11 citations · 21 across the 9 of their papers we have counts for
9 papers
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Hong Zhang, John K. Hutchison, Rao Kotamarthi +8
Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. A central challenge is to produce high-resoluti…
Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
Xiaolong Ma, Xu Dong, Ashley Tarrant +5
High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse t…
Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting
Jason Stock, Troy Arcomano, Rao Kotamarthi
Diffusion models offer a physically grounded framework for probabilistic weather forecasting, but their typical reliance on slow, iterative solvers during inference makes them impr…
AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Väinö Hatanpää, Eugene Ku, Jason Stock +12
Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Joint modeling of wind speed and wind direction through a conditional approach
Eva Murphy, Whitney Huang, Julie Bessac +2
Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to cl…
A Deep Learning Approach to Probabilistic Forecasting of Weather
Nick Rittler, Carlo Graziani, Jiali Wang +1
We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information t…
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang +6
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical we…
Fast and accurate learned multiresolution dynamical downscaling for precipitation
Jiali Wang, Zhengchun Liu, Ian Foster +3
This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computati…