Development of a tropical cyclone microphysical model

A tropical cyclone (TC) simulation using Spectral (bin) Microphysics (SBM) was produced for Hurricane Katrina using the dynamical framework of NOAA's Hurricane WRF (HWRFX) prediction system. The outer simulation domain contains an inner, moving nest that follows the TC center, where clouds are resolved explicitly. The hurricane model is coupled with the Princeton Ocean Model and the WAVEWATCH III wave model. The wind-wave-current interaction framework at the air-sea interface includes sea spray effects on the parameterization of turbulence within the surface layer and the momentum, heat and moisture fluxes. The generation of sea spray depends on the wave parameters predicted by the wave model and wind speed predicted in the hurricane model. The SBM scheme in HWRFX calculates at each time step and in each grid point size distributions of atmospheric aerosols, water drops, cloud ice (ice crystals and aggregates) and graupel/hail. Each size distribution is defined on a hydrometeor mass grid containing 33 bins. The SBM scheme allows one to take into account aerosol effects on dynamics and microphysics of clouds, as well on the TC intensity and structure. The model calculates accurately (by solving non-parameterized kinetic equations for size distribution functions) convective heating/cooling related to condensational growth/evaporation of drops, deposition and sublimation of ice, freezing and melting. It solves equations for stochastic collisions to describe formation of raindrops, aggregates (snow) and grauplel/hail. The coupled model output includes spatial distribution of precipitation rate, radar reflectivity (calculated according to its definition), wind, wave, and ocean fields, etc. The newly developed coupled model can be used for the investigation of physical processes determining TC genesis, development, spatial structure, precipitation. It can also be utilized as a benchmark model for testing and calibration of different bulk cloud microphysics schemes to improve the current TC forecast models.