化学和气溶胶多尺度基础设施0版(MUSICAv0)在非洲空气质量研究中的应用

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, Pieternel Levelt
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引用次数: 0

摘要

摘要化学和气溶胶多尺度基础设施版本0 (MUSICAv0)是一个新的社区建模基础设施,可以跨所有相关尺度研究大气成分和化学。我们开发了一个非洲细化的MUSICAv0网格(非洲上空约28公里× 28公里)。我们通过现场观测评估了2017年的MUSICAv0模拟,并将模型结果与非洲上空的卫星产品进行了比较。天气研究与预报模式与化学(WRF-Chem)耦合的模拟(一个在非洲研究中广泛使用的区域模式)也作为参考纳入了分析。总体而言,MUSICAv0的性能与WRF-Chem相当。与现场观测和对流层污染测量(MOPITT)卫星仪器的卫星CO柱反演相比,这两种模式都低估了一氧化碳(CO)。MUSICAv0倾向于高估臭氧(O3),可能是由于高估了平流层到对流层的臭氧通量。这两个模型都显著低估了东非两个地表站点的细颗粒物(PM2.5)。与WRF-Chem相比,MUSICAv0模拟结果与中分辨率成像光谱仪(MODIS)的气溶胶光学深度(AOD)和臭氧监测仪(OMI)的对流层二氧化氮(NO2)柱反演结果更吻合。MUSICAv0的对流层甲醛(HCHO)柱始终低于OMI检索。基于MUSICAv0与WRF-Chem、MOPITT CO、MODIS AOD和OMI对流层NO2之间的模式-卫星差异,我们发现未来在东非地区(5°s - 5°N, 30-45°E)的野外活动和更多的现场观测可以大大提高大气化学模式的预测能力。这一重点区域显示出最大的模式原位观测差异,以及高人口密度、土地覆盖变率和人为污染源的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa
Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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