1. Application of T382 CFS Forecasts for Dynamic Hurricane Season Prediction J. Schemm, L. Long, S. Saha and S. Moorthi NOAA/NWS/NCEP October 21, 2008 The 33rd Climate Diagnostics and Prediction Workshop, Lincoln, NE

2. Outline Description of the CFS experiment Description of the CFS experiment Datasets Used Datasets Used IRI Detection and Tracking Method IRI Detection and Tracking Method Analysis of storm activity statistics Analysis of storm activity statistics Focus on the Atlantic Basin Focus on the Atlantic Basin Statistics Statistics A look at CFS predictions for 2008 A look at CFS predictions for 2008 Future plan Future plan

3. CFS T382 hurricane season experiments 1.One of the FY07/08 CTB internal projects 2.AGCM - 2007 operational NCEP GFS in T382/L64 resolution LSM - Noah LSM OGCM - GFDL MOM3 3. All runs initialized with NCEP/DOE R2 and NCEP GODAS. Initial conditions at 0Z, Apr. 19, 20, (21) and May 15(FY07) for 1981-2007. Forecasts extended to December 1. 4.For May 15 cases, runs in T62 and T126 resolutions also performed.

4. Datasets  CFS hindcasts at T382 May 15 th Initial ConditionMay 15 th Initial Condition Output at every 3 hours Output at every 3 hours 1981-2007, 27 years 1981-2007, 27 years April 19 th and April 20 th Initial ConditionApril 19 th and April 20 th Initial Condition Output at every 6 hours Output at every 6 hours 1981-2008, 28 years 1981-2008, 28 years More appropriate ICs for CPC Operational Hurricane Season Outlook More appropriate ICs for CPC Operational Hurricane Season Outlook  Observations from the HURDAT and JTWC Best Track Dataset -Tropical depressions and subtropical storms-Tropical depressions and subtropical storms are not included in storm counts. are not included in storm counts.

5. Tropical Cyclone Detection Method Based on method devised by Camargo and Zebiak (2002) at IRI Based on method devised by Camargo and Zebiak (2002) at IRI Detection algorithm uses basin-dependent threshold criteria for three variables Detection algorithm uses basin-dependent threshold criteria for three variables Vorticity,  thresh Vorticity,  thresh   thresh = 2   10-m Wind Speed,  thresh 10-m Wind Speed,  thresh   thresh =  gl +     gl = wind speed averaged over all global basins all global basins Vertically integrated temp anomaly, T thresh Vertically integrated temp anomaly, T thresh o T thresh =  T /2 o Calculated using only warm-core systems

6. 1.850-hPa relative vorticity >  thresh 2.Maximum 10-m wind speed in a 7x7 box >  thresh 3.SLP is the minimum in the centered 7x7 box 4.Temp anomaly averaged over the 7x7 box and three pressure levels (300, 500, & 700 mb) > T thresh 5. Temperature anomaly averaged over the 7x7 box is positive at all levels (300, 500, & 700 mb) * 6.Temperature anomaly averaged over the 7x7 box at 300mb > 850mb * 7.Wind speed averaged over the 7x7 box at 850mb > 300mb 8.The storm must last for at least 6 hours. * Criteria 5 & 6 define a warm core system. * Criteria 5 & 6 define a warm core system. Eight conditions must be met for a point to be considered a tropical cyclone

7. Storm Tracking Once a point is designated as a tropical cyclone, the cyclone is tracked forward and backward in time to create a full storm track. Once a point is designated as a tropical cyclone, the cyclone is tracked forward and backward in time to create a full storm track. The maximum vorticity in a 5x5 grid around the cyclone is found and a new 3x3 box is formed around it. The maximum vorticity in a 5x5 grid around the cyclone is found and a new 3x3 box is formed around it. At the next time step, if the maximum vorticity in this new box is greater than 3.5 x 10 -5 s -1, the procedure is repeated. At the next time step, if the maximum vorticity in this new box is greater than 3.5 x 10 -5 s -1, the procedure is repeated. This point has become part of the storm track. This point has become part of the storm track. If two storm tracks are the same, they are considered the same cyclone and counted as one. If two storm tracks are the same, they are considered the same cyclone and counted as one.

8. Four NH Ocean Basins

9. Western North PacificNorth Indian AtlanticEastern North Pacific Examples of Storm Tracks for 4 NH Basins

10. AtlanticBasin Atlantic Tropical Storms

11. EasternPacificBasin Eastern Pacific Tropical Storms

12. JJA Nino 3.4 SST Index 0.722IC=0419 0.671IC=0420 0.872IC=0515 R2T382

13. JJA Atlantic MDR SST Index 0.629IC=0419 0.624IC=0420 0.781IC=0515 R2T382

14. OBSIC=0515 IC=0419IC=0420 Tropical Storm Origins, 1981-2007 ASO

15. Atlantic Basin Correlations 0.080.850.550.400.180.35IC=0515 0.30-0.390.320.33 IC=0420 0.510.160.860.14-0.100.44IC=0419 NeutralLa NinaEl Nino94-0681-93Total N=27 N=13 N=13 N=8 N=5 N=14 Red = Statistically Significant at 0.95 Correlations based on 27-year anomaly 0.79 Anomalies of Atlantic Storms

16. 0.370.800.520.690.610.66IC=0515 0.49 0.220.150.700.58IC=0420 0.350.650.740.230.050.47IC=0419 NeutralLa NinaEl Nino94-0681-93Total Correlations Red = Statistically Significant at 0.95 0.90

17. 2008 Atlantic Season Summary Obs NOAA Outlook Outlook Update CFS-Stat May CFS-Stat Update Named Storm 1512-1614-189-1613-16 Hurricane 76-97-105-97-10 Major Hurricane 42-53-62-43-5 As of October 17, 2008

18. Atlantic Basin Prediction for 2008 Two additional runs were made using July 15 th and 16 th initial conditions for 2008 only Two additional runs were made using July 15 th and 16 th initial conditions for 2008 only Used as a trial run for the CPC Hurricane Outlook Update Used as a trial run for the CPC Hurricane Outlook Update

19. 13.330.67132.333.671.331ENSM 16128410716 93420715 154431Obs 11133310422 152423310421 1411235110419 TotalNovOctSepAugJulJunMay2008 Atlantic Basin CFS 2008 Monthly Storm Count 3

20. (Courtesy of Unisys) Large number of storms over the Gulf of Mexico this year 2008 Atlantic Storm Tracks

21. Resemble Bertha and Cristobal

22. . Summary  CFS in T382 resolution exhibits robust climatological seasonal cycle of tropical cyclone over four NH basins.  Warming trend and intensification of hurricane activity in the Atlantic basin captured in the CFS hindcasts.  Fair level of skill in predicting interannual variability of seasonal storm activities based on the limited number of forecast runs.  Continue to increase number of ensemble members for better climatology and storm statistics. Hope to provide input for 2009 Hurricane Season Outlook with real time prediction runs.  Prospect of multi-model ensemble approach for dynamical hurricane season prediction