Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis , our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.

中文翻译：

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预测偏差校准的基于概率密度的视觉分析方法

由于对混沌大气系统建模的理想化数值假设，数值天气预报 (NWP) 不可避免地会出现偏差。因此，快速准确地识别和校准偏差对于 NWP 在天气预报中至关重要。传统方法（例如各种模拟后处理预测方法）旨在帮助进行偏差校准。然而，这些方法未能考虑预测偏差的时空相关性，这会极大地影响校准效果。在本文中，我们通过合并历史预测和观察数据集，提出了一种基于预测-观察概率密度的新型偏差模式提取方法。给定一个时空范围，我们的方法提取和融合偏差模式，并自动划分具有相似偏差模式的区域。BicaVis 是我们的时空偏差模式可视化分析系统，旨在帮助专家根据这些偏差模式绘制校准曲线。为了验证我们方法的有效性，我们使用真实世界的再分析数据集进行了两个案例研究。从领域专家那里收集的反馈证实了我们方法的有效性。