Bayesian inversion and computation applied to atmospheric flux fields

该项目旨在利用来自遥感和现场数据的前所未有的测量来源来估计温室气体的源和汇。地球大气中过多的温室气体可以说是对地球生态系统最严重的长期威胁。该项目将结合测量不确定性、物理传输模型中的过程不确定性和任何参数不确定性，为估计提供可靠的不确定性量化。这将通过新的贝叶斯时空反转和大数据计算策略实现。由此产生的关于温室气体通量场的统计推断将有助于制定关键的缓解战略。这些新的统计推断将成为世界各地政策制定者的宝贵资源，他们正在评估实现全球承诺的进展情况。此外，最终产品可能有助于在存在不确定性的情况下制定具有成本效益的缓解战略。

This project aims to make use of unprecedented sources of measurements, from remote sensing and in situ data, to estimate the sources and sinks of greenhouse gases. An overabundance of greenhouse gases in Earth's atmosphere is arguably the most serious long-term threat to the planet's ecosystems. This project will combine measurement uncertainties, process uncertainties in the physical transport models, and any parameter uncertainties, to provide reliable uncertainty quantification for the estimates. This will be achieved with new Bayesian spatio-temporal inversions and big-data computational strategies. The resulting statistical inferences on greenhouse-gas flux fields will enable the development of critical mitigation strategies. These new statistical inferences will be a valuable resource to policy-makers worldwide, who are assessing progress towards global commitments. Further, the final product may assist in developing cost-effective mitigation strategies in the presence of uncertainty.