Related papers: Predicting Lake Erie Wave Heights using XGBoost
Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable. We reformulate wind…
Estimating climate effects on future ocean storm severity is plagued by large uncertainties, yet for safe design and operation of offshore structures, best possible estimates of climate effects are required given available data. We explore…
High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While…
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the…
Reliable dynamic sea level forecasts are hindered by numerous sources of uncertainty on daily-to-seasonal timescales (1-180 days) due to atmospheric boundary conditions and internal ocean variability. Studies have demonstrated that certain…
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term…
Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority class, which is of extreme interest, often proves difficult…
Forecasting ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based…
Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not…
Weather forecasting presents several challenges, including the chaotic nature of the atmosphere and the high computational demands of numerical weather prediction models. To achieve the most accurate predictions, the ideal scenario involves…
The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the…
Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from…
We report the outcome of evaluations of the skill of long-range forecasts from the ocean wave model component of the Navy's global coupled modeling system. Specifically, the model output is taken from a single member of the ensemble system,…
Accurate prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment…
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\circ}$ resolution. FourCastNet accurately forecasts…
Upscaling is central to offshore wind's cost-reduction strategy, with increasingly large rotors and nacelles requiring taller and stronger towers. In Floating Offshore Wind Turbines (FOWTs), this trend amplifies fatigue loads due to coupled…
Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models…
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water depth prediction are nowadays important as urban floods are…
This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the…
We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and…