Weather Derivative Pricing and the Year-Ahead Forecasting of Surface Air Temperature: An Empirical Evaluation of Damped Linear Detrending
The pricing of temperature derivatives is often based on statistical methods that predict the surface air temperature a year in advance. We perform an empirical comparison of three simple methods for such year-ahead temperature forecasting and draw clear conclusions about their relative merits. The three methods we consider are the standard flat-line and best-fit linear detrending methods and the recently introduced damped linear detrending method.
Weather Derivative Pricing and the Impact of El Nino on Us Temperature: Empirical Tests of an Optimal Categorical Forecasting Scheme
Weather derivative pricing for US locations can potentially be improved through the judicious use of seasonal forecasts. However, the number of El Nino and La Nina events that appear in the historical record is small and for many locations the signals are weak. We run some simulation-based tests of an optimal categorical forecasting scheme that takes the small sample size and weak signal into account and tries to produce RMSE-optimal seasonal forecasts.
Theoretical Skill of Monte Carlo Forecasts
Abstract The theoretical skill of Monte Carlo approximations to the stochastic dynamic forecasting technique proposed by Epstein is examined by means of an extension of earlier atmospheric predictability studies that used the test-field model of two-dimensional turbulence. The fundamental statistical hydrodynamical concept of an ensemble of phase paths evolving in a dynamical phase space is reviewed and used to define the statistical properties of a finite Monte Carlo sample. The application of a linear regression step to arrive at a final best estimate of the state of the atmosphere is also discussed. The resulting forecasts approach the climatological mean at forecast times so late that all skill has been lost. For an ideal case with an observing resolution, hopefully achievable in the 1980s with satellite-based sensors, it is found that the. Monte Carlo procedure leads to the greatest improvement in mean-square vector wind forecast skill in the 6- to 10-day range. For another case corresponding roughly...