Fine-resolution simulations of Hurricane Erin 2001 are conducted using the Penn State University/National Center for Atmospheric Research mesoscale model (MM5) to investigate roles of thermodynamic, boundary layer and microphysical processes in Erin’s structure and evolution, and their effects on horizontal and vertical distributions of hydrometeors. Through comparison against radar reflectivity (Z) and Doppler velocity (Vdop) measured by the NASA ER-2 Doppler radar and Z measured by the NOAA P-3 during the Convection and Moisture Experiment (CAMEX) 4, it is seen that MM5 simulations over predict Z, rainfall rate (R) and hence hydrometeor mixing ratios, and the magnitudes of updrafts around the freezing level. Comparisons against microwave radiometer data also suggest simulations over predict graupel. A simulation with a new iterative condensation scheme, that limits unphysical increases of equivalent potential temperature produced by most condensation schemes, best matches observed Z and gives lower updrafts more consistent with observed Vdop, but gives a moister eye not matching inner eye thermodynamic structures sampled by dropsondes released from the ER-2.

Simulations with varying parameterization schemes show how the release of latent heat feedbacks upon Erin’s structure. Different microphysical schemes give different Z, R, hydrometeor profiles, updrafts and downdrafts. The differences are partly related to use of varying coefficients describing conversions between hydrometeor categories. In particular, the choice of coefficients used to describe hydrometeor fall velocities has as big of an impact on simulations as does the choice of microphysical parameterization scheme, with increasing fall velocity giving more intense hurricanes, but not as clear relationships on how increasing fall velocity affects magnitudes of updrafts, downdrafts, R and Z. In order for the simulated Erin to intensify, only certain boundary layer schemes (e.g., Burk-Thompson, Eta) could be used to represent boundary layer processes. Ramifications of these findings for quantitative precipitation forecasts (QPFs) of tropical cyclones are discussed.