Interpretable deep learning for probabilistic MJO prediction
Geophysical Research Letters Wiley 49:16 (2022) e2022GL098566
Abstract:
The Madden-Julian oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so sub-seasonal forecasts are generally probabilistic. We present a deep convolutional neural network (CNN) that produces skilful state-dependent probabilistic MJO forecasts. Importantly, the CNN's forecast uncertainty varies depending on the instantaneous predictability of the MJO. The CNN accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using Monte-Carlo dropout. Interpretation of the CNN mean forecasts highlights known MJO mechanisms, providing confidence in the model. Interpretation of forecast uncertainty indicates mechanisms governing MJO predictability. In particular, we find an initially stronger MJO signal is associated with more uncertainty, and that MJO predictability is affected by the state of the Walker Circulation.Interpretable Deep Learning for Probabilistic MJO Prediction
(2022)
Abstract:
The fractal nature of clouds in global storm-resolving models
Geophysical Research Letters American Geophysical Union 48:23 (2021) e2021GL095746
Abstract:
Clouds in observations are fractals: they show self-similarity across scales ranging from one to 1000 km. This includes individual storms and large-scale cloud structures typical of organised convection. It is not known whether global storm-resolving models reproduce the observed fractal scaling laws for clouds and organised convection. We compute the fractal dimension of clouds using Himawari satellite data and compare this to global storm-resolving model simulations completed as part of the DYAMOND intercomparison project. We find cloud fields in these simulations are indeed fractal, and reproduce the observed fractal dimension to within 10%. We find the fractal dimension is sensitive to the choice of boundary layer parametrisation scheme used in each model simulation, and not to the convection parametrisation as might have been expected.Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 379:2194 (2021) ARTN 20200083
Abstract:
In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.Scale‐aware space‐time stochastic parameterization of subgrid‐scale velocity enhancement of sea surface fluxes
Journal of Advances in Modeling Earth Systems American Geophysical Union (AGU) (2021)