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Precipitation nowcasting (forecasting locally for 0–6 h) serves both public security and industries, facilitating the mitigation of losses incurred due to e.g. flash floods, and is usually done by predicting weather radar echoes, which provides better performance than NWP at that scale. Probabilistic nowcasts are especially useful as they provide a desirable framework for operational decision-making. Many extrapolation-based statistical nowcasting methods exist, but they all suffer from a limited ability to capture the nonlinear growth and decay of precipitation, leading to a recent paradigm shift towards deep learning methods, more capable of representing these patterns. Despite of its potential advantages, the application of deep learning in probabilistic nowcasting has only recently started to be explored. Here we develop a novel probabilistic precipitation nowcasting method, based on Bayesian neural networks with variational inference and the U-Net architecture, named DEUCE. The method estimates the total predictive uncertainty of precipitation by combining estimates of the epistemic (knowledge-related, reducible) and heteroscedastic aleatoric (data-dependent, irreducible) uncertainties, and produces an ensemble of development scenarios for the following 60 minutes. DEUCE is trained and verified using Finnish Meteorological Institute radar composites against established classical models. Our model is found to produce both skillful and reliable probabilistic nowcasts based on various evaluation criteria. It improves ROC Area Under the Curve scores 1–5 % over STEPS and LINDA-P baselines, and comes close to the best-performer STEPS on a CRPS metric. The reliability of DEUCE is demonstrated with, e.g., having the lowest Expected Calibration Error at 20 and 25 dBZ reflectivity thresholds, and coming second at 35 dBZ. On the other hand, deterministic performance of ensemble means is found to be worse than that of extrapolation and LINDA-D baselines. Lastly, the composition of the predictive uncertainty is analysed and described, with the conclusion that aleatoric uncertainty is more significant and informative than epistemic uncertainty in the DEUCE model.

openaire: EC/H2020/803937/EU//InterActive Funding Information: J.V.I.T. acknowledges funding from ERC (803937) and Academy of Finland (316219). We acknowledge the facilities and technical support by Aalto University at OtaNano Nanomicroscopy Centre (Aalto-NMC). Publisher Copyright: © 2022, The Author(s). Ferrofluids are magnetic liquids known for the patterns they form in external magnetic fields. Typically, the patterns form at the interface between a ferrofluid and another immiscible non-magnetic fluid with a large interfacial tension γ ∼ 10−2 N m−1, leading to large pattern periodicities. Here we show that it is possible to reduce the interfacial tension several orders of magnitude down to ca. γ ∼ 10−6 N m−1 by using two immiscible aqueous phases based on spontaneous phase separation of dextran and polyethylene glycol and the asymmetric partitioning of superparamagnetic maghemite nanoparticles into the dextran-rich phase. The system exhibits classic Rosensweig instability in a uniform magnetic field with a periodicity of ∼200 μm, significantly lower than in traditional systems (∼10 mm). This system paves the way towards the science of pattern formation at the limit of vanishing interfacial tension and ferrofluid applications driven by small external magnetic fields.