使用标准、空间和面向对象方法验证区域云预报

H. Christophersen, J. Nachamkin, W. Davis
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摘要

本研究评估了海洋/大气中尺度耦合预报系统(COAMPS)对稳定和不稳定环境中的云(以下简称 "稳定云 "和 "不稳定云")预报的准确性。这项评估是通过结合传统、空间和基于对象的方法,将这些预报与卫星检索结果进行比较。为便于评估,使用了模式评估工具(MET)社区工具。研究结果强调了对 MET 参数进行微调的重要性,以便更准确地呈现所审查的特征。研究结果表明,在使用传统的按点统计(如频率偏差和公平威胁分值)时,无论是根据基于对象的诊断评估方法(MODE)计算的对象,还是根据完整字段得出的结果,都具有一致性。此外,基于对象的统计数据提供了有价值的见解,表明 COAMPS 通常能准确预测云对象的位置,尽管这些预测位置的分布往往会随着时间的推移而增加。随着时间的推移,COAMPS 往往会对不稳定云的云对象面积预测过高,而对稳定云的云对象面积预测不足。这些结果与整个网格的传统点状偏差分数一致。总之,基于对象的验证方法所提供的空间度量是验证云预报的关键和实用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regional Cloud Forecast Verification using Standard, Spatial and Object-Oriented Methods
This study assesses the accuracy of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study's results reveal that when employing traditional point-wise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to over-predict the object area for unstable clouds while under-predicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts.
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