Kara Sulia

David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia et al.

Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memory (LSTM) networks can successfully predict forecast errors in the High-Resolution Rapid Refresh (HRRR) model using mesonet observations, but we believe performance degradation is linked to periods of complex vertical atmospheric evolution. To address this limitation, we develop a hybrid LSTM-Vision Transformer (LSTM-ViT) framework that combines temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network. The LSTM-ViT framework is trained to predict HRRR hourly precipitation, 10 m wind speed, and 2 m temperature forecast errors at individual mesonet stations. Across all three predictors, incorporation of profiler-derived atmospheric structure improves forecast error prediction skill relative to the baseline LSTM architecture, with the largest gains occurring at shorter forecast lead times and during periods of enhanced PBL activity. Improvements are particularly pronounced for precipitation forecast error, where the LSTM-ViT framework achieves approximately a twofold increase in predictive skill relative to the baseline LSTM while better capturing convectively driven error evolution and reducing degradation associated with PBL processes. These results demonstrate that combining temporal sequence learning with vertically informed attention mechanisms provides a physically meaningful pathway for improving forecast error prediction in operational NWP systems. Our research offers forecasters enhanced guidance regarding model bias and forecast confidence.

Carly Sutter, Kara J. Sulia, Nick P. Bassill et al.

The New York State Department of Transportation (NYSDOT) has a network of roadside traffic cameras that are used by both the NYSDOT and the public to observe road conditions. The NYSDOT evaluates road conditions by driving on roads and observing live cameras, tasks which are labor-intensive but necessary for making critical operational decisions during winter weather events. However, machine learning models can provide additional support for the NYSDOT by automatically classifying current road conditions across the state. In this study, convolutional neural networks and random forests are trained on camera images and weather data to predict road surface conditions. Models are trained on a hand-labeled dataset of ~22,000 camera images, each classified by human labelers into one of six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, and the weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras.

Joseph Ko, Hariprasath Govindarajan, Fredrik Lindsten et al.

Ice-containing clouds strongly impact climate, but they are hard to model due to ice crystal habit (i.e., shape) diversity. We use self-supervised learning (SSL) to learn latent representations of crystals from ice crystal imagery. By pre-training a vision transformer with many cloud particle images, we learn robust representations of crystal morphology, which can be used for various science-driven tasks. Our key contributions include (1) validating that our SSL approach can be used to learn meaningful representations, and (2) presenting a relevant application where we quantify ice crystal diversity with these latent representations. Our results demonstrate the power of SSL-driven representations to improve the characterization of ice crystals and subsequently constrain their role in Earth's climate system.

Joseph Ko, Jerry Harrington, Kara Sulia et al.

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generate synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we use synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_{e}$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. When tested on unseen synthetic images, we find that our ML models can predict microphysical properties with high accuracy. For $ρ_{e}$ and $A_e$, respectively, our best-performing single view models achieved $R^2$ values of 0.99 and 0.98. For $N_b$, our best single view model achieved a balanced accuracy and F1 score of 0.91. We also quantify the marginal prediction improvements from incorporating a second view. A stereo view ResNet-18 model reduced RMSE by 40% for both $ρ_e$ and $A_e$, relative to a single view ResNet-18 model. For $N_b$, we find that a stereo view ResNet-18 model improved the F1 score by 8%. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.