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Met Office Ensemble System Current Status and Plans

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Presentation on theme: "Met Office Ensemble System Current Status and Plans"— Presentation transcript:

1 Met Office Ensemble System Current Status and Plans
Neill Bowler © Crown copyright 2005

2 Outline Current status and plans Initial conditions perturbations
Model physics perturbations Latest results © Crown copyright 2005

3 LAMEPS MOGREPS © Crown copyright 2005

4 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 © Crown copyright 2005

5 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 © Crown copyright 2005

6 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! © Crown copyright 2005

7 Initial conditions perturbations
© Crown copyright 2005

8 EnKF Ensemble Kalman filter is a data assimilation scheme which solves
The analysis error covariance matrix is updated according to © Crown copyright 2005

9 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 © Crown copyright 2005

10 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) © Crown copyright 2005

11 Model physics perturbations
© Crown copyright 2005

12 Random parameters Model error: parameterisations Parameter Scheme
QUMP (Murphy et al., 2004) Initial stoch. Phys. Scheme for the UM (Arribas, 2004) Random parameters Parameter Scheme min/std/Max Entrainment rate CONVECTION 2 / 3 / 5 Cape timescale 30 / 30 / 120 RH critical LRG. S. CLOUD 0.6 / 0.8 / 0.9 Cloud to rain (land) 1E-4/8E-4/1E-3 Cloud to rain (sea) 5E-5/2E-4/5E-4 Ice fall 17 / 25.2 / 33 Flux profile param. BOUNDARY L. 5 / 10 / 20 Neutral mixing length 0.05 / 0.15 / 0.5 Gravity wave const. GRAVITY W.D. 1E-4/7E-4/7.5E-4 Froude number 2 / 2 / 4 © Crown copyright 2005

13 Short-range impacts Intense snowfall over the UK (poorly forecast)
© Crown copyright 2005

14 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 © Crown copyright 2005

15 SKEB Streamfunction forcing: Example: u increments at H500
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 © Crown copyright 2005

16 SKEB Preliminary results: SKEB
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 © Crown copyright 2005

17 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 © Crown copyright 2005

18 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 © Crown copyright 2005

19 Case study 1 – Spanish plume
© Crown copyright 2005

20 Mean and Spread PMSL © Crown copyright 2005

21 Spread of the Ensemble with Latitude
RMS innovations for sonde observations of T Perturbation spread (at observation locations) © Crown copyright 2005

22 The Effect Of Stochastic Physics
With stochastic physics Without stochastic physics © Crown copyright 2005

23 Case study 2 – 8 January 2005 Contours – PMSL Colours – θw on 850hPa
© Crown copyright 2005

24 Postage Stamps - PMSL © Crown copyright 2005

25 Spread & Error NH extra-tropics
Error in ensemble mean (wrt radiosonde observations) Spread Error in ensemble mean, with correction for obs errors © Crown copyright 2005

26 Rank Histogram Saetra et al., MWR, 132 (6) P1487 (2004)
© Crown copyright 2005

27 Rank Histogram © Crown copyright 2005

28 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) © Crown copyright 2005

29 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 © Crown copyright 2005

30 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 © Crown copyright 2005

31 Local ETKF Spread Error in ensemble mean (wrt radiosonde observations)
Error in ensemble mean, with correction for obs errors © Crown copyright 2005

32 Conclusions ETKF performance is promising Positives are: Issues are:
Near-flat rank histograms Seems to capture the major errors in the forecast Issues are: Spread in tropics Speed of growth of spread © Crown copyright 2005

33 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 © Crown copyright 2005

34 Innovation distrubtion – Sonde Temp
Innovations Perturbations © Crown copyright 2005

35 Innovation distrubtion – ATOVS channel 26
Innovations Perturbations © Crown copyright 2005

36 Innovation distrubtion – ATOVS channel 40
Innovations Perturbations © Crown copyright 2005

37 Innovation distrubtion – Aircraft Temp
Innovations Perturbations © Crown copyright 2005

38 New approaches Model error: excessive diffusion
Stochastic Backscatter (Shutts, 2004) Hypothesis: model KE dissipation rate is too large No Stoch. Back. With Stoch. Back. Missing energy! © Crown copyright 2005

39 Inflation factors © Crown copyright 2005

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