基于时间序列的气温预报后处理集合模型输出统计

IF 3 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
David Jobst, Annette Möller, Jürgen Groß
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引用次数: 0

摘要

如今,数值天气预报模式的不确定性已通过使用集合预报得到量化。尽管这些预报在不断改进,但仍存在系统偏差和分散误差。统计后处理方法,如集合模式输出统计(EMOS),已被证明可以大大纠正预报。这项工作提出了 EMOS 在时间序列框架内的扩展。除了考虑预测分布的位置和规模参数的季节性和趋势外,还考虑了平均预测误差或标准化预测误差的自回归过程。这些模型可以通过允许广义自回归条件异方差来进一步扩展。此外,还概述了如何将这些模型用于任意预测期限。为了说明所建议的 EMOS 模型在时间序列方面的性能,我们介绍了一个案例研究,利用五个不同的前置时间和一组德国观测站对 2 米地表温度预报进行后处理。结果表明,在大多数领先时间站的情况下,时间序列 EMOS 扩展模型的性能明显优于基准模型 EMOS 和自回归 EMOS(AR-EMOS)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing
The uncertainty in numerical weather prediction models is nowadays quantified by the use of ensemble forecasts. Although these forecasts are continuously improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods, such as the ensemble model output statistics (EMOS), have been shown to substantially correct the forecasts. This work proposes an extension of EMOS in a time‐series framework. Besides taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity. Furthermore, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time‐series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time‐series EMOS extensions are able to significantly outperform the benchmark models EMOS and autoregressive EMOS (AR‐EMOS) in most of the lead time–station cases.
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来源期刊
CiteScore
16.80
自引率
4.50%
发文量
163
审稿时长
3-8 weeks
期刊介绍: The Quarterly Journal of the Royal Meteorological Society is a journal published by the Royal Meteorological Society. It aims to communicate and document new research in the atmospheric sciences and related fields. The journal is considered one of the leading publications in meteorology worldwide. It accepts articles, comprehensive review articles, and comments on published papers. It is published eight times a year, with additional special issues. The Quarterly Journal has a wide readership of scientists in the atmospheric and related fields. It is indexed and abstracted in various databases, including Advanced Polymers Abstracts, Agricultural Engineering Abstracts, CAB Abstracts, CABDirect, COMPENDEX, CSA Civil Engineering Abstracts, Earthquake Engineering Abstracts, Engineered Materials Abstracts, Science Citation Index, SCOPUS, Web of Science, and more.
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