Development of a next-generation ensemble prediction system for atmospheric composition

We propose to develop a next-generation modeling framework for the real-time simulation of reactive gases and aerosols in the atmosphere. The core innovations of this project are (a) the deployment of computationally efficient parameterizations of atmospheric chemistry and transport and (b) the development of generative models based on machine learning (ML) to predict model uncertainties. Combined, these innovations will enable improved and novel applications related to atmospheric composition, including probabilistic air quality forecasts at increased horizontal resolution, advanced use of satellite observations using ensemble-based data assimilation techniques, and scenario simulations for real-time event analysis. The proposed simulation capability will be developed and tested within the NASA GEOS Earth System Model (ESM), and its utility will first be demonstrated in the GEOS Composition Forecast system (GEOS-CF). In a second step, we propose to transfer the technology to NOAA's air quality forecasting (AQF) system. This project will greatly advance NASA's capability to monitor, simulate, and understand reactive trace gases and aerosols in the atmosphere. It directly supports NASA's TEMPO mission – scheduled to launch in November 2022 – and other upcoming NASA missions including PACE and MAIA. It alleviates a major limitation of existing ESMs, namely the prohibitive computational cost of full chemistry models. We address this issue by implementing simplified parameterizations for the slowest model components, the simulation of atmospheric chemistry and the advection of chemical species. We further propose the use of conditional generative adversarial networks (cGAN) ML algorithms for the estimation of probability distributions to enable ensemble-based applications. A key aspect of the proposed system is that the original numerical model and the accelerated models can be used in tandem. This way, the full physics model can be deployed for the main analysis stream, and the accelerated system is used to improve overall analytic and predictive power during forecast and data assimilation. This minimizes the impact of compounding errors that can arise from the use of ML models alone. The project comprises two science tasks: Task 1 is to implement simplified parameterizations for atmospheric chemistry and tracer transport. This task builds on extensive previous work by the proposal team. For instance, PI Keller has developed a ML emulator for atmospheric chemistry based on gradient boosted regression trees, but this algorithm has not yet been tested for high-resolution applications such as GEOS-CF. Here we propose to do so, along with the development of an accelerated tracer transport capability based on optimizing the number of advected tracers and time stepping. Computation of atmospheric chemistry and transport consume more than 80% of the total compute time of full chemistry simulations, and we project that the accelerated system will deliver a 3-5 fold model speed-up. The second task is to develop an efficient methodology for generating probabilistic estimates of atmospheric composition. This task leverages the fact that cGANs offer a natural way to estimate probability distributions from a limited set of samples. The accelerated model developed in Task 1 will make it feasible to produce such samples, and we will combine that model sampling capability with the density estimation power of cGANs to dynamically estimate model uncertainties. Combined, Tasks 1 and 2 will offer an ensemble-style modeling framework for reactive trace gas and aerosol simulations that is applicable to a wide array of systems and applications. We will demonstrate these capabilities by integrating the framework into the NASA GEOS-CF system, and transfer the technology to NOAA's air quality forecasting system in Task 3. Thus, the proposed project will greatly advance the composition modeling, prediction, and monitoring capabilities of NASA and NOAA.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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