Session 1

The 9^th Workshop on Adjoint Model Applications

In Dynamic Meteorology

Cefalu, Sicily, Italy

10-14 October 2011

Sponsored by

NASA's Global Modeling and Assimilation Office

Chief Organizer: Ronald Errico (GMAO and GESTAR)

Organizing committee:

Susan Ballard (Met-Office)

Jan Barkmeijer (KNMI)

Mark Buehner (Environment Canada)

Carla Cardinali (ECMWF)

Gerald Desroziers (Meteo-France)

Marta Janiskova (ECMWF)

Nikki Privé (GMAO/GESTAR)

Liang Xu (NRL)

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PROGRAM

If a presentation has multiple authors affiliated with the same institution, that institution is indicated only once. If an author has multiple affiliations, only the primary one is indicated. A presentation number in bold italics indicates a long (invited) talk. All times are tentative.

Session 0 Sunday Pre-Workshop Tutorials 9 Oct 2011

1000	Ronald Errico	0.1	Fundamentals of Adjoint Models
1200	Lunch
1330	Andrew Lorenc	0.2	Fundamentals of Data Assimilation
1500	Close of Tutorial Session

Session 1 Monday Morning 10 Oct 2011
Session Chair: Liang Xu
0830	Ronald Errico		Welcoming remarks and announcements
Adjoint Development
0845	Marta Janiskova Carla Cardinali	1.1	Forecast error contribution of the global observing system using different energy norms and different representation of physical processes in the adjoint model
0910	Cristina L. Charlton-Perez S. P. Ballard, Z. Li, D. Simonin, H. Buttery, N. Gaussiat, L. Hawkness-Smith	1.2	Adapting the UKMO linear model for NWP-based nowcasting
0935	Tim Payne	1.3	Accounting for linearisation error in the Extended Kalman Filter and 4D-Var
1000	Roel Stappers Jan Barkmeijer	1.4	Optimal linearization trajectories
1025	Break
1045	Adrian Sandu	1.5	Properties of discrete adjoints for adaptive models
1110	Nils van Velzen	1.6	Generic parallelization strategies for data assimilation applications
Sensitivity Analysis
1135	Brett Hoover Michael Morgan	1.7	Dynamical sensitivity analysis of tropical cyclogenesis: a barotropic mode in the eastern Pacific
1200	Michael C. Morgan	1.8	An adjoint description of geostrophic adjustment
1300	Lunch

Session 2 Monday Afternoon 10 Oct 2011
Session Chair: Marta Janiskova
1430	Brian Ancell Lynn McMurdie Rolf Langland	2.1	The predictability of North American land-falling cyclones
1455	Colette Kerry Brian Powell	2.2	Quantifying the sensitivity of nonlinear tides in the Philippine Sea
1520	Oger N., Olivier Pannekoucke Doerenbecher, A. Arbogast, P	2.3	Sensitivity of the KFS to the trajectory of reference
1545	Break
Observation Operators
1605	Steven J. Fletcher Glen E. Liston Christopher A. Hiemstra Steven D. Miller	2.5	Assimilation of MODIS and AMSR-E Snow Parameter Observations into a Physical Snow Model
1630	Zhiquan Liu Quanhua Liu Hui-Chuan Lin Craig Schwartz	2.6	Variational assimilation of MODIS aersosol optical depth over east Asia region
1655	Martin Leutbecher	2.6	On ensemble forecasts, singular vectors and reliability

Session 3 Tuesday Morning 11 Oct 2011
Session Chair: Gerald Desroziers
Error Formulations
0900	Andrew Moore Hernan Arango Gregoire Broquet	3.1	Estimates of analysis error, forecast error, and predictability derived from the adjoint of 4D-Var
0925	Dr. Yann Michel	3.2	Estimating deformations of random processes for correlation modeling in data assimilation
0950	Ricardo Todling	3.3	A smoother-based strategy to estimate system error covariances
1015	Nedjeljka Zagar	3.4	Comparison of balance and flow-dependency of large-scale background-error variances in two ensembles
1040	Break
1100	Chiara Piccolo Mike Cullen	3.5	Adaptive mesh method in the Met Office variational data assimilation system
1125	Thibaut Montmerle Loik Berre	3.6	Use of heterogeneous background error covariances accounting for precipitations at convective scale
1150	Stefano Migliorini	3.7	Information-based data selection for ensemble data assimilation
Variational DAS
1215	Xin Zhang Xiang-Yu Huang Nils Gustafsson	3.8	Control of lateral boundary conditions in WRF 4D-Var
1300	Lunch

Session 4 Tuesday Afternoon 11 Oct 2011
Session Chair: Andrew Lorenc
Variational DAS
1430	Amal El Akkraoui Ricardo Todling Yannick Tremolet	4.1	Using a Bi-Conjugate Gradient minimization algorithm for variational data assimilation
1455	M.A. Freitag Nancy K. Nichols C.J. Budd	4.2	Resolution of sharp fronts in the presence of model error using L1-regularized variational assimilation
1520	Joanna S. Pelc Ehouarn Simon Laurent Bertino Ghada El Serafy Arnold W. Heemink	4.3	Model-reduced 4D-Var data assimilation in application to 1D ecosystem model
1545	Serge Gratton Selime Gurol Philippe Toint	4.4	Preconditioning of conjugate-gradients in observation space with an application to 4D-Var data assimilation
1610	Break
1610	Poster Session
Javier Amezcua Eugenia Kalnay Kayo Ide		4.5		Addressing the nonlinear problem of low order clustering found in deterministic filters by using mean-preserving non-symmetric solutions of the ETKF
Loik Berre Gerald Desroziers Laure Raynaud Hubert Varella Laurent Descamps Carole Labadie		4.6		Variational ensemble data assimilation at Meteo-France for error covariance modelling and ensemble prediction
Vanja Blazica Nedjeljka Zagar		4.7		Quantification of divergence in a mesoscale model
Jean-François Caron		4.8		How to optimally treat large scale information in limited area ensemble-based data assimilation?
Dan Holdaway Ron Errico		4.9		Jacobians of the GEOS5 Relaxed Arakawa-Schubert convection scheme
Erin Kashawlic Brian Ancell		4.10		Comparing observation impact on low-level wind forecasts between an ensemble Kalman filter and a 3DVAR data assimilation scheme
Benjamin Menetrier Thibaut Montmerle Loik Berre Yann Michel		4.11		Variational ensemble-based forecast error variance maps filtering, a toy-models approach
Tamas Prager Éva König Fanni Kelemen		4.12		On the possibilities and limits of direct physical interpretation and synoptical use of mathematical objects related to the adjoint hydro-thermodynamical equations
Tom Rosmond Craig Bishop Dave Kuhl Liz Satterfield		4.13		Balanced Ensemble Localization with Normal Mode Initialization
Elizabeth Satterfield Craig H. Bishop David D. Kuhl Tom Rosmond		4.14		Deriving optimal weights for combining static and flow-dependent covariance models
Polly Smith Andrew Moore		4.15		Application of weak constraint dual formulation 4D-Var to the California Current System
Julius Sumihar Martin Verlaan Stef Hummel Nils van Velzen		4.16		File-based model connections for data-assimilation with OpenDa
Xudong Sun Peter Steinle		4.17		4-Dvar spectral covariance with horizontal anisotropic transformation
Hubert Varella Loik Berre Gerald Desroziers		4.18		Modelling of flow-dependent ensemble-based background error correlations using a wavelet formulation
Martin Verlaan Julius Sumihar		4.19		Ensemble based observations sensitivity applied to storm surge forecasting

Session 5 Wednesday Morning 12 Oct 2011
Session Chair: Jan Barkmeijer
Particle Filters
0900	Peter Jan van Leeuwen	*5.1*	Introduction to particle filters
0950	Melanie Ades Peter Jan van Leeuwen	5.2	Particle filters for large-dimensional problems
1015	Anne Cuzol Etienne Mémin	5.3	Image assimilation with the weighted ensemble Kalman filter
1040	Break
Model Error
1100	Yannick Tremolet	5.4	Towards a longer 4D-Var assimilation window
1125	Carla Cardinali Roberto Buizza Gabor Radnoti Nedjeljka Zagar	5.5	Representing model error in Ensemble Data Assimilation
1150	Laure Raynaud Loik Berre Gerald Desroziers	5.6	Accounting for model error in global and regional ensemble data assimilation systems
1215	Joanne A. Pocock A. S. Lawless S. L. Dance N. K. Nichols	5.7	Errors of representativity
1300	Lunch

Wednesday Afternoon 12 Oct 2011
Free time (weather permitting)

1830	Dinner

Session 6 Thursday Morning 13 Oct 2011
Session Chair: Nikki Privé
Ensemble DAS
0900	Gerald Desroziers Loik Berre	6.1	Accelerating and parallelizing minimizations in ensemble and deterministic variational assimilation
0925	Lisa Neef Katja Matthes	6.2	Assimilation of Earth Rotation Parameters into an Atmosphere Model
0950	Tijana Janjic L.Nerger A. Albertella J.Schroeter S. Skachko	6.3	Domain localization in ensemble based Kalman filter algorithms
1015	Mohamad El Gharamti U. Altaf I. Hotiet A. W. Heemink	6.4	Data assimilation into groundwater contaminant models using an ensemble variational approach
1040	Break
Hybrid Techniques
1100	Adam Clayton Dale Barker Neill Bowler Peter Jermey Andrew Lorenc Rick Rawlins Mike Thurlow	6.5	The Met Office's hybrid ensemble-4D-Var scheme
1125	David D. Kuhl Tom Rosmond Craig H. Bishop Elizabeth Satterfield	6.6	Which matters more in Hybrid Ensemble 4D-VAR, variances or correlations?
1150	Craig H. Bishop Elizabeth Satterfield David D Kuhl Tom Rosmond	6.7	Errors in ensemble-based error covariance estimates and Hybrid ensemble 4D-VAR
1215	Olivier Titaud Jean-Michel Brankart Jacques Verron	6.8	On the use of Lagrangian Coherent Structures in direct assimilation of ocean tracer images
1240	Lunch

Session 7 Thursday Afternoon 13 Oct 2011
Session Chair: Nikki Privé
Other Data Assimilaton
1430	Hajoon Song Ibrahim Hoteit Bruce Cornuelle Aneesh Subramanian	7.1	An adjoint-based adaptive ensemble Kalman filter
1455	Anthony Weaver Isabelle Mirouze	7.2	On the diffusion equation and its application to isotropic and anisotropic correlation modelling in variational assimilation
1520	Brian Powell Bruce Cornuelle	7.3	Quantifying the role of observations in ocean state estimation
1545	Ruth Petrie Ross Bannister	7.4	Background error covariance modeling using normal modes
1615	Break
1635	Zhijin Li Yi Chao James C. McWilliams Kayo Ide	7.5	A multi-scale three dimensional variational data assimilation scheme and its application to coastal oceans
1700	Daryl Kleist John Derber David Parrish Kayo Ide Jeff Whitaker	7.6	Evaluation of a hybrid ensemble-variational data assimilation scheme using an OSSE
1725	Dale Barker	7.7	Strategies For The Use Of Ensemble Information in Data Assimilation
1740		7.8	Discussion

Session 8 Friday Morning 14 Oct 2011
Session Chair: Carla Cardinali
Observation Impacts
0830	Liang Xu Wei Kang	8.1	Optimal Sensor Placement for Data Assimilations
0855	Clark Amerault James D. Doyle	8.2	Adjoint observation impact for a limited area model
0945	Richard Marriott Andrew Lorenc	8.3	Forecast-error-sensitivity to observations in the UM
0945	Break
0945	Alison Fowler Peter Jan Van Leeuwen	8.4	Measures of observation impact in non-Gaussian data assimilation
1010	Break
1030	Patrick E. Farrell S. W. Funke	8.5	A high-level abstraction for developing adjoint models
1055	Ronald Errico Nikki Privé King-Sheng Tai	8.6	The design and validation of Observing System Simulation Experiments at NASA's Global Modeling and Assimilation Office
1120	Nikki Privé Ronald Errico	8.7	Observing System Simulation Experiments OSSEs as tools for the investigation of data assimilation systems
1145	Ronald Errico		Closing Remarks
1200	Lunch