This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular, the unconditional mean for each series in the vector autoregression is centered around long-horizon survey forecasts. Heavy shrinkage toward the democratic prior is found to give good real-time predictions of a range of...

In the last decade, increases in fire activity and suppression expenditures have caused budgetary problems for federal land management agencies. Spatial forecasts of upcoming fire activity and costs have the potential to help reduce expenditures, and increase the efficiency of suppression efforts, by enabling them to focus resources where they have the greatest effect. In this paper, we present...

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual fore...

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on events, biased conclusions readily made. In this context, classical verification methods tailored such as thresholded weighted scoring rules, have undesirable properties that cannot be mitigated, the well-known continuous ranked probability sco...

This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed defines weighted version, which emphasizes regions of interest, such as tails or center variable’s range. The (weighted) ACPS extends symmetric CRPS by allowing asymmetries in preferences underlying scoring rule. A test is used to statistical...