A5063050629- Air Quality Monitoring and Forecasting
- Atmospheric and Environmental Gas Dynamics
- Atmospheric chemistry and aerosols
- Atmospheric aerosols and clouds
- Forecasting Techniques and Applications
- Climate variability and models
- Cryospheric studies and observations
- Meteorological Phenomena and Simulations
- Methane Hydrates and Related Phenomena
- Arctic and Antarctic ice dynamics
- Climate Change and Geoengineering
- Radiative Heat Transfer Studies
- demographic modeling and climate adaptation
- Energy Load and Power Forecasting
ETH Zurich
2021-2024
European Space Research Institute
2024
Abstract Many different emission pathways exist that are compatible with the Paris climate agreement, and many more possible miss target. While some of most complex Earth System Models have simulated a small selection Shared Socioeconomic Pathways, it is impractical to use these expensive models fully explore space possibilities. Such explorations therefore mostly rely on one‐dimensional impulse response models, or simple pattern scaling approaches approximate physical given scenario. Here...
Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions among the largest uncertainties in global climate models. This work uses three years satellite reanalysis data to study link between cirrus drivers cloud properties. We use a gradient-boosted machine learning model Long Short-Term Memory (LSTM) network with an attention layer predict ice water content crystal number concentration. The models show that can properties $R^2 = 0.49$....
Many different emission pathways exist that are compatible with the Paris climate agreement, and many more possible miss target. While some of most complex Earth System Models have simulated a small selection Shared Socioeconomic Pathways, it is impractical to use these expensive models fully explore space possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches approximate physical given scenario. Here we present...
Remote sensing observations of cloud ice in cirrus and mixed-phase clouds have been playing a crucial role advancing our understanding processes validating climate models. On the one hand, many studies used polar-orbiting active satellite instruments like CALIPSO’s lidar CloudSat’s radar to analyze microphysical properties clouds. These are able provide vertical profile structures thus allow detailed view on properties. But, due their long revisiting times it is...
Abstract. The microphysical and radiative properties of cirrus clouds are strongly dependent on the ice nucleation mechanism origin crystals. Due to sparse temporal coverage satellite data limited observations nucleating particles (INPs) at levels it is notoriously hard determine in observations. In this work we combine three years from DARDAR-Nice retrieval product with Lagrangian trajectories reanalysis meteorological aerosol variables calculated 24 h backward time for each observed cloud....
IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage resolution of geostationary satellite observations (SEVIRI) vertical active retrievals (DARDAR). consists ConvNeXt-based U-Net 3D PatchGAN discriminator model trained by predicting DARDAR profiles from co-located SEVIRI images. Despite sparse...
In recent years our understanding of cirrus cloud processes has been significantly advanced. However, a large uncertainty regarding the influence formation mechanisms on microphysical properties, and hence radiative properties clouds still remains. This leads to in global climate models change projections. this work we aim identify different regimes analyze their properties. We combine DARDAR-Nice satellite observations with Lagrangian back trajectories meteorological aerosol...
Abstract Cirrus clouds are key modulators of Earth’s climate. Their dependencies on meteorological and aerosol conditions among the largest uncertainties in global climate models. This work uses 3 years satellite reanalysis data to study link between cirrus drivers cloud properties. We use a gradient-boosted machine learning model long short-term memory network with an attention layer predict ice water content crystal number concentration. The models show that can properties R 2 = 0.49....
Clouds containing ice particles play a crucial role in the climate system. Yet they remain source of great uncertainty models and future projections. In this work, we create new observational constraint regime-dependent microphysical properties at spatio-temporal coverage geostationary satellite instruments quality active retrievals. We achieve by training convolutional neural network on three years SEVIRI DARDAR data sets. This work will enable novel research to improve cloud process...
<p>Exploration of future emissions scenarios mostly relies on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate a given scenario. Such are unable reliably predict variables which respond non-linearly forcing (such as precipitation) and must rely heavily simplified representations e.g., aerosol, neglecting important spatial dependencies.</p><p>Here we present ClimateBench - benchmark...
<p><span>Cirrus cloud microphysics and their interactions with aerosols remain one of the largest uncertainties in global climate models change projections. The uncertainty originates from high spatio-temporal variability non-linear dependence on meteorological drivers like temperature, updraft velocities, aerosol environment. We combine ten years CALIPSO/CloudSat satellite observations cirrus clouds ERA5 MERRA-2 reanalysis data variables to create a spatial cube....
Earth and Space Science Open Archive This preprint has been submitted to is under consideration at Journal of Advances in Modeling Systems (JAMES). ESSOAr a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go new versionClimateBench: A benchmark dataset data-driven climate projectionsAuthorsDuncanWatson-ParrisiDYuhanRaoDirkOliviéØyvindSelandPeer...