The Issue of Chaos Seeding within Experiments Designed to Study Inadvertent Weather Modification

Perturbation experiments are used widely within numerical weather prediction model frameworks to study predictability by investigating the effects of initial condition error and data assimilation on atmospheric forecasts. With respect to inadvertent weather modification, such experiments can be very useful to understand how differences in initial conditions (e.g. soil moisture, which can represent the effects of irrigation) evolve downstream to affect the atmosphere on timescales of days and spatial scales of thousands of miles. However, it has been discovered that perturbations within the Weather Research and Forecasting (WRF) model can create numerical noise that propagates horizontally at speeds substantially faster than any realistic physical mode. The resulting noise is very small, and likely does not affect the atmospheric state within model simulations in areas where dry dynamics dominate. However, in areas of moist convection or precipitation, the noise can grow rapidly through chaos by nonlinear processes to significantly alter the state, potentially growing upscale. The growth of noise thus has the ability to cause severe misinterpretations of the realistic effects of the perturbation in the first place. This work details the propagation and growth of numerical noise in the WRF model for simulated convection, and compares it to a number of perturbation experiments for which realistic perturbation growth is expected. Two techniques designed to mitigate these effects, EOF analysis and sensitivity analysis, are also presented as methods that may effectively eliminate the effects of rapidly-propagating numerical noise, leading to substantially more valuable interpretations.