利用地统计填图方法预测年际天基二氧化碳浓度

Shrutilipi Bhattacharjee, Katharina Dill, Jia Chen
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引用次数: 2

摘要

美国宇航局轨道碳观测2号(OCO-2)是最近的一项卫星任务,主要目的是测量大气中二氧化碳的柱浓度。大气CO2浓度以平行四边形足迹的连续条来测量,这些足迹可作为宽度约为10.3公里的2级样本。在一个地方检索的时间频率约为16天。本研究试图利用基于地理统计时空克里格的制图方法,从附近地点的过去可用样本中预测年际时间尺度上的OCO-2样本。这种预测对于事先了解二氧化碳的未来季节性行为是必要的。为了验证,我们在2015 - 2019年的研究区域使用了OCO-2的XCO2条带,并在2018年和2019年进行了预测。其中一种变体方法发现产生1.52 ppm的均方根误差(RMSE),这在有限的样本中是一个很好的结果。该方法能够对OCO-2的其他产品进行时空预测和预报,并可以通过考虑研究区域的相关辅助变量进一步改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Interannual Space-based CO2 Concentration using Geostatistical Mapping Approach
NASAs Orbiting Carbon Observatory-2 (OCO-2) is a recent satellite mission primarily aimed at measuring the column concentration of the carbon dioxide (CO2) in the atmosphere. The atmospheric CO2 concentration is measured as continuous swaths of the parallelogram footprints which are available as the Level-2 samples with a swath width of 10.3 km approximately. The temporal frequency of the retrieval at one place is 16 days approximately. This work attempts to forecast the OCO-2 samples at an interannual time scale from the available past samples at the nearby locations using geostatistical spatiotemporal kriging-based mapping approaches. This forecasting is needed to understand the future seasonal behavior of CO2 beforehand. For the validation, we have used the XCO2 swaths of OCO-2 in a study region from 2015 - 2019 and foretasted in the year of 2018 and 2019. One of the variant approaches found to produce 1.52 ppm root mean square error (RMSE), which is a good result with limited samples. This approach is capable of spatio-temporal prediction and forecasting of other products of OCO-2 and might be improved further by considering correlated auxiliary variables in the study region.
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