12 citations · 13 across the 4 of their papers we have counts for
4 papers
Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction
Marwa Alfouly, Smajil Halilovic, Nils Bochow +3
Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data…
Extrapolation from historical data cannot reliably predict the time of a potential AMOC collapse
Andreas Morr, Maya Ben-Yami, Brian Groenke +6
Ditlevsen and Ditlevsen [Nature Communications, 2023] (DD23 hereafter) propose a statistical framework to estimate the timing of a potential collapse of the Atlantic Meridional Ove…
Generative deep learning improves reconstruction of global historical climate records
Zhen Qian, Teng Liu, Sebastian Bathiany +7
Accurate assessment of anthropogenic climate change relies on historical instrumental data, yet observations from the early 20th century are sparse, fragmented, and uncertain. Conv…
Reconstructing Historical Climate Fields With Deep Learning
Nils Bochow, Anna Poltronieri, Martin Rypdal +1
Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and mod…