the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Apr 2025
Meteorological Landscape of Tropical Cyclone
Abstract. A tropical cyclone is a meteorological phenomenon that produces heavy rainfall, damaging winds, thunderstorms, storm surges, among others. This is also a system of complex interactions between local sea-surface temperatures vertical atmospheric conditions, such as shear winds, and regional steering flows. A single discipline cannot rise to the challenge posed by the understanding of the mechanisms governing the birth, maturity, and decay of a tropical cyclone. Collaborative work between earth science and other disciplines can address such a challenge, by offering new angles of thinking and new techniques of research to apprehend such a complex phenomenon. In this study, we apply biological concepts such as the Waddington’s epigenetic landscape, and bioinformatics techniques like the graph-Hodge decomposition, to meteorology, to introduce an innovative way to characterize the evolution of three tropical cyclones: "Dolphin," "Nepartak," and "Meari". When applied to an ensemble prediction system, the result is a meteorological landscape depicting the creodes reflecting possible paths and their associated probabilities of realization.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-1458', Anonymous Referee #1, 27 Feb 2026
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RC2: 'Comment on egusphere-2025-1458', Anonymous Referee #2, 06 Mar 2026
The authors utilized biological principles alongside bioinformatics methods to elucidate the branching patterns observed in forecast tracks of tropical cyclones (TCs). These tracks were derived from ensemble forecasts pertaining to three typhoon cases within the JMA-MEPS model. By employing concepts such as bifurcation, separatrix, and both potential and rotational flow, the study establishes analogies for the uncertainties associated with tropical cyclones' projected movements in the ensemble predictions of these cases.
I appreciate the authors’ interdisciplinary efforts in drawing connections between the two fields. However, the proposed framework does not demonstrate its compatibility with the physics of TCs, and I do not see how it can meaningfully improve TC predictability. The method used to distinguish between two states/fates in future TC tracks is overly complex and fails to offer significant improvements over simpler clustering methods, including the DBSCAN method used in Oettli & Kotsuki (2024). Irregular TC tracks can be far more complex than just having two possible fates/paths. TC forecasts can sometimes be so uncertain that the ensemble tracks are widely dispersed across the map. However, these situations are not examined in this study. The "Naparka" case in this study demonstrates some evidence of this issue—there are considerable track points going eastward but are awkwardly classified as “Westward.”
I understand that the authors want to promote a deeper understanding of TC evolution using biological concepts. It is a creative approach, to be sure. However, there are several obvious caveats that call into question the suitability of using these concepts on TC evolution. First, projected TC tracks reflect only a small portion of the evolution of TCs. TC evolution involves motion, intensification/weakening, and structural changes. The latter two aspects depend heavily on the physics of TCs and the underlying environment, and they are not discussed in the study. TC motions, the focus of this study, are mostly controlled by upper-level steering winds/flows. Therefore, rather than viewing the ensemble forecast track points and the projected translation speed as the primary factors governing TC motion, a deeper and more direct physical connection can be established between the upper-level steering flow (streamlines) in observations and forecasts and future TC movement. For example, the "saddle points" observed in some irregular TC tracks are typically caused by saddle fields in atmospheric pressure/geopotential fields. A challenge in the modeling world is determining the fate of TCs that fall into these saddle fields, as well as how the large-scale atmospheric flow interacts with the intensity and structural changes in TCs.
I agree with Referee #1 that the authors should consider collaborating with tropical cyclone experts to develop a stronger foundation of the atmospheric science component. While I see the potential in bridging these novel concepts to the field of meteorology and TCs, the current manuscript does not demonstrate sufficient quality in establishing the connection.
Citation: https://doi.org/10.5194/egusphere-2025-1458-RC2
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This manuscript describes the application of techniques developed in the biological sciences to describe tropical cyclone (TC) track forecasts from an ensemble prediction system. Specifically, the technique can be used to identify bifurcation points and the evolution of the flow from a irrotational and solenoidal (divergent and rotational) wind perspective. The authors apply this technique to three TCs that impacted Japan during 2020-2022 and provide a brief explanation of the results of applying these techniques to these forecasts.
In the abstract, the authors make the case that advancing knowledge on a topic like TCs would benefit from knowledge from other areas. I fully agree with this statement and as an atmospheric scientist who has worked on TC predictability for years, I welcome input and techniques from other disciplines on this topic. With that being said, I have difficulty identifying how these concepts would advance knowledge on TCs, especially since TC motion is not something like a cell division with lots of degrees of motion and a random-appearing evolution. Instead, TC motion is primarily driven by the concept of steering flow, which evolves based on the dynamics of large-scale atmospheric features. I would encourage the authors to review the current state of the science on this, particularly for irregular tracks (see: Magnusson et al. 2019). Track bifurcation is an important problem, but the atmospheric science community has developed methods for addressing this using methods that are much less complex, yet give similar information, such as clustering (e.g., Kowaleski and Evans, 2020), empirical orthogonal function decomposition (e.g., Torn et al. 2025) among others. The paper would be far more compelling if the authors demonstrated how these compare to what is already present in the atmospheric science literature, along with what new information could be gained from adopting these approaches. I don’t see a compelling case for these methods when I can get similar information from simpler techniques. Finally, I assume the audience for this paper is atmospheric sciences working on TCs. If so, I think this paper is going to be very dense and difficult to follow for them because it provides an extensive description of the mathematics behind the method and relatively little about how this method could be useful. Most of the analysis is superficial and does not make a compelling case for adopting or trying out this method. I would encourage the authors to work with TC expertise to help with these concerns.
With these concerns, I cannot recommend publication of this journal at this time. Addressing the concerns I have listed above will take time and allow the authors to craft a potentially more impactful paper for the TC community.
References:
Kowaleski, A. M. and Evans, J. L.: Use of multiensemble track clustering to inform medium-range tropical cyclone forecasts, Weather Forecast., 35, 1407–1426, DOI: 10.1175/WAF-D-20-0003.1, 2020.
Magnusson, L., J. D. Doyle, R. D. Torn, C. K. Tang, M. Yamaguchi, F. Zhang, 2019: Advances in Understanding Difficult Cases of Track Forecasts. Trop. Cyclone Research 8, 109–122, DOI: 10.6057/2019TCRR03.01
Torn, R. D., Brennan, M. J. and Dunion, J. P., 2025: Application of ensemble sensitivity for hurricane track forecast sensitivity and flight planning. Wea. Forecasting, 40, 411–424, DOI: 10.1175/WAF-D-24-0179.1