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Met Office Ensemble System Current Status and Plans. Neill Bowler [email protected] Outline. Current status and plans Initial conditions perturbations Model physics perturbations Latest results. MOGREPS. LAMEPS. Global Ensemble Prediction Developments.

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Met Office Ensemble System

Current Status and Plans

Neill Bowler

[email protected]

  • Current status and plans
  • Initial conditions perturbations
  • Model physics perturbations
  • Latest results



global ensemble prediction developments
Global Ensemble Prediction Developments
  • Ensemble under development for short-range
    • ETKF perturbations
    • Stochastic physics
    • T+72 global, N144 resolution (~90km in mid-latitudes), 38 levels
    • Run at 0Z & 12Z
lam ensemble prediction developments
LAM Ensemble Prediction Developments
  • Ensemble under development for short-range
    • Regional ensemble over N. Atlantic and Europe (NAE)
    • Nested within global ensemble for LBCs
    • IC perturbations taken directly from global model
    • Stochastic physics
    • T+36 regional, 24km resolution, 38 levels
    • Run at 6Z & 18Z


time line for technical developments
Time-line for technical developments

ETKF to generate NAE IC perturbations

Global ensemble run operationally

NAE ensemble run operationally

Development begins

Ensemble products available in real time?

Summer ‘03

10 June ‘05

2 August ‘05

Spring ‘06

Summer ‘06

We are here!

  • Ensemble Kalman filter is a data assimilation scheme which solves
  • The analysis error covariance matrix is updated according to
  • The ETKF uses the fact that the analysis error covariance for the EnKF can be written as
  • So, the updated perturbations are given by
  • Thus, for the ETKF, the set of analysis perturbations are a linear combination of the forecast perturbations
initial conditions perturbations1
Initial conditions perturbations
  • Perturbations centred around 4D-Var analysis
  • Transforms calculated using same set of observations as used in 4D-Var (including all satellite obs) within +/- 3 hours of data time
  • Ensemble uses 12 hour cycle (data assimilation uses 6 hour cycle)

Model error: parameterisations

  • QUMP (Murphy et al., 2004)
  • Initial stoch. Phys. Scheme for the UM (Arribas, 2004)

Random parameters


Short-range impacts

Intense snowfall over the UK (poorly forecast)



Stochastic Kinetic Energy Backscatter (SKEB)

Based on original idea and previous work by Shutts (2004)

Aim: To backscatter (stochastically) into the forecast model some of the energy excessively dissipated by it at scales near the truncation limit

In the case of the UM, a total dissipation of 0.75 Wm-2 has been estimated from the Semi-lagrangian and Horizontal diffusion schemes. (Dissipation from Physics to be added later on)

Each member of the ensemble is perturbed by a different realization of this backscatter forcing



Streamfunction forcing:

K.- Kinetic En.; R.- Random field;

D.- Dissipated en. in a time-step

R is designed to reproduce some statistical properties found with CRMs


u increments at H500

  • Largest at the jets/storm track


  • Preliminary results:
    • Positive increase in spread (comparable to that seen at ECMWF)

Increase in spread respect to an IC-only ensemble

500 hPa geopotential height



latest results
Latest results
  • Have run a global ensemble forecast with 16 members + control for 2 case studies
  • Overall the results are promising
  • There was a bug in the code for the first case study which may have a minor effect on the results
500hpa height power spectra
500hPa Height Power Spectra
  • The perturbations have similar spectra to full forecast fields (as one might expect).

Avoid perturbing largest scales

Full Forecast Field

Need greater influence from stochastic physics to generate small-scale perturbations


spread of the ensemble with latitude
Spread of the Ensemble with Latitude

RMS innovations for sonde observations of T

Perturbation spread (at observation locations)

the effect of stochastic physics
The Effect Of Stochastic Physics

With stochastic physics

Without stochastic physics

case study 2 8 january 2005
Case study 2 – 8 January 2005

Contours – PMSL

Colours – θw on 850hPa

spread error nh extra tropics
Spread & Error NH extra-tropics

Error in ensemble mean (wrt radiosonde observations)


Error in ensemble mean, with correction for obs errors

rank histogram
Rank Histogram

Saetra et al., MWR, 132 (6) P1487 (2004)

local enkf
Local EnKF


For each grid-point, draw a box around the point, and update ensemble at that point using information in the box only.

I. Szunyogh (with permission)

local etkf
Local ETKF
  • Calculate transform matrix using observations local to a limited set of points, approximately evenly distributed around globe
  • Interpolate transform matrix to intermediate grid points
spherical simplex etkf
Spherical simplex ETKF

Traditional ETKF

Spherical simplex (analysis perturbations are all orthogonal)

(Wang, Bishop & Julier, 2004)

  • Using the spherical simplex transform constrains the transform matrix to have a certain form, helping to ensure consistency of perturbations
local etkf spread
Local ETKF Spread

Error in ensemble mean (wrt radiosonde observations)


Error in ensemble mean, with correction for obs errors

  • ETKF performance is promising
  • Positives are:
    • Near-flat rank histograms
    • Seems to capture the major errors in the forecast
  • Issues are:
    • Spread in tropics
    • Speed of growth of spread
future work
Future work
  • Technical work in preparation for implementation
  • Develop stochastic physics scheme (SKEB)
  • Work on local ETKF scheme, and problems with tropics
  • Longer runs for objective comparisons with other schemes (e.g. error breeding)
  • Investigate usefulness of singular vectors

Model error: excessive diffusion

New approaches

  • Stochastic Backscatter (Shutts, 2004)
  • Hypothesis: model KE dissipation rate is too large

With Stoch. Back.

No Stoch. Back.