34 research outputs found
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
In weather forecasting, nonhomogeneous regression is used to statistically
postprocess forecast ensembles in order to obtain calibrated predictive
distributions. For wind speed forecasts, the regression model is given by a
truncated normal distribution where location and spread are derived from the
ensemble. This paper proposes two alternative approaches which utilize the
generalized extreme value (GEV) distribution. A direct alternative to the
truncated normal regression is to apply a predictive distribution from the GEV
family, while a regime switching approach based on the median of the forecast
ensemble incorporates both distributions. In a case study on daily maximum wind
speed over Germany with the forecast ensemble from the European Centre for
Medium-Range Weather Forecasts, all three approaches provide calibrated and
sharp predictive distributions with the regime switching approach showing the
highest skill in the upper tail
Predicting Inflation: Professional Experts Versus No-Change Forecasts
We compare forecasts of United States inflation from the Survey of
Professional Forecasters (SPF) to predictions made by simple statistical
techniques. In nowcasting, economic expertise is persuasive. When projecting
beyond the current quarter, novel yet simplistic probabilistic no-change
forecasts are equally competitive. We further interpret surveys as ensembles of
forecasts, and show that they can be used similarly to the ways in which
ensemble prediction systems have transformed weather forecasting. Then we
borrow another idea from weather forecasting, in that we apply statistical
techniques to postprocess the SPF forecast, based on experience from the recent
past. The foregoing conclusions remain unchanged after survey postprocessing
Using proper divergence functions to evaluate climate models
It has been argued persuasively that, in order to evaluate climate models,
the probability distributions of model output need to be compared to the
corresponding empirical distributions of observed data. Distance measures
between probability distributions, also called divergence functions, can be
used for this purpose. We contend that divergence functions ought to be proper,
in the sense that acting on modelers' true beliefs is an optimal strategy.
Score divergences that derive from proper scoring rules are proper, with the
integrated quadratic distance and the Kullback-Leibler divergence being
particularly attractive choices. Other commonly used divergences fail to be
proper. In an illustration, we evaluate and rank simulations from fifteen
climate models for temperature extremes in a comparison to re-analysis data