Met Office Ensemble System
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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

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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

  • 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

  • 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

NAE


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!


Initial conditions perturbations


EnKF

  • Ensemble Kalman filter is a data assimilation scheme which solves

  • The analysis error covariance matrix is updated according to


ETKF

  • 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 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 physics perturbations


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)


SKEB

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


SKEB

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

Example:

u increments at H500

  • Largest at the jets/storm track


SKEB

  • 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

SKEB

RP+SCV


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

  • 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

Perturbation


Case study 1 – Spanish plume


Mean and Spread PMSL


Spread of the Ensemble with Latitude

RMS innovations for sonde observations of T

Perturbation spread (at observation locations)


The Effect Of Stochastic Physics

With stochastic physics

Without stochastic physics


Case study 2 – 8 January 2005

Contours – PMSL

Colours – θw on 850hPa


Postage Stamps - PMSL


Spread & Error NH extra-tropics

Error in ensemble mean (wrt radiosonde observations)

Spread

Error in ensemble mean, with correction for obs errors


Rank Histogram

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


Rank Histogram


Local EnKF

LEKF:

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

  • 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

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

Error in ensemble mean (wrt radiosonde observations)

Spread

Error in ensemble mean, with correction for obs errors


Conclusions

  • 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

  • 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


Innovation distrubtion – Sonde Temp

Innovations

Perturbations


Innovation distrubtion – ATOVS channel 26

Innovations

Perturbations


Innovation distrubtion – ATOVS channel 40

Innovations

Perturbations


Innovation distrubtion – Aircraft Temp

Innovations

Perturbations


Model error: excessive diffusion

New approaches

  • Stochastic Backscatter (Shutts, 2004)

  • Hypothesis: model KE dissipation rate is too large

With Stoch. Back.

No Stoch. Back.

Missing

energy!


Inflation factors


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