Expedited modeling of burn events results (EMBER): A screening-level dataset of 2023 ozone fire impacts in the US

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Heather Simon , James Beidler , Kirk R. Baker , Barron H. Henderson , Loren Fox , Chris Misenis , Patrick Campbell , Jeff Vukovich , Norm Possiel , Alison Eyth
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引用次数: 0

Abstract

The Expedited Modeling of Burn Events Results (EMBER) dataset consists of 36-km grid-spacing Community Multiscale Air Quality (CMAQ) photochemical modeling for the summer of 2023. For emissions, these simulations utilized representative monthly and day-of-week anthropogenic emissions from a recent year and preliminary day-specific 2023 fire emissions derived using BlueSky pipeline. The base model run simulated ozone concentrations across the contiguous US during Apr 11-Sep 29, 2023. Two zero-out model runs simulated ozone levels that would have occurred in the US (1) in the absence of fire emissions (“Zero Fires”) and (2) in the absence of only Canadian wildfire emissions (“Zero Canadian Fires”). Fire impacts on ozone were then estimated as the difference between ozone simulated in the base EMBER run compared to the ozone simulated in each of the zero out model runs. EMBER is presented as a screening level dataset due to the emissions limitations and the 36-km grid-spacing used in these simulations.
燃烧事件结果的快速建模(EMBER):美国2023年臭氧火灾影响的筛选级数据集。
燃烧事件结果快速模拟(EMBER)数据集包括 2023 年夏季 36 公里网格间距的社区多尺度空气质量(CMAQ)光化学模拟。在排放方面,这些模拟利用了最近一年具有代表性的月度和周日人为排放,以及利用 BlueSky 管道得出的 2023 年特定日期的初步火灾排放。基础模型运行模拟了 2023 年 4 月 11 日至 9 月 29 日期间美国毗连地区的臭氧浓度。两个 "零 "模型运行模拟了美国(1)没有火灾排放("零火灾")和(2)只有加拿大野火排放("加拿大零火灾")时的臭氧水平。火灾对臭氧的影响是根据 EMBER 基本运行模拟的臭氧与每个 "零火灾 "模型运行模拟的臭氧之间的差异来估算的。由于排放限制以及模拟中使用的 36 千米网格间距,EMBER 是作为筛选级数据集提出的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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