The UCSB/MFEWS forecast interpretation method, developed by Greg Husak and Joel Michaelsen, uses Monte Carlo resampling to translate probabilistic forecast terciles into a matching probability distribution function (pdf). The method begins with a 2 or 3 element vector of parameters, ¦Á, which typically define either a normal or conditional gamma distribution. The theoretical distribution is then resampled in accordance with a set of forecast probabilities. If a forecast calls for a 45%, 35%, 20% probability of rainfall within the wet, middle and dry terciles, the method might draw 45, 35, and 20 samples from the highest, middle and lowest terciles of the theoretical probability distribution. A new set of parameters is calculated and stored. The resampling process is repeated many times, and the median parameter values are used to define a new distribution (¦Á'). The new theoretical distribution can then be used to answer specific questions tailored to user end needs. Example question might be:
I manage a commercial bean agribusiness venture, and I know that beans generally require 700 mm of rainfall during August, September and October. What is the chance of getting at least this much rainfall?
This forecast interpretation method brings a new set of tools to decision makers and agroclimatic outlooks. This research effort will target a small set of end users, and track the utility of the interpreted seasonal forecasts through interviews. We describe several of these forecast applications, comment on their success and failures, and sketch the future scope of this collaborative activity.