Conceptual Model

TATHU proposes a conceptual model to address the problem of tracking and analyzing the CS lifecycle. The entities of the model are:

Basically, a geospatial database contains the observed elements of interest, represented by the ConvectiveSystem class. This class has an identifier, uuid, at least one spatial attribute, geom, which indicates the geographic limits of the system, and n other attributes, fields. Thus, four different entities are used:

Detector

Interface for detecting the CS present at a given time. This interface takes an image as parameter and should return a list of ConvectiveSystem as a result. For each element, the geom attribute is defined. As an example, detection can be performed from a thresholding operation, i.e. ThresholdDetector;

class Detector(object):
    def __init__(self):
        pass

    def detect(self, image):
        return list(systems)

Descriptor

Responsible for the characterization of CS. This entity defines, for each system, a list of descriptive attributes. It receives as parameters auxiliary data and a list of ConvectiveSystem. For example, calculating statistical attributes such as mean, minimum and maximum temperatures - StatisticalDescriptor;

class Descriptor(object):
    def __init__(self):
        pass

    def describe(self, image, systems):
        pass

Tracker

This interface aims to tracking the CS (i.e. associate in time the different elements detected in each observation). The abstract method takes as parameters two lists containing ConvectiveSystem of different time instants - previous and current. As an example, the association can be performed from the topological relationship between the CS and the analysis of the intersection areas - OverlapAreaTracker;

class Tracker(object):
    def __init__(self, previous):
        self.previous = previous

    def track(self, current):
        pass

Forecaster

This interface is built to provide predictions for the CS. One option is to consider a conservative movement, based only on the current speed of the system - ConservativeForecaster.

class Forecaster(object):
    def __init__(self, previous, intervals):
        self.previous = previous
        self.intervals = intervals

    def forecast(self, current):
        pass

Pseudocode for detection, characterization, tracking and forecast of CS using the abstract interfaces:

images = load()
previous = None
for each image in images:
    systems = detector.detect(images[i])
    descriptor.describe(systems)
    tracker.track(previous, systems)
    forecaster.forecast(previous, systems)
    previous = systems