Multi-Scale Analysis of Observations of Tropical Cyclones with Applications to High-Resolution Hurricane Modeling

Author/Creator ORCID

Date

2011-01-01

Department

Physics

Program

Physics, Atmospheric

Citation of Original Publication

Rights

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Abstract

Tropical cyclone numerical models, a critical tool to forecasters, have been run at resolutions of around 9-30 km in operational centers until recently. It is currently possible to run in the range of 1-4 km resolution, which may allow a model to resolve small-scale dynamical processes critical to tropical cyclone intensity. A 3km version of the NCEP HWRF model is developed for that purpose and its competitive track and intensity forecasting abilities are demonstrated. To determine if the small scales are resolved correctly, a statistical framework for comparison to observations of small-scales is developed. The standard definition of a model's forecast intensity is examined, and found to have a systematic, resolution-dependent bias. A database of TRMM overpasses of over eight hundred tropical cyclones is produced and used to show a relationship between storm-scale cloud top temperature and storm wind intensity. However, all storms, regardless of strength, produce near-tropopause cloud tops, and storms undergoing rapid intensification (RI) tend to have higher cloud tops than non-RI storms. In an analysis of in-situ wind data, vertical wind is shown to be scale-invariant, with no correlation beyond, nominally, 2 km scales. This new framework for comparison is used to show that model's cloud tops have the right relationships with intensity and intensification, but that downdrafts are weak and rare. Model ""spin-up"" issues are seen: in the first six hours, some storms rapidly gain fine-scale 3 km resolution wind maxima that hurt the forecast and others weaken uniformly at all resolutions. In addition, a model bug is found in this and operational HWRF: all microphysics type fractions are discarded when the nest moves. Overall, the research presented in this demonstrates the value of statistical diagnostics for high-resolution models. In addition, this research presents a framework for a deeper investigation of tropical cyclone small-scale dynamics.