The Environment of Hurricane Debby (1982). Part II: Thermodynamic Fields

Abstract

A three-dimensional analysis of temperature and relative humidity in the environment of Hurricane Debby (1982) has been completed. Observations from Omega dropwindsondes (ODWs) within 1000 km of the storm have been combined with rawinsondes over the continental United States and the Caribbean and with observations from surface ships and aircraft data where possible. The temperature and relative humidity analyses, together with wind analyses from a previous study, form a dataset that can be used an an initial condition in a multilevel prognostic model when combined with analyses over area larger than our analysis domain. In this paper a series of diagnostic tests has been applied to the dataset to evaluate its performance without using a prognostic model. These tests include horizontal maps of the moist convective instability, calculation of the heat and moisture budgets in the vicinity of Bermuda, which was 350 km to the northeast of the storm center, and diagnosis of precipitation from these budgets and from the Arakawa-Schubert cumulus parameterization. Results show that the horizontal distribution of moist convective instability is strongly affected by the low-level moisture field upstream of the main inflow region to the storm. The total surface heat flux, estimated with a bulk aerodynamic method, matches the vertically integrated eddy flux of moist static energy to within observational errors. Precipitation estimates from the budgets give rates of approximately 20 mm day?1, which are consistent with an estimated rate from radar. Partition of the rainfall rate into convective scale and resolvable scale (stratiform) shows about equal contributions. Our results lead us to believe that, within the limitations determined by the horizontal distribution of the observations, the final dataset for Hurricane Debby provides a realistic depiction of the various physical processes that were occurring in Debby's environment. Future work will include data sensitivity experiments with a three-dimensional forecast model.