Articles | Volume 10, issue 9
https://doi.org/10.5194/amt-10-3547-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-3547-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Sep 2017
Research article |
| 29 Sep 2017
Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
Deutsches Zentrum für Luft- und Raumfahrt, Institut für
Physik der Atmosphäre, Oberpfaffenhofen, Germany
Luca Bugliaro
Deutsches Zentrum für Luft- und Raumfahrt, Institut für
Physik der Atmosphäre, Oberpfaffenhofen, Germany
Frank Sehnke
Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden Württemberg, Systemanalyse, Stuttgart, Germany
Leon Schröder
Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden Württemberg, Systemanalyse, Stuttgart, Germany
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34 citations as recorded by crossref.
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- A scalable system to measure contrail formation on a per-flight basis S. Geraedts et al. 10.1088/2515-7620/ad11ab
- Physics-Driven Machine Learning Algorithm Facilitates Multilayer Cloud Property Retrievals From Geostationary Passive Imager Measurements W. Li et al. 10.1109/TGRS.2024.3369621
- On estimation of cloudiness characteristics and parameters of DOAS retrieval from spectral measurements using a neural network O. Postylyakov et al. 10.1088/1755-1315/489/1/012031
- Air traffic and contrail changes over Europe during COVID-19: a model study U. Schumann et al. 10.5194/acp-21-7429-2021
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems S. Pfreundschuh et al. 10.5194/amt-11-4627-2018
- Aviation Contrail Cirrus and Radiative Forcing Over Europe During 6 Months of COVID‐19 U. Schumann et al. 10.1029/2021GL092771
- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
- An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions M. Plu et al. 10.5194/nhess-21-2973-2021
- Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint V. Kristollari & V. Karathanassi 10.1080/01431161.2020.1714776
- A Lagrangian analysis of pockets of open cells over the southeastern Pacific K. Smalley et al. 10.5194/acp-22-8197-2022
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1 S. Larosa et al. 10.3390/rs15071798
- A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG E. Ricciardelli et al. 10.1109/TGRS.2023.3303268
- Contrasting characteristics of continental and oceanic deep convective systems at different life stages from CloudSat observations J. Ge et al. 10.1016/j.atmosres.2023.107157
- Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI J. Strandgren et al. 10.5194/amt-10-4317-2017
- Formation and radiative forcing of contrail cirrus B. Kärcher 10.1038/s41467-018-04068-0
- Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. 10.5194/amt-15-5701-2022
- A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. 10.5194/amt-13-2257-2020
- The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON H. Rybka et al. 10.5194/acp-21-4285-2021
- Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic Z. Wang et al. 10.5194/acp-23-1941-2023
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization Y. Cheng et al. 10.3390/s24020541
- The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation B. STEVENS et al. 10.2151/jmsj.2020-021
- An update on global atmospheric ice estimates from satellite observations and reanalyses D. Duncan & P. Eriksson 10.5194/acp-18-11205-2018
- Investigating the radiative effect of Arctic cirrus measured in situ during the winter 2015–2016 A. Marsing et al. 10.5194/acp-23-587-2023
- The retrieval of ice cloud parameters from multi-spectral satellite observations of reflectance using a modified XBAER algorithm L. Mei et al. 10.1016/j.rse.2018.06.007
- VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model L. Bugliaro et al. 10.5194/nhess-22-1029-2022
- Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks L. Rao et al. 10.1109/JSTARS.2022.3196843
- A benchmark dataset for binary segmentation and quantification of dust emissions from unsealed roads A. De Silva et al. 10.1038/s41597-022-01918-x
- Cloud Top Thermodynamic Phase from Synergistic Lidar-Radar Cloud Products from Polar Orbiting Satellites: Implications for Observations from Geostationary Satellites J. Mayer et al. 10.3390/rs15071742
- 基于神经网络反演中国南海海域透明卷云参数 陆. Lu Wenqiang et al. 10.3788/AOS230605
Latest update: 29 Jun 2024
Short summary
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque pixels and retrieves the corresponding optical thickness, cloud top height and ice water path from the geostationary imager MSG/SEVIRI. CiPS utilises a set of four artificial neural networks trained with space-borne lidar data, thermal MSG/SEVIRI observations, model data and auxiliary data.
To demonstrate the capabilities of CiPS, the life cycle of a thin cirrus cloud is analysed.
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque...
