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NWP 4: Probabilistic and ensemble forecasting at short and medium-range 13/09/2013

“ High resolution ensemble analysis: linking correlations and spread to physical processes ” S . Dey , R . Plant , N . Roberts and S . Migliorini. NWP 4: Probabilistic and ensemble forecasting at short and medium-range 13/09/2013. Overview.

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NWP 4: Probabilistic and ensemble forecasting at short and medium-range 13/09/2013

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  1. “High resolution ensemble analysis: linking correlations and spread to physical processes ”S. Dey, R. Plant, N. Roberts and S. Migliorini NWP 4: Probabilistic and ensemble forecasting at short and medium-range 13/09/2013

  2. Overview Objective: Investigate methods of evaluating high resolution ensembles • Linking ensemble evolution with physical processes • Understanding of convective events • Evaluating on believable scales Background Case study Results

  3. Background 1: spatial predictability Predictability limits “certain turbulent systems, possibly including the earth’s atmosphere, possess for practical purposes a finite range of predictability” (Lorentz 1969) Scale dependence • Faster error growth at smaller scales (Hohenegger and Schär 2007, BAMS) • Need ensembles at convective scale Upscale error growth: A forecast can be unpredictable at grid scale but predictable at larger scales. • Should be evaluating on scales that are believable

  4. Background 2: correlations Data Assimilation: Background error covariance matrix (B) (x…,y…,z…) Autocross- correlations Auto-correlations (x…,y…,z…) • Sampling uncertainties • Localization Bannister 2008, QJRMS • Present method of analysing the ensemble using correlations. • Present one case study to show utility of techniques: future work to test on more cases

  5. Method 1: case study • MOGREPS-UK domain, UK Met Office UM 7.7 • 11 members + control • 8th July 2011 • 2.2km grid spacing 13:00- 14:00 >10mm >2mm

  6. Method 2: Analysis 2σ 1. Vertical auto- and autocross-correlations 2. Neighbourhood approach Gaussian weighting of perturbations Width set by FSS scale • Believable scale • Variable dependant • Spatially varying

  7. Results 1: Gaussian width 15:00 on 8th July 2013 Rain rate spatial scales Horizontal divergence spatial scales 0 4 8 12 16 Grid points 0 4 8 12 16 Grid points

  8. Results 2: rain rate correlations 09:00 12:00 15:00 18:00 Single point sampling error Convective layer

  9. Results 3: auto-correlations Single column • 12:00 on 8th July 2013 • Horizontal divergence Height [km] Height [km] Spatially augmented ensemble Height [km] Height [km]

  10. Results 4: autocross-correlations Spatially augmented ensemble Single column Cloud Fraction Height [km] Height [km] Height [km] Height [km] Horizontal divergence +ve correlation Divergence -ve correlation Convergence

  11. Conclusions • Extra information from convective scale ensemble using correlations. • Neighbourhood sampling for analysis on meaningful scales. • Reduce sampling error and increase confidence. • Application to one case: future work to look at multiple cases.

  12. Questions? Thanks for listening. Bannister, R. N., 2008: A review of forecast error covariance statistics in atmospheric variational data assimilation. i: Characteristics and measurements of forecast error covariances. Quart. J. Roy. Meteor. Soc., 134, 1951–1970 Hohenegger, C. and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud- resolving scales. Bull. Amer. Meteor. Soc., 88 (7), 1783–1793. Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21 (3), 289–307. Roberts, N., 2008: Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model. Meteorol. Appl., 15 (1), 163–169. Roberts, N. M. and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136 (1), 78– 97. s.dey@pgr.reading.ac.uk

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