Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop.

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Desublimation Frosting on Nanoengineered Surfaces.

Ice nucleation from vapor presents a variety of challenges across a wide range of industries and applications including refrigeration, transportation, and energy generation. However, a rational comprehensive approach to fabricating intrinsically icephobic surfaces for frost formation-both from water condensation (followed by freezing) and in particular from desublimation (direct growth of ice crystals from vapor)-remains elusive. Here, guided by nucleation physics, we investigate the effect of material composition and surface texturing (atomically smooth to nanorough) on the nucleation and growth mechanism of frost for a range of conditions within the sublimation domain (0 °C to -55 °C; partial water vapor pressures 6 to 0.02 mbar). Surprisingly, we observe that on silicon at very cold temperatures-below the homogeneous ice solidification nucleation limit (<-46 °C)-desublimation does not become the favorable pathway to frosting. Furthermore, we show that surface nanoroughness makes frost formation on silicon more probable. We experimentally demonstrate at temperatures between -48 °C and -55 °C that nanotexture with radii of curvature within 1 order of magnitude of the critical radius of nucleation favors frost growth, facilitated by capillary condensation, consistent with Kelvin's equation. Our findings show that such nanoscale surface morphology imposed by design to impart desired functionalities-such as superhydrophobicity-or from defects can be highly detrimental for frost icephobicity at low temperatures and water vapor partial pressures (<0.05 mbar). Our work contributes to the fundamental understanding of phase transitions well within the equilibrium sublimation domain and has implications for applications such as travel, power generation, and refrigeration.