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Can deep learning beat numerical weather prediction? [PDF]
Philos Trans A Math Phys Eng Sci, 2021 The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology.
Schultz MG +7 more
europepmc +2 more sources
Advances in weather prediction [PDF]
Science, 2019 Better weather and environmental forecasting will continue to improve well ...
Kerry Emanuel +2 more
openaire +5 more sources
The birth of numerical weather prediction [PDF]
Tellus A: Dynamic Meteorology and Oceanography, 1991 The paper describes the major events leading gradually to operational, numerical, short-range predictions for the large-scale atmospheric flow. The theoretical foundation starting with Rossby's studies of the linearized, barotropic equation and ending a decade and a half later with the general formulation of the quasi-geostrophic, baroclinic model by ...
A. Wiin‐Nielsen
openalex +6 more sources
Numerical Weather Prediction [PDF]
Annual Review of Fluid Mechanics, 1995 This review highlights a number of current areas of emphasis in research and operational numerical weather prediction. Detailed accounts of each area of activity are not presented; some key references are provided within each section for interested readers who may wish to explore further. The review outlines the types of weather prediction models where
Eugenia Kalnay
+6 more sources
Mean radiant temperature from global-scale numerical weather prediction models. [PDF]
Int J Biometeorol, 2020 Di Napoli C, Hogan RJ, Pappenberger F.
europepmc +2 more sources
Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models [PDF]
Journal of Advances in Modeling Earth Systems, 2021 We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts six key atmospheric variables with six‐hour time resolution. This computationally efficient model uses convolutional neural networks (
Jonathan A. Weyn +3 more
semanticscholar +1 more source
Extension of the WRF-Chem volcanic emission preprocessor to integrate complex source terms and evaluation for different emission scenarios of the Grimsvötn 2011 eruption [PDF]
Natural Hazards and Earth System Sciences, 2020 Volcanic eruptions may generate volcanic ash and sulfur dioxide (SO2) plumes with strong temporal and vertical variations. When simulating these changing volcanic plumes and the afar dispersion of emissions, it is important to provide the best available ...
M. Hirtl +8 more
doaj +1 more source
NOAA MODIS SST Reanalysis Version 1
Remote Sensing, 2023 The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed.
Olafur Jonasson +4 more
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Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere [PDF]
Journal of Advances in Modeling Earth Systems, 2020 We present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid.
Jonathan A. Weyn, D. Durran, R. Caruana
semanticscholar +1 more source
Accelerating Weather Prediction Using Near-Memory Reconfigurable Fabric [PDF]
ACM Transactions on Reconfigurable Technology and Systems, 2021 Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption ...
Gagandeep Singh +6 more
semanticscholar +1 more source