TempestExtremes v2.1: A Community Framework for Feature
Detection, Tracking and Analysis in Large Datasets

Ullrich, P. A.; Zarzycki, Colin M.; McClenny, Elizabeth; Pinheiro, M. C.; Stansfield, Alyssa M.; Reed, Kevin A.

doi:10.5194/gmd-2020-303

Cited by 17 publications

(29 citation statements)

References 63 publications

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“…For the tracking of pointwise features, such as TCs, it provides two functions: DetectNodes that find candidates "nodes" corresponding to local extrema of a given variable, and optionally satisfying a set of additional criteria (closed-contours, thresholds); and StitchNodes that links candidates within a given distance of one another into a track. In this paper, we use TempestExtremes to implement two vastly different TC trackers, UZ and OWZ, respectively described by Ullrich et al (2021) and Tory et al (2013d). We describe both algorithms below and provide the associated codes in the appendix C.…”

Section: Tempestextremesmentioning

confidence: 99%

“…We implemented the physics-based algorithm UZ in TempestExtremes as described by Ullrich et al (2021). The thresholds were calibrated by Zarzycki and Ullrich (2017) using sensitivity analysis to several metrics and the data of four reanalysis products.…”

Section: Uz Algorithmmentioning

confidence: 99%

“…When using reanalysis products like ERA5, detected tracks can tentatively be associated with observed tracks (Murakami, 2014;Hodges et al, 2017;Ullrich et al, 2021). We used the following matching algorithm.…”

Section: Tracks Matchingmentioning

confidence: 99%

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Intercomparison of Four Tropical Cyclones Detection Algorithms on ERA5

Bourdin

Fromang

Dulac

et al. 2022

Preprint

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Abstract. The assessment of Tropical Cyclones (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of algorithms, often referred to as trackers, that aim to detect the positions of tropical cyclones in gridded datasets. This paper compares four trackers with very different formulations in detail. We assess their performances by tracking tropical cyclones in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. The first section of the paper finds typical detection rates of the trackers ranging from 75 to 85 %. At the same time, false alarm rates (FAR) greatly vary across the four trackers and can sometimes exceed the number of detected genuine cyclones. Based on the finding that many of these false alarms are extra-tropical cyclones, we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 to 36 % in our final catalogs of tropical cyclones tracks. We then show that different traditional metrics can be very sensitive to the particular choice of the tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 tropical cyclones intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known properties.

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Section: Tempestextremesmentioning

confidence: 99%

Section: Uz Algorithmmentioning

confidence: 99%

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Intercomparison of Four Tropical Cyclones Detection Algorithms on ERA5

Bourdin

Fromang

Dulac

et al. 2022

Preprint

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“…As features strongly associated with convection, we expect TCs to be particularly sensitive to the choice of dynamical core (Roberts et al., 2020). To this end, we track TCs using TempestExtremes (Ullrich & Zarzycki, 2017; Ullrich et al., 2021) and assess differences in the composite horizontal structure of these systems between the NH and H model. Notably, Qi et al.…”

Section: Realistic Seasonal Simulationsmentioning

confidence: 99%

An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SM)

Liu

Ullrich

Guba

et al. 2022

J Adv Model Earth Syst

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The hydrostatic (H) approximation is widely used in numerical atmospheric models to simplify the underlying fluid equations and avoid the timestep restrictions imposed by vertically propagating waves (Saito et al., 2007;Salmon, 1988). Under this approximation the atmosphere is assumed to be continuously in hydrostatic equilibrium, with a balance between gravity and vertical pressure gradient; in essence, any hydrostatic imbalances are assumed to be corrected instantaneously. The hydrostatic approximation can be shown to be a reasonable assumption when the horizontal scale is much larger than the vertical scale. However, nonhydrostatic (NH) effects are crucial to the simulation of certain fine-scale phenomena, such as deep convection and flow over topography; in these circumstances, departures from hydrostatic equilibrium need to be taken into account. While the horizontal grid spacing necessary for producing NH effects is generally around 10 km, recent studies have also been shown that the inclusion of moist physics can lead to significant divergence between H and NH models even around 36 km grid spacing (Gao et al., 2017;Yang et al., 2017). While this grid spacing is finer than the typical grid spacing for global climate models, recent pushes toward higher horizontal resolution in atmospheric models have placed us squarely in this regime (Caldwell et al., 2021;Haarsma et al., 2016;Stevens et al., 2019). Consequently, it is both important and timely to investigate NH effects in global climate modeling systems, particularly in the context of seasonal to climatological scale simulations.

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“…We used TempestExtremes (Ullrich et al, 2021; to track TUTTs. TempestExtremes is a flexible software package for identifying and tracking meteorological features in time and space in historical or simulated datasets.…”

Section: Identification and Trackingmentioning

confidence: 99%