1. October 17, 2002National Hurricane Center Current and Future Tropical Cyclone Projects at CIRA/NESDIS: An Update and Outlook Presented by John Knaff CIRA

2. October 17, 2002National Hurricane Center Acknowledgments Nick Shea, Michelle Manelli, Jim Kossin, John Kaplan, Julie Demuth, Ray Zehr, Mark DeMaria, and many others…. A special thanks to Kristina Katsaros for paying my way!

3. October 17, 2002National Hurricane Center Outline JHT –AMSU –SHIPS Insurance Friends –Ensemble SHIPS –Wind Probablities Continuing and Future Projects

4. October 17, 2002National Hurricane Center Tropical Cyclone Intensity and Size Estimation From AMSU Data CIRA algorithm –Physical retrieval of T, P and wind from all AMSU-A channels –Statistical estimation of max wind, min pressure, radii of 34, 50 and 64 kt winds using input from retrievals

5. October 17, 2002National Hurricane Center AMSU Instrument Properties AMSU-A1 –13 frequencies 50-89 GHz –48 km maximum resolution –Vertical temperature profiles 0-45 km AMSU-A2 –2 frequencies 23.8, 31.4 GHz –48 km maximum resolution –Precipitable water, cloud water, rain rate NOAA – 15, Launched May 13, 1998

6. October 17, 2002National Hurricane Center Horizontal Resolution: AMSU-A vs. MSU Useful Region

7. October 17, 2002National Hurricane Center Vertical Weighting: AMSU-A

8. October 17, 2002National Hurricane Center Typical AMSU Data Coverage (NOAA-15) Total Precipitable Water

9. October 17, 2002National Hurricane Center Hurricane Temperature Profile

10. October 17, 2002National Hurricane Center 1.Apply a statistical retrieval to obtain vertical temperature (T) profiles and integrated cloud liquid water (CLW) from all available AMSU limb- corrected brightness temperatures at the satellite footprint locations. The storm must be within 600 km of the swath center for the algorithm to run. 2.Apply statistical correction to T in regions of high CLW to account for attenuation. 3.Interpolate T and CLW to an evenly spaced lat/lon grid using a two-pass Barnes analysis 4.Apply “Laplacian” filter to remove localized cold anomalies resulting from ice scattering 5.Using a smoothness upper boundary condition, integrate the hydrostatic equation downwards from 50 hPa to the surface to obtain the pressure field 6.Assume gradient balance to obtain the tangential wind from the pressure field as a function of radius and height 7.Statistically relate parameters from the retrieved CLW, temperature, pressure and wind fields to the observed MSLP

11. October 17, 2002National Hurricane Center Without Correction With Correction TaTa V TaTa V

12. October 17, 2002National Hurricane Center AMSU Surface Pressure for Floyd 14 Sept 1999 UncorrectedCorrected

13. October 17, 2002National Hurricane Center Statistically relate parameters from the retrieved CLW, temperature, pressure and wind fields to the observed MSLP and Wind

14. October 17, 2002National Hurricane Center

15. October 17, 2002National Hurricane Center Statistical Coefficients/ Wind, MSLP

16. October 17, 2002National Hurricane Center Performance Tests

17. October 17, 2002National Hurricane Center 2002 Verification through 11 Oct What was produced in real-time at CIRA Most got to NHC 243 Cases

18. October 17, 2002National Hurricane Center Bias = -1.9 mb MAE = 7.6 mb (6.7) RMSE = 10.28 mb (9.3)

19. October 17, 2002National Hurricane Center Skeleton Storms

20. October 17, 2002National Hurricane Center Bias = 3.0 kt MAE = 10.9 kt (11.0) RMSE = 14.1 kt (14.1)

21. October 17, 2002National Hurricane Center AMSU Good Bad Dvorak Good Skeleton Storms

22. October 17, 2002National Hurricane Center AMSU Wind Radii Estimates Predict azimuthally averaged winds with multiple linear regression Apply asymmetries based upon motion.

23. Estimation of Wind Radii 20 potential predictors are used to predict the mean radii of 35, 50, and 65 knot winds if they exist. Asymmetries are accounted for by a simple relationship Actual mean radii are estimated using But are solved using a variational method since all radii rarely exist.

24. Cost Function and Variational Analysis Cost Function (thing to minimize iteratively) Last two terms are used to constrain the results close to climatology (penalty terms) – climatological r and x are functions of intensity.

25. October 17, 2002National Hurricane Center Coefficients/ Wind Radii

26. October 17, 2002National Hurricane Center Performance Tests for Symmetric Winds

27. October 17, 2002National Hurricane Center

28. October 17, 2002National Hurricane Center Performance Tests for Asymmetric Winds

29. October 17, 2002National Hurricane Center Verification Will be done following the season with g- deck information.

30. October 17, 2002National Hurricane Center Future Plans for AMSU Analyses Finish program to access data from BUFR files –Dostalek/Krautkramer collaboration Perform validation of 2002 season Investigate method to remove high bias for “skeleton” storms Generalize wind model to improve asymmetric wind radii estimation –Current method only includes wavenumber one asymmetry due to motion –Obtain higher-order asymmetries from AMSU nonlinear balance winds

31. October 17, 2002National Hurricane Center Balance Equation Variational Solution - Hurricane Floyd AMSU 850 hPa heightFirst guess wind: (   u=0,   v=0) Nonlinear balance wind

32. October 17, 2002National Hurricane Center SHIPS Improvements Using Satellite Data (NESDIS/CIRA JHT Project) Parallel version of SHIPS with predictors from GOES and satellite altimetry data –% Pixel counts < -20 C –BT standard deviation –Ocean Heat Content along storm track > 50 kJ/m 2 Provides correction to operational SHIPS forecast (No land and decay version of SHIPS) Implemented on NCEP/IBM Aug 20, 2002 beginning with Dolly

33. October 17, 2002National Hurricane Center Sample Image and Radial/Time Profiles for Hurricane Floyd 1999 Mean Tb Tb std dev Pixels < -30 C Pixels < -40 C Pixels < -50 C Pixels < -60 C

34. October 17, 2002National Hurricane Center SAMPLE OCEANIC HEAT CONTENT (26 Sept 2002)

35. October 17, 2002National Hurricane Center Preliminary 2002 Intensity Validation Arthur-Lili as of 11 Oct 2002 NHC validation rules with working best track –186 cases at 12 hr, 77 cases at 120 hr Conclusion: It was a difficult year for intensity forecasting

36. October 17, 2002National Hurricane Center Impact of GOES/OHC Predictors on Decay-SHIPS Forecasts Dolly-Lili as of 11 Oct 2002 NHC validation rules with working best track –169 cases at 12 hr, 78 cases at 120 hr Positive Impact 0-60 hr, Negative impact 72-120 hr Negative impact influenced by Isidore track forecast errors

37. October 17, 2002National Hurricane Center Separation of GOES and OHC Effects GOES data provide modest corrections (~0-8 kt) for most storms OHC has little effect for most storms, but large increases (up to 20 kt) over limited regions Impacts of GOES and OHC will be evaluated separately after the season Preliminary analysis: –OHC effects: Isidore, Lili –GOES effects: Dolly, Edouard, Fay, Gustav, Hanna, Josephine, Kyle

38. October 17, 2002National Hurricane Center Impact of GOES Predictors on Decay-SHIPS Forecasts Sample for cases without OHC impacts –Dolly-Kyle without Isidore Positive Impact 0-108 hr, Negative impact 120 hr –GOES predictors are independent of track forecast –GOES data reduced intensity of sheared cases (2002 sample ideal for this effect)

39. October 17, 2002National Hurricane Center Future Improvements to SHIPS Short Term –Evaluate SST and OHC with smaller time step –Update SST analysis daily (already done by C. Sisko) –Test GOES predictors in east Pacific SHIPS Longer Term (Possible new JHT project) –Neural Network Prediction Time step approach –(0-12, 12-24, … instead of 0-12, 0-24, etc) –Extend developmental sample back to 1981 using re- analysis data –Investigate “False Start” tropical cyclones –Develop specialized prediction for hurricanes Account for inner core structure (eye wall cycles, etc) Incorporate aircraft data, possibly microwave imagery

40. October 17, 2002National Hurricane Center “False Start” Tropical Cyclones Significant intensification predicted by SHIPS, but storm decays in low-shear environment with warm SSTs –Danny 1991, Cindy, Bret 1993, TD05 1997, Alex 1998, Ernesto, Joyce 2000, Chantal, Erin 2001, Dolly 2002 Large source of SHIPS (and NHC forecast errors Develop objective measure for “False Starts” Examine storm environment to aid identification –Emphasis on thermodynamics –Possible connection with Saharan Air Layer

41. October 17, 2002National Hurricane Center D-SHIPS forecasts for Chantal 2001 and Dolly 2002 (“False Start” Storms)

42. October 17, 2002National Hurricane Center SHIPS Ensemble Forecasts (“Insurance Friends” Project) Provide a range of plausible forecast tracks using error characteristics of NHC official forecasts –Errors fitted to analytic distributions –Errors sampled at 0.5  and 1.0  Run ensemble SHIPS and Decay-SHIPS Calculate ensemble mean and ensemble spread Preliminary results for Dolly through Lili –Includes depression stage –Validated using working best track –239 cases at 12 hr, 121 at 120 hr

43. October 17, 2002National Hurricane Center Sample 16-member Track Ensemble (East Pacific case)

44. October 17, 2002National Hurricane Center Control and Ensemble Mean SHIPS Errors, and Average Ensemble Spread (No land correction)

45. October 17, 2002National Hurricane Center Control and Ensemble Mean D-SHIPS Errors, and Average Ensemble Spread (With land correction)

46. October 17, 2002National Hurricane Center Sample N-AWIPS Ensemble Product (Developed by M. Mainelli)

47. October 17, 2002National Hurricane Center Preliminary Conclusions from SHIPS Ensembles SHIPS ensembles of limited utility over the ocean due to small forecast spread More useful for storms near land masses –3-5 day forecast improvements of ~10% Analysis will be performed using final best track as well as the parallel versions of SHIPS at the end of the 2002 season Results will be presented at the IHC

48. October 17, 2002National Hurricane Center Monte Carlo Wind Speed Probabilities (New “Insurance Friends” Project) Determine ensemble of forecast tracks using probability distributions from NHC official track forecast errors –Generalization of method used for tracks for SHIPS ensembles For each track, determine intensity forecast by sampling from NHC intensity error distribution –Adjustment for movement over land using empirical decay equation from SHIPS For ensemble members with high enough wind speeds, estimate wind radii (34, 50 and 64 kt) from observed sample Calculate field of probabilities for various wind thresholds from ensemble track/radii forecasts Plans for Atlantic prototype by June 2003

49. October 17, 2002National Hurricane Center Future Directions Expand the tropical cyclone genesis parameter to a multibasin, basin-wide application. Track probability density functions derived from Jonathan Vigh’s Kilo-ensemble track forecasts Satellite based tropical cyclone surface wind analysis

50. October 17, 2002National Hurricane Center Tropical Cyclone Applications Center Fix - Location of min surface pressure Intensity Estimation - From r,z analyses Size Estimation - From r,z analyses Asymmetric vortex structure - From x,y,p winds Steering flow - From x,y,P winds Impact on data assimilation

51. October 17, 2002National Hurricane Center AMSU-A Moisture Algorithms Total Precipitable Water (TPW) –TPW = cos(Z) * f[T B (23.8),T B (31.4)] Cloud Liquid Water (CLW) –CLW = cos(Z) * g[T B (23.8),T B (31.4)] Rain Rate (RR) –RR = 0.002 * Q 1.7, where Q is clw.

52. October 17, 2002National Hurricane Center 15 AMSU-A channels included Radiances adjusted for side lobes before conversion to brightness temperatures (BT) BT adjusted for view angle Statistical algorithm converts from BT to temperature profiles 40 vertical levels 0.1-1000 mb RMS error 1.0-1.5 K compared with

53. October 17, 2002National Hurricane Center Hydrostatic Integration, Gradient Winds Neglect virtual temperature effects, interpolate T to evenly spaced analysis grid Start with NCEP T s, P s on analysis boundary Integrate AMSU T upwards to 50 mb on boundaries to give Z b (P) Assume no curvature at 50 mb (  2 Z=0) Integrate AMSU T downwards (50 mb to surface) in domain interior –Z(x,y,P), P s (x,y) or Z(r,P), P s (r) –Calculate gradient wind from Z(r,P) Alternate procedure: Start with NCEP Z at 100 mb and integrate AMSU T downwards to surface

54. October 17, 2002National Hurricane Center

55. October 17, 2002National Hurricane Center Temperature Retrieval Algorithm 15 AMSU-A channels included Radiances adjusted for side lobes before conversion to brightness temperatures (BT) BT adjusted for view angle Statistical algorithm converts from BT to temperature profiles 40 vertical levels 0.1-1000 mb RMS error 1.0-1.5 K compared with rawinsondes