Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …

The report documents the DOE Town Halls held during 2019 at Argonne National
Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in …

Weather radars play an important role in in situ rainfall monitoring owing to their ability to
measure instantaneous rain rates and rainfall distributions. Currently, the Korea …

Fatigue life estimation is essential for life safety and cost reasons. Fatigue parameters at low
cycles must be estimated with high accuracy to correctly estimate fatigue life. Conventional …

Parameterizations for physical processes in weather and climate models are
computationally expensive. We use model output from the Weather Research Forecast …

近年来, 机器学习理论和方法应用蓬勃发展, 已在强对流天气监测和预报中广泛应用.
各类机器学习算法, 包括传统机器学习算法(如随机森林, 决策树, 支持向量机, 神经网络等) …

Artificial neural networks (ANNs) are a widely used machine learning approach for
estimating low‐cycle fatigue parameters. ANN structure has its parameters such as hidden …

A deep‐learning neural network (DLNN) model was developed to predict thunderstorm
occurrence within 400 km2 South Texas domains for up to 15 hr (±2 hr accuracy) in …

In numerical weather prediction models, point thunderstorm forecasts tend to have little
predictive value beyond a few hours. Thunderstorms are difficult to predict due largely to …

Rainfall prediction is a very challenging task due to its dependence on many meteorological
parameters. Because of the complex nature of rainfall, the uncertainty associated with its …