arXiv:2606.09313v1 Announce Type: new Abstract: Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solving inverse problems from high-spectral-resolution satellite radiance measurements. However, these algorithms are computationally expensive, which makes real-time estimation at scale difficult. Machine-learning models have therefore been proposed as fast emulators of retrieval algorithms. Most existing studies, however, evaluate them only on test data from the same period as the training data. We
The increasing computational demands of climate monitoring and the maturity of machine learning techniques are driving the need for more efficient solutions for environmental data processing.
This development allows for faster, more scalable, and potentially real-time, monitoring of greenhouse gases, enabling more responsive policy decisions and scientific understanding of climate change.
The methodology for processing satellite greenhouse gas data can become significantly more efficient, moving from computationally intensive inverse problems to machine-learning emulations.
- · Climate scientists
- · Environmental monitoring agencies
- · Satellite data providers
- · Developers of ML for scientific applications
- · Providers of traditional, computationally expensive retrieval algorithms
More timely and widespread access to greenhouse gas concentration data.
Improved accuracy and resolution in climate models due to better input data.
Potentially accelerated policy-making and international cooperation on emissions reduction.
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