Articles | Volume 10, issue 11
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-10-4317-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Nov 2017
Research article |
| 14 Nov 2017
| 14 Nov 2017
Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Jennifer Fricker
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
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Cited
17 citations as recorded by crossref.
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
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- A scalable system to measure contrail formation on a per-flight basis S. Geraedts et al. 10.1088/2515-7620/ad11ab
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- 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
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- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
- Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. 10.5194/amt-13-3447-2020
- The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation B. STEVENS et al. 10.2151/jmsj.2020-021
- 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
- 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
- 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
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks J. Strandgren et al. 10.5194/amt-10-3547-2017
16 citations as recorded by crossref.
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. 10.5194/amt-17-4015-2024
- Contrail altitude estimation using GOES-16 ABI data and deep learning V. Meijer et al. 10.5194/amt-17-6145-2024
- A scalable system to measure contrail formation on a per-flight basis S. Geraedts et al. 10.1088/2515-7620/ad11ab
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- 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
- 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
- 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
- Aviation Contrail Cirrus and Radiative Forcing Over Europe During 6 Months of COVID‐19 U. Schumann et al. 10.1029/2021GL092771
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
- Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. 10.5194/amt-13-3447-2020
- The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation B. STEVENS et al. 10.2151/jmsj.2020-021
- 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
- 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
- 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
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
1 citations as recorded by crossref.
Latest update: 14 Dec 2024
Short summary
We characterise the the performance of a set of artificial neural networks used for the remote sensing of cirrus clouds from the geostationary Meteosat Second Generation satellites. The retrievals show little interference with the underlying land surface type as well as with possible liquid water clouds or aerosol layers below the cirrus cloud. We also characterise the retrievals as a funtion of optical thickness and top height and gain better understanding of the retrival uncertainties of CiPS
We characterise the the performance of a set of artificial neural networks used for the remote...
