1、Application of the CRA Method,William A. Gallus, Jr. Iowa State UniversityBeth Ebert Center for Australian Weather and Climate Research Bureau of Meteorology,Idealized cases - geometric,Which forecast is best?,Traditional verification yields same statistics for cases 1 and 2,forecast,5th case tradit
2、ional verification,forecast,THE WINNER,forecast,1st case CRA verification,2nd case CRA verification,CRA Technique yields similar results with cases 3 and 4,5th case CRA verification,RESULTS ARE SENSITIVE TO SEARCH BOX FOR DISPLACEMENTS,Increase of size of rectangle (extra 90 instead of 30 pts) affec
3、ts results,Further increase from 90 pts to 150 pts does not result in additional change,1st case vs. 5th case,THE WINNER,Perturbed cases,“Observed“,(2) Shift 12 pts right, 20 pts down, intensity*1.5,(1) Shift 24 pts right, 40 pts down,Which forecast is better?,1st case traditional verification,(1) S
4、hift 24 pts right, 40 pts down,2nd case traditional verification,(2) Shift 12 pts right, 20 pts down, intensity*1.5,THE WINNER,CRA verification,Threshold=5 mm/h,Case 1,Case 2,CRA verification,Threshold=5 mm/h,Case 1,Case 2,CRA verification,Threshold=5 mm/h,Case 1,Case 2,System far from boundary in C
5、ase 1 small shift behaves as expected,Problem?,System closer to boundary yields unexpected results not all error is displacement,Problem is more serious for smaller system at edge of domain,Central system works well through medium displacements,But. Larger displacement yields odd results,Summary,CRA
6、 requirement for forecast and observed systems to be contiguous may limit some applications Problems occur for systems near the domain boundaries not yet clear what causes the problems,Results from separate study using object-oriented techniques to verify ensembles,Both CRA and MODE have been applie
7、d to 6-hr forecasts from two 15km 8 member WRF ensembles integrated for 60 h for 72 cases This results in 10 x 16 x 72 = 11,520 evaluations (plots, tables.) from each approach Results were compared to Clark et al (2008) study,Clark et al. study,Clark et al. (2008) looked at two 8 member WRF ensemble
8、s, one using mixed IC/LBC, the other mixed physics/dynamic cores Spread & skill initially may have been better in mixed physics ensemble vs. IC/LBC one, but spread grew much faster in the IC/LBC one, and it performed better than the mixed physics ensemble at later times (after 30-36 h) in these 120
9、h integrations.,Areas under ROC curves for both ensembles (Clark et al. 2007),Skill initially better in mixed ensemble but IC/LBC becomes better after hour 30-36,0.5 mm,2.5 mm,Variance continues to grow in IC/LBC ensemble but levels off after hour 30 in mixed ensemble. MSE always worse for mean of m
10、ixed ensemble and performance worsens with time relative to IC/LBC ensemble.,Diurnal Cycle,Spread Ratio also shows dramatically different behavior with increasing spread in IC/LBC ensemble but little or no growth in mixed ensemble after first 24 hours,0.5 mm,2.5 mm,Questions:,Do the object parameter
11、s from the CRA and MODE techniques show the different behaviors between the Mix and IC/LBC ensembles? Do the object parameters from the CRA and MODE techniques show an influence from the diurnal trends in observed precipitation?,Rain Rate Standard Deviation (in.) mean usually around .5 inch,06 12 18
12、 24 30 36 42 48 54 60Forecast Hour,Mix-CRA,IC/LBC-CRA,Mix-MODE,IC/LBC-MODE,Wet times in blue,Diurnal signal not pronounced, only weak hint of IC/LBC tendency to have increasing spread with time and only in MODE results,06 12 18 24 30 36 42 48 54 60,Mix-CRA,IC/LBC-MODE,IC/LBC-CRA,Mix-MODE,Standard De
13、viation of Rain Volume (km3) MODE values multiplied by 10 (mean 1),No diurnal signal, hard to see different trends between 2 ensembles,06 12 18 24 30 36 42 48 54 60,Mix-CRA,IC/LBC-CRA,Mix-MODE,IC/LBC-MODE,Areal Coverage Standard Deviation (number of points above .25 inch) Mean 800 pts,CRA results sh
14、ow both ensembles with growing spread, and IC/LBC having faster growth,Mix-MODE,IC/LBC-MODE,Mix-CRA,IC/LBC-CRA,06 12 18 24 30 36 42 48 54 60,No clear diurnal signal, both CRA & MODE show max in 24-48 h,06 12 18 24 30 36 42 48 54 60,Mix-CRA,Mix-MODE,IC/LBC-CRA,IC/LBC-MODE,No diurnal signal, no obviou
15、s differences in behavior of Mix and IC/LBC,Other questions:,Is the mean of the ensembles distribution of object-based parameters a good forecast (better than ensemble mean put into CRA/MODE)? Does an increase in spread imply less predictability? How should a forecaster handle a case where only a su
16、bset of members show an object?,These questions have been examined using CRA results,Mix Ensemble in general, slight positive bias in rain rate, with Probability Matching forecast slightly less intense than mean of rates from members (PM usually better but not by much). Only during 06-18 period does
17、 observed rate not fall within forecasted range.,wet,mean,PM,dry,IC/LBC usually too dry with rain rate (at all hours except 06-18), Probability Matching forecast exhibits much more variable behavior, again its performance is comparable to mean of rates of members,wet,mean,dry,PM,06 12 18 24 30 36 42
18、 48 54 60,06 12 18 24 30 36 42 48 54 60,Notice that at all times, the observed rain rate falls within the range of values from the full 16 member ensemble indicating potential value for forecasting,06 12 18 24 30 36 42 48 54 60,06 12 18 24 30 36 42 48 54 60,Mix Ensemble clear diurnal signal, usually
19、 too much rain volume except at times of observed peak, when it is too small. Probability Matching equal in skill to mean of member volumes,IC/LBC Ensemble also clear diurnal signal, less volume than Mix ensemble, Probability Matching usually a little wetter but generally comparable to mean of membe
20、rs,wet,mean,PM,dry,wet,dry,PM,mean,Mix,Mix,Mix,IC/LBC,IC/LBC,IC/LBC,Mix-PM,IC/LBC-PM,06 12 18 24 30 36 42 48 54 60,NOTE: Even with all 16 members, there are still times when observed volume does NOT fall within range of predictions - not enough spread (indicated with red bar),Areal Coverage,IC/LBC,M
21、ix,Rate - Mix,Rate-IC/LBC,Volume-Mix,Volume-IC/LBC,Percentage of times the observed value fell within the min/max of the ensemble,Skill (MAE) as a function of spread ( 1.5*SD cases vs .5*SD cases),Rate*10 low SD,Rate*10 big SD,Vol big SD,Vol low SD,Area/1000 big SD,Area/1000 low SD,CRA applied to Mi
22、x Ensemble (IC/LBC similar),06 12 18 24 30 36 42 48 54 60,It thus appears that total system rain volume and total system areal coverage of rainfall show a clear signal for better skill when spread is smaller Rain rate does not show such a clear signal (especially when 4 bins of SDs are examined). Pe
23、rhaps average rain rate for systems is not as big a problem in the forecasts as areal coverage (and thus volume)? Seems to be 5-10% error for rate, 10-20% for volume, 10-20% for area,Summary,Ensemble spread behavior for object-oriented parameters may not behave like traditional ensemble measures Som
24、e similarities but some differences also in output from CRA vs MODE Some suggestion that ensembles may give useful information on probability of systems having a particular size, intensity, volume,Acknowledgments,Thanks to Eric (and others?) for organizing the workshop Thanks to John Halley-Gotway and Randy Bullock for help with MODE runs for ensemble work, and Adam Clark for precip forecast output Partial support for the work was provided by NSF Grant ATM-0537043,Gulf near-boundary system with increased rainfall,
