Workshop on Flow-dependent aspects of data assimilation

The Workshop on Flow-dependent aspects of data assimilation was from 11 to 13 June 2007.

Description

The workshop considered advances in data assimilation methods that address the flow-dependence of the analysis problem. Current assimilation methods for operational numerical weather prediction rely on time-averaged covariance statistics that may be close to optimal on average, but which are quite incorrect in extreme situations such as intense baroclinic development, strong organised convection and tropical cyclones. Such cases are of particular interest, both to weather forecasters and to the general public, because of their unusual nature, high intensity, and impact on society. Flow dependence is important in less extreme situations too. Properly accounting for the day-to-day variation of error statistics, as well as their anisotropies and inhomogeneities, has the potential to significantly improve analysis quality.

Flow dependence would also allow more effective use of the available observations, and facilitate quality control that could retain extreme observations. Topics for the workshop included: ensemble-assimilation methods, state-dependent modelling of background-error statistics, flow-dependent quality control, data selection, and targeting of observations.

Programme

PDF iconIntroduction and working group reports

PDF iconParticipants

PDF iconProgramme

 

Presentations

Flow dependence in global data assimilation systems  

Ideas for adding flow-dependence to the Met Office VAR system

Andrew Lorenc (Met Office)

PDF icon

Covariance modelling in a grid-point analysis

Jim Purser (NCEP)

PDF icon

Use of analysis ensembles in estimating flow-dependent background error variances

Mike Fisher (ECMWF)

PDF icon
Ensemble approaches  

Ensemble Jb modelling

Mark Buehner (Environment Canada)

PDF icon

Impacts of flow-dependent background-error covariances in the NCEP Global Forecast System

Jeff Whitaker (NOAA/ESRL)

PDF icon

Use of analysis ensembles in estimating flow-dependent background error variances

Lars Isaksen (ECMWF)

PDF icon

Implementation of the LETKF on the NCEP Global Forecast System

Istvan Szunyogh (University of Maryland)

PDF icon
Change of variable approaches  

Flow-dependent transforms

Ian Roulstone (University of Surrey)

-

A regime-dependent balanced control variable based on potential vorticity

Ross Bannister (University of Reading)

PDF icon

Background errors in HIRLAM variational data assimilation

Magnus Lindskog (HIRLAM)

PDF icon

Diabatic omega equation

Luc Fillion (Environment Canada)

PDF icon

Humidity control variable and total water

Elias Holm (ECMWF)

PDF icon
Implementation issues  

Spatial filtering of analysis ensemble statistics

Olivier Pannekoucke (Meteo-France)

PDF icon

Representation of inhomogeneous, non-separable covariances by sparse wavelet transformed matrices

Andreas Rhodin (DWD)

PDF icon

Ensemble forecasting and flow-dependent estimates of initial uncertainty

Martin Leutbecher

PDF icon

Proceedings

A regime-dependent balanced control variable based on potential vorticity

R N Bannister

PDF icon

A variational assimilation ensemble and the spatial filtering of its error covariances: increase of sample size by local spatial averaging

L Berre

PDF icon

Ensemble-based background-error covariances in variational data assimilation

M Buehner

PDF icon

Case dependent implicit normal mode balance operators

S Lee

PDF icon

The sensitivity of analysis errors to the specification of background error covariances

M Fisher

PDF icon

Humidity control variable and supersaturation

E V Holm

PDF icon

Use of analysis ensembles in estimating flow-dependent background error variance

L Isaksen

PDF icon

Ensemble forecasting and flow-dependent estimates of initial uncertainty

M Leutbecher

PDF icon

Background errors in HIRLAM variational data assimilation

M Lindskog

PDF icon

Ideas for adding flow-dependence to the Met Office VAR system

A Lorenc

PDF icon

Covariance modelling in a grid-point analysis

R Purser

PDF icon

Representation of inhomogeneous, non-separable covariances by sparse wavelet- transformed matrices

A Rhodin

PDF icon

Flow-dependent transforms

I Roulstone

PDF icon

The Local Ensemble Transform Kalman Filter and its implementation on the NCEP global model at the University of Maryland

I Szunyogh

PDF icon

Impacts of flow-dependent background-error covariances in the NCEP Global Forecast System

J Whittaker

PDF icon

Local information


You can also see an archive of past workshops

View full calendar of events