SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models

Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. This is done by running many physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample hundreds to tens of thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in important statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While this work demonstrates the utility of the methodology by focusing on weather forecasting, the generative artificial intelligence methodology can be extended for uncertainty quantification in climate modeling, where we believe the generation of very large ensembles of climate projections will play an increasingly important role in climate risk assessment.Comment: fixed a mistake of the previous version; the paper has not been submitted to neurips 202

Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. However, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user-centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile-based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different probabilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty

Uncertainty in climate science and climate policy

This essay, written by a statistician and a climate scientist, describes our view of the gap that exists between current practice in mainstream climate science, and the practical needs of policymakers charged with exploring possible interventions in the context of climate change. By `mainstream' we mean the type of climate science that dominates in universities and research centres, which we will term `academic' climate science, in contrast to `policy' climate science; aspects of this distinction will become clearer in what follows. In a nutshell, we do not think that academic climate science equips climate scientists to be as helpful as they might be, when involved in climate policy assessment. Partly, we attribute this to an over-investment in high resolution climate simulators, and partly to a culture that is uncomfortable with the inherently subjective nature of climate uncertainty.Comment: submitted as contribution to Conceptual Foundations of ClimateModeling, Winsberg, E. and Lloyd, E., eds., The University of Chicago Pres

Seasonal Arctic sea ice forecasting with probabilistic deep learning

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss