Linguistic and Scientific
Harmonies of the Quran

Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show that the difficulty is benign and introduce a self-supervised learning task that defines a categorical loss for a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network

We aim to characterize the U-band variability of young brown dwarfs in the Taurus Molecular Cloud and discuss its origin. We used the XMM-Newton Extended Survey of the Taurus Molecular Cloud, where a sample of 11 young bona fide brown dwarfs (spectral type later than M6) were observed simultaneously in X-rays with XMM-Newton and in the U-band with the XMM-Newton Optical/UV Monitor (OM). We obtained upper limits to the U-band emission of 10 brown dwarfs (U>19.6-20.6 mag), whereas 2MASSJ04141188+2

We aim to characterize the U-band variability of young brown dwarfs in the Taurus Molecular Cloud and discuss its origin. We used the XMM-Newton Extended Survey of the Taurus Molecular Cloud, where a sample of 11 young bona fide brown dwarfs (spectral type later than M6) were observed simultaneously in X-rays with XMM-Newton and in the U-band with the XMM-Newton Optical/UV Monitor (OM). We obtained upper limits to the U-band emission of 10 brown dwarfs (U>19.6-20.6 mag), whereas 2MASSJ04141188+2

Precipitation generates small-scale turbulent air flows the energy of which ultimately dissipates to heat. The power of this process has previously been estimated to be around 2-4 W m-2 in the tropics: a value comparable in magnitude to the dynamic power of the global circulation. Here we suggest that this previous power estimate is approximately double the true figure. Our result reflects a revised evaluation of the mean precipitation path length Hp. We investigate the dependence of Hp on surfa

Precipitation generates small-scale turbulent air flows the energy of which ultimately dissipates to heat. The power of this process has previously been estimated to be around 2-4 W m-2 in the tropics: a value comparable in magnitude to the dynamic power of the global circulation. Here we suggest that this previous power estimate is approximately double the true figure. Our result reflects a revised evaluation of the mean precipitation path length Hp. We investigate the dependence of Hp on surfa

Because the opacity of clouds in substellar mass object (SMO) atmospheres depends on the composition and distribution of particle sizes within the cloud, a credible cloud model is essential for accurately modeling SMO spectra and colors. We present a one--dimensional model of cloud particle formation and subsequent growth based on a consideration of basic cloud microphysics. We apply this microphysical cloud model to a set of synthetic brown dwarf atmospheres spanning a broad range of surface gr

Because the opacity of clouds in substellar mass object (SMO) atmospheres depends on the composition and distribution of particle sizes within the cloud, a credible cloud model is essential for accurately modeling SMO spectra and colors. We present a one--dimensional model of cloud particle formation and subsequent growth based on a consideration of basic cloud microphysics. We apply this microphysical cloud model to a set of synthetic brown dwarf atmospheres spanning a broad range of surface gr

Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show that the difficulty is benign and introduce a self-supervised learning task that defines a categorical loss for a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network

Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show that the difficulty is benign and introduce a self-supervised learning task that defines a categorical loss for a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network