'Background error' presentation slideshows

WRF-Var System Overview

WRF-Var System Overview. Xin Zhang, Yong-Run Guo, Syed R-H Rizvi, and Michael Duda. Outline. WRF-Var in the WRF Modeling System WRF-Var Software WRF-Var Implementation. WRF-Var in the WRF Modeling System. WRF-Var in the WRF Modeling System. WRF-Var. Blue --> Supported by WRF-Var Team.

753 views • 53 slides

Introduction to Data Assimilation: Lecture 2

566 views • 48 slides

GSI Tutorial 2010

GSI Tutorial 2010. Background and Observation Error Estimation and Tuning. Background Error. Background error covariance Multivariate relation Covariance with fat-tailed power spectrum Estimate background error.

815 views • 48 slides

Numerical Weather Prediction (Met DA) The Analysis of Satellite Data lecture 2 Tony McNally

Numerical Weather Prediction (Met DA) The Analysis of Satellite Data lecture 2 Tony McNally ECMWF. 1. REVIEW OF KEY CONCEPTS Radiance information content 2. BACKGROUND ERROR STRUCTURES Why are they important ? How do we estimate them ? 3. AMBIGUITY BETWEEN VARIABLES

655 views • 45 slides

Overview of GSI

567 views • 45 slides

GSI Overview and Recent Application at CWB

GSI Overview and Recent Application at CWB. Wan-Shu Wu NOAA/NWS/NCEP/EMC. Acknowledgements: John Derber, Yong Han, Daryl Kleist, Dave Parrish, Manuel Pondeca, Jim Purser, Russ Treadon, Paul vanDelst, ??? ??? ??? ???. Overview. History Current system Ongoing / future development

569 views • 42 slides

Data assimilation and forecasting the weather (!)

457 views • 35 slides

Surface objective analyses (O 3 ,PM2.5,NO 2 ) for data assimilation

422 views • 34 slides

Data Assimilation

Data Assimilation. Andrew Collard. Overview. Introduction to Atmospheric Data Assimilation Control Variables Observations Background Error Covariance Summary. Overview. Introduction to Atmospheric Data Assimilation Control Variables Observations Background Error Covariance Summary.

480 views • 31 slides

434 views • 30 slides

D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao Mon. Wea. Rev., 132, 897-914

A Three-Dimensional Variational Data Assimilation System for MM5 : Implementation and Initial Results. D. M. Barker, W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao Mon. Wea. Rev., 132, 897-914. Introduction. Goals of 3DVAR for MM5 :

423 views • 29 slides

Handling Cloud-Affected Infrared Radiances in the GSI

Handling Cloud-Affected Infrared Radiances in the GSI. Will McCarty GSFC/Global Modeling and Assimilation Office JCSDA Workshop 10 October 2012. Introduction. In GMAO forward processing, infrared radiances are assimilated from IASI, AIRS , and HIRS

357 views • 28 slides

Estimating background-error covariance parameters in a variational ocean data

NEMO Users meeting, Daget and Weaver, 2006/06/09. Estimating background-error covariance parameters in a variational ocean data assimilation system through the use of ensemble-based methods. OPAVAR / NEMOVAR Introduction. 2 . Objectives.

378 views • 24 slides

CO 2 Data Assimilation at ECMWF

412 views • 22 slides

Hyperspectral Infrared Water Vapor Radiance Assimilation

Hyperspectral Infrared Water Vapor Radiance Assimilation. James Jung Contributions From: Paul Van Delst, Fanglin Yang, Chris Barnet, John Le Marshall, Lars Peter Riishojgaard and many others. Water Vapor Radiance Assimilation. Problems Innovations do not have a normal distribution

350 views • 19 slides

Maturation of Data Assimilation Over the Last Two Decades

Maturation of Data Assimilation Over the Last Two Decades. John C. Derber Environmental Modeling Center NCEP/NWS/NOAA. Roger Daley (1996) ECMWF seminar on data assimilation.

275 views • 17 slides

CMA report

CMA report. Xueshun Shen WGNE-29, Melbourne, 10-14 Mar.2014. Outline. New HPC Production systems Current status The coming upgrades in 2014/15 Research activities New dynamical core New Chinese satellite. CMA New HPC System SS1 started operation since July/2013 SS2 is ready soon.

421 views • 16 slides

Panel 3: New Science Issues and Possibilities in Data Assimilation

Panel 3: New Science Issues and Possibilities in Data Assimilation. Ensemble Kalman Filtering: Square root filters, Local Ensemble Kalman Filtering 4D-Var: should we consider it? Surface data reanalysis (pre-1948) Model deficiencies: detection and correction

175 views • 10 slides

High-resolution assimilation and weather forecasting

High-resolution assimilation and weather forecasting. Ross Bannister and Stefano Migliorini (NCEO, Reading) Mark Dixon (ex MetO , JCMM, Reading) NCEO Annual Conference, September 2010, Leicester. Oct 29 2008 Jul 7 2007.

186 views • 9 slides

Basic concepts

Maximum Likelihood Estimation and Simplified Kalman Filter tecniques for real time Data Assimilation. Basic concepts. State vector : atmospheric state of the forecast model True state : best representation of the reality

192 views • 8 slides