Probabilistic temperature forecasting: a comparison of four spread-regression models
大气与海洋物理
2007-05-23 v1
摘要
Spread regression is an extension of linear regression that allows for the inclusion of a predictor that contains information about the variance. It can be used to take the information from a weather forecast ensemble and produce a probabilistic prediction of future temperatures. There are a number of ways that spread regression can be formulated in detail. We perform an empirical comparison of four of the most obvious methods applied to the calibration of a year of ECMWF temperature forecasts for London Heathrow.
引用
@article{arxiv.physics/0410053,
title = {Probabilistic temperature forecasting: a comparison of four spread-regression models},
author = {Stephen Jewson},
journal= {arXiv preprint arXiv:physics/0410053},
year = {2007}
}