  Analog forecasting has been applied in a variety of fields for predicting
future states of complex nonlinear systems that require flexible forecasting
methods. Past analog methods have almost exclu- sively been used in an
empirical framework without the structure of a model-based approach. We propose
a Bayesian model framework for analog forecasting, building upon previous
analog methods but accounting for parameter uncertainty. Thus, unlike
traditional analog forecasting methods, the use of Bayesian modeling allows one
to rigorously quantify uncertainty to obtain realistic posterior predictive
distributions. The model is applied to the long-lead time forecasting of
mid-May averaged soil moisture anomalies in Iowa over a high-resolution grid of
spatial locations. Sea Surface Tem- perature (SST) is used to find past time
periods with similar trajectories to the current pre-forecast period. The
analog model is developed on projection coefficients from a basis expansion of
the soil moisture and SST fields. Separate models are constructed for locations
falling in each Iowa Crop Reporting District (CRD) and the forecasting ability
of the proposed model is compared against a variety of alternative methods and
metrics.
