Computer simulation models have been widely used to generate hydrometeorological
forecasts. As the raw forecasts contain uncertainties arising from various sources, including …

Ensemble weather predictions require statistical postprocessing of systematic errors to
obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with …

Probabilistic forecasts in the form of probability distributions over future events have become
popular in several fields including meteorology, hydrology, economics, and demography. In …

Raw forecasts from numerical weather prediction models suffer from systematic bias and
cannot be directly used in applications such as hydrological forecasting. Statistical post …

集合数值模式预报已在定量降水预报业务中广泛应用, 以获得预报不确定性,
最可能预报结果以及极端天气预警. 由于集合系统的数值模式不完善, 且不能提供所有的不确定 …

Isotonic distributional regression (IDR) is a powerful non-parametric technique for the
estimation of conditional distributions under order restrictions. In a nutshell, IDR learns …

Probability forecasts for binary events play a central role in many applications. Their quality
is commonly assessed with proper scoring rules, which assign forecasts numerical scores …

The crch package provides functions for maximum likelihood estimation of censored or
truncated regression models with conditional heteroscedasticity along with suitable standard …

Probabilistic forecasts of wind speed are important for a wide range of applications, ranging
from operational decision making in connection with wind power generation to storm …

Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts.
Usually only ensemble forecasts of the predictand variable are used as input, but other …