8th EnKF Workshop Discussion.

Slides:

Advertisements

Similar presentations

1 -Classification: Internal Uncertainty in petroleum reservoirs.

Advertisements

Data-Assimilation Research Centre

Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.

Effects of model error on ensemble forecast using the EnKF Hiroshi Koyama 1 and Masahiro Watanabe 2 1 : Center for Climate System Research, University.

Ensemble Forecasting of Hurricane Intensity based on Biased and non-Gaussian Samples Zhan Zhang, Vijay Tallapragada, Robert Tuleya HFIP Regional Ensemble.

Jidong Gao and David Stensrud Some OSSEs on Assimilation of Radar Data with a Hybrid 3DVAR/EnKF Method.

Ibrahim Hoteit KAUST, CSIM, May 2010 Should we be using Data Assimilation to Combine Seismic Imaging and Reservoir Modeling? Earth Sciences and Engineering.

1 Graphical Models in Data Assimilation Problems Alexander Ihler UC Irvine Collaborators: Sergey Kirshner Andrew Robertson Padhraic Smyth.

1/20 Accelerating minimizations in ensemble variational assimilation G. Desroziers, L. Berre Météo-France/CNRS (CNRM/GAME)

Spatially clustered processes are very pervasive in nature Can we do more to insure that our estimates are physically realistic? How can we incorporate.

Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter April 2006, EnKF Wildflower Meeting Greg Hakim & Ryan Torn University of Washington.

Simultaneous Estimation of Microphysical Parameters and State Variables with Radar data and EnSRF – OSS Experiments Mingjing Tong and Ming Xue School of.

ASSIMILATION of RADAR DATA at CONVECTIVE SCALES with the EnKF: PERFECT-MODEL EXPERIMENTS USING WRF / DART Altuğ Aksoy National Center for Atmospheric Research.

Andrew Schuh, Scott Denning, Marek Ulliasz Kathy Corbin, Nick Parazoo A Case Study in Regional Inverse Modeling.

WWRP/THORPEX WORKSHOP on 4D-VAR and ENSEMBLE KALMAN FILTER INTER-COMPARISONS BUENOS AIRES - ARGENTINA, NOVEMBER 2008 Topics for Discussion in Session.

Validation and intercomparation discussion Items Size of assimilation ensembles. Is it to be determined only by numerical stability of algorithms? (O.

Experimenting with the LETKF in a dispersion model coupled with the Lorenz 96 model Author: Félix Carrasco, PhD Student at University of Buenos Aires,

Accounting for correlated AMSU-A observation errors in an ensemble Kalman filter 18 August 2014 The World Weather Open Science Conference, Montreal, Canada.

Current Status of the Development of the Local Ensemble Transform Kalman Filter at UMD Istvan Szunyogh representing the UMD “Chaos-Weather” Group Ensemble.

Consortium Meeting June 3, Thanks Mike! Hit Rates.

Models for model error –Additive noise. What is Q(x 1, x 2, t 1, t 2 )? –Covariance inflation –Multiplicative noise? –Parameter uncertainty –“Structural”

Classification: Internal Status: Draft Using the EnKF for combined state and parameter estimation Geir Evensen.

LAMEPS Development and Plan of ALADIN-LACE Yong Wang et al. Speaker: Harald Seidl ZAMG, Austria Thanks: Météo France, LACE, NCEP.

EnKF Overview and Theory

Nonlinear Data Assimilation and Particle Filters

WWOSC 2014, Aug 16 – 21, Montreal 1 Impact of initial ensemble perturbations provided by convective-scale ensemble data assimilation in the COSMO-DE model.

ISDA 2014, Feb 24 – 28, Munich 1 Impact of ensemble perturbations provided by convective-scale ensemble data assimilation in the COSMO-DE model Florian.

Assimilation of HF Radar Data into Coastal Wave Models NERC-funded PhD work also supervised by Clive W Anderson (University of Sheffield) Judith Wolf (Proudman.

Common Set of Tools for Assimilation of Data COSTA Data Assimilation Summer School, Sibiu, 6 th August 2009 COSTA An Introduction Nils van Velzen

10/18/2011 Youngsun Jung and Ming Xue CAPS/OU with help from Tim Supinie.

Assimilating Reflectivity Observations of Convective Storms into Convection-Permitting NWP Models David Dowell 1, Chris Snyder 2, Bill Skamarock 2 1 Cooperative.

Ensemble Data Assimilation for the Mesoscale: A Progress Report Massimo Bonavita, Lucio Torrisi, Francesca Marcucci CNMCA National Meteorological Service.

A unifying framework for hybrid data-assimilation schemes Peter Jan van Leeuwen Data Assimilation Research Center (DARC) National Centre for Earth Observation.

Sensitivity Analysis of Mesoscale Forecasts from Large Ensembles of Randomly and Non-Randomly Perturbed Model Runs William Martin November 10, 2005.

A Sequential Hybrid 4DVAR System Implemented Using a Multi-Grid Technique Yuanfu Xie 1, Steven E. Koch 1, and Steve Albers 1,2 1 NOAA Earth System Research.

. Outline  Evaluation of different model-error schemes in the WRF mesoscale ensemble: stochastic, multi-physics and combinations thereof  Where is.

Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.

Adaptive Hybrid EnKF-OI for State- Parameters Estimation in Contaminant Transport Models Mohamad E. Gharamti, Johan Valstar, Ibrahim Hoteit European Geoscience.

Ensemble Kalman Filter in a boundary layer 1D numerical model Samuel Rémy and Thierry Bergot (Météo-France) Workshop on ensemble methods in meteorology.

Errors, Uncertainties in Data Assimilation François-Xavier LE DIMET Université Joseph Fourier+INRIA Projet IDOPT, Grenoble, France.

Quality of model and Error Analysis in Variational Data Assimilation François-Xavier LE DIMET Victor SHUTYAEV Université Joseph Fourier+INRIA Projet IDOPT,

Reservoir Uncertainty Assessment Using Machine Learning Techniques Authors: Jincong He Department of Energy Resources Engineering AbstractIntroduction.

Implementation and Testing of 3DEnVAR and 4DEnVAR Algorithms within the ARPS Data Assimilation Framework Chengsi Liu, Ming Xue, and Rong Kong Center for.

Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo(1997) Using the Ensemble Kalman Filter Chen Deng-Shun 16 Apr, 2013, NCU Godinez,

A Random Subgrouping Scheme for Ensemble Kalman Filters Yun Liu Dept. of Atmospheric and Oceanic Science, University of Maryland Atmospheric and oceanic.

Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 LETKF 앙상블 자료동화 시스템 테스트베드 구축 및 활용방안 Implementation and application of LETKF.

Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,

Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.

Multi-scale Analysis and Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Assimilating Radar and Surface Network Data using EnKF Ting.

Hybrid Data Assimilation

Xuexing Qiu and Fuqing Dec. 2014

Japan Meteorological Agency / Meteorological Research Institute

Grid Point Models Surface Data.

Update on the Northwest Regional Modeling System 2013

Data Assimilation Training

BACY = Basic Cycling A COSMO Data Assimilation Testbed for Research and Development Roland Potthast, Hendrik Reich, Christoph Schraff, Klaus.

MO – Design & Plans UERRA GA 2016 Peter Jermey

Peter May and Beth Ebert CAWCR Bureau of Meteorology Australia

Radar Data Assimilation

background error covariance matrices Rescaled EnKF Optimization

Ensemble variance loss in transport models:

New Approaches to Data Assimilation

FSOI adapted for used with 4D-EnVar

WMO NWP Wokshop: Blending Breakout

2. University of Northern British Columbia, Prince George, Canada

Lightning Assimilation Techniques

Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer

Sarah Dance DARC/University of Reading

Yalchin Efendiev Texas A&M University

Presentation transcript:

8th EnKF Workshop Discussion

Limits to EnKF: Great progress over the last 10 years thanks to ensemble based data assimilation. limits: model error (boundary layer + stratosphere + microphysics). How to deal with slowly evolving model error?

Hybrids Ensemble size: we would like to use very large ensembles with 1000 members or more. How to obtain such a big ensemble efficiently? Mix-resolution ensembles? ETLM? An alternative is the use of hybrids. Does this work at high resolution?

Radar The direct assimilation of radar observations with forward operators based on our best microphysics has a only modest impact beyond 6h. The Frankenstate directly links the radar image with a best member. Is this a good way to avoid the complexity of microphysics in data- assimilation and have longer lasting impacts?

Particle filters Are non-Gaussian issues best solved using particle filters? How many particles should we have? 1000? Should we use iterative smoother algorithms or particle filters to link radar measurements with large-scale precursors of precipitation?

B-localization B-localization seems to give best result. In EnKF formulations this is expensive (factor of 10). Is it worth it? Study by Mitchell showed the main problem for AMSU-A data assimilation is the structure of B, not the localization. What would create deep modes in B? Should we sample uncertainty in bias correction? Something else? Should we do a Principal Component Analysis of radiance observations and does this affect the above?

System (aka model) error Many different ways to account for system error are in use: RTPP, RTPS, Additive covariance inflation, adaptive multiplicative inflation. Schemes are often combined and used in an engineering fashion (tuned for optimal results). A few schemes are/seem “physically informed”: SKEB, randomized transport. No schemes feed back to system/model improvement. How to connect system error simulation with system development?

Forecast sensitivity to observations Should system error parameterizations (additive inflation, RTPP, etc. ) be undone?

Next workshop Any suggestions for a 9th EnKF workshop? Issue of having too many similar meetings. The EnKF workshop is every two years. ISDA every year. Also note the yearly Bergen workshop for a slightly different crowd (oceanography + oil reservoir modeling).

Closure