'Model error' diaporamas de présentation

Intelligent Use of LAPS

Intelligent Use of LAPS. By Ed Szoke and Steve Albers 16 December 1999. LAPS. A system designed to: Exploit all available data sources Create analyzed and forecast grids Build products for specific forecast applications Use advanced display technology

1.03k views • 90 slides

Judith Berner , NCAR

Representing model uncertainty in weather and climate: stochastic versa multi-physics representations. Judith Berner , NCAR. Key Points. There is model error in weather and climate models from the need to parameterize subgrid-scale fluctuations

727 views • 50 slides

Judith Berner , NCAR

Representing model uncertainty in weather and climate: stochastic versa multi-physics representations. Judith Berner , NCAR. Key Points. There is model error in weather and climate models from the need to parameterize subgrid-scale fluctuations

653 views • 50 slides

Non-linear System Identification: Possibilities and Problems

716 views • 44 slides

Model Selection and Validation

514 views • 39 slides

Working with Uncertainty in Model Predictive Control

Working with Uncertainty in Model Predictive Control. Bob Bitmead University of California, San Diego. Nonlinear MPC Workshop. 4 April, 2005, Sheffield UK. Outline. Model Predictive Control Constrained receding horizon optimal control

575 views • 36 slides

Ensemble Kalman Filter

Ensemble Kalman Filter. Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado. Guest Lecture at AT 753: Atmospheric Water Cycle 21 April 2006, CSU/ATS Dept., Fort Collins, CO. Acknowledgements: M. Zupanski, C. Kummerow, S. Denning, and M. Uliasz, CSU

647 views • 28 slides

SWOT spatio-temporal errors from in-situ measurements

S. Biancamaria (1) , N. Mognard (1) , Y. Oudin (1) , M. Durand (2) , E. Rodriguez (3) , E. Clark (4) , K. Andreadis (4) , D. Alsdorf (2) , D. Lettenmaier (4)? (1) LEGOS, FR (2) Ohio State University, US (3) Jet Propulsion Laboratory, US

372 views • 26 slides

A General Ensemble-Based Approach to Data Assimilation Model Error and Parameter Estimation

A General Ensemble-Based Approach to Data Assimilation Model Error and Parameter Estimation. Dusanka Zupanski CIRA/Colorado State University Fort Collins, Colorado. LSCE Presentation CEA/Saclay, Gif-sur-Yvette, October 19, 2005. Outline State augmentation approach

384 views • 25 slides

Five Years of Development of New Sources of Information for a Canadian Brigade

372 views • 25 slides

MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski

MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins, CO 80523-1375. ATS Colloquium series 29 January 2004. ftp://ftp.cira.colostate.edu/Zupanski/presentations.

352 views • 24 slides

Balance in the EnKF

Balance in the EnKF. Jeffrey D. Kepert Centre for Australian Weather and Climate Research A partnership between the Australian Bureau of Meteorology and CSIRO WWRP/Thorpex Workshop on 4D-Var and EnKF Intercomparisons 10-13 November, 2008. EnKF and Slow Manifold.

477 views • 23 slides

Update on the Inclusive Measurement of the b ? s? Transition Rate Using a Lepton Tag

Update on the Inclusive Measurement of the b ? s? Transition Rate Using a Lepton Tag. Philip Bechtle (until 5/07) * , Rainer Bartoldus SLAC Colin Jessop, Kyle Knoepfel ? , Postdoc (TBD) Notre Dame University Al Eisner, Bruce Schumm, Luke Winstrom ? UC Santa Cruz Minghui Lu

308 views • 22 slides

Use of Model Error With the EnKF

Use of Model Error With the EnKF. S. I. Aanonsen Voss Workshop 18 – 20 June 2008. Motivation. An unkown model error is present in all real problems Originally, the Kalman filter and the EnKF was developed to update state variables for models with error

346 views • 19 slides

Critical issues of ensemble data assimilation in application to carbon cycle studies

Critical issues of ensemble data assimilation in application to carbon cycle studies. Dusanka Zupanski And Scott Denning Colorado State University Fort Collins, CO 80523-1375. CMDL Workshop on Modeling and Data Analysis of Atmospheric CO 2 Observations in North America

266 views • 19 slides

A Method for the More Accurate Measurement and Communication of Model Error

A Method for the More Accurate Measurement and Communication of Model Error. Scott Fortmann-Roe University of California, Berkeley. Predictions. 1) More accurate assessment of prediction error. 2 ) More accurate models. Inferences. 3) More accurate measures of significance.

281 views • 17 slides

Model configuration

Model configuration. 2-layer spectral PE model, run at T127 (truth), T63, and T31. Model state is vorticity, divergence, and layer thickness of Exner function. Wavenumber 2 terrain peaked at 45N&S. No land/water. Forced by Newtonian relaxation of interface

273 views • 17 slides

V Gorin and M Tsyrulnikov Can model error be perceived from routine observations?

V Gorin and M Tsyrulnikov Can model error be perceived from routine observations?. Motivation : all existing approaches in model-error modeling are largely ad-hoc . Any objective knowledge on real model errors is very welcome. Model error: definition. The forecast equation:.

239 views • 15 slides

Ensemble forecasting/data assimilation and model error estimation algorithm Prepared by

Ensemble forecasting/data assimilation and model error estimation algorithm Prepared by Dusanka Zupanski and Milija Zupanski CIRA/CSU. References Zupanski, M., 2004: The Maximum Likelihood Ensemble Filter. Theoretical aspects. Submitted to Mon. Wea. Rev .

159 views • 9 slides

Snow assimilation using EOS snow products

Snow assimilation using EOS snow products. Why do we need snow data assimilation? Models are imperfect, so are observations: we need to combine model and observation in an optimal way (by taking into account their errors) to provide:

155 views • 3 slides