Hurricane/Typhoon Forecasts at Florida State University

1. Hurricane/Typhoon Forecasts at Florida State University Department of Meteorology, Florida State University, Tallahassee, FL 32306, USA. T. N. Krishnamurti International Conference on South Atlantic Cyclones, Track Prediction and Risk Evaluation, Rio de Janeiro, May 19-21, 2008

2. Outline • Track and intensity forecasts over the Atlantic basin: • Superensemble methodology • A walk through a superensemble forecast • Skills during 2004, 2005 • Case studies on Katrina, Rita • Tropical storm forecasts using mesoscale superensemble. • Tropical Storm frequency forecast a season in advance: • Methodology • Application to north Atlantic basin tropical storms: • Using four models • Using superensemble • Tropical storms in single, double and triple CO2 scenarios. • Conclusions and future work.

3. Hurricane Track and Intensity Forecasts over the North Atlantic Basin

4. Description of FSU Superensemble • This technique demonstrates a way to post-process a set of multi-model forecasts to produce a new "optimal" forecast. • It operates by applying unequal weights to each model for each forecast lead time.  It differs from the simple ensemble mean, which assigns a weight of 1/N to each of the N multi-models.  • In the end, the Superensemble tends to reduce the individual model biases while giving more weight to the more accurate member forecasts.  • For this purpose, the Superensemble requires a training period made of up previous forecast cases in order to produce the regression coefficients (weights) for each model.

5. Multimodel FSU Conventional Superensemble • The superensemble forecast is constructed as, where, are the ith model forecasts. are the mean of the ith model forecasts over the training period. is the observed mean of the training period. are the regression coefficient obtained by a minimization procedure during the training period. Those may vary in space but are constant in time. is the number of forecast models involved. The coefficients ai are derived from estimating the minimum of function, the mean square error. • Multimodel bias removed ensemble is defined as, • In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.

6. Training Phase Forecast Phase Weights Model 1 a1 Multiple Linear Regression: Superensemble Forecasts: Model 2 a2 Model 3 a3 Forecasts Training Time Series Forecast Time Series a4 Model 4 F => Forecasts O=>Observations ai => Weights. Overbar represents climatology. Minimization of error term G. Model N aN Statistical weights obtained in the training phase are passed on to the forecast phase. • In addition to removing the bias, the superensemble scales the individual model forecasts contributions according to their relative performance in the training period in a way that, mathematically, is equivalent to weighting them.

7. A Walk Through a Superensemble Forecast S(60  72 hrs) = 0.44 + 0.45 (1.10 – (0.38)) Model 1 + -0.07 (0.40 – (0.62)) Model 2 + 0.08 (1.00 – (0.55)) Model 3 + 0.40 (1.20 – (0.41)) Model 4 + 0.26 (0.90 – (0.45)) Model 5 S(60  72 hrs) = 1.24 S(at 72 hours) = S(at 60 hours) + S(60 72hours) = 79.30 + 1.24 = 80.54 Observed (at 72 hours) = 80.40

8. Models used in the construction of the track FSU Superensemble • OFCI – Previous cycle OFCL (Official NHC forecast) interpolated • GFDI – Previous cycle GFDL (Geophysical Fluid Dynamics Laboratory model) interpolated • GFSI – Previous cycle GFS interpolated • UKMI – Previous cycle UKM (United Kingdom Met Office model) interpolated • NGPI – Previous cycle NGPS (Navy Operational Global Atmospheric Prediction System) interpolated • GUNA – Mean consensus of GFDI, UKMI, NGPI, and GFSI

9. Models used in the construction of the intensity FSU Superensemble • OFCI – Previous cycle OFCL (Official NHC forecast) interpolated • GFSI – Previous cycle GFS interpolated • UKMI – Previous cycle UKM (United Kingdom Met Service model) interpolated • SHF5 – SHIFOR5 (Climatology and Persistence model) • DSHP – SHIPS with inland decay algorithm

10. Current Operational Skills NHC’s Official annual average track errors (nm) in Atlantic basin for hurricanes and tropical storms Official Intensity skill trend with respect to SHIFOR5 (in %) in Atlantic Basin from 1990

11. Mean absolute track errors (in nm) for 2004 season Mean absolute intensity errors (in kts) for 2004. The number of cases is shown on the top of the illustration

12. Mean absolute track errors (in nm) for 2005 Mean absolute intensity errors (in kts) for 2005. The number of cases is shown on the top of the illustration.

13. Official homogeneous comparisons for selected Atlantic basin early track guidance models for 2004 with respect to the CLIPER5 Official homogeneous comparisons for selected Atlantic basin early intensity guidance models for 2004 with respect to the SHIFOR5

14. Official homogeneous comparisons for selected Atlantic basin early track guidance models for 2005 with respect to the CLIPER5 Official homogeneous comparisons for selected Atlantic basin early intensity guidance models for 2005 with respect to the SHIFOR5

15. Mean absolute intensity errors in kts for (c) forecast hour 72 for Charley, Danielle, Frances, Ivan Jeanne, Karl and Lisa (in kts) Mean absolute intensity errors in kts for forecast hour 120 for Frances, Ivan, Jeanne and Karl (in kts)

16. MAE for 96-hr fcst is 69 n mi (34 cases) MAE for 96-hr fcst is 89 n mi (34 cases) MAE for 96-hr fcst is 85n mi (34 cases) MAE for 96-hr fcst is178n mi (34 cases)

17. Landfall

18. 120 hour track forecasts for hurricane Katrina with initial time at 27 August 2005 00 UTC 84 hour track forecasts for hurricane Katrina with initial time at 27 August 2005 18 UTC. Thered, blue andgreenlines show thesuperensemble track, the observed best track and themember model forecasttracks at 12 hour intervals

19. 60 hour intensity forecasts for Katrina at initial time 26 August 2005 18 UTC. The red line indicates the superensemble forecast and the blue line denotes the observed at 12 hour intervals.

20. 120 hour track forecasts for hurricane Rita with initial time at 19 September 12 UTC. The red, blue and green lines show the superensemble track, the observed best track and the member model forecast tracks at 12 hour intervals 120 hour intensity forecasts (in mph) for Katrina at initial time 19 September 2005 06 UTC. The red line indicates the superensemble forecast and the blue line denotes the observed at 12 hour intervals.

21. Mean absolute intensity errors (in kts) for Hurricane Rita

22. Mesoscale Superensemble Forecasts of Tropical Storms

23. A suite of mesoscale models Member models for the mesoscale superensemble: • HWRF • MM5 • WRFA • WRFB • GFDL • DSHIP

24. Microphysics Ice, graupel, snow, cloud, liquid water, rain water, hail Sensitivity Studies HWRF LATERAL BOUNDARY CONDITIONS Global Spectral Model T255 28 Layer Angular momentum perspective WRFA MEMBER MODELS Multi model mesoscale superensemble WRFB Intensity forecasts and interpretation of skills MM5 Meso scale Data Assimilation Scale interaction between hurricane and cloud scale Post Processing GFDL DSHP Meso scale data sets

25. The NMM-WRF Modeling System • Regional-Scale, Moving Nest, Atmospheric Modeling System. • Non-Hydrostatic system of equations formulated on a rotated latitude-longitude, Arakawa E-grid and a vertical, pressure hybrid (sigma_p-P) coordinate. • Advanced HWRF,3D Variational analysis that includes vortex relocation and adjustment to actual storm intensity. • Uses SAS convection scheme, GFS/GFDL surface, boundary layer physics, GFDL/GFS radiation and Ferrier Microphysical Scheme. • Ocean coupled modeling system (POM GFDL).

26. HWRF Grid Configuration Arakawa E-grid

27. HWRF GFDL

29. Source: James Franklin, NHC

30. Storm cases used for computing mesoscale superensemble

31. Helene Intensity Helene Sea Level Pressure

32. Mesoscale Superensemble Track Errors

33. Mesoscale superensemble intensity errors

34. Seasonal Forecasts of Hurricane Frequency

35. The notion of seasonal tropical storm frequency prediction using global models or reanalysis data sets is based on the fact that large scale models carry the signature of a tropical storm in several of its forecasted atmospheric parameters. • Identification of these parameters and their threshold values to come up with an algorithm that identifies tropical storm in a large scale model is the purpose of this section of the talk. • A goal of this study is also to construct superensemble forecasts for the seasonal frequencies.

36. Model Description • Florida State University (FSU) coupled global spectral model (GSM) was used for our study. • Atmospheric Model: • The atmospheric model had T63 horizontal resolution (~1.875o). • It had 14 vertical sigma levels. • Global clouds in the radiative transfer are defined using a diagnostic cloud algorithm, which is based on the difference between the model’s initial relative humidity, and certain threshold predefined relative humidity, Krishnamurti et al. (1990). • Ocean Model: • The ocean model was an extended version of the model developed by Ernst Maier-Reimer, also called the HOPE model. • It is a primitive equation model using the Boussinesq and hydrostatic approximation on a horizontally staggered Arakawa E-grid. • The meridional resolution was highest near the equator (0.5o) and decreases to 5.0o near the south and north boundaries.. • The zonal resolution was 5.0o constant. • 17 irregularly spaced levels. 10 levels within upper 300 meters. Continued…

37. …Continued • Four versions of the model were used in our study. • These four versions were the combinations of two different cumulus parameterization schemes and two different radiative transfer schemes:

38. Coupled Assimilation Procedure In this study we perform data assimilation using Newtonian relaxation technique. That constitutes the coupled assimilation of our model. Within the coupled model the SST field is continually assimilated toward the observed fields of Reynolds and Smith (1994).

39. Coupled Assimilation Methodology Atmospheric Model I Other Models Ensemble Mean and Superensemble Forecasts Model II Model IV Model I Atmospheric Model II Model III FSU Coupled Global Models Forecasts Atmospheric Model III IC-2 Coupled Assimilation IC-1 Atmospheric Model IV Forecasts Ensemble Mean and Superensemble Forecasts Model I Model II Ocean Model 11 Year Spin-up Model II Model IV Other Models

40. Seasonal Forecast Experiments • Three-months long seasonal forecasts experiments were performed with the four versions of the FSU CGCM for the summer seasons of the years 1987 to 2001. • The model was initiated at the middle and end of every month during this period (24 forecast experiments per year) and was integrated up to 90 days. • In this study we use forecasts starting on 29 July of every month. • 6-hourly model outputs were considered for our analysis.

41. Previous Studies on Calculation of Storm Frequency from Large Scale Data Sets (Observations or Models) (from Walsh et al. 2007)

42. Basin Domains Basin domains used to calculate global average wind speed at 1000 hPa to construct the wind speed threshold. Red: After Camargo and Zebiak (2002); Black: This study.

43. Configuration-dependent thresholds for the Atlantic basin

44. Conditions for Detection of a Tropical Strom 1. The 850-mb relative vorticity exceeds the vorticity threshold. 2. The maximum 1000 mb wind speed in a centered 7 by 7 box exceeds the wind speed threshold. 3. The sea level pressure is the minimum in a centered 7 by 7 box. 4. The vertically-integrated temperature anomaly at the grid point exceeds the temperature anomaly threshold. 5. The mean wind speed averaged over a 7 by 7 box is larger at 850 mb than at 300 mb. 6. If there exists a grid point at a following timestep that satisfies these criteria within two grid points of latitude and longitude (approximately 3.75°) of the original point, then they are connected. 7. If the storm lasts at least one day (5 consecutive grid points), then it is identified as a model tropical cyclone.

45. Relative Vorticity at 850 hPa vs. Vertical Integrated Temperature Anomaly, KNR Vertical Integrated Temperature Anomaly (K) Relative Vorticity (x 10-5 s-1) Thresholds are shown in Red line.

46. Structure of a Strong Storm in KNR Wind at 1000 hPa Vertically integrated temperature anomaly (shaded) and sea level pressure (contour) Relative vorticity at 850 hPa (shaded) and sea level pressure (contour)

47. Latitude-vertical cross section of wind speed Longitude-vertical cross section of wind speed

48. Latitude-vertical cross section of temperature anomaly (shaded) and total temperature (contour) Longitude-vertical cross section of temperature anomaly (shaded) and total temperature (contour)

49. The strongest storms in KNR and KOR possess similar surface wind characteristics as the strongest storms in ANR and AOR. • The strongest winds of each are located generally to the north, northeast, and east of the center of circulation. • The strong KNR storm is the strongest storm in any configuration of the model, and it is one the looks the most realistic. • There is a bulls-eye structure in the pressure field around the center, and the lowest closed isobar, 974 mb, is by far the strongest out of any of the other storms. • The vertical wind profile and the vertical temperature structures also look very realistic, especially the heating maximum’s location at 300 mb. • The strongest storm in the KOR does show a pressure drop as well at the center. The near-zero wind speed is still present at the core. • The level of maximum heating, however, is not 300 mb. Although the vertical structure of the heating is very uniform and not sheared, the anomaly maximum is located at 500 mb, which is below observational records.

50. Tropical Cyclone Frequency, July 29 to October 27 During 1987 to 2001, the model with Kuo convection scheme and new radiation scheme (KNR) performs the best compared to the other three models in the suite.