Beating the Uncertainties: Ensemble Forecasting and Ensemble‐Based Data Assimilation in Modern Numerical Weather Prediction
Hailing Zhang, Zhaoxia Pu
Abstract
Open-access reader
Hailing Zhang, Zhaoxia Pu
Abstract
Open-access reader
Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble‐based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble‐based data assimilation. The usefulness of these methods in improving high‐impact weather forecasting is also discussed.
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Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble‐based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble‐based data assimilation. The usefulness of these methods in improving high‐impact weather forecasting is also discussed.
Key concepts: Data assimilation, Numerical weather prediction, Ensemble forecasting, Meteorology, North American Mesoscale Model, Model output statistics, Global Forecast System, Weather prediction