Representing Serial Correlation of Meteorological Events and Forecasts in Dynamic Decision–Analytic Models

View More View Less
  • 1 Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York
© Get Permissions
Restricted access

Abstract

A recursive solution for optimal sequences of decisions given uncertainty in future weather events, and forecasts of those events, is presented. The formulation incorporates a representation of the autocorrelation that is typically exhibited. The general finite-horizon dynamic decision–analytic framework is employed, with the weather forecast for the previous decision period included as a state variable. Serial correlation is represented through conditional probability distributions of the forecast for the current decision period, given the forecast for the previous period. Autocorrelation of the events is represented by proxy through the autocorrelation of the forecasts. The formulation is practical to implement operationally, and efficient in the sense that the weather component can be represented through a single state variable.

A compact representation of the required conditional distributions, based on an autoregressive model for forecast autocorrelation, is presented for the em of 24-h probability of precipitation forecasts. Parameters describing operationally available precipitation forecasts are given. The overall procedure is illustrated for the case of these forecasts in the context of the generalized cost/loss ratio problem.

Abstract

A recursive solution for optimal sequences of decisions given uncertainty in future weather events, and forecasts of those events, is presented. The formulation incorporates a representation of the autocorrelation that is typically exhibited. The general finite-horizon dynamic decision–analytic framework is employed, with the weather forecast for the previous decision period included as a state variable. Serial correlation is represented through conditional probability distributions of the forecast for the current decision period, given the forecast for the previous period. Autocorrelation of the events is represented by proxy through the autocorrelation of the forecasts. The formulation is practical to implement operationally, and efficient in the sense that the weather component can be represented through a single state variable.

A compact representation of the required conditional distributions, based on an autoregressive model for forecast autocorrelation, is presented for the em of 24-h probability of precipitation forecasts. Parameters describing operationally available precipitation forecasts are given. The overall procedure is illustrated for the case of these forecasts in the context of the generalized cost/loss ratio problem.

Save