A Model-Based Approach for Analog Spatio-Temporal Dynamic Forecasting

TL;DR

This paper introduces a Bayesian model-based analog forecasting framework that quantifies uncertainty in predictions of complex nonlinear systems, demonstrated through soil moisture anomaly forecasts in Iowa.

Contribution

It develops a novel Bayesian analog forecasting model that accounts for parameter uncertainty, improving upon traditional empirical analog methods.

Findings

The Bayesian model provides realistic posterior predictive distributions.

The model effectively forecasts soil moisture anomalies using SST-based analogs.

Compared to alternative methods, it shows improved forecasting accuracy.

Abstract

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.