAR). Data Assimilation Research Testbed - DART. . 8Deltares. The OpenDA data-assimilation toolbox. 9Lars Nerger and Wolfgang Hiller (2013). “Software for ensemble-based data assimilation systems—Implementation strategies and scalability”. In: Computers and Geosciences 55.0. Ensemble Kalman filter for data assimilation, pp. 110 –118. issn:Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 25 / 52
- 26. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART DART Models Directory Details Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 26 / 52
- 27. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Assimilation Tools Module This module provides subroutines that implement the parallel versions of the sequential scalar filter algorithms. Ensemble Filters • 1 = EAKF (Ensemble Adjustment Kalman Filter, see Anderson 2001) • 2 = ENKF (Ensemble Kalman Filter) • 3 = Kernel filter • 4 = Particle filter • 5 = Random draw from posterior (talk to Jeff before using) • 6 = Deterministic draw from posterior with fixed kurtosis (ditto) • 7 = Boxcar kernel filter • 8 = Rank histogram filter (see Anderson 2010) Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 27 / 52
- 28. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Questions to be addressed • What models do we use? • What assimilation algorithms do we use? • What type of observations do we assimilate? • What are the observation errors? • What are the model and analysis errors? Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 28 / 52
- 29. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Observation to Assimilate SOURCE: NOAA (San Francisco Operational System) Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 29 / 52
- 30. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Observation to Assimilate Temperature loggers and ADCP at the MN mooring. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 30 / 52
- 31. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Observing System Simulation Experiments - OSSEs The primary strategy is to use (OSSEs) to evaluate the impact of new OR planned observing systems. 1 (create_obs_sequence ) to generate the type of observation (and observation error) desired. 2 (create_fixed_network_seq ) to define the temporal distribution of the desired observations. 3 perfect_model_obs: to advance the model from a known initial condition - and harvest the ’observations’ (with error) from the (known) true state of the model. 4 filter: to assimilate the ’observations’. Since the true model state is known, it is possible to evaluate the performance of the assimilation. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 31 / 52
- 32. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Questions to be addressed • What models do we use? • What assimilation algorithms do we use? • What type of observations do we assimilate? • What are the observation errors? • What are the model and analysis errors? Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 32 / 52
- 33. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Dealing with Ensemble Filters Errors Source: https://proxy.subversion.ucar.edu/DAReS/DART/releases/Lanai/tutorial/section_09.pdf Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 33 / 52
- 34. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Filter Module most common namelist settings and features built into DART • Ensemble Size: ensemble sizes between 20 and 100 seem to work best. • Localization: To minimizes spurious correlations and reduce the spatial domain of influence of the observations . Also, for large models it improves run-time performance because only points within the localization radius need to be considered. • Inflation: The spread of the members in a systematic way to avoid problems of filter divergence. • Outlier Rejection: Can be used to avoid bad observations. • Sampling Error: For small ensemble sizes a table of expected statistical error distributions, corrections accounting for these errors are applied during the assimilation. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 34 / 52
- 35. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Perfect Model Experiment Seamount True State True State of the model for the OSSE Experiment, observation control at grid point (nx,ny,nz)=(64,16,10), the total run is 6 hours, data is stored every 10 minutes • Experiment 1: 1 single observation to identify the best localization parameter • Experiment 2: 50 observation sea surface at 21 random depth. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 35 / 52
- 36. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Initial Ensemble Members A proper ensemble has sufficient spread to encompass our uncertainty in our knowledge of the system • Perturb a single state • Climatological ensemble The initial ensemble member for GCCOM • This techniques assume that the variance of the short-term model forecast can approximate the error distribution of the model. • The temporal window used to extract previews model output is typically smaller than the entire system • Helps to resolves physical processes caused by rapid changes in internal forcing. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 36 / 52
- 37. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 1: Perfect Model Experiment Sea-mount Impact of Localization Innov = PosteriorDiag − PriorDiag Velocities U− from Innov X-Y (Different localization ) assim tools nml • filter kind =1 1= EAKF, 2= EKF, 3 = Kernel filter, 4 = particle filter... • cutoff = 0.000010 (radians) about 63.66 meters • select localization = 1 valid values: 1=Gaspari-Cohn; 2=Boxcar; 3=Ramped Boxcar Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 37 / 52
- 38. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 1: Perfect Model Experiment Seamount How is the output different from the input? Innov = PosteriorDiag − PriorDiag Velocities U− from Innov (Plane X−Z. Time Step 1) The vertical layers are tens of meters apart. location nml • horiz dist only = .false. Then full 3D separation • vert normalization height = 6370000.0 Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 38 / 52
- 39. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Perfect Model Experiment Seamount Total of 1000 observation (50 at the top, each one with 21 observation in the vertical) Experiment Set up • 3.5km (lon) x 2.5 km (lat) x 1km (depth) . • 30 m horizonta.l • 10 m vertical resolution. • 10 minutes assimilation window. • Assimilation variable U component. • Total Time 6 hours • 30 Ensemble Members • Observation variance (0.1 - 1.00) • Localization =250 m, 500m, 1000 m, 2000m Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 39 / 52
- 40. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 2: Perfect Model Experiment Seamount Observation control for the State-Space evolution analysis Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 40 / 52
- 41. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 2: State-Space evolution Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 41 / 52
- 42. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 1: Perfect Model Experiment Seamount Was the Assimilation Effective? • Ensemble Spread • how many observations are getting rejected by the assimilation • Rank Histogram • RMSE Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 42 / 52
- 43. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 1: Perfect Model Experiment Sea-mount Ensemble Spread Number of observations available and the number of observations successfully assimilated. • U CURRENT COMPONENT spread: DART QC == 7, prior/post 1 1 Any observations with a QC value greater than ’maxgoodQC’ will get plotted on a separate figure color-coded to its QC value, notGarcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 43 / 52
- 44. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Experiment 2: Perfect Model Experiment Sea-mount Rank Histogram for all time steps • Rank histogram, that the probability that the observation will fall in each bin is equal. • If this is true, then over a large enough sample, the histogram should be flat or roughly so. • Then one can conclude that on the average, the ensemble spread correctly represents the uncertainty in the forecast. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 44 / 52
- 45. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Profile Time Evolution Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 45 / 52
- 46. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART RMSE Vs Spread 00:00 00:20 00:40 01:00 01:20 01:40 02:00 02:20 02:40 03:00 03:20 03:40 04:00 04:20 04:40 05:00 05:20 05:40 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Date Since 2015−01−01 (HH:MM) ForecastDepthAverageRMSE Depth Average Ensemble Mean RMS Error, GCOM ’u’ Level nz=lev([1:32]), lat(nx=64), lon(ny= 16), PriorDiag.nc’ Loc 2000m | ObsErrVar 1.0 | Total Error =0.69536 Loc 2000m | ObsErrVar 0.9 | Total Error =0.70941 Loc 2000m | ObsErrVar 0.8 | Total Error =0.77495 Loc 2000m | ObsErrVar 0.5 | Total Error =0.81652 00:00 00:20 00:40 01:00 01:20 01:40 02:00 02:20 02:40 03:00 03:20 03:40 04:00 04:20 04:40 05:00 05:20 05:40 0 0.005 0.01 0.015 0.02 0.025 0.03 Date Since 2015−01−01 (HH:MM) ForecastDepthAverageEnsembleSpread Depth Average Ensemble Spread, GCOM ’u’ Level nz=lev([1:32]), lat(nx=64), lon(ny= 16), PriorDiag.nc’ Loc 2000m | ObsErrVar 1.0 | Total Spread =0.56466 Loc 2000m | ObsErrVar 0.9 | Total Spread =0.54438 Loc 2000m | ObsErrVar 0.8 | Total Spread =0.52337 Loc 2000m | ObsErrVar 0.5 | Total Spread =0.42281 Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 46 / 52
- 47. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Time Evolution and Profile Diagnostic Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 47 / 52
- 48. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART State evolution Plane X-Z Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 48 / 52
- 49. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Ensemble size and Computational Cost Can we go Operational? CSRC cluster 16-core Xeon nodes each w/ 64GB RAM. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 49 / 52
- 50. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Conclusion We demonstrate how data assimilation can be used, with a non-hydrostatic coastal ocean model, to study sub-mesoscale processes and accurately estimate the state variables. Sensitivity Analysis Summary • The ensemble adjustment Kalman filter (EAKF) has been shown to successfully assimilate very high resolution data in the DA-GCCOM model. • Increasing the ensemble size from 30 to 100 was not crucial for the current prediction • For small domains (kilometers), every observation impacted every state variable. However, the spread of the ensembles tended to reduce over time. Adding inflation factor is need it. • The assimilation system also exhibited some sensitivity to observation error variance, but in general it can handle large observation error variance from 0.8-1.0 • results suggest that the DA-GCCOM ensemble-based system is able to extract the dynamically important information from the model to provide reliable statistics to map the information from Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 50 / 52
- 51. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Southern Monterey Bay Project Stratification and mixing events associated with nearshore internal bores in southern Monterey Bay Temperature loggers and ADCP at the MN mooring. Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 51 / 52
- 52. Motivation UCOAM Governing Equations Data Assimilation GCOM-DART Thank you! Garcia M. PhD Candidate 13th August 2015 SIAM-ICIAM 2015, Beijing, China 52 / 52