On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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9 results for “subseasonal”

Adaptive Bias Correction for Improved Subseasonal Forecastingcs.LG

ABC method improves subseasonal weather forecasting by 60-90%.

problem Improving subseasonal temperature and precipitation forecasting accuracy.

method Combines dynamical forecasts with machine learning-based bias correction.

result Significant improvement in temperature and precipitation forecasting skills.

subseasonal forecasting bias correction machine learning temperature prediction precipitation prediction

2022-09-21abs ↗pdf ↗

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarkingphysics.ao-ph

SubseasonalClimateUSA dataset improves subseasonal weather forecasting.

problem Challenges in subseasonal weather forecasting, especially skill of physics-based models and integration of local and global variables.

method Curated dataset for training and benchmarking subseasonal forecasting models, including various methods.

result Benchmarking suggests simple and effective ways to improve current operational models.

subseasonal forecasting weather prediction machine learning climate variables dynamical models

2021-09-21abs ↗pdf ↗

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correctioncs.LG

PBC improves AI and dynamical subseasonal forecasts by reducing biases.

problem Subseasonal forecast accuracy drops due to model biases and compounding errors.

method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.

result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.

weather forecasting subseasonal bias correction machine learning AI

2026-04-17abs ↗pdf ↗

Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Modelsmath.NA

Gaussian process models improve MJO predictions with better uncertainty quantification.

problem Lack of uncertainty quantification in MJO predictions by machine learning models.

method Developed a nonparametric strategy based on Gaussian process models, calibrating them using empirical correlations and proposing a posteriori covariance correction.

result Gaussian process models provide better prediction skills and extended probabilistic coverage for MJO forecasts.

Madden-Julian Oscillation Gaussian process machine learning weather prediction uncertainty quantification

2025-05-21abs ↗pdf ↗

Improving Subseasonal Forecasting in the Western U.S. with Machine Learningstat.AP

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…

2018-09-19abs ↗pdf ↗

DiffObs: Generative Diffusion for Global Forecasting of Satellite Observationsphysics.comp-ph

DiffObs predicts global precipitation with realistic wave modes and low frequency variations.

problem Predicting global precipitation evolution using satellite observations.

method Autoregressive generative diffusion model trained on satellite data.

result Model generates realistic wave modes and low frequency variations, validating its potential for climate prediction.

satellite observations global precipitation diffusion model Madden-Julian oscillation convectively coupled waves

2024-04-04abs ↗pdf ↗

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecastingstat.ML

TelePiT improves S2S forecasting by integrating physics and teleconnections.

problem Challenges in subseasonal-to-seasonal climate forecasting due to chaotic dynamics and complex interactions.

method Integrates physics and teleconnections into a transformer architecture with spherical embedding and multi-scale physics-informed neural ODE.

result Significantly outperforms state-of-the-art methods across all forecast horizons.

teleconnections physics-informed transformer S2S forecasting climate prediction

2025-06-08abs ↗pdf ↗

Online Learning with Optimism and Delaycs.LG

New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.

problem Real-time climate forecasting with delayed feedback.

method Developed optimistic online learning algorithms that reduce regret under delayed feedback.

result Optimistic learning algorithms achieve optimal regret guarantees under delayed feedback.

online learning optimism delayed feedback forecasting regret minimization

2021-06-13abs ↗pdf ↗

Generative emulation of chaotic dynamics with coherent priorcs.LG

Cohesion uses deep Koopman operators to generate long-range forecasts of chaotic dynamics.

problem Challenges in data-driven emulation of chaotic dynamics, especially long-range skill decay.

method Generative modeling with coherent priors estimated using reduced-order models.

result Superior long-range forecasting skill on chaotic systems, including climate dynamics.

chaotic dynamics generative modeling coherent priors long-range forecasting reinforcement learning

2025-04-19abs ↗pdf ↗