Probabilistic Modeling / Uncertainty Analysis

Complex questions in environmental science are not usually answered by a single model simulation generated by one set of idealized input parameters. Rather, the best solution may be a collection or ensemble of model results that characterize the range of environmental conditions observed in the real world. We use probabilistic modeling to understand the range and likelihood of outcomes and to place quantitative bounds on uncertainty in results.

Analysis begins by generating probability distributions of model input parameters based on the best available spatial and temporal data for real world observations. Depending on the number of uncertain inputs, we select a sampling technique, such as Monte Carlo or Latin Hypercube Sampling, to create realizations of model input parameters. Then we scale our computer resources on site or in the cloud to efficiently execute hundreds to thousands of model simulations from which distributions of results are derived.