Statistical Modeling of Rainfall Enhancement


  • Stephen Beare Analytecon, 4 Johnston Street, Narrabundah, ACT 2604, Australia
  • Ray Chambers Centre for Statistical and Survey Methodology, University of Wollongong, Wollongong, Australia
  • Scott Peak Australian Rain Technologies, Pier 8/9, 232 Hickson Road, Millers Point, NSW 2000 Australia


Non-stationary spatial variation makes it difficult to establish real-time areas of control and effect in weather modification. Non-stationary temporal variation makes the comparison of long-term averages from limited climatic records open to question. Here we describe a statistical methodology which addresses both problems explicitly, in a trial of a ground-based ionization technology known as Atlant, and which could be applied to other weather modification technologies more generally. The approach adopted here is based on a statistical model for daily rainfall that achieves a high level of real-time control by the inclusion of both spatial and temporal components.  In particular, it makes use of daily gauge level rainfall data, orographic and daily meteorological covariates, as well as dynamically defined downwind areas, to model the impact of Atlant operation on rainfall. Subject to the caveat that the trial was not randomized in any way, this type of dynamic control demonstrates a clear rainfall enhancement effect at both a simple observational level and when a spatial random effects model is used to control for covariates. Rainfall downwind of the Atlant test site was 15% higher than rainfall in the control (crosswind or upwind) areas. Based on these results, randomized trials with multiple sites are currently being conducted in the same area.




Scientific Papers