Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Misun Kang, Woojeong Lee
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Abstract

This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.

利用 KMA 全球季节预报系统评估韩国春季亚洲沙尘的季节预报潜力
本研究旨在利用韩国气象局全球季节预报系统第 6 版(KMA GloSea6)评估韩国春季亚洲沙尘暴日的季节预报潜力。与 GloSea5-ADAM 相比,该系统对春季亚洲沙尘暴日的预测性能相似,GloSea5-ADAM 将亚洲沙尘和气溶胶模型的排放算法纳入了 GloSea5。与 GloSea5-ADAM 相似,KMA GloSea6 在后报期间的春季亚洲沙尘源区域表现出强烈的风、冷和潮湿偏差,但它显示出更好的 ACC 空间分布。沙尘源区亚洲沙尘发生频率异常年变率的异常相关系数也相似,KMA GloSea6 为 0.41,GloSea5-ADAM 为 0.43。此外,在利用后报数据集评估春季亚洲沙尘日的季节预报时,KMA GloSea6 准确预报了 24 次中的 12 次,预报精度为 0.5,与 GloSea5-ADAM 一致。将该标准应用于推导中未使用的 2022 年和 2023 年春季预报数据时,2022 年春季亚洲沙尘预测日数为 6.14 天,预测结果为 "接近正常",与观测到的 "低于正常 "不同。然而,空间分布显示为 "低于正常",与某些地区的观测结果一致。2023 年春季,9.78 个亚洲沙尘暴日的预测结果为 "高于正常值",与观测结果一致。这些发现将有助于推进韩国的季节性亚洲沙尘预报。
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来源期刊
Atmospheric Pollution Research
Atmospheric Pollution Research ENVIRONMENTAL SCIENCES-
CiteScore
8.30
自引率
6.70%
发文量
256
审稿时长
36 days
期刊介绍: Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.
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