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Blocking morphology, with an example from the TIGGE forecasts

Blocking morphology, with an example from the TIGGE forecasts. Ф on PV2. Giacomo Masato B. J. Hoskins T. J. Woollings. lat. lon. NCAS Blocking Workshop, Reading, 15*12*2010. Outline. The ‘Wave-breaking’ Approach.

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Blocking morphology, with an example from the TIGGE forecasts

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  1. Blocking morphology, with an example from the TIGGE forecasts Ф on PV2 Giacomo Masato B. J. Hoskins T. J. Woollings lat lon NCAS Blocking Workshop, Reading, 15*12*2010

  2. Outline • The ‘Wave-breaking’ Approach. • Blocking, Direction of Breaking and Relative Intensity Indices. • A case-study. European event and the usage of TIGGE data-set. • TIGGE data-set (THORPEX project). • Oct 2006-present. • 15 days EPS forecast. • 1 control + n perturbed runs. • 10 different centres (not all have theta on PV2). • Results and Conclusions. • Methodology applied to the ECMWF and NCEP centres.

  3. BlockingIndex • First calculated by Pelly and Hoskins (2003). • Developed in a 2-dimensional form (lon,lat) by Berrisford et al. (2007).

  4. Warm-Anticyclonic Cold-Cyclonic Direction of Breaking Relative Intensity

  5. 10 Dec 11 Dec 12 Dec 13 Dec The event 14 Dec 15 Dec 16 Dec 17 Dec 18 Dec 19 Dec 20 Dec 21 Dec 22 Dec 23 Dec 24 Dec 25 Dec 26 Dec 27 Dec 28 Dec 29 Dec 30 Dec 31 Dec 01 Jan 02 Jan

  6. The Tracking Algorithm A local maximum in B (>0) is detected at day n (blocking centre). At day n+1 the closest maximum within a box 36x27 degrees in longitude-latitude is identified as the evolution of blocking. Values of DB, RI are retained from the blocking centre and archived as a function of time. The process is iterated as long as the condition at point 2 is satisfied. The algorithm is stopped if the location of ANY blocking centre is outside a box 3/2 times the box at point 2 and centred on the onset blocking centre.

  7. 1st 2nd 3rd wave-breaking wave-breaking wave-breaking ECMWFNCEP Days Days

  8. x = B, (EPS members) X = B, (analysis) I = number of memebers T = number of days Days (x,y) = (DB,RI), (EPS members) (X,Y) = (DB,RI), (analysis)

  9. 1st 2nd 3rd wave-breaking wave-breaking wave-breaking 0.71 0.8 0.36 0.3 0.74 0.29 1.17 1.32 0.87 0.76 1.48 0.6 Lead-time 3 days Lead-time 7 days ECMWFNCEP

  10. ECMWF, WB #1 θ B Lead-time 3 days (d=0.71) θ B θ B θ B θ B Lead-time 7 days (d=1.17) θ B

  11. ECMWF, WB #2 θ B Lead-time 3 days (d=0.8) θ B θ B θ B θ B Lead-time 7 days (d=1.32) θ B

  12. ECMWF, WB #3 θ B Lead-time 3 days (d=0.36) θ B θ B θ B θ B Lead-time 7 days (d=0.87) θ B

  13. NCEP, WB #1 θ B Lead-time 3 days (d=0.3) θ B θ B θ B θ B Lead-time 7 days (d=0.76) θ B

  14. NCEP, WB #2 θ B Lead-time 3 days (d=0.74) θ B θ B θ B θ B Lead-time 7 days (d=1.48) θ B

  15. NCEP, WB #3 θ B Lead-time 3 days (d=0.29) θ B θ B θ B θ B Lead-time 7 days (d=0.6) θ B

  16. Conclusions A case study has been performed and the wave-breaking approach has been applied to 2 NWP, EPS forecasts. Three wave-breakings have been identified by the TA. The identification of the second episode (days 14, 15 and 16) was by far the most problematic and both the centres had equally wide spread of ensemble. It can be inferred that the evolution of the second wave-breaking might have been heavily affected by the decaying phase of the previous one, which has been identified as a clear source of uncertainty for the model. The relatively smaller wave amplitude characterising the breaking might have also contributed to the poor model performance for its correct identification. The third wave-breaking (days 24, 25 and 26) exhibits a rather large spread of the ensemble members (first of all for greater lead-time runs), but this does not prevent the forecast capturing correctly the wave-breaking.

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