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“New tools for the evaluation of convective scale ensemble systems” Seonaid Dey Supervisors: Bob Plant, Nigel Roberts and Stefano Migliorini.

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Presentation on theme: "“New tools for the evaluation of convective scale ensemble systems” Seonaid Dey Supervisors: Bob Plant, Nigel Roberts and Stefano Migliorini."— Presentation transcript:

1 “New tools for the evaluation of convective scale ensemble systems” Seonaid Dey Supervisors: Bob Plant, Nigel Roberts and Stefano Migliorini

2 Introduction 1.What 2.Why – Mesoscale – Data assimilation 3.How – Case study – Skilful scale – correlations

3 What? Statistics: Link statistical measures with physical processes and spatial verification measures Ensembles: Investigate how deterministic techniques can be extended to ensembles Predictability: look into meteorological aspects and scales that are useful for determining predictability

4 Why DA? Bannister et. al. (2011) Pressure increments Geostrophic balance Wind increments

5 Why? Forecasting Better forecasts Probabilistic forecasts Courtesy of Nigel Roberts © Crown copyright Met Office

6 Assumptions/cautions Many degrees of freedom- should have 100’s of members Expensive use 12 members Assume ensemble is perfect Correlation does not imply causality! Limited number of cases

7 Case study : background UK Met Office Analysis, 06:00 8 th July 2011 over Edinburgh Convective precipitation, flooding Nimrod 1.5km radar

8 Case study: experiment 100km domain over Edinburgh MOGREPS-UK 2.2km 12 member ensemble

9 Predictability : Skilful scale Fields different fields the same – skilful Fractions skill score (FSS) Large scale low predictability Small scale high predictability Courtesy of Nigel Roberts

10 Predictability : results 1 Spreadskill

11 Predictability : results 2 Average over 100km domain No rain above 4mm/hr Predictability increases

12 Correlations Field x Field y z Member 1 deviations from ensemble mean Multiply for fields x and y Repeat for all members. Average Normalise by standard deviation of x and y Repeat for members 2, 3, …, 11

13 Correlations: point to point 1 Convergence Divergence +ve correlation -ve correlation

14 More convective cloud warmer More information Physical relationships T, cloud fraction, divergence, wind speed, RH Correlations: point to point 2

15 Correlations 4: vertical results

16 Future work Correlations – horizontal correlations – Temporal correlations – Points within an area More cases – DIAMET, COPE – Operational runs

17 Questions?

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