Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System

Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress)

We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by machine learning approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a $>$50\% reduction in errors from a current state-of-the-art model (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the `new frontier' of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts

Software and data release v1.0.0: Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning

<p>Data and software release for publication in Journal of Advances in Modeling Earth Systems (JAMES).</p&gt

Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process‐Based Modeling With Deep Learning

Abstract Hybrid Knowledge‐Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process‐based model simulations, have shown improved performance over their process‐based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub‐components that can be process‐based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process‐based model. To achieve this, we first train individual deep learning models with the output of the process‐based models. In a second step, we fine‐tune one deep learning model with observed field data. In this study, we replaced process‐based calculations of vertical diffusive transport with deep learning. Finally, this fine‐tuned deep learning model is integrated into the process‐based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process‐based model. We further compare the performance of the hybrid MCL model with the process‐based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process‐based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

International audienceLand surface water area (hereafter LSWA) is of paramount importance to the survival of all life forms (Karpatne et al., 2016). Water not only provides habitat for aquatic organisms but also affects various aspects of human life, such as for agricultural, domestic and industrial purposes (Vörösmarty & Sahagian, 2000). LSWA is highly dynamic and variations therein can be used as a direct indicator of climate change (Williamson et al., 2009) or human-induced changes (Pekel et al., 2016). LSWA is thus an essential variable in ecological, hydrological, climatic, and economic studies (Hirabayashi et al., 2013; Raymond et al., 2013; Willner et al., 2018). For such applications, accurate water information at adequate spatiotemporal resolution is crucial. Estimation of LSWA relies on three methods: ground surveys, remote sensing, and models. Among these methods, ground surveys cannot fully describe the water dynamics due to their slow updating frequency (Carroll et al., 2009; Lehner & Döll, 2004) and the significant cost of covering a large spatial domain. Remote sensing using satellites is an outstanding method that can provide regular large-scale observations of water surfaces. Various satellites have been used to identify LSWA, including Landsat