Copertina

Identifying Lightning Processes in ERA5 Soundings with Deep Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ehrensperger, Gregor, Simon, Thorsten, Mayr, Georg J., Hell, Tobias
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Atmospheric and Oceanic Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Deep Learning Model of Lightning Stroke Density
di: Jones II, Randall, et al.
Pubblicazione: (2025)

Equatorial African Lightning: Past. Present and Future
di: Chakraborty, Rohit, et al.
Pubblicazione: (2025)

Impact of Non-Classical Gravity-Wave Dynamics on Middle-Atmosphere Mean Flow and Solar Tides
di: Kühner, Tobias, et al.
Pubblicazione: (2025)

Long-term foehn reconstruction combining unsupervised and supervised learning
di: Stauffer, Reto, et al.
Pubblicazione: (2024)

Direct Observations of X-Rays Produced by Upward Positive Lightning
di: Oregel-Chaumont, Toma, et al.
Pubblicazione: (2023)

Error in ERA5 2m Temperature identified using GraphCast
di: Christensen, Hannah M., et al.
Pubblicazione: (2026)

Lightning declines over shipping lanes following regulation of fuel sulfur emissions
di: Wright, Chris J., et al.
Pubblicazione: (2024)

Characteristics of Gravity Waves in Opposing Phases of the QBO: A Reanalysis Perspective with ERA5
di: Pahlavan, Hamid A., et al.
Pubblicazione: (2023)

The impact of two-dimensional filtering on white noise spectra in SWOT along-track observations
di: Dù, Ryan Shìjié, et al.
Pubblicazione: (2026)

New Physical Mechanism for Lightning
di: Artekha, S. N., et al.
Pubblicazione: (2024)

Learning from Translation: Seasonal Errors and Feature Importance of the ERA5 Turbulence Predictions
di: Tolentino, Arial, et al.
Pubblicazione: (2026)

Revisiting the Quasi Biennial Oscillation as Seen in ERA5.Part II: Evaluation of Waves and Wave Forcing
di: Pahlavan, Hamid A., et al.
Pubblicazione: (2020)

Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability?
di: Mayer, Kirsten J., et al.
Pubblicazione: (2024)

Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
di: Lee, ChangJae, et al.
Pubblicazione: (2025)

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
di: Behrens, Gunnar, et al.
Pubblicazione: (2024)

Lightning as a natural accelerator of relativistic particles and a source of synchrotron radiation
di: Petrov, N. I.
Pubblicazione: (2021)

Climatology of severe local storm environments and synoptic-scale features over North America in ERA5 reanalysis and CAM6 simulation
di: Li, Funing, et al.
Pubblicazione: (2020)

Electrostatic Disturbances of Aerosol Atmospheric Plasma: Beaded Lightning
di: Izhovkina, N. I., et al.
Pubblicazione: (2024)

Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
di: Toride, Kinya, et al.
Pubblicazione: (2024)

Similarities in the Initiation of Upward Positive and Downward Negative Lightning Flashes
di: Oregel-Chaumont, Toma, et al.
Pubblicazione: (2025)

Smoothing and spatial verification of global fields
di: Skok, Gregor, et al.
Pubblicazione: (2024)

Spatial verification of global precipitation forecasts
di: Skok, Gregor, et al.
Pubblicazione: (2024)

Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis
di: Vonich, P. Trent, et al.
Pubblicazione: (2024)

Machine Learning of Vertical Fluxes by Unresolved Midlatitude Mesoscale Processes
di: Ismaili, Erisa, et al.
Pubblicazione: (2026)

Glacial abrupt climate change as a multi-scale phenomenon resulting from monostable excitable dynamics
di: Riechers, Keno, et al.
Pubblicazione: (2023)

Climate Downscaling of Tropical Cyclone Intensity using Deep Learning
di: Luong, Minh-Khanh, et al.
Pubblicazione: (2025)

An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
di: Gultekin, Cem, et al.
Pubblicazione: (2024)

The Design and Performance of Meteorological Sensors for WindBorne Global Sounding Balloons
di: Spisak, Jake, et al.
Pubblicazione: (2026)

A neural network-based observation operator for weather radar data assimilation
di: Stefanelli, Marco, et al.
Pubblicazione: (2025)

Forecast error diagnostics in neural weather models
di: Perkan, Uros, et al.
Pubblicazione: (2025)

Zeeman: A Deep Learning Regional Atmospheric Chemistry Transport Model
di: Pang, Mijie, et al.
Pubblicazione: (2025)

Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
di: Lieberman, Kelsey, et al.
Pubblicazione: (2026)

Reconstructing Pre-Satellite Tropical Cyclogenesis Climatology Using Deep Learning
di: Kieu, Chanh, et al.
Pubblicazione: (2025)

Extratropical Atmospheric Circulation Response to ENSO in Deep Learning Pacific Pacemaker Experiments
di: Hua, Zhanxiang, et al.
Pubblicazione: (2025)

A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
di: Cresswell-Clay, Nathaniel, et al.
Pubblicazione: (2024)

A Hierarchical Deep Learning Model for Predicting Pedestrian-Level Urban Winds
di: Snaiki, Reda, et al.
Pubblicazione: (2025)

A Generalized Richardson Number Diagnostic for Turbulence in the Free Atmosphere
di: Foudad, Mohamed, et al.
Pubblicazione: (2026)

Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
di: Hu, Zeyuan, et al.
Pubblicazione: (2024)

A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models
di: Pallotta, Giuliana, et al.
Pubblicazione: (2026)

Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning
di: Mbarak, Mahmoud, et al.
Pubblicazione: (2025)