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Two innovative algorithms for motion tracking and monitoring of rainy clouds from radar images are proposed. The methods are generalizations of classical optical flow techniques, including a production term (modelling formation, growth or depletion of clouds) in the model to be fit to the data. Multiple images are processed and different smoothness constraints are introduced. When applied to simulated maps (including additive noise up to 10 dB of SNR) showing formation and propagation of objects with different directions and velocities, the algorithms identified correctly the production and the flow, and were stable to noise when the number of images was sufficiently high (about 10). The average error was about 0.06 pixels (px) per sampling interval (ΔT) in identifying the modulus of the flow (velocities between 0.25 and 2 px/ΔT were simulated) and about 1° in detecting its direction (varying between 0° and 90°). An example of application to X-band radar rainfall rate images detected during a stratiform rainfall is shown. Different directions of the flow were detected when investigating short (10 min) or long time ranges (8 h), in line with the chaotic behaviour of the weather condition. The algorithms can be applied to investigate the local stability of meteorological conditions with potential future applications in nowcasting.

Short range tracking of rainy clouds by multi-image flow processing of X-band radar data