Using Machine Learning for Model Physics: an Overview

In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.Comment: 50 pages, 3 figures, 1 tabl

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

APPLICATION OF NEURAL NETWORKS TO EMULATION OF RADIATION PARAMETERIZATIONS IN GENERAL CIRCULATION MODELS

A novel approach based on using neural network (NN) techniques for approximation of physical components of complex environmental systems has been applied and further developed in this dissertation. A new type of a numerical model, a complex hybrid environmental model, based on a combination of deterministic and statistical learning model components, has been explored. Conceptual and practical aspects of developing hybrid models have been formalized as a methodology for applications to climate modeling and numerical weather prediction. The approach uses NN as a machine or statistical learning technique to develop highly accurate and fast emulations for model physics components/parameterizations. The NN emulations of the most time consuming model physics components, short and long wave radiation (LWR and SWR) parameterizations have been combined with the remaining deterministic components of a general circulation model (GCM) to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The high accuracy, which is of a paramount importance for the approach, and a speed-up of model calculations when using NN emulations, open the opportunity for model improvement. It includes using extended NN ensembles and/or more frequent calculations of full model radiation resulting in an improvement of radiation-cloud interaction, a better consistency with model dynamics and other model physics components. First, the approach was successfully applied to a moderate resolution (T42L26) uncoupled NCAR Community Atmospheric Model driven by climatological SST for a decadal climate simulation mode. Then it has been further developed and subsequently implemented into a coupled GCM, the NCEP Climate Forecast System with significantly higher resolution (T126L64) and time dependent CO2 and tested for decadal climate simulations, seasonal prediction, and short- to medium term forecasts. The developed highly accurate NN emulations of radiation parameterizations are on average one to two orders of magnitude faster than the original radiation parameterizations. The NN approach was extended by introduction of NN ensembles and a compound parameterization with quality control of larger errors. Applicability of other statistical learning techniques, such as approximate nearest neighbor approximation and random trees, to emulation of model physics has also been explore

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