Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Yunjae Cho, Hyun Mee Kim, Eun-Gyeong Yang, Yonghee Lee, Jae-Bum Lee, Soyoung Ha
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Abstract

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a type of online coupled chemistry-meteorology model (CCMM), considers the interaction between air quality and meteorology to improve air quality forecasting. Meteorological data assimilation (DA) can be used to reduce uncertainty in meteorological field, which is one factor causing prediction uncertainty in the CCMM. In this study, WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula. The nesting model domains were configured over East Asia (outer domain) and the Korean Peninsula (inner domain). Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA. When the meteorological DA was performed in the outer domain or both the outer and inner domains, the root-mean-square error (RMSE), bias of the predicted particulate matter (PM) concentrations, and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain. This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain, subsequently improving air quality and meteorological predictions. Compared to the experiment without meteorological DA, the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA. The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58–66 h, depending on the case. Therefore, the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

气象数据同化对朝鲜半岛区域空气质量预报的影响
化学耦合天气研究和预报模式(WRF-Chem)是一种在线化学气象耦合模式(CCMM),它考虑了空气质量和气象之间的相互作用,以改进空气质量预报。气象数据同化(DA)可用于减少气象领域的不确定性,而气象领域的不确定性是导致 CCMM 预测不确定性的因素之一。本研究利用 WRF-Chem 和三维变分 DA 研究了气象数据同化对朝鲜半岛空气质量和气象预报的影响。嵌套模式域分别配置在东亚(外域)和朝鲜半岛(内域)。通过使用不同的DA域进行了三次实验,以确定气象DA的最佳模式域。在外域或同时在外域和内域进行气象数据分析时,预测的颗粒物(PM)浓度的均方根误差(RMSE)、偏差以及预测的气象变量与观测值的均方根误差(RMSE)均小于仅在内域进行气象数据分析的实验。这表明,外域的气象数据分析改善了同步气象场,增强了内域的气象初始条件和边界条件,从而改善了空气质量和气象预测。与未进行气象DA的实验相比,有DA的实验中气象和PM变量的均方根误差和偏差都较小。气象数据加密对改善 PM 预测的影响持续了约 58-66 小时,视情况而定。因此,气象数据分析减少了气象初始条件的不确定性,有助于减少气象和空气质量的预报误差。
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来源期刊
Journal of Meteorological Research
Journal of Meteorological Research METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
6.20
自引率
6.20%
发文量
54
期刊介绍: Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.
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