A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases
A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases
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DOI：
10.5194/amt-16-3787-2023
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发表时间：
2023-08
影响因子：
3.8
通讯作者：
Nicholas Balasus;D. Jacob;A. Lorente;J. Maasakkers;R. Parker;H. Boesch;Zichong Chen;M. Kelp;H. Nesser;D. Varon
中科院分区：
文献类型：
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作者：
Nicholas Balasus;D. Jacob;A. Lorente;J. Maasakkers;R. Parker;H. Boesch;Zichong Chen;M. Kelp;H. Nesser;D. Varon
抽象的。来自短波红外(SWIR)太阳背向散射辐射的干柱甲烷混合比(XCH4)的卫星观测为量化服务于气候行动的甲烷排放提供了强有力的资源。2017年10月发射的对流层监测仪(Tropomi)以5.5×7平方公里(最低点)的像素分辨率提供全球每日覆盖，但其甲烷反演可能受到与SWIR地表反照率、气溶胶和卷云散射以及跨轨道可变性(条带性)相关的偏差的影响。2009年发射的温室气体观测卫星(GOSAT)仪器具有更好的光谱特性，其甲烷反演受偏差的影响要小得多，但其数据密度比Tropomi稀疏250倍。在这里，我们提供了一个混合的Tropomi+GOSAT甲烷产品，通过训练机器学习(ML)模型来预测Tropomi和GOSAT共同定位测量之间的差异，仅使用Tropomi检索中包括的预测变量，然后将校正应用于2018年4月至今的完整Tropomi记录。我们发现，最大的改正与粗大的气溶胶粒子、高的SWIR地表反照率和跨轨迹像素指数有关。我们的混合产品纠正了Tropomi和GOSAT在水上的系统差异，它的特点是在干旱土地、持续多云地区和高北纬地区的校正超过10 ppb。在0.25∘×0.3125∘分辨率下，它将陆地上的特罗波米空间变量偏差(参考全球卫星组织的数据)从14.3ppb减少到10.4ppb。总碳柱观测网络(TCCON)地面柱测量的验证表明，与原始的Tropomi数据相比，可变偏差从4.7 ppb降低到4.4 ppb，单次反演精度从14.5 ppb降低到11.9 ppb。TCCON数据都是在SWIR表面反照率低于0.4(Tropomi偏差往往相对较低)的位置，但它们证实了Tropomi偏差对SWIR表面反照率和粗大气溶胶粒子的依赖，以及混合产品中这些偏差的减少。对阿拉伯半岛的精细检查显示，原始Tropomi数据中的一些热点被作为混合产品中的人工制品删除。该混合产品还纠正了单轨Tropomi数据中的条带化和气溶胶/云偏差，从而能够更好地检测和量化超辐射源。残留的沿海偏差可以通过应用额外的过滤器来消除。本文提出的最大似然方法可以更普遍地应用于验证和校正来自任何新的卫星仪器的数据，只需参考更成熟的仪器。
Abstract. Satellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25∘ × 0.3125∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument.