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Rain Mixing Ratio Characterizing the Structure of Hurricane Karl (2010) Jennifer C. DeHart and Robert A. Houze, Jr. Department of Atmospheric Sciences, University of Washington, Seattle, WA Introduction Hurricane Karl WRF Simulations In the literature, the orographic modification of the three- • Hurricane Karl made landfall in Mexican state of Veracruz • WRF modeling study to examine structure of Karl. dimensional structure of tropical cyclones is understudied. on September 17th, 2010. • Different microphysical schemes produce similar tracks In order to improve prediction of surface precipitation, the • Rainfall totals were highest near Misantla, MX (Stewart • General intensity captured, but falls a bit short manner in which topography impacts dynamical and 2010). microphysical processes must be better understood. Hurricane Karl’s landfall in southern Mexico in 2010 provides an opportunity to study this interaction in greater detail. Before teasing out the relative contribution of the topography, this study first seeks to characterize the existing thermodynamic, microphysical and dynamical structures. This will enable further study of the role the Mexican mountains and plateau play in organizing and enhancing precipitation. Rainfall associated with Hurricane Karl from Stewart 2010 Track and Intensity of Hurricane Karl from NHC Best Track data and simulations. Characteristic Structure – WSM Rain Mixing Ratio Graupel Mixing Ratio Snow Mixing Ratio Snow Profile Rain Profile 15 15 Box 1 Box 1 Box 2 0.5 km 1 km 6 km Box 2 6 km Box 1 Box 1 Box 1 10 10 Altitude (km) Altitude (km) 5 Latitude 5 Latitude Box 2 Box 2 Box 2 0 0 0123456789 0 0.2 0.4 0.6 0.8 1 1.2 -4 kg/kg -3 kg/kg Mixing Ratio (kg/kg) ×10 Longitude Mixing Ratio (kg/kg) ×10 Longitude Latitude 2 km 4 km Left: Graupel at 6 km altitude at hour 63. • Lighter particles like snow have a Left: Snow at 6 km altitude at hour 63. Right: Graupel profiles in denoted areas. • Graupel more prevalent in Box 1 tendency to circle away from origin Right: Snow profiles in denoted areas. Theta-E and W Theta E Profile 15 1 km Box 1 Conclusions and Future Work kg/kg Box 2 Box 1 • Consistent with the location of maximum surface rainfall, 10 graupel mixing ratios are maximized along the northern Longitude Above: Rain mixing ratio at four edge of the terrain altitude levels at hour 63. Latitude Altitude (km) 5 • Theta-e profile indicates greater instability, supported by Rain Profile 15 Box 2 Box 1 stronger mean ascent present in the same region Box 2 • Convection in the outer bands is strongest in the northern 0 • Strong rain signature in the Longitude K 344 346 348 350 352 354 356 358 10 Mixing Ratio (kg/kg) region, though there are several potential factors at play W Profile inner core / outer bands 15 Box 1 Box 2 • Box 1 has higher average • Future Plans Altitude (km) • Stronger decrease in θ in Box 1 5 mixing ratios through 4 km 10 e • Comparison of the simulated structures with airborne radar • Mean w is stronger in Box 1 data from APR-2 on the NASA DC-8 Altitude (km) 5 0 • Isolate the role that orography plays in the structural 0 0.2 0.4 0.6 0.8 1 Mixing Ratio (kg/kg) ×10 -3 Top Left: θ at 1 km altitude at hour 63. e patterns by modifying the terrain in WRF experiments Top Right: θ profiles from data in denoted areas. Bottom: Rain mixing ratio profiles from data in boxes e 0 drawn above. 0 0.1 0.2 0.3 0.4 0.5 0.6 Bottom: W profiles from data in denoted areas. Wind Speed (m/s) Work supported by NASA grants NNX13AG71G and NNX12AJ82G .
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