Trustworthy predictive distributions for rare events via diagnostic transport maps

Forecast systems in science and technology are increasingly moving beyond point prediction toward methods that produce full predictive distributions of future outcomes y, conditional on high-dimensional and complex sequences of inputs x. However, even when forecast systems provide a full predictive distribution, the result is rarely calibrated with respect to all x and y. The estimated density can be especially unreliable in low-frequency or out-of-distribution regimes, where accurate uncertainty quantification and a means for human experts to verify results are most needed to establish trust in models. In this paper, we take an initial predictive distribution as given and treat it as a useful but potentially misspecified base model. WE then introduce diagnostic transport maps, covariate-dependent probability-to-probability maps that quantify how the base model's probabilities should be adjusted to better match the true conditional distribution of calibration data. At deployment, these maps provide the user with real-time local diagnostics that reveal where the model fails and how it fails (including bias, dispersion, skewness, and tail errors), while also producing a recalibrated predictive distribution through a simple composition with the base model. We apply diagnostic transport maps to short-term tropical cyclone intensity forecasting and show that an easy-to-fit parametric version identifies evolutionary modes associated with local miscalibration and improves the predictive performance for rare events, including 24-hour rapid intensity change, as compared to the operational forecasts of the National Hurricane Center.

Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models

Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.