IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input
learned from sparse supervision
- URL: http://arxiv.org/abs/2310.03499v1
- Date: Thu, 5 Oct 2023 12:24:25 GMT
- Title: IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input
learned from sparse supervision
- Authors: Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux,
David Neubauer, and Ulrike Lohmann
- Abstract summary: Ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections.
In this work, we create a new observational constraint of regime-dependent ice-physical properties at geostationary satellite instruments and the quality of active satellite retrievals.
- Score: 26.970640961908032
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Clouds containing ice particles play a crucial role in the climate system.
Yet they remain a source of great uncertainty in climate models and future
climate projections. In this work, we create a new observational constraint of
regime-dependent ice microphysical properties at the spatio-temporal coverage
of geostationary satellite instruments and the quality of active satellite
retrievals. We achieve this by training a convolutional neural network on three
years of SEVIRI and DARDAR data sets. This work will enable novel research to
improve ice cloud process understanding and hence, reduce uncertainties in a
changing climate and help assess geoengineering methods for cirrus clouds.
Related papers
- 3D Cloud reconstruction through geospatially-aware Masked Autoencoders [1.4124182346539256]
This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures.
We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs.
arXiv Detail & Related papers (2025-01-03T12:26:04Z) - Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion [33.025831091005784]
Large-scale Sea Surface Temperature (SST) monitoring relies on satellite infrared radiation detection.
Cloud cover presents a major challenge, creating extensive observational gaps.
We employ deep neural networks to reconstruct cloud-covered portions of satellite imagery.
arXiv Detail & Related papers (2024-12-04T15:49:49Z) - MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions.
Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations.
We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
arXiv Detail & Related papers (2024-08-20T13:45:49Z) - Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset.
Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors.
Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z) - Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph
Neural Networks [0.38073142980732994]
We propose a machine learning model that uses adaptive, recurrent graph convolutional networks to predict snow accumulation.
We found that our model performs better and with greater consistency than our previous model as well as equivalent non-temporal, non-geometric, and non-adaptive models.
arXiv Detail & Related papers (2023-06-22T19:59:54Z) - HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects
of Cloud Properties on Climate Patterns [4.716196892532721]
Marine Cloud Brightening (MCB) refers to modification in cloud reflectivity, thereby cooling the surrounding region.
We propose a hybrid AI-assisted visual analysis framework to drive such scientific studies.
We work with a team of climate scientists to develop a suite of hybrid AI models emulating cloud-climate response function.
arXiv Detail & Related papers (2023-05-13T07:55:47Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - Deep Learning Based Cloud Cover Parameterization for ICON [55.49957005291674]
We train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON simulations.
Globally trained NNs can reproduce sub-grid scale cloud cover of the regional simulation.
We identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained data.
arXiv Detail & Related papers (2021-12-21T16:10:45Z) - Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya [54.12023102155757]
Glacier mapping is key to ecological monitoring in the hkh region.
Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems.
We present a machine learning based approach to support ecological monitoring, with a focus on glaciers.
arXiv Detail & Related papers (2020-12-09T12:48:06Z) - Classification and understanding of cloud structures via satellite
images with EfficientUNet [0.0]
classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN)
Dice coefficient has been used for the final evaluation metric, which gave the score of 66.26% and 66.02% for public and private (test set) leaderboard on Kaggle competition respectively.
arXiv Detail & Related papers (2020-09-27T19:50:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.