An Object-based Approach to Model Verification in Tropical Cyclone Forecasting

Authors: Stephanie Zick*, Virginia Tech
Topics: Climatology and Meteorology
Keywords: tropical cyclones, hurricanes, rainfall, precipitation, spatial analysis
Session Type: Poster
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download


An intense tropical cyclone (TC) presents a dramatic picture from space. TC convection is multi-scalar, spanning the large-scale synoptic (~2000 km) environment over which TCs gather moisture, the characteristic meso-α scale (~200-2000 km) spiral rainbands seen in radar and satellite imagery, and the meso-β scale (~20-200 km) eye and eyewall(s) in the TC inner core. As a TC intensifies, it produces structures in these predictable patterns, evolving from a disorganized cluster of clouds and precipitation into a compact and symmetric storm. These consistent patterns throughout the TC lifecycle facilitate the use of object-based metrics to quantify spatial attributes of the overall storm and individual rainbands. Further, object-based verification techniques are useful for comparing model predictions of precipitation (or reflectivity) versus observations. Here, I present two proof of concept studies of Hurricane Isabel (2003) and Hurricane Harvey (2017) to demonstrate an objective method for measuring TC precipitation forecast skill. In both cases, model forecast fields are compared against observations. Results demonstrate the value of object-based, quantitative measures for assessing TC model forecasts rather than relying on subjective visual comparisons of forecasts and observations. Additionally, forecast skill varies by rainfall/reflectivity threshold. Therefore, it is important to assess forecast skill over a range of thresholds that cover stratiform and convective precipitation to uncover any systematic biases that may exist in these regimes.

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