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First 3416 results for ”JI van Gent”

  1. Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?

    Convection-permitting climate models (CPMs) are becoming increasingly used in climate change stud...

    Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley Fowler, and Hylke de Vries | Journal: Hydrology and Earth System Sciences | Year: 2024 | doi: https://doi.org/10.5194/hess-2024-132,

    Publication

  2. Detecting Spatiotemporal Dynamics of Western European Heatwaves Using Deep Learning

    Heatwaves over western Europe are increasing faster than elsewhere, which recent studies have att...

    T. Happé, J. S. Wijnands, M. Á. Fernández-Torres, P. Scussolini, L. Muntjewerf, and D. Coumou | Journal: Artificial Intelligence for the Earth Systems | Volume: 3(4) | Year: 2024 | doi: https://doi.org/10.1175/AIES-D-23-0107.1

    Publication

  3. Hamiltonian Monte Carlo to Characterize Induced Earthquakes: Application to a ML 3.4 Event in the Groningen Gas Field and the Role of Prior

    The Hamiltonian Monte Carlo algorithm is known to be highly efficient when sampling high-dimensio...

    La Ode Marzujriban Masfara, Cornelis Weemstra | Journal: Earth and Space Science | Volume: 11 | Year: 2024 | doi: 10.1029/2023EA003184

    Publication

  4. High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid

    Urban air pollution poses a significant health risk, with over half the global population living ...

    Bas Mijling, Henk Eskes, Sascha Hofmann, Pau Moreno, David García Falin, María Encarnación de Vega Pastor | Journal: Geoscientific Model Development | Volume: 18 | Year: 2025 | First page: 6439 | Last page: 6460 | doi: 10.5194/gmd-18-6439-2025

    Publication

  5. Probabilistic Forecasting of Cloud-Base Height and Visibility Using Quantile Regression Forests, Based on NWP and Observation Features

    We have applied quantile regression forests (QRFs) to generate probabilistic forecasts of weather...

    D. Wolters, M. Schmeits, and K. Whan | Journal: Wea. Forecasting | Volume: 40 | Year: 2025 | First page: 543 | Last page: 559 | doi: https://doi.org/10.1175/WAF-D-23-0231.1

    Publication