“High resolution ensemble analysis: linking correlations and spread to physical processes ” S. Dey, R. Plant, N. Roberts and S. Migliorini NWP 4: Probabilistic.

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Presentation transcript:

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

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

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

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

Method 2: Analysis

Results 1: Gaussian width Rain rate spatial scales Horizontal divergence spatial scales Grid points 15:00 on 8 th July Grid points

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

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

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

Conclusions 1.Extra information from convective scale ensemble using correlations. 2.Neighbourhood sampling for analysis on meaningful scales. 3.Reduce sampling error and increase confidence. 4.Application to one case: future work to look at multiple cases.

Thanks for listening. Questions? 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.