This package provides methods to track and normalize heatwave (HW) objects. It enables the identification, tracking, and unification of extreme events across spatial and temporal scales. By mapping multiscale HW events into a unified structural framework, this method allows direct comparison of event dynamics. Its applications are not limited to HWs and MHWs, but can also be extended to other extremes such as precipitation and drought.
The tracking method to identify and track spatially contiguous extremes that allows nouniform lon-lat grids.
%Example
tracks = mhwtrack(lon_used, lat_used, hw_ts, nums);
Here, lon_used and lat_used are 1D vectors representing the longitudes and latitudes of the input data, respectively. hw_ts is a 3D binary field [lon, lat, time], where a value of 1 indicates the presence of a temperature extreme event and 0 indicates its absence. nums defines the minimum number of pixels required for a detected object. The output tracks contains the spatiotemporal trajectories of tracks.
Analyzing track entities as sequences of moving objects from a composite perspective is substantially more challenging than interpreting fixed-grid outputs, as each event exhibits distinct spatiotemporal dynamics, with evolving shapes, sizes, and durations. To overcome this issue, we develop a normalization method that scales the entities in space and time so that they can be more directly compared. To compare the spatiotemporal evolution of individual tracks, we normalize their shapes to fit within a unit circle (radius = 1) and standardize their durations from t=0 (onset) to t=1 (end). Specifically, we first compute the centroid of each track at every time step and identify the maximum distance (r) from these centroids to their outermost boundaries over the event's entire lifespan. Subsequently, we reposition each objects by translating all grid points so that the centroid aligns with the origin, and scale spatial dimensions uniformly by a factor 1/r to constrain the maximum radius within unity. Pixels covered by land remain blank in this and future composite process.
%Example
[radius_s,sst_sp,sst_spn_full,x_full,y_full]=spn(tracks,lon_used,lat_used,data_anom);
Examples of an output
Spatial incoherence is commonly observed in extreme events identified from raw data. Therefore, spatial smoothing is typically applied to refine connected extreme objects in 2D maps. This step helps regularize object shapes while removing fragmented small-scale features, making subsequent tracking more meaningful and practically applicable. An example of this process at a given spatial scale can be found in this example.
A very rough and unpolished example for tracking and normalizing can be found here. Better documentions and more examples will be included later.