A framework for probabilistic weather forecast post-processing across models and lead times using machine learning.

ABSTRACT: Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP m

SUBMITTER: Kirkwood C 

PROVIDER: S-EPMC7898129 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature