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A Climatology and Model Validation of Rossby Wave Packets Brian Colle, Matthew Souders, and Edmund Chang Stony Brook University School of Marine and Atmospheric.

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Presentation on theme: "A Climatology and Model Validation of Rossby Wave Packets Brian Colle, Matthew Souders, and Edmund Chang Stony Brook University School of Marine and Atmospheric."— Presentation transcript:

1 A Climatology and Model Validation of Rossby Wave Packets Brian Colle, Matthew Souders, and Edmund Chang Stony Brook University School of Marine and Atmospheric Sciences

2 Rossby Wave Packet Background Phase Group Hovmoller Diagram: 300 hPa Meridional Wind (left) and Wave Packet Envelope Amplitude (right) for March 2, 2009 NYC Snow Event

3 Motivation & Goals ● Wave packets linked to extreme weather, regime changes (e.g. Archambault et al. 2009) and predictability issues (Majumdar et al., 2010). ● Need to better understand spatial and temporal distribution of wave packets (no robust climatology to date) ● Goals: Develop a wave packet tracking method, and use it to produce a climatology, and an ensemble validation. life.com March 2, 2009 – New York City

4 Wave Packet Amplitude: Data and Methods ● Use NCEP/NCAR global 2.5 degree reanalysis ( ) – 300 hPa wind ● Implemented the Hilbert transform stream flow technique (Zimin et al. 2006) to extract wave packet envelope amplitude (WPA) ● 14-day running mean 300 hPa wind used to establish the stream flow along which packets propagate

5 Wave Packet Tracking Approach ● Relative maxima in wave packet amplitude (WPA) used to track wave packets ● Raw WPA spectrally filtered (Cholesky Decomposition – e.g. Wilks, 2006) at T21 resolution to reduce noise ● Modified Hodges (2009) TRACK program for packets to obtain cost optimization of user specified maximum displacement and smoothness parameters ● Also, to be included in the climatology: --A packet must propagate for at least 48 hours and attain a WPA maximum of 20 m s -1 sometime during its life cycle.

6 Verification and Some Challenges ● WPA maxima often split or move erratically when encountering obstacles (split flows, wave breaks, mountain barrier crossings) – this may cause TRACK to assign multiple track IDs to one wave packet. X X ● Verification over three months (JAN-MAR, 2009), using ageostrophic geopotential flux divergence (AGFD) (from Chang, 2000 – eq. 2) ↔ Probability of significant packet detection: ~93% (2366 points) ↔ False alarm rate: ~8% (74 tracked packets, 6 of them likely not significant)

7 Object Attribution WPA (> 10 m s-1 masked), AGFD, and Raw Track Labels Nearest Neighbor Object Attribution for Each Feature Point X X

8 Track Merging Rules ● Two types of merges supported: One Time Step Before Merge Occurs Time of Merge If more than 50% of the new object area 6-h later is within the previous packet (green shaded area), then the packets are merged.

9 Long Durating Wave Packet Track and AGFD (JAN 29-FEB 12, 2009)

10 Spatial Climatology of Wave Packet Activity Average WPA for the (in m s -1 ): Found by isolating all WPA values > 10 m s-1 associated with significant wave packets, summing those values spatially and dividing by the number of time steps in the climatology. m s -1

11 Wave Packet Formation and Dissipation Locations Formation Dissipation # per 2.5 deg from

12 Wave Packet Propagation and Longevity Mean Propagation: o Median Propagation: 97.9 St. Dev: 87.6 Mean Duration: 5.7 days Median Duration: 4.8 days St. Dev: 3.4 days Northern Hemisphere Winter (DJF) Maximum Eastward Propagation (Degrees Longitude) Northern Hemisphere Winter (DJF) Duration (Days)

13 Average WPA in Significant Wave Packets by Month (m s -1 ) Summer to Fall Transition: Rapid Increase from Aug to Sept. Max over central Pacific and NE N America by Oct. Wave packets move across N Tibetan Plateau. AUG SEP OCT NOV

14 Average WPA in Significant Wave Packets by Month (ms-1) Fall to Midwinter Transition: Activity weakens throughout the winter months in the C Pacifc. Atlantic weakens less. Fewer tracks across Tibetan Plateau – some splitting around. Park et al. (2010 JAS) show the role of Asian mountains on mid- winter supression. NOV DEC JAN FEB

15 Average WPA in Significant Wave Packets by Month (ms-1) Winter/Summer Transition: Tracks increase again in central Pacific by April. More packets north of Tibetan Plateau by April. Tracks shift north and weaken by early summer. MAR APR MAY JUN

16 DJF Mean WPA (m s -1 )- MEI (Wolter, 1987) > 1.0 (El Nino) and < -1.0 (La Nina) Spatial counts of WPA max El Nino WPA average for all packets La Nina Northern Hemisphere Winter ENSO Signals

17 Two stndev GFS +/- SLP Errors for Day (Colle and Charles 2011) WPA Climo ( ) WPA: 27 Underdeepen Events WPA: 25 Overdeepen Events weaker stronger weaker GFS OBS 1200 UTC 16 JAN 2004

18 Summary ● An objective, object-based algorithm for tracking and analysis of wave packets has been developed for gridded data. ● Wave packets are most active (and intense) in the known mid- latitude storm track belts (East of Southern tip of Africa and in the North Pacific and Atlantic basins) along both 45 N and 45 S (as expected by Blackmon 1977). ● N. Atlantic activity peaks between October and December. The N. central Pacific activity drops during the midwinter months, which may be related to fewer packets crossing Tibetan Plateau. ● During La Nina there is increased pattern amplitude over N. America, while El Ninos favor more activity in the subtropical Pacific. ● Medium range cyclone errors in models may be associated with particular wave packet evolutions and difficulties simulating these packets – Future Work….


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