Slide #1.

USING THE ROSSBY RADIUS OF DEFORMATION AS A FORECASTING TOOL FOR TROPICAL CYCLOGENESIS  Philippe Papin (Faculty Advisor: Chris Hennon)
More slides like this


Slide #2.

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


Slide #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
More slides like this


Slide #4.

How Tropical Cyclones Develop (Gray 1968)     Sufficient Sea Surface Temperatures (at or greater than 26.5oC ~80oF)  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 Dr Wind Shear Diagram yA ir MUpper Levels oi st Ai r Mid Levels Low Levels Strong Wind Shear Weak Wind Shear
More slides like this


Slide #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
More slides like this


Slide #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/textbo ok/ch10/tropcyclone_10_3_1.html Using Rossby Radius of Deformation?
More slides like this


Slide #7.

Rossby Radius of Deformation  Defined as N = Brunt–Väisälä frequency  H = Depth of the system  ζ = Relative Vorticity  f0 = Coriolis parameter (planetary vorticity)   Critical Boundary where rotation becomes as important as buoyancy Brunt–Väisälä frequency g = Gravity  θ = Potential Temperature  Z = Geometric Height
More slides like this


Slide #8.

RROD - Illustration 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
More slides like this


Slide #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
More slides like this


Slide #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
More slides like this


Slide #11.

Methodology for RROD(cont.)  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 
More slides like this


Slide #12.

Three Obvious RROD Minimums Preliminary RROD Field 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 •
More slides like this


Slide #13.

September 2004 Cluster Tracks Individual Tropical Cloud Cluster RROD 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 11.3oN 28.0oW (note IR image is just a hypothetical diagram)
More slides like this


Slide #14.

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


Slide #15.

Non-Developing Case vs. Developing Case(cont.) 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) •
More slides like this


Slide #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
More slides like this


Slide #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
More slides like this


Slide #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)
More slides like this


Slide #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.
More slides like this


Slide #20.

http://www.nhc.noaa.gov/gifs/SST/AL_08_AUG_1971-2000_RSST.gif http://www.stuffintheair.com/images/abstractrotation.jpg 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_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 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/tc_pages/gif89/SEL_20040909.1145.goes12.x.ir1km_bw.09LIVAN.140kts925mb-137N-695W.gif
More slides like this