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A Comparison of the ECMWF and NCEP Reanalyses Using Synoptic Classification

A Comparison of the ECMWF and NCEP Reanalyses Using Synoptic Classification. Chen Zhang 2011/9/19. Motivation. Why do we compare the reanalyses ? What do we want to know from the comparison? Synoptic scale Cloud parameterization How do we compare? Synoptic classification

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A Comparison of the ECMWF and NCEP Reanalyses Using Synoptic Classification

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  1. A Comparison of the ECMWF and NCEP ReanalysesUsing Synoptic Classification Chen Zhang 2011/9/19

  2. Motivation • Why do we compare the reanalyses? • What do we want to know from the comparison? • Synoptic scale • Cloud parameterization • How do we compare? • Synoptic classification • Cloud data associated • Correspondence in time series • Cloud structures • meteorology

  3. Method • Initial clustering: sort atmospheric patterns into classes using basic variables • Resulting states can be interpreted in a number of ways: • recurring weather patterns • a collection of similar events • the average of the atmospheric variables across the cluster

  4. Example of 2D clustering Courtesy Stuart Evans

  5. Example of 2D clustering Courtesy Stuart Evans

  6. Example of 2D clustering Courtesy Stuart Evans

  7. Method • Improving cluster quality • A certain number of states is required in the initial clustering • Use cloud occurrence data associated with each state to perform two statistical tests of quality • Stability: are the profiles for the first and second halves of the state the same? • Distinctness: are the profiles for each state different from one another?

  8. Method • ARM cloud data • Vertically pointed mm radar • Observation every 10s • Sample 30 vertical levels • Aggregate into 8x daily fractional occurrence values • Radar threshold of -40 dBz • ARM data represents the best, long-term (decade +) record of cloud properties

  9. Example of 2D clustering Large states: divide them Small states: delete them Courtesy Stuart Evans

  10. Method Input data (T,U,V,RH,SP) Neural Network Classifier Initial states • Advantages • Cluster on large-scale state variables (well-defined in atmospheric analyses and models) • Iterate to refine state definitions through tests using cloud properties Resort observations fail Delete / Divide up to four bad states (defined as least stable / distinct states) Stability test pass fail pass Distinctiveness test Final states

  11. Method • Evaluating states • Time evolution • Meteorology: monsoon, front, cyclonic system • State properties: cloud structure, precipitation, LWC, OLR, etc.

  12. Past work: ECMWF at SGP • ERA-Interim reanalysis • Dec. 1996 – Mar. 2010 • every 6h, 4 times a day • About 19,000 events • At each time step • centered on SGP • 9 x 9 horizontal grid • 1.5° x 1.5° spacing • 7 vertical levels • Variables at each point • temp, winds, humidity, surface pressure

  13. ECMWF: cloud profiles

  14. ECMWF: state transition

  15. ECMWF: monthly distribution

  16. ECMWF: hourly distribution State 14 <-> State 16: Diurnal Cycle

  17. ECMWF State 16: summertime morning

  18. ECMWF State 14: summertime afternoon

  19. ECMWF: cloud profile

  20. Ongoing work • Difference • Past work: ERA-I reanalysis from ECMWF • Ongoing work: CFS-R from NCEP • Consistency • Same location (slightly lower resolution) • Same time scale • Progress • comparison

  21. Ongoing work: NCEP CFS-R • Newly completed and released • Three major differences with earlier NCEP reanalysis: • Much higher horizontal and vertical resolution (T382L64) of the atmosphere (earlier efforts were made with T62L28) • The guess forecast generated from a coupled atmosphere - ocean - seaice- land system • Radiance measurements from the historical satellites are assimilated From: CFSR Overview

  22. NCEP: cloud profile

  23. Ongoing work: comparison • Time series • C • A • A • A • D • B ERA-I states Time • A′ • B’ • G’ • A’ • E’ • A’ CFSR states

  24. NCEP: state correspondence ECMWF CFSR State 14 -> State 9 State 16 -> State 3

  25. NCEP: monthly and hourly distribution

  26. NCEP State 3 – summer time morning

  27. NCEP State 9 – summertime afternoon

  28. NCEP: state transition

  29. Future work Additional physical properties • NCEP • Other data • Robust states Robustness test • ECMWF Models outputs

  30. Acknowledgement • Thomas P. Ackerman • Roger Marchand • Stuart Evans • Other group members • Grads 10

  31. Thank you!

  32. Supplement

  33. Supplement State 9 -> State 14 percentage: 67.43% State 10 -> State 17 percentage: 68.06% State 11 -> State 6 percentage: 55.91% State 13 -> State 13 percentage: 70.78% State 14 -> State 15 percentage: 57.25% State 16 -> State 2 percentage: 71.29% State 17 -> State 7 percentage: 52.53% State 18 -> State 9 percentage: 41.86% Threshold = 3*std + mean CFS-R ERA-I State 1 -> State 4 percentage: 88.90% State 2 -> State 10 percentage: 70.19% State 3 -> State 16 percentage: 49.05% State 6 -> State 12 percentage: 86.14% State 7 -> State 11 percentage: 68.35% State 8 -> State 1 percentage: 59.68%

  34. Supplement

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