Improving satellite measurements of clouds and precipitation using machine learning
Doktorsavhandling, 2022
Inferring properties of clouds or precipitation from satellite observations is a non-trivial task. Due to the limited information content of the observations and the complex physics of the atmosphere, such retrievals are endowed with significant uncertainties. Traditional methods to perform these retrievals trade-off processing speed against accuracy and the ability to characterize the uncertainties in their predictions.
This thesis develops and evaluates two neural-network-based methods for performing retrievals of hydrometeors, i.e., clouds and precipitation, that are capable of providing accurate predictions of the retrieval uncertainty. The practicality and benefits of the proposed methods are demonstrated using three real-world retrieval applications of cloud properties and precipitation. The demonstrated benefits of these methods over traditional retrieval methods led to the adoption of one of the algorithms for operational use at the European Organisation for the Exploitation of Meteorological Satellites. The two other algorithms are planned to be integrated into the operational processing at the Brazilian National Institute for Space Research, as well as the processing of observations from the Global Precipitation Measurement, a joint satellite mission by NASA and the Japanese Aerospace Exploration Agency.
The principal advantage of the proposed methods is their simplicity and computational efficiency. A minor modification of the architecture and training of conventional neural networks is sufficient to capture the dominant source of uncertainty for remote sensing retrievals. As shown in this thesis, deep neural networks can significantly improve the accuracy of satellite retrievals of hydrometeors. With the proposed methods, the benefits of modern neural network architectures can be combined with reliable uncertainty estimates, which are required to improve the characterization of the observed hydrometeors.
precipitation
clouds
remote sensing
machine learning
Författare
Simon Pfreundschuh
Chalmers, Rymd-, geo- och miljövetenskap, Geovetenskap och fjärranalys
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This thesis employs methods from artificial intelligence to improve satellite measurements of clouds and rain. These measurements are difficult because the atmosphere is highly complex and the information provided by the satellite is limited. Providing reliable measurements thus requires quantifying the associated uncertainties. This thesis proposes a way to employ novel machine learning methods in a way that allows reliable quantification of the uncertainties.
Using three different satellite applications, the potential of the proposed methods is demonstrated. They yield significant improvements compared to current methods and improve the handling of uncertainties in the measurements. Because of these advantages, the methods are already used by the European Organisation for the Exploitation of Meteorological Satellites and planned to be used for the processing of observations from the Global Precipitation Measurement, a joint satellite mission by NASA and the Japanese Aerospace Exploration Agency.
Robust Estimation of Global precipitation using Neural networks (REGN)
Rymdstyrelsen (154/19), 2020-01-01 -- 2023-12-31.
Ämneskategorier
Matematik
Data- och informationsvetenskap
Geovetenskap och miljövetenskap
ISBN
978-91-7905-657-5
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5123
Utgivare
Chalmers
EB, Edit
Opponent: Dr. Peter Dueben, Coordinator of Machine Learning and AI activities at the European Centre for Medium Range Weather Forecasts