1. SLP Cyclone anti-cyclone asymmetry and time mean field
July 8, 2008

2. NCEP reanalysis , DJF
No filtering

4. NH Time mean (raw) reflected about equator
Total wavenumber >=6

5. Normalized by STD^3

6. Skewness (contours)
Time mean (colors

7. Example Fit

10. Histograms of extrema
Locate local (time) extrema in daily time series at each gridpoint
Can use a threshold to define unique extrema (doesn’t change much of the results)
Compile the histograms of positive and negative extrema
Do these look the same as raw data histogram?

11. Mid-Atlantic

14. North Pacific

15. Mid_pacific

16. South Pacific

17. Tracking of SLP All data is NCEP mean SLP from 1978-2008 DJFM, daily
Two pre-filtering routines are comparesd
Spatial filter that keeps total planetary wavenumber >= 6 (Hoskins);
A temporal, high pass filter (double pass, sixth order butterworth) with cutoff period of 20 days.

19. hPa

21. Define Regions over which the time mean, spatially filtered field is large

22. North Pacific Histogram

23. North Atlantic

24. South (low lat) Pacific

25. Can we explain the histogram differences by simple adding a time mean to the temporal data
Find the spatial average of the spatially filtered time mean SLP in each boxed domain
Add this constant to all the tracked data magnitudes
Compute the histograms alongside the spatially filtered tracked data

29. TRACKS AND COMPOSITES BY REGION
Take the 100 largest storms in each region (that way number of tracked features doesn’t get in the way)
Plot tracks, all is lagrangrian data defined by the filter used
Composite around the 100 features onto the RAW SLP Eulerian fields relative to storm center
In plots, the spatial mean of the composite field is subtracted for visual purposes.

42. Look in the middle of the Atlantic, where there is a node in the mean

51. Possible explanation; MPAC composite STD