CyTRACK: Cyclone Tracking framework ================================= Overview ---------------- CyTRACK is a new open-source, comprehensive and user-friendly Python toolbox for detecting and tracking cyclones in model and reanalysis datasets. It detects and tracks cyclones also using the MSLP. Like most algorithmic Lagrangian trackers, the procedure for detecting cyclones is divided into two parts: (i) critical centres detection and (ii) pairing centres in continuous time steps. These procedures are described below. Detecting critical centres ---------------------------- Critical centres are detected as mean sea level pressure (MSLP) minima and must satisfy the following conditions: - The MSLP value is lower than a specified MSLP threshold (**min_slp_threshold**). - The MSLP anomaly computed as the difference between the MSLP at time t0 and the mean MSLP in the previous N days (**prev_days**) is lower than a critical value (`mslp_anomaly_threshold`). - The MSLP increases (**dmslp_great_circle_distance**) over a specified distance (**great_circle_distance**) from the candidate point. - The maximum wind speed (**max_wind_speed_threshold**) around the critical centre (**radius_for_msw**) is higher than a predefined value. - The surface relative vorticity (**vorticity_threshold**) is greater than a critical threshold. - The mean radial distance to the last closed isobar is higher than a critical value (**critical_outer_radius**). The radial distance to the last closed isobar is computed following the procedure developed by Rudeva and Gulev (2007). - When several centres exist in a critical radial distance (**filter_­center_threshold**), the centre that has the lowest MSLP is retained. Additionally, CyTRACK allows discarding centres positioned over terrain higher than a critical high (**terrain_filter**). .. seealso:: Rudeva, I., Gulev, S.K., 2007. Climatology of cyclone size characteristics and their changes during the cyclone life cycle. Mon. Weather Rev. 135, 2568–2587. https:// doi.org/10.1175/MWR3420.1. Paring cyclone centres in continuous time steps --------------------------------------------- - Storm centres are linked together if they reoccur in the next time step (**t=t0 + dt**) within a critical distance (**dist_threshold**) from the previous low-pressure centre detected at time t0. - If there are multiple identified centres within the critical distance, then the point with the lowest MSLP is chosen as the cyclone centre at the second time step. - CyTRACK also allows a one-time step gap. If no centre is detected at time t0+dt, CyTRACK searches for a candidate point at time t0+2dt. If at least one point is found at t0+2dt within a radial distance of two times dist_threshold from the cyclone centre at time t0, then the cyclone centre at time t0+dt is computed as the average latitude and longitude at time t0 and t0+2dt. To account for the “natural evolution” of the cyclone trajectory, the angle between the lines formed by the centres at time t0 and t0+dt and t0+dt and t0+2dt must be less than 10°. If the last condition is satisfied, the algorithm continues searching for the next centre at time t0+3dt; otherwise, the track ends at time t0. - After that, CyTRACK evaluates the lifetime (**dt_lifetime**) of the cyclone, the minimum distance travelled (**minimum_distance_travelled**) from genesis to dissipation, the maximum intensity (**intensity_threshold**) in terms of the maximum wind speed along the track. - If **checking_upper_levels_parameters** is set to “yes” in the configuration file, CyTRACK classifies the cyclone core based on the thermal wind and thermal asymmetry according to the CPS. For a more detailed understanding of CyTRACK, Please refer to .. seealso:: Pérez-Alarcón, A.; Coll-Hidalgo, P.; Trigo, R.M.; Nieto, R.; Gimeno, L. (2024). CyTRACK: An open-source and user-friendly python toolbox for detecting and tracking cyclones. Environmental Modelling & Software, 176, 106027. https://doi.org/10.1016/j.envsoft.2024.106027.