Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach …
%0 Journal Article
%1 novitasari2024cloudy
%A Novitasari, Maria Carolina
%A Quaas, Johannes
%A Rodrigues, Miguel RD
%D 2024
%I Cambridge University Press
%J Environmental Data Science
%K imported topic_earthenvironment
%P e23
%T Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning
%V 3
%X Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach …
@article{novitasari2024cloudy,
abstract = {Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach …},
added-at = {2024-11-29T12:02:08.000+0100},
author = {Novitasari, Maria Carolina and Quaas, Johannes and Rodrigues, Miguel RD},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2789274d737ff7c486df2f8f03c5a6d86/joum576e},
citation = {Environmental Data Science 3, e23, 2024},
interhash = {af139c6c11ca615fdc9aa1f94ff428e4},
intrahash = {789274d737ff7c486df2f8f03c5a6d86},
journal = {Environmental Data Science},
keywords = {imported topic_earthenvironment},
pages = {e23},
publisher = {Cambridge University Press},
timestamp = {2024-11-29T12:02:08.000+0100},
title = {Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning},
volume = 3,
year = 2024
}
