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The Met Office Ensemble of Regional Reanalyses

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Presentation on theme: "The Met Office Ensemble of Regional Reanalyses"— Presentation transcript:

1 The Met Office Ensemble of Regional Reanalyses
EMS 2015 The Met Office Ensemble of Regional Reanalyses Peter Jermey Dale Barker, Jemma Davie, Amy Doherty, Sana Mahmood, Adam Maycock, Richard Renshaw

2 Reanalysis at the Met Office
2008 & 2009 European Reanalysis 12km Deterministic Reanalysis 40 year European Reanalysis 12km Deterministic Reanalysis 20 member Ensemble Reanalysis

3 Uncertainties in Ensembles of Regional Reanalyses
Ensemble using static 4DVAR Provides lower resolution fields with uncertainty estimation i.e. mean and spread at 24km Production start: Dec 2015 Deterministic reanalysis using hybrid 4DVAR Uses ensemble reanalysis uncertainty to improve assimilation (B) Provides higher resolution deterministic fields at 12km Production start: late 2016

4 Uncertainties in Ensembles of Regional Reanalyses
Ensemble & Deterministic systems coupled 4DVAR minimises weighted sum of differences with background & obs Weights are dependent on background error covariance matrix (B) Ensemble uses fixed bg error cov (B=Bc) Ensemble provides EOTD to ensemble EDA - "hybrid" 4DVAR - weighted sum of bg error covs (B=bcBc+beBe) Bc + Be H 4 Y D B V R A I R D 4 D V A R 4 D V A R 4 D V A R UM UM UM UM

5 Ensemble System Represent every uncertainty in the system via perturbations Uncertainty in Observations Model Boundary Conditions Each ensemble member has a different realisation of these Set of (input) realisations represent the span of all possible realisations Therefore (output) spread should estimate uncertainty in the system

6 Observations For every observation we have a measurement and an uncertainty estimate... To obtain several realisations randomly perturb the measurement within the uncertainty estimate...

7 Model To perturb the model we need an estimate of model error...
Assuming the analyses are drawn from the same distribution as the truth.

8 Boundary Conditions * * 80km *planned

9 RMS v Spread T2 & 500H (10 members)
Germany Temperature 1.5m Geopotential Height 500hPa

10 Examples of Product 18Z on 18/02/10 mean uncertainty

11 Probability Maps Probability 50m wind 3ms-1 < speed < 15ms-1
>0.9 <0.1 Probability 50m wind 3ms-1 < speed < 15ms-1 18Z on 18/02/10 Probability of 1mm/6h 12Z to 18Z 18/02/10

12 Improve quality of ensemble
What’s Next ..? Improve quality of ensemble Port to ECMWF Regional SURF Ensemble production aim to start December 2015 Regional hybrid Deterministic production aim to start late 2016

13 Thank you for listening

14 Initial Test Run (no inflation) T2 & 500H
1. Each member equally likely 2. Mean Error < Ctrl Error 3. En Spread = Mean RMSE 4. Model Freq. = Obs. Freq. 500H T2


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