Abstract
Abstract. Potential intensity (PI) is the maximum speed limit of a tropical cyclone found by modeling the storm as a thermal heat engine. Because there are significant correlations between PI and actual storm wind speeds, PI is a useful diagnostic for evaluating or predicting tropical cyclone intensity climatology and variability. Previous studies have calculated PI given a set of atmospheric and oceanographic conditions, but although a PI algorithm – originally developed by Kerry Emanuel – is in widespread use, it remains under-documented. The Tropical Cyclone Potential Intensity Calculations in Python (pyPI, v1.3) package develops the PI algorithm in Python and for the first time details the full background and algorithm (line by line) used to compute tropical cyclone potential intensity constrained by thermodynamics. The pyPI package (1) provides a freely available, flexible, validated Python PI algorithm, (2) carefully documents the PI algorithm and its Python implementation, and (3) demonstrates and encourages the use of PI theory in tropical cyclone analyses. Validation shows pyPI output is nearly identical to the previous potential intensity computation but is an improvement on the algorithm's consistency and handling of missing data. Example calculations with reanalyses data demonstrate pyPI's usefulness in climatological and meteorological research. Planned future improvements will improve on pyPI's assumptions, flexibility, and range of applications and tropical cyclone thermodynamic calculations.
Highlights
Tropical cyclones pose significant risks to coastal societies, being among the costliest and deadliest of global natural hazards (e.g., Pielke et al, 2008; Rappaport, 2014; Hsiang and Jina, 2014)
Driven by thermodynamic disequilibrium – which is largest in the summer and autumn seasons – an existing mature tropical cycle will transfer heat from the surface to the atmospheric boundary layer, largely through latent heat release of evaporation and from the sea surface and dissipative heating (Bister and Emanuel, 1998)
Potential intensity may be derived following Bister and Emanuel (2002) idealizing a tropical cyclone as a Carnot heat engine (e.g., Emanuel, 1987) and assuming the following: (1) the work done against friction by the outflow is ignored, (2) when the storm intensity reaches its maximum, the anticyclone at the top of the storm is fully developed, and (3) the gradient wind may be approximated by cyclostrophic wind at the radius of maximum winds (RMW)
Summary
Tropical cyclones pose significant risks to coastal societies, being among the costliest and deadliest of global natural hazards (e.g., Pielke et al, 2008; Rappaport, 2014; Hsiang and Jina, 2014). Gilford: pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in Python storms (Emanuel, 2000), so it can be used to assess and interpret real-world intensity trends and variability (e.g., Wing et al, 2007; Gilford et al, 2019; Shields et al, 2019).
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