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My research utilizes data science methods, including machine learning (ML) techniques, to improve our understanding and prediction of the following areas:
My research utilizes data science methods, including machine learning (ML) techniques, to improve our understanding and prediction of the following areas:
Tropical Cyclone Intensity Change
Tropical Cyclone Intensity Change
Although progress has recently been made in the skill of both track and intensity forecasts, accurately predicting periods of rapid intensification (RI), usually defined as a swift increase in the storm’s peak winds, has proven quite challenging. Because societal preparations for an imposing storm are dependent upon the expected intensity of the storm at the time of impact, the consequences of an unanticipated RI event can be catastrophic. With the goal of improving our understanding and prediction of RI, I am interested in answering the following questions:
1) Are RI events associated with unique vortex and convective characteristics?
2) If so, over what time scales do these characteristics emerge prior to the onset of RI?
3) To what extent can ML methods improve our forecast skill of RI events?
Tropical Cyclone Structure
Tropical Cyclone Structure
The hazards posed by a tropical cyclone are intimately linked to the structure of the storm. The structure of the storm, however, is dictated by how convective processes interact with the storm's environment. To better tropical cyclone structure, and the drivers of changes in structure, I led the development of a novel airborne Doppler radar database: the Tropical Cyclone Radar Archive of Doppler Analyses with Recentering (TC-RADAR). Using this database, as well as a combination of other observational platforms, I am interested in answering the following questions:
1) How is the tropical cyclone convective mode related to changes in the structure of the storm?
2) Why do some tropical cyclones continue to intensify while experiencing eyewall replacement cycles?
3) Can machine learning methods be used to skillfully predict the three-dimensional tropical cyclone kinematic structure based off the storm's satellite appearance?
Tropical Cyclone Seasonal Prediction
Tropical Cyclone Seasonal Prediction
Tropical cyclone activity, in terms of both number of storms as well as integrated measures of intensity, exhibits significant interannual variability. Research over the last few decades has demonstrated that skillful predictions of seasonal tropical cyclone activity can be made months before the majority of the activity is realized. However, the limits of this predictability are not clear. I am interested in answering the following questions:
1) To what extent can predictions of seasonal tropical cyclone activity be improved upon?
2) Can skillful predictions of the spatial distribution of tropical cyclone activity be made?
3) How far in advance can skillful predictions of seasonal tropical cyclone activity be made?
Publications:
Publications:
Fischer, M. S., P. D. Reasor, J. P. Dunion, and R. F. Rogers, 2025: Are rapidly intensifying tropical cyclones associated with unique vortex and convective characteristics? Mon. Wea. Rev., 153, 183–203.
Rios-Berrios, R., P. M. Finocchio, J. J. Alland, X. Chen, M. S. Fischer, S. Stevenson, and D. Tao, 2024: A review of the effects of vertical wind shear on tropical cyclone structure and intensity. J. Atmos. Sci., 81, 713–741.
Shimada, U., P. Reasor, R. Rogers, Fischer, M. S., F. Marks, J. Zawislak, and J. Zhang, 2024: Shear-relative asymmetric kinematic characteristics of intensifying hurricanes as observed by airborne Doppler radar. Mon. Wea. Rev., 152, 491–512.
Fischer, M. S., R. F. Rogers, P. D. Reasor, and J. P. Dunion, 2024: An observational analysis of the relationship between tropical cyclone vortex tilt, precipitation structure, and intensity change. Mon. Wea. Rev., 152, 203–225.
Wadler, J. B., J. J. Cione, R. F. Rogers, and M. S. Fischer, 2023: On the distribution of convective and stratiform precipitation in tropical cyclones from airborne Doppler radar and its relationship to intensity change and environmental wind shear direction. Mon. Wea. Rev., 151, 3209–3233.
Wadler, J. B., J. E. Rudzin, B. J. de la Cruz, J. Chen, M. S. Fischer, G. Chen, N. Qin, B. Tang, and Q. Li, 2023: A review of recent research progress on the external influences of tropical cyclone intensity change. Tropical Cyclone Res. and Rev., https://doi.org/10.1016/j.tcrr.2023.09.001.
DesRosiers, A. J., M. M. Bell, P. J. Klotzbach, M. S. Fischer, and P. Reasor, 2023: Observed relationships between tropical cyclone vortex height, intensity, and intensification rate. Geophys. Res. Lett., 50, e2022GL101877.
Stone, Z., G. R. Alvey III, J. P. Dunion, Fischer, M. S., D. J. Raymond, R. F. Rogers, S. Sentic, and J. Zawislak, 2023: Thermodynamic contribution to vortex alignment and rapid intensification of Hurricane Sally (2020). Mon. Wea. Rev., 151, 931–951.
Fischer, M. S., P. D. Reasor, B. H. Tang, K. L. Corbosiero, R. D. Torn, and X. Chen, 2023: A tale of two vortex evolutions: Using a high-resolution ensemble to assess the impacts of ventilation on a tropical cyclone rapid intensification event. Mon. Wea. Rev., 151, 297–320.
Hazelton, A., G. J. Alaka, M. S. Fischer, R. D. Torn, and S. Gopalakrishnan, 2023: Factors influencing the track of Hurricane Dorian (2019) in the West Atlantic: Analysis of a HAFS ensemble. Mon. Wea. Rev., 151, 175–192.
Fischer, M. S., P. D., Reasor, R. F. Rogers, and J. F. Gamache, 2022: An analysis of tropical cyclone vortex and convective characteristics in relation to storm intensity using a novel airborne Doppler radar database. Mon. Wea. Rev., 150, 2255–2278.
Alvey, G., M. S. Fischer, P. Reasor, J. Zawislak, and R. Rogers, 2022: Processes underlying the vortex repositioning during Dorian’s (2019) Early Stages that increased its favorability for rapid intensification. Mon. Wea. Rev., 150, 193–213.
Zawislak, J., R. Rogers, S. Aberson, G. Alaka, G. Alvey, A. Aksoy, L. Bucci, J. Cione, N. Dorst, J. Dunion, M. S. Fischer, J. Gamache, S. Gopalakrishnan, A. Hazelton, H. Holbach, J. Kaplan, H. Leighton, F. Marks, S. Murillo, P. Reasor, K. Ryan, K. Sellwood, J. Sippel, and J. Zhang, 2022: Accomplishments of NOAA’s Airborne Hurricane Field Program and a broader future approach to forecast improvement. Bull. Amer. Meteor. Soc., 102, 1–79.
Hazelton, A., G. J. Alaka, L. Cowan, M. S. Fischer, S. Gopalakrishnan, 2021: Understanding the processes causing the early intensification of Hurricane Dorian through an ensemble of the Hurricane Analysis and Forecast Systems (HAFS). Atmos., 12, 93.
Fischer, M. S., R. F. Rogers, and P. D. Reasor, 2020: The rapid intensification and eyewall replacement cycles of Hurricane Irma (2017). Mon. Wea. Rev., 148, 981–1004.
Fischer, M. S., B. H. Tang, and K. L. Corbosiero, 2019: A climatological analysis of tropical cyclone rapid intensification in environments of upper-tropospheric troughs. Mon. Wea. Rev., 147, 3693–3719.
Fischer, M. S., B. H. Tang, K. L. Corbosiero, and C. M. Rozoff, 2018: Normalized convective characteristics of tropical cyclone rapid intensification events in the North Atlantic and eastern North Pacific basins. Mon. Wea. Rev., 146, 1133–1155.
Fischer, M. S., B. H. Tang, and K. L. Corbosiero, 2017: Assessing the influence of upper-tropospheric troughs on tropical cyclone intensification rates after genesis. Mon. Wea. Rev., 145, 1295–1313.
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