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Evaluating Boundary Layer Types in Weather Forecast Models Using Long-Term Doppler Lidar Data

This study assesses how boundary layer types are modeled in weather forecast models, specifically utilizing long-term observations from Doppler lidar. By comparing the Met Office's 4km model with actual observations, we investigate the model's predictive skill regarding stability and cloud types. The outcome reveals significant seasonal and diurnal cycles, and differences in model skill across various boundary layer scenarios. Future work will extend the analysis to additional sites and models, aiming to improve boundary layer parameterizations and pollutant distribution forecasting.

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Evaluating Boundary Layer Types in Weather Forecast Models Using Long-Term Doppler Lidar Data

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  1. Evaluation of Boundary-Layer Type in Weather Forecast Models Using Long-Term Doppler LidarObservations Natalie Harvey Supervisors: Helen Dacre & Robin Hogan

  2. Questions • How is the boundary layer modelled? • Observational diagnosis of boundary-layer type? • How does the Met Office 4km model boundary-layer type compare to the observed? • What next?

  3. How is the boundary layer modelled? + Type 7: unstable shear dominated Lock et al. (2000)

  4. Stability + Type 7: unstable shear dominated Lock et al. (2000)

  5. Cloud type - stratocumulus + Type 7: unstable shear dominated Lock et al. (2000)

  6. Cloud type - cumulus + Type 7: unstable shear dominated Lock et al. (2000)

  7. Decoupled layer + Type 7: unstable shear dominated Lock et al. (2000)

  8. 2 layers of cloud + Type 7: unstable shear dominated Lock et al. (2000)

  9. Model Boundary Layer Diagnosis stable? N Y cumulus? cumulus? Y N Y N decoupled stratocumulus? decoupled stratocumulus? decoupled stratocumulus? N Y N Y N Y Type 1 Type 5 Type 6 Type 2 Type 3 Type 4

  10. What about observations? • Unstable? • Cloud type? • Decoupled cloud layer? • 2 cloud layers? Sonic anemometer Doppler lidar – wskewness and variance Doppler lidar – w variance Doppler lidar backscatter

  11. Example day – 18/10/2009 most probable boundary layer type IV: decoupled stratocumulus IIIb: well mixed stratocumulus topped II: decoupled stratocumulus over a stable layer Harvey, Hogan and Dacre (2012, in revision) Usually the most probable type has a probability greater than 0.9

  12. Observational decision tree stable? stable? stratocumulus & decoupled? stratocumulus? stable, well mixed unstable, well mixed decoupled? stratocumulus over cumulus cumulus capped unstable, well mixed & cloudy stable, well mixed and cloudy stratocumulus over stable decoupled stratocumulus

  13. Most probable transitions 12% of the time “Textbook” boundary layer evolution

  14. Diurnal comparison:01/09/2009 – 31/08/2011

  15. Temporal comparison01/09/2009 – 31/08/2011 • Perfect match would have all numbers along diagonal. • Stable/unstable distinction is well matched in model and observations

  16. Forecast skill • Many different measures that could be used • A SEDI value of 1 indicates perfect forecasting skill. • Robust for rare events • Equitable • Difficult to hedge. where and Symmetric extremal dependence index (Ferro & Stephenson, 2011)

  17. Forecast skill random

  18. Forecast skill Stable? a b • Model very skilful at predicting stability (day or night!) d c random

  19. Forecast skill Cumulus present? a b d c • Not as skilful as stability but better than persistance random

  20. Forecast skill Decoupled? a b • Not significantly better than persistence d c random

  21. Forecast skill More than 1 cloudlayer? a b d c • Not significantly more skilful than a random forecast random

  22. Forecast skill decoupled stratocuover a stable surface? • slightly more skilful than a persistence forecast b a c d random

  23. Summary • Boundary layer processes are turbulent and are parameterised in weather forecast models. • A new method using Doppler lidar and sonic anemometer data diagnose observational boundary-layer type has been presented. • Clear seasonal and diurnal cycle is present in the Met Office 4km model and observations with similar distributions. • The model has the greatest skill at forecasting the correct stability, the other decisions are much less skilful.

  24. What next? • Extend to other models without explicit types (e.g. ECMWF) • Do same analysis over another site, possibly London • Does misdiagnosis of the boundary-layer type affect the vertical distribution of pollutants and if so how long does this difference in pollutant distribution last? • Can this be used to improve boundary-layer parameterisations? • Can observational mixing profiles be found using the lidar ?

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