The result is an emulator that (i) enjoys the flexibility of statistical machine learning models and can learn from observations, and (ii) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system.
We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers.
The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented.
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.
The original M7 model is used to generate data of input-output pairs to train a neural network on it.
In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i. e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity.
Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN).
This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time.
no code implementations • 17 Dec 2020 • Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world.
1 code implementation • 13 Nov 2020 • Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánell Quesada-Chacón, Aris Marcolongo, Sofija Stefanović, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance.
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.