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我怎么把Sentinel-5p / TROPOMI垂直列密度浓度?

我正在做一个研究Sentinel-5p TROPOMI图像。我想把数据从摩尔/ m²μg /米³,我知道我需要测量的高度数据。我找他们,我只发现了对流层臭氧(6公里),你会知道有限公司₂也没有₂纵列的高度吗?

非常感谢你,艾琳B。

哨兵产品给你不同的层,图像中可以看到,NO2给一层对流层和平流层的另一个数据…通过这种分离,他们不应该假设限制的高度分离的数据层的列?我理解的高度限制这些可以有所不同,但我想抵达一个近似平均值。

回答

你的建议在理论上是可能的但徒劳的任何实际用途。在大气中污染物的浓度不同垂直,所以计算一个值代表的平均浓度在垂直板本身是有缺陷的。试图选择一个垂直高度,仅仅代表了边界层也不会工作,由于卫星检索敏感自由对流层,而不是表面。你也有下面的“云”部分的垂直列处理有时,完全是基于模型数据。如果TROPOMI VCD产品可以很容易地转换成表面浓度,他们已经做到了并提供产品。(NASA)[1]: >可以卫星测量“头”浓度?>简短的回答是“不”,因为大多数的卫星仪器测量感兴趣的污染物向下方向的AQ社区,提供有限的信息在大气中污染物的垂直结构。卫星仪器测量臭氧、二氧化氮、甲醛和二氧化硫检测仪器之间的分子数和地球表面(即。,一个垂直列密度或VCD)。你可以找到二氧化硫和公司层和一些混合比“配置文件”,但它是真的很差相比,表面测量。然而,您可以集成探空仪测量得到类似于VCD,如果使用(平均内核)[2]。 You can also calculate a modeled VCD (e.g. from CMAQ or other CTM) using the TROPOMI averaging kernels, which is then comparable to the satellite retrieval. The vertical concentration profile assumption is part of the algorithm that goes into the column retrieval. So, any attempt to derive surface concentration from the VCD is inherently based on model data and is not sensitive to the actual surface concentrations. To complicate matters, your surface measurements will vary in time due to local sources (think neighborhood airshed), meteorology, and photochemistry. Your VCD retrievals will be for one timestep per day over a large multi-km area which will vary depending on surface albedo, cloud cover, season, and which pixel on the detector was used, how old the detector is, etc. ... even if the column itself remained the same. Another factor is sensitivity to a clean atmosphere, because if the atmosphere is not heavily polluted, the radiance signal will not be strong and you will basically be looking at noise. Take a look at these peer reviewed papers, where they compare to surface measurements but do not attempt to convert VCD to surface concentrations: - [Nitrogen Dioxide (NO2) Pollution Monitoring with Sentinel-5P Satellite Imagery over Europe during the Coronavirus Pandemic Outbreak][3] - [Comparison of TROPOMI/Sentinel-5 Precursor NO2 observations with ground-based measurements in Helsinki][4] And you can see this tangentially related post: - [Flawed estimates of the effects of lockdown measures on air quality derived from satellite observations][5] And if you really want to go down the rabbit hole, you can see this effort to use machine learning to estimate surface NO2 from TropOMI: - [Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method][6] [1]: https://so2.gsfc.nasa.gov/no2/FAQs.htm [2]: https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Nitrogen-Dioxide [3]: https://www.researchgate.net/publication/346221881_Nitrogen_Dioxide_NO2_Pollution_Monitoring_with_Sentinel-5P_Satellite_Imagery_over_Europe_during_the_Coronavirus_Pandemic_Outbreak [4]: https://amt.copernicus.org/articles/13/205/2020/amt-13-205-2020.pdf [5]: https://atmosphere.copernicus.eu/flawed-estimates-effects-lockdown-measures-air-quality-derived-satellite-observations?q=flawed-estimates-effects-lockdown-measures-air-quality-satellite-observations [6]: https://elib.dlr.de/141411/1/remotesensing-13-00969-v2.pdf

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