1. USING THE ROSSBY RADIUS OF DEFORMATION AS A FORECASTING TOOL FOR TROPICAL CYCLOGENESIS USING THE ROSSBY RADIUS OF DEFORMATION AS A FORECASTING TOOL FOR TROPICAL CYCLOGENESIS Philippe Papin (Faculty Advisor: Chris Hennon)

2. Outline  Tropical Cyclogenesis background  Forecasting TCG  Rossby Radius of Deformation  Equation and Diagrams  Using RROD  Methodology  Results Preliminary Global Field Developing vs. Non-Developing clusters  Complications and Future Work

3. Tropical Cyclogenesis (TCG)  Formation of a tropical cyclone through an initial disturbance over open waters  Tropical Cloud Clusters Areas of thunderstorms that have potential to develop into a tropical cyclone

4. How Tropical Cyclones Develop (Gray 1968)  Sufficient Sea Surface Temperatures (at or greater than 26.5 o C ~80 o F)  Source of Latent Heat for tropical cyclones  Weak Vertical Wind Shear  Small change of winds with height  Low Level Relative Vorticity  Initial spin  Moist Mid Levels  High relative humidity Dry Air Moist Air Strong Wind Shear Weak Wind Shear Wind Shear Diagram Low Levels Mid Levels Upper Levels

5. Forecasting Tropical Cyclogenesis  Rare Event  90% of all Atlantic Basin tropical cyclone ‘seedlings’ fail to develop despite favorable conditions. (Hennon et. al 2005)  Challenges  Insufficient Computer Model resolution  Few In-situ observations in Atlantic Satellite and Computer Models used for forecasting

6. Potential For Operational Forecasting Parameter for TCG  “Advances in theoretical understanding and observational analysis of tropical cyclogenesis suggest new diagnostics of genesis potential applicable to analysis of the operational models.”  http://www.meted.ucar.edu/tropical/textbook/ch10/tr opcyclone_10_3_1.html http://www.meted.ucar.edu/tropical/textbook/ch10/tr opcyclone_10_3_1.html  Using Rossby Radius of Deformation?

7. Rossby Radius of Deformation  Defined as N = Brunt–Väisälä frequency H = Depth of the system ζ = Relative Vorticity f 0 = Coriolis parameter (planetary vorticity)  Critical Boundary where rotation becomes as important as buoyancy  Brunt–Väisälä frequency g = Gravity θ = Potential Temperature Z = Geometric Height

8. If RROD smaller than radius of disturbance, then it persists Winds rotate as a result of mass adjustment (disturbance maintains size). Latent heat is maintained within system (more convection is likely to develop) If RROD is larger than radius of the disturbance, system disperses, and dissipates. Atmospheric waves disperse system, latent heat is not contained within circulation RROD - Illustration

9. RROD as a Forecasting Parameter  Decreasing Values of RROD typically indicate where conditions are more favorable for development  A RROD value can be assigned to a tropical cloud cluster  Synoptic Conditions = Model Analysis  Storm Height = Cloud Top Height

10. Methodology for RROD  Dataset  Used Archive Global Forecasting System (GFS) gridded binary (GRIB) files to obtain these variables Temperature Pressure Geopotential Height Absolute Vorticity

11.  Dataset  Atlantic Tropical Cloud Cluster Dataset (Helms et al. 2008) was incorporated to test RROD for particular disturbances Cloud shield of cluster was used as storm radius Cloud top height used as storm height Methodology for RROD (cont.)

12. Preliminary map was created to show if RROD was a feasible value to use for tropical cyclones Compare the RROD field with the satellite imagery at the same time. Notice the correlation of low RROD values with clusters/tropical cyclones Correlation will be pursued to see if it is useful for tropical cyclogenesis Preliminary RROD Field Three Obvious RROD Minimums

13. September 2004 Cluster Tracks RROD was calculated for every tropical cloud cluster (developing and non-developing) over the September-October 2004 period RROD was calculated in each grid point within the cloud radius of the cluster Example – Pre-Karl – 8 grid points calculated within 194km from center at Latitude 11.3 o N 28.0 o W (note IR image is just a hypothetical diagram) Individual Tropical Cloud Cluster RROD

14. Two clusters, cluster1097 and cluster1171 Cluster 1097 is more organized on Satellite Less elongated, banding features, deeper convection Non-Developing Case vs. Developing Case Cluster 1097 Cluster 1171

15. RROD Numbers vs. Max Radius Cluster 1097 – RROD: 1944 km Max Radius: 400 km Ratio: 4.86 Cluster 1171 – RROD: 3346 km Max Radius: 213 km Ratio: 15.71 The Lower the ratio, the more latent heat is contained in the cluster Cluster 1097 goes on to develop into a tropical cyclone (Hurricane Ivan) Non-Developing Case vs. Developing Case (cont.)

16. All Developing vs. Non-Developing Cases  RROD  Developing Clusters: 2590 km  Non-Developing Clusters: 3687 km  Results suggest that RROD is a useful parameter to indicate tropical cyclogenesis  The lower the RROD value associated with the cluster, the higher likelihood of development  Matches theoretical expectations

17. Complications to Calculating RROD  Cluster Track Data  Challenges for tracking clusters Vorticity center and convection center not always correlating  GRIB GFS Files  Resolution not high enough for best results

18. Future Work  Work to combine RROD with other forecasting predictors  Hennon et al. 2005  Fine tune calculation of RROD  Develop a real time RROD number that can be assigned to current tropical cloud clusters  Expand test cases to other years (2005 and beyond)

19. References  Bister M (2001) Effect of peripheral convection on tropical cyclone formation. J Atmos Sci 58: 3463–3476  Emanuel, K. A.,1994 :Atmospheric Convecfion. Oxford University Press, New York  Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon.Wea. Rev., 96, 669- 700.  Helms, C., C.C. Hennon, and K.R. Knapp, 2008: An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary Infrared Imagery.28th Conference on Hurricanes and Tropical Meteorology, (Orlando FL), American Meteorological Society.  Hennon, C. C., C. Marzban, and J. S. Hobgood, 2005: Improving tropical cyclogenesis statistical model forecasts through the application of a neural network classifier. Wea. Forecasting, 20, 1073–1083.  Lee C-S, Lin Y-L, Cheung KKW. 2006. Tropical cyclone formations in the South China Sea associated with the Mei-yu front. Monthly Weather Review 134: 2670–2687.  Vitart, F., J. L. Anderson, and W. F. Stern, 1999: Impact of largescale circulation on tropical storm frequency, intensity, and location, simulated by an ensemble of GCM integrations. J. Climate, 12, 3237–3254.

20. http://www.stuffintheair.com/images/abstractrotation.jpg http://www.nhc.noaa.gov/gifs/SST/AL_08_AUG_1971-2000_RSST.gif http://www.meted.ucar.edu/nwp/pcu1/d_adjust/media/images/adjust2.jpg http://weather.unisys.com/archive/sat_ir/0409/04090100.gif http://www.nrlmry.navy.mil/htdocs_dyn/tc_pages/gif89/SEL_20040909.1145.goes12.x.ir1km_bw.09LIVAN.140kts- 925mb-137N-695W.gif http://www.nrlmry.navy.mil/htdocs_dyn_apache/tc_pages/thumbnails/thumbs/tc04/ATL/09L.IVAN/ir/geo/1km_bw/thumb/ 20040901.1500.meteo7.x.ir1km_bw.98LINVEST.25kts-NAmb-100N-220W.jpg http://www.nrlmry.navy.mil/htdocs_dyn_apache/tc_pages/thumbnails/thumbs/tc04/ATL/12L.KARL/ir/geo/1km_bw/thumb/ 20040915.1745.goes12.x.ir1km_bw.91LINVEST.25kts-1009mb-107N-249W.jpg http://www.ncdc.noaa.gov/gibbs/image/GOE-12/IR/2004-09-17-12