Statistical postprocessing techniques are nowadays key components of the forecasting
suites in many national meteorological services (NMS), with, for most of them, the objective …

A probabilistic forecast takes the form of a predictive probability distribution over future
quantities or events of interest. Probabilistic forecasting aims to maximize the sharpness of …

Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather
scenarios is crucial for important decisions, from warning the public about hazardous …

We present an ensemble prediction system using a Deep Learning Weather Prediction
(DLWP) model that recursively predicts six key atmospheric variables with six‐hour time …

综合论述了近年来已在国际上引起高度重视的新一代动力随机预报方法———集合预报.
随着计算机技术的迅猛发展和由于大气初值和数值模式中物理过程存在着不确定性的事实 …

Despite the advances in solar forecasting methods, and their ever-increasing accuracy, little
is known about their value for real applications, eg, bidding in the electricity market, power …

We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called
support vector machine (SVM), and four years of data from the Solar Dynamics …

This comprehensive text and reference work on numerical weather prediction covers for the
first time, not only methods for numerical modeling, but also the important related areas of …

Forecast Verification: A Practioner's Guide in Atmospheric Science, 2nd Edition provides an
indispensible guide to this area of active research by combining depth of information with a …