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Abstract An ensemble postprocessing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States (CONUS). The method combines a 3D vision transformer (ViT) for bias correction with a latent diffusion model (LDM), a generative artificial intelligence (AI) method, to postprocess 6-hourly precipitation ensemble forecasts and produce an enlarged generative ensemble that contains spatiotemporally consistent precipitation trajectories. These trajectories are expected to improve the characterization of extreme precipitation events and offer skillful multiday accumulated and 6-hourly precipitation guidance. The method is tested using the Global Ensemble Forecast System (GEFS) precipitation forecasts out to day 6 and is verified against the Climatology-Calibrated Precipitation Analysis (CCPA) data. Verification results indicate that the method generated skillful ensemble members with improved continuous ranked probabilistic skill scores (CRPSSs) and Brier skill scores (BSSs) over the raw operational GEFS and a multivariate statistical postprocessing baseline. It showed skillful and reliable probabilities for events at extreme precipitation thresholds. Explainability studies were further conducted, which revealed the decision-making process of the method and confirmed its effectiveness on ensemble member generation. This work introduces a novel, generative AI–based approach to address the limitation of small numerical ensembles and the need for larger ensembles to identify extreme precipitation events. Significance StatementWe use a new artificial intelligence (AI) technique to improve extreme precipitation forecasts from a numerical weather prediction ensemble, generating more scenarios that better characterize extreme precipitation events. This AI-generated ensemble improved the accuracy of precipitation forecasts and probabilistic warnings for extreme precipitation events. The study explores AI methods to generate precipitation forecasts and explains the decision-making mechanisms of such AI techniques to prove their effectiveness. 

Abstract Improving the skill of medium-range (3–8 day) severe weather prediction is crucial for mitigating societal impacts. This study introduces a novel approach leveraging decoder-only transformer networks to post-process AI-based weather forecasts, specifically from the Pangu-Weather model, for improved severe weather guidance. Unlike traditional post-processing methods that use a dense neural network to predict the probability of severe weather using discrete forecast samples, our method treats forecast lead times as sequential “tokens”, enabling the transformer to learn complex temporal relationships within the evolving atmospheric state. We compare this approach against post-processing of the Global Forecast System (GFS) using both a traditional dense neural network and our transformer, as well as configurations that exclude convective parameters to fairly evaluate the impact of using the Pangu-Weather AI model. Results demonstrate that the transformer-based post-processing significantly enhances forecast skill compared to dense neural networks. Furthermore, AI-driven forecasts, particularly Pangu-Weather initialized from high resolution analysis, exhibit superior performance to GFS in the medium-range, even without explicit convective parameters. Our approach offers improved accuracy, and reliability, which also provides interpretability through feature attribution analysis, advancing medium-range severe weather prediction capabilities. 

Abstract An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction. Significance StatementWe use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction. 

Abstract As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, and performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (i) how they make guidance use decisions and (ii) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting the probability of severe hail or a 2D convolutional neural network model predicting the probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful andusedtools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts. Significance StatementWe used a mixed-methods quantitative and qualitative approach to understand how National Weather Service (NWS) forecasters 1) make guidance use decisions within their operational forecasting process and 2) assess the trustworthiness of prototype guidance developed using artificial intelligence (AI). When taken as a whole, our findings illustrate that forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically and suggest that developers must center the end user when creating new AI guidance tools to ensure that the developed tools are useful andused. 

This project developed a pre-interview survey, interview protocols, and materials for conducting interviews with expert users to better understand how they assess and make use decisions about new AI/ML guidance. Weather forecasters access and synthesize myriad sources of information when forecasting for high-impact, severe weather events. In recent years, artificial intelligence (AI) techniques have increasingly been used to produce new guidance tools with the goal of aiding weather forecasting, including for severe weather. For this study, we leveraged these advances to explore how National Weather Service (NWS) forecasters perceive the use of new AI guidance for forecasting severe hail and storm mode. We also specifically examine which guidance features are important for how forecasters assess the trustworthiness of new AI guidance. To this aim, we conducted online, structured interviews with NWS forecasters from across the Eastern, Central, and Southern Regions. The interviews covered the forecasters’ approaches and challenges for forecasting severe weather, perceptions of AI and its use in forecasting, and reactions to one of two experimental (i.e., non-operational) AI severe weather guidance: probability of severe hail or probability of storm mode. During the interview, the forecasters went through a self-guided review of different sets of information about the development (spin-up information, AI model technique, training of AI model, input information) and performance (verification metrics, interactive output, output comparison to operational guidance) of the presented guidance. The forecasters then assessed how the information influenced their perception of how trustworthy the guidance was and whether or not they would consider using it for forecasting. This project includes the pre-interview survey, survey data, interview protocols, and accompanying information boards used for the interviews. There is one set of interview materials in which AI/ML are mentioned throughout and another set where AI/ML were only mentioned at the end of the interviews. We did this to better understand how the label “AI/ML” did or did not affect how interviewees responded to interview questions and reviewed the information board. We also leverage think aloud methods with the information board, the instructions for which are included in the interview protocols. 

Abstract While convective storm mode is explicitly depicted in convection-allowing model (CAM) output, subjectively diagnosing mode in large volumes of CAM forecasts can be burdensome. In this work, four machine learning (ML) models were trained to probabilistically classify CAM storms into one of three modes: supercells, quasi-linear convective systems, and disorganized convection. The four ML models included a dense neural network (DNN), logistic regression (LR), a convolutional neural network (CNN) and semi-supervised CNN-Gaussian mixture model (GMM). The DNN, CNN, and LR were trained with a set of hand-labeled CAM storms, while the semi-supervised GMM used updraft helicity and storm size to generate clusters which were then hand labeled. When evaluated using storms withheld from training, the four classifiers had similar ability to discriminate between modes, but the GMM had worse calibration. The DNN and LR had similar objective performance to the CNN, suggesting that CNN-based methods may not be needed for mode classification tasks. The mode classifications from all four classifiers successfully approximated the known climatology of modes in the U.S., including a maximum in supercell occurrence in the U.S. Central Plains. Further, the modes also occurred in environments recognized to support the three different storm morphologies. Finally, storm mode provided useful information about hazard type, e.g., storm reports were most likely with supercells, further supporting the efficacy of the classifiers. Future applications, including the use of objective CAM mode classifications as a novel predictor in ML systems, could potentially lead to improved forecasts of convective hazards.