'Analysis error' diaporamas de présentation

Zoltan Toth Global Systems Division, ESRL/OAR/NOAA Acknowledgements: Isidora Jankov, Malaquias Pena, Yuanfu Xie, Paula McCaslin, Paul Schultz, Linda Wharton, Roman Krysztofowicz, Yuejian Zhu, Andre Methot, Tom Hamill, Kathy Gilbert, et al.

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ECMWF Data Assimilation Training Course - Kalman Filter Techniques

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Mesoscale Atmospheric Predictability

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Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping

Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping. Presented by John Clark March 24, 2014. Paper Summary. Describes algorithmic changes to existing Dynamic Time Warping (DTW) calculations to increase search efficiency

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Assessing a Local Ensemble Kalman Filter

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Assessing a Local Ensemble Kalman Filter

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The RTMA

The RTMA. Real-time Mesoscale Analysis. 2006 NWS Southern Region SOO Conference 11July 2006. Jeffrey M. Medlin Science and Operations Officer National Weather Service Mobile, Alabama. Outline. Presentation Overview What is the RTMA? RTMA Procedure Outline RTMA Details

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Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC)

Pertinent Issues and Open Questions Regarding the Use of Ensembles for Weather Analysis and Prediction. Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC) Global Modeling and Assimilation Office (NASA). Outline. 1. Determination of analysis error

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Status of DART-CAM

Status of DART-CAM. Kevin Raeder; CCSM interface to DART Jeff Anderson; DART development and organization Hui Liu; Observations Tim Hoar; Software, hardware, grayware Nancy Collins; Software Engineering. are the Data Assimilation Research Section of

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Hybrid Variational/Ensemble Data Assimilation for the NCEP GFS

Hybrid Variational/Ensemble Data Assimilation for the NCEP GFS. Tom Hamill, for Jeff Whitaker NOAA Earth System Research Lab, Boulder, CO, USA jeffrey.s.whitaker@noaa.gov Daryl Kleist, Dave Parrish and John Derber National Centers for Environmental Prediction, Camp Springs, MD, USA

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Single versus dual component wind observations

Single versus dual component wind observations. L. P. Riishojgaard, UMBC/GSFC R. M. Atlas, NOAA G. D. Emmitt, SWA O. Reale, UMBC/GSFC J.-C. Jusem, UMBC/GSFC G. Brin, SAIC/GSFC J. Terry, SAIC/GSFC. Overview. Background 1. Single-level analysis framework and experimental result

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Forecasting Forecast Error

Forecasting Forecast Error. Andy Moore & Kevin Smith UC Santa Cruz Hernan Arango Rutgers University. How can we predict forecast error ?. Ensemble methods. 0. t. Issues with ensemble methods: - how to choose the perturbations? - how large should the ensemble be?

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Rod Frehlich and Robert Sharman University of Colorado, Boulder RAP/NCAR Boulder

The Importance of Atmospheric Variability for Data Requirements, Data Assimilation, Forecast Errors, OSSEs and Verification. Rod Frehlich and Robert Sharman University of Colorado, Boulder RAP/NCAR Boulder Funded by NSF (Lydia Gates) and FAA/AWRP. In Situ Aircraft Data.

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NIWA, New Zealand

Hindcasting mesoscale variability: Statistical projection of altimetry to subsurface T-S, and assimilation into ROMS by intermittent optimal interpolation John Wilkin. NIWA, New Zealand. Rutgers University. East Auckland Current. Western boundary current upwelling. Wind driven upwelling.

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Advanced data assimilation methods with evolving forecast error covariance

Advanced data assimilation methods with evolving forecast error covariance. Four-dimensional variational analysis (4D-Var) Shu-Chih Yang (with EK). Find the optimal analysis. (forecast). Best estimate the true value. (observation). Least squares approach

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The aim of the study

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Recent Developments in Ensemble Research

NAVAL RESEARCH LABORATORY. Recent Developments in Ensemble Research Carolyn Reynolds, Justin McLay , Elizabeth Satterfield, Daniel Hodyss Naval Research Laboratory, Marine Meteorology Division, Monterey , CA, USA Michael Sestak

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Assemble from some archive a matrix of forecast perturbations. Obtain a new matrix by carrying out an

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