Methane (CH4) is the second most critical greenhouse gas after carbon
dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands
are the primary natural source of methane emissions globally. However, wetland
methane emission estimates from biogeochemistry models contain considerable
uncertainty. One of the main sources of this uncertainty arises from the
numerous uncertain model parameters within various physical, biological, and
chemical processes that influence methane production, oxidation, and transport.
Sensitivity Analysis (SA) can help identify critical parameters for methane
emission and achieve reduced biases and uncertainties in future projections.
This study performs SA for 19 selected parameters responsible for critical
biogeochemical processes in the methane module of the Energy Exascale Earth
System Model (E3SM) land model (ELM). The impact of these parameters on various
CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types.
Given the extensive number of model simulations needed for global
variance-based SA, we employ a machine learning (ML) algorithm to emulate the
complex behavior of ELM methane biogeochemistry. ML enables the computational
time to be shortened significantly from 6 CPU hours to 0.72 milliseconds,
achieving reduced computational costs. We found that parameters linked to CH4
production and diffusion generally present the highest sensitivities despite
apparent seasonal variation. Comparing simulated emissions from perturbed
parameter sets against FLUXNET-CH4 observations revealed that better
performances can be achieved at each site compared to the default parameter
values. This presents a scope for further improving simulated emissions using
parameter calibration with advanced optimization techniques like Bayesian
optimization.

利用敏感性分析方法对 Energy Exascale Earth System Model (E3SM) 的甲烷模型的 19 个关键生物地球化学过程参数进行研究，并通过机器学习算法模拟复杂的生物地球化学行为以减少计算成本，发现甲烷生产和扩散参数通常具有最高的敏感性，并通过参数校准进一步提高模拟的排放性能。

基于机器学习的 E3SM 陆地模型参数对湿地甲烷排放的敏感性分析

Machine Learning Driven Sensitivity Analysis of E3SM Land Model  Parameters for Wetland Methane Emissions

Methane is one of the most potent greenhouse gases, and its short atmospheric
half-life makes it a prime target to rapidly curb global warming. However,
current methane emission monitoring techniques primarily rely on approximate
emission factors or self-reporting, which have been shown to often dramatically
underestimate emissions. Although initially designed to monitor surface
properties, satellite multispectral data has recently emerged as a powerful
method to analyze atmospheric content. However, the spectral resolution of
multispectral instruments is poor, and methane measurements are typically very
noisy. Methane data products are also sensitive to absorption by the surface
and other atmospheric gases (water vapor in particular) and therefore provide
noisy maps of potential methane plumes, that typically require extensive human
analysis. Here, we show that the image recognition capabilities of deep
learning methods can be leveraged to automatize the detection of methane leaks
in Sentinel-2 satellite multispectral data, with dramatically reduced false
positive rates compared with state-of-the-art multispectral methane data
products, and without the need for a priori knowledge of potential leak sites.
Our proposed approach paves the way for the automated, high-definition and
high-frequency monitoring of point-source methane emissions across the world.

利用深度学习方法可以自动化检测卫星多光谱数据中的甲烷泄漏，无需事先了解潜在泄漏点，从而大幅降低误报率，为全球点源甲烷排放的自动化、高清晰度和高频率监测铺平了道路。

利用深度学习在多光谱卫星数据中进行甲烷排放的自主检测

Autonomous Detection of Methane Emissions in Multispectral Satellite  Data Using Deep Learning

Methane (CH$_4$) is the chief contributor to global climate change. Recent
Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has
been very useful in quantitative mapping of methane emissions. Existing methods
for analyzing this data are sensitive to local terrain conditions, often
require manual inspection from domain experts, prone to significant error and
hence are not scalable. To address these challenges, we propose a novel
end-to-end spectral absorption wavelength aware transformer network,
MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces
two novel modules that help to locate the most relevant methane plume regions
in the spectral domain and uses them to localize these accurately. Thorough
evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces
the model size (by 5x) compared to the current state of the art. In addition,
we also introduce a large-scale dataset of methane plume segmentation mask for
over 1200 AVIRIS-NG flight lines from 2015-2022. It contains over 4000 methane
plume sites. Our dataset will provide researchers the opportunity to develop
and advance new methods for tackling this challenging green-house gas detection
problem with significant broader social impact. Dataset and source code are
public

提出了一种新型的光谱吸收波长感知变换器网络 MethaneMapper，它可以定位和准确地定量化甲烷的排放，通过对 1200 AVIRIS-NG 飞行线的超过 4000 个甲烷排放数据集的详细评估表明，MethaneMapper 在检测中获得了 0.63 mAP，并将模型大小与现有技术相比减小了 5 倍。