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Forecasts of tropical cyclone (TC) tornadoes are less skillful than their non‐TC counterparts at all lead times. The development of a convection‐allowing regional ensemble, known as the Warn‐on‐Forecast System (WoFS), may help improve short‐fused TC tornado forecasts. As a first step, this study investigates the fidelity of convective‐scale kinematic and thermodynamic environments to a preliminary set of soundings from WoFS forecasts for comparison with radiosondes for selected 2020 landfalling TCs. Our study shows reasonable agreement between TC convective‐scale kinematic environments in WoFS versus observed soundings at all forecast lead times. Nonetheless, WoFS is biased toward weaker than observed TC‐relative radial winds, and stronger than observed near‐surface tangential winds with weaker winds aloft, during the forecast. Analysis of storm‐relative helicity (SRH) shows that WoFS underestimates extreme observed values. Convective‐scale thermodynamic environments are well simulated for both temperature and dewpoint at all lead times. However, WoFS is biased moister with steeper lapse rates compared to observations during the forecast. Both CAPE and, to a lesser extent, 0–3‐km CAPE distributions are narrower in WoFS than in radiosondes, with an underestimation of higher CAPE values. Together, these results suggest that WoFS may have utility for forecasting convective‐scale environments in landfalling TCs with lead times of several hours.
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Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance
Abstract Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts.
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- PAR ID:
- 10430775
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 38
- Issue:
- 6
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- 985 to 998
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/WAF-D-22-0111.1
