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Abstract:

Machine learning has the potential to improve the physical realism and/or computational efficiency of parameterizations. A typical approach has been to feed concatenated vertical profiles to a dense neural network. However, feed‐forward networks lack the connections to propagate information sequentially through the vertical column. Here we examine if predictions can be improved by instead traversing the column with recurrent neural networks (RNNs) such as Long Short‐Term Memory (LSTMs). This method encodes physical priors (locality) and uses parameters more efficiently. Firstly, we test RNN‐based radiation emulators in the Integrated Forecasting System. We achieve near‐perfect offline accuracy, and the forecast skill of a suite of global weather simulations using the emulator are for the most part statistically indistinguishable from reference runs. But can radiation emulators provide both high accuracy and a speed‐up? We find optimized, state‐of‐the‐art radiation code on CPU generally faster than RNN‐based emulators on GPU, although the latter can be more energy efficient. To test the method more broadly, and explore recent challenges in parameterization, we also adapt it to data sets from other studies. RNNs outperform reference feed‐forward networks in emulating gravity waves, and when combined with horizontal convolutions, for non‐local unified parameterization. In emulation of moist physics with memory, the RNNs have similar offline accuracy as ResNets, the previous state‐of‐the‐art. However, the RNNs are more efficient, and more stable in autoregressive semi‐prognostic tests. Multi‐step autoregressive training improves performance in these tests and enables a latent representation of convective memory. Recently proposed linearly recurrent models achieve similar performance to LSTMs.

Abstract:

Abstract:

<jats:title>Abstract</jats:title> <jats:p>The fractions skill score (FSS) is a widely used metric for assessing forecast skill, with applications ranging from precipitation to volcanic ash forecasts. By evaluating the fraction of grid squares exceeding a threshold in a neighborhood, the intuition is that it can avoid the pitfalls of pixelwise comparisons and identify length scales at which a forecast has skill. The FSS is typically interpreted relative to a “useful” criterion, where a forecast is considered skillful if its score exceeds a simple reference score. However, the typical reference score used is problematic, since it is not derived in a way that provides obvious meaning, does not scale with neighborhood size, and may not be exceeded by forecasts that have skill. We, therefore, provide a new method to determine forecast skill from the FSS, by deriving an expression for the FSS achieved by a random forecast, which provides a more robust and meaningful reference score to compare with. Through illustrative examples, we show that this new method considerably changes the length scales at which a forecast would be regarded as skillful and reveals subtleties in how the FSS should be interpreted.</jats:p> <jats:sec> <jats:title>Significance Statement</jats:title> <jats:p>Forecast verification metrics are crucial to assess accuracy and identify where forecasts can be improved. In this work, we investigate a popular verification metric, the fractions skill score, and derive a more robust method to decide if a forecast has sufficiently high skill. This new method significantly improves the quality of insights that can be drawn from this score.</jats:p></jats:sec>

Abstract:

Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of SHAP values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, US. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future.