A new phenomenological hierarchical stochastic model is developed to robustly simulate rainfall fields consistent with 10-minute 1-km² pixel radar images. The hierarchical framework has three levels. The first level simulates a latent Gaussian random field conditioned on the previous time step. In the second level, first-order autoregressive (AR(1)) models are used to describe the within-storm variations of the level-one parameters that control the evolution of rain fields. The third level is designed for simulation of storm sequences. Calibration and validation of the first two levels using an observed storm event (typical in Sydney, Australia) demonstrate that this two-level model produces realistic sequences of rain images which capture the physical hierarchical structure of clusters, patchiness of rain fields and the persistence exhibited during storm development. A variety of important statistics are adequately reproduced at both 10-minute and 1-hour time scales over various space scales. Application of this model to short-term rainfall forecasting is also presented.
Joint International Convention of 8th IAHS Scientific Assembly and 37th IAH Congress. New Approaches to Hydrological Prediction in Data-Sparse Regions: Proceedings of Symposium HS.2 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and The International Association of Hydrogeologists (IAH) (Hyderabad, India 6-12 September, 2009) p. 330-340