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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Retrieval of Total Column Ozone From Imagers Onboard Geostationary Satellites
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Retrieval of Total Column Ozone From Imagers Onboard Geostationary Satellites

机译:从地球静止卫星上的成像仪中检索总柱状臭氧

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Geostationary imagers such as the Advanced Baseline Imager (ABI) proposed for the next generation of Geostationary Operational Environmental Satellites (GOESs), i.e., GOES Series R and beyond, and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard METEOSAT 8 provide atmospheric total column ozone (TCO) with high temporal (better than 15 min) and spatial (better than 5 km) resolutions. The purpose of this paper is to present a method that evolved from the current GOES sounder TCO retrieval that can be applied to ABI, with SEVIRI serving as a proxy for ABI. Although ABI and SEVIRI have fewer infrared spectral bands than the current series of GOES (-8 to -P) sounders, ABI and SEVIRI can provide TCO with an accuracy that is comparable to the current GOES sounder by using forecast temperature profiles as additional a priori information. Despite the need for additional data, the greatest advantage of ABI and SEVIRI is their fast 15-min coverage of the full disk. The SEVIRI TCO demonstrates how well geostationary imagers can capture ozone transport and change at high temporal and spatial resolutions. The estimated TCO has very good agreement ( $R = 0.92$ and $ hbox{root-mean-square error} = 3.7%$) with the total ozone measurements from the Ozone Monitoring Instrument (OMI) onboard the Earth Observing System Aura platform.
机译:对地静止成像仪,例如为下一代对地静止作战环境卫星(GOESs)(例如,GOES R系列及更高版本)建议的高级基线成像仪(ABI),以及METEOSAT 8机载的旋转增强型可见光和红外成像仪(SEVIRI),可提供大气总量具有高时间分辨率(小于15分钟)和空间分辨率(小于5 km)的柱臭氧(TCO)。本文的目的是提出一种方法,该方法是从可应用于ABI的当前GOES探听仪TCO检索中发展而来的,其中SEVIRI充当ABI的代理。尽管ABI和SEVIRI的红外光谱带少于当前系列的GOES(-8至-P)测深仪,但ABI和SEVIRI通过使用预报温度曲线作为先验,可以提供与当前GOES测深仪相当的精度。信息。尽管需要其他数据,但ABI和SEVIRI的最大优势是它们可以在15分钟内快速覆盖整个磁盘。 SEVIRI TCO演示了地球静止成像仪如何在高时空分辨率下捕获臭氧传输和变化。估算的总拥有成本与地球观测系统Aura平台上的臭氧监测仪(OMI)的臭氧总测量值具有很好的一致性($ R = 0.92 $和$ hbox {root-mean-square error} = 3.7%$)。

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