Deterministic numerical weather prediction (NWP) models and ensemble NWP models are routinely run worldwide to assist weather forecasting. Deterministic forecasts are capable of capturing more detailed spatial features, while ensemble forecasts, often with a coarser resolution, have the ability to predict uncertainty in future conditions. A comparative understanding of the performance of these two types of forecasts is valuable for both users of NWP products and model developers. Past published comparisons tended to be limited in scope, for example, for only specific locations and weather events, and involving only raw forecasts. In this study, we conduct a comprehensive comparison of the performance of a deterministic model and an ensemble model of the Australian Bureau of Meteorology in forecasting daily precipitation across Australia over a period of 3 years. The deterministic model has a horizontal grid spacing of approximately 25 km, and the ensemble model 60 km. Despite the coarser resolution, the ensemble forecasts are found to be superior by a number of measures, including correlation, accuracy and reliability. This finding holds true for both raw forecasts from the NWP models and forecasts post‐processed using the recently developed seasonally coherent calibration (SCC) model. Post‐processing is shown to greatly improve the forecasts from both models; however, the improvement is greater for the deterministic model, narrowing the performance gap between the two models. This study adds strong evidence to the general notion that coarser‐resolution ensemble NWP forecasts perform better than deterministic forecasts.

中文翻译：

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使用哪个降水预报？确定性与较粗分辨率的整体NWP模型

确定性数值天气预报（NWP）模型和集成NWP模型在世界范围内常规运行，以协助天气预报。确定性预测能够捕获更详细的空间特征，而总体预测（通常具有较粗的分辨率）具有预测未来条件下不确定性的能力。对这两种类型的预测的性能进行比较的理解对于NWP产品的用户和模型开发人员都是有价值的。过去发布的比较的范围往往受到限制，例如，仅针对特定位置和天气事件，并且仅涉及原始预测。在这项研究中，我们对澳大利亚气象局的确定性模型和整体模型的性能进行了全面的比较，以预测澳大利亚在3年内的日降水量。确定性模型的水平网格间距约为25 km，整体模型的间距为60 km。尽管分辨率较差，但通过许多措施（包括相关性，准确性和可靠性），发现集合预报是优越的。这一发现对于NWP模型的原始预测和使用最近开发的季节性相干校准（SCC）模型进行后处理的预测都是正确的。结果表明，后处理可以极大地改善两种模型的预测。但是，确定性模型的改进更大，从而缩小了两个模型之间的性能差距。