scholarly journals Continuos Monitoring of Liquid Water Clouds and Aerosols with Dual-FOV Lidar Polarization Technique

2020 ◽  
Vol 237 ◽  
pp. 07005
Author(s):  
Cristofer Jimenez ◽  
Albert Ansmann ◽  
Ronny Engelmann ◽  
Patric Seifert ◽  
Robert Wiesen ◽  
...  
Keyword(s):  
Liquid Water ◽  
Cloud Properties ◽  
A Value ◽  
Scattering Technique ◽  
Polarization Technique ◽  
Water Cloud ◽  
Aerosol Cloud Interactions ◽  
Short Time ◽  
Lidar Observations ◽  
Aerosol Extinction Coefficient

In this work we evaluate the possibilities to assess aerosol-cloud interactions in short time scales (2 min.) on an observational base. Retrievals of the cloud effective radius and number concentration in a liquid-water cloud by using the multiple scattering technique Dual-FOV Polarization lidar, together with the aerosol extinction coefficient in the boundary layer has shown a correspondence between the aerosol and cloud properties in the 6 hours case presented, obtaining a value of ACIN = 0.76 ± 0.29, which corroborates the potential of lidar observations to study the relation between aerosols and clouds on small scales.

scholarly journals Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks

2017 ◽  
Author(s):  
Hendrik Andersen ◽  
Jan Cermak ◽  
Julia Fuchs ◽  
Reto Knutti ◽  
Ulrike Lohmann
Keyword(s):  
Neural Networks ◽  
Liquid Water ◽  
Meteorological Parameters ◽  
Reanalysis Data ◽  
Main Determinant ◽  
Boundary Layer Height ◽  
Water Cloud ◽  
Aerosol Cloud Interactions ◽  
Water Amount

Abstract. The role of aerosols, clouds and their interactions with radiation remain among the largest unknowns in the climate system. Even though the processes involved are complex, aerosol-cloud interactions are often analyzed by means of bivariate relationships. In this study, 15 years (2001–2015) of monthly satellite-retrieved nearly-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine liquid-water cloud occurrence and properties by means of regionally-specific artificial neural networks. The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability. At this monthly scale, lower tropospheric stability is shown to be the main determinant of cloud fraction and droplet size, especially in stratocumulus regions, while boundary layer height controls the liquid-water amount and thus the optical thickness of clouds. While aerosols show the expected impact on clouds, at this scale they are less relevant than some meteorological factors. Global patterns of the derived sensitivities point to regional characteristics of aerosol and cloud processes.

scholarly journals A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part 2: Near‐global retrievals and evaluation against A‐Train products

2016 ◽  
Vol 142 (701) ◽  
pp. 3063-3081 ◽  
Author(s):  
Odran Sourdeval ◽  
Laurent C.‐Labonnote ◽  
Anthony J. Baran ◽  
Johannes Mülmenstädt ◽  
Gérard Brogniez
Keyword(s):  
Liquid Water ◽  
Cloud Properties ◽  
Water Cloud

Liquid water cloud properties during the Polarimeter Definition Experiment (PODEX)

2015 ◽  
Vol 169 ◽  
pp. 20-36 ◽  
Author(s):  
Mikhail D. Alexandrov ◽  
Brian Cairns ◽  
Andrzej P. Wasilewski ◽  
Andrew S. Ackerman ◽  
Matthew J. McGill ◽  
...  
Keyword(s):  
Liquid Water ◽  
Cloud Properties ◽  
Water Cloud

scholarly journals A methodology for simultaneous retrieval of ice and liquid water cloud properties. Part I: Information content and case study

2014 ◽  
Vol 141 (688) ◽  
pp. 870-882 ◽  
Author(s):  
Odran Sourdeval ◽  
Laurent C.‐Labonnote ◽  
Anthony J. Baran ◽  
Gérard Brogniez
Keyword(s):  
Information Content ◽  
Liquid Water ◽  
Cloud Properties ◽  
Water Cloud

scholarly journals Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

2019 ◽  
Vol 12 (3) ◽  
pp. 1697-1716 ◽  
Author(s):  
Antonio Di Noia ◽  
Otto P. Hasekamp ◽  
Bastiaan van Diedenhoven ◽  
Zhibo Zhang
Keyword(s):  
Neural Network ◽  
Liquid Water ◽  
Effective Radius ◽  
Neural Network Ensemble ◽  
Atmospheric Sciences ◽  
Cloud Properties ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Water Cloud ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

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