Publications
Following publications have been announced by our department Hydrodynamics and Data Assimilation. For further information please contact the marked co-authors of the publications:
Mehra, A., Staneva, J., Kim, H.-S., Joseph, S., & Glenn, S. (2024): Editorial: Impact of oceans on extreme weather events (tropical cyclones). Front. Mar. Sci., 11:1428063, doi:10.3389/fmars.2024.1428063
Excerpt from the editorial:
In the last decade, extreme Weather Events (Tropical Cyclones) have caused significant damage to global coastlines as the climate continues to change and both the world population and economic activity near the coasts continue to increase. The World Climate Research Programme’s Grand Challenge on Weather and Climate Extremes has highlighted the need for reliable predictions of weather extremes, including Tropical Cyclones in all oceanic basins. The challenge ahead is for the research community to make progress in model improvements, data assimilation, observations, and design of observing systems in light of the most pressing needs from the end user community for improved forecast information on extreme events which impact these coastlines.
This Research Topic highlights some of recent progress made by the research community in model improvements, data assimilation, machine learning methods and use of physical and biogeochemical marine observations for an enhanced understanding of impact of extreme weather events (tropical cyclones, marine heat waves) in oceanic basins across the globe.
Contributing articles have considered observations describing oceanic states influencing extreme events – like warm eddies with a warm deep mixed layer restricting the cooling and contributing to TCs intensification with higher availability of latent heat flux (Kang et al.) or that the presence of a freshwater barrier layer which likely inhibited additional sea surface cooling and enhanced enthalpy flux as factors behind lack of strengthening of developing TC’s (Miles et al.) or that the highest surface intensity of a marine heat wave often aligned with low surface salinity anomalies while subsurface temperature and salinity anomaly peaks which often corresponded (Sims et al.).
Other research studies in this Research Topic include modeling studies which describe a new Machine Learning (ML) based ensemble modeling system that produces noteworthy improvements for rapid intensification (RI) predictions in the Western North Pacific basin (Kim et al.). Other studies consider the impact of surface wave assimilation on hurricane track and intensity forecasts (Chen et al.) or that enhancing the regional upper-ocean observing system and improving the fidelity with which the observations are assimilated into ocean data assimilation systems could directly benefit TC intensity forecasts (Chiodi et al.).
Couto de Souza, D., da Silva Dias, P.L., Gramcianinov, C.B., da Silva, M.B.L., & de Camargo, R. (2024): New perspectives on South Atlantic storm track through an automatic method for detecting extratropical cyclones‘ lifecycle. International Journal of Climatology, 44(10), 3568–3588, doi:10.1002/joc.8539
Abstract:
This study introduces new insights into the climatology of South Atlantic (SAt) cyclones by employing a novel cyclone life cycle detection method, the CycloPhaser. Utilizing the minimum relative vorticity series and its derivative at the cyclone centre, the program effectively identifies distinct phases in the cyclone life cycle. Cyclone tracks are obtained through the analysis of relative vorticity at 850 hPa, using the ERA5 dataset. The study identified six main cyclone life cycle patterns from the analysis of 28,458 systems. The predominant cyclone type, accounting for approximately 60% of the analysed systems, exhibited a four-phase configuration: incipient, intensification, mature and decay. Detailed statistics for each developmental phase and the overall life cycle are presented, offering valuable comparisons and new insights while corroborating previous research findings. Key genesis regions in the SAt are identified, along with track density maps that reveal distinct preferences in cyclone developmental cycle. The main outcome of this study is the demonstration that the automated classification procedure enables the analysis of cyclones‘ life cycles to be conducted promptly and with low computing costs, facilitating the comprehensive study of cyclone behaviour with high efficiency.
