scholarly journals Low-Level, Liquid-Only and Mixed-Phase Cloud Identification by Polarimetric Lidar

2016 ◽  
Author(s):  
Robert A. Stillwell ◽  
Ryan R. Neely III ◽  
Jeffrey P. Thayer ◽  
Matthew D. Shupe ◽  
Michael O'Neill

Abstract. The measurement of low-level, liquid-only and mixed-phase clouds in the polar regions is a necessary building block to understand the regional surface energy and mass budgets over ice sheets. The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes alter the transmitted polarization. However, due to clouds varying in range, optical depth, and scatterer size and shape, most atmospheric lidar systems must observe high dynamic ranges in scattered signal strengths. Depending on the polarization component measured, these signals can far exceed the linear range of a detection system. Thus, due to the high optical thickness and predominately low-lying nature of liquid-only and mixed-phase clouds in the polar regions, relative to ice only clouds, a systematic overestimate of the traditional depolarization ratio, which uses the co-polarized and cross-polarized signals, can occur due to the large dynamic range signals. For both liquid-only and mixed-phase clouds, this results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar (CAPABL) at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. For example, an examination of observations of liquid-only and mixed-phase clouds at Summit shows as much as a 2 kilometer offset in median cloud height of those identified as liquid due to a systematic bias in photon counting signals. At a constant altitude, more than 94 % of the liquid pixels identified with analog signals can be misidentified as ice with photon counting signals. This results in a possible error of fractional occurrence of cloud liquid of approximately 30 %. It is shown that by observing polarization planes that are non-orthogonal, the dynamic range of observed signals is reduced, the coverage of the expected signal dynamic range is increased, and more linear response can be captured. Using non-orthogonal polarization observations is shown to enhance measurement sensitivity increasing the effective sampling range for CAPABL by as much as 18 % or approximately 1.5 km.

2018 ◽  
Vol 176 ◽  
pp. 08006
Author(s):  
Robert A. Stillwell ◽  
Matthew D. Shupe ◽  
Jeffrey P. Thayer ◽  
Ryan R. Neely ◽  
David D. Turner

Liquid-only and mixed-phase clouds in the Arctic strongly affect the regional surface energy and ice mass budgets, yet much remains unknown about the nature of these clouds due to the lack of intensive measurements. Lidar measurements of these clouds are challenged by very large signal dynamic range, which makes even seemingly simple tasks, such as thermodynamic phase classification, difficult. This work focuses on a set of measurements made by the Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland and its retrieval algorithms, which use both analog and photon counting as well as orthogonal and non-orthogonal polarization retrievals to extend dynamic range and improve overall measurement quality and quantity. Presented here is an algorithm for cloud parameter retrievals that leverages enhanced dynamic range retrievals to classify mixed-phase clouds. This best guess retrieval is compared to co-located instruments for validation.


2018 ◽  
Vol 11 (2) ◽  
pp. 835-859 ◽  
Author(s):  
Robert A. Stillwell ◽  
Ryan R. Neely III ◽  
Jeffrey P. Thayer ◽  
Matthew D. Shupe ◽  
David D. Turner

Abstract. The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system nonlinear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud-phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud-phase fractional occurrence estimates on the order of 30 % causing errors in attribution of cloud radiative effects on the order of 10–30 %. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to evaluate this method. Enhanced measurements are achieved in this work with non-orthogonal polarization retrievals as well as analog and photon counting detection facilitating a more complete attribution of radiative effects linked to cloud properties.


2017 ◽  
Author(s):  
Robert A. Stillwell ◽  
Ryan R. Neely III ◽  
Jeffrey P. Thayer ◽  
Matthew D. Shupe ◽  
David D. Turner

Abstract. The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system non-linear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud phase fractional occurrence estimates on the order of 30% causing errors in attribution of cloud radiative effects on the order of 10%-30%. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to validate this method.


2021 ◽  
Author(s):  
Christoph Braun ◽  
Aiko Voigt ◽  
Johannes Hörner ◽  
Joaquim G. Pinto

<p>Stable waterbelt climate states with close to global ice cover challenge the classical Snowball Earth hypothesis because they provide a robust explanation for the survival of advanced marine species during the Neoproterozoic glaciations (1000 – 541 Million years ago). Whether Earth’s climate stabilizes in a waterbelt state or rushes towards a Snowball state is determined by the magnitude of the ice-albedo feedback in the subtropics, where dark, bare sea ice instead of snow-covered sea ice prevails. For a given bare sea-ice albedo, the subtropical ice-albedo feedback and thus the stable range of the waterbelt climate regime is sensitive to the albedo over ice-free ocean, which is largely determined by shortwave cloud-radiative effects (CRE). In the present-day climate, CRE are known to dominate the spread of climate sensitivity across global climate models. We here study the impact of uncertainty associated with CRE on the existence of geologically relevant waterbelt climate regimes using two global climate models and an idealized energy balance model. We find that the stable range of the waterbelt climate regime is very sensitive to the abundance of subtropical low-level mixed-phase clouds. If subtropical cloud cover is low, climate sensitivity becomes so high as to inhibit stable waterbelt states.</p><p>The treatment of mixed-phase clouds is highly uncertain in global climate models. Therefore we aim to constrain the uncertainty associated with their CRE by means of a hierarchy of global and regional simulations that span horizontal grid resolutions from 160 km to 300m, and in particular include large eddy simulations of subtropical mixed-phase clouds located over a low-latitude ice edge. In the cold waterbelt climate subtropical CRE arise from convective events caused by strong meridional temperature gradients and stratocumulus decks located in areas of large-scale descending motion. We identify the latter to dominate subtropical CRE and therefore focus our large eddy simulations on subtropical stratocumulus clouds. By conducting simulations with two extreme scenarios for the abundance of atmospheric mineral dust, which serves as ice-nucleating particles and therefore can control mixed-phase cloud physics, we aim to estimate the possible spread of CRE associated with subtropical mixed-phase clouds. From this estimate we may assess whether Neoproterozoic low-level cloud abundance may have been high enough to sustain a stable waterbelt climate regime.</p>


2018 ◽  
Vol 18 (18) ◽  
pp. 13345-13361 ◽  
Author(s):  
Matthew S. Norgren ◽  
Gijs de Boer ◽  
Matthew D. Shupe

Abstract. The interactions that occur between aerosols and a mixed-phase cloud system, and the subsequent alteration of the microphysical state of such clouds, are a problem that has yet to be well constrained. Advancing our understanding of aerosol–ice processes is necessary to determine the impact of natural and anthropogenic emissions on Earth's climate and to improve our capability to predict future climate states. This paper deals specifically with how aerosols influence ice mass production in low-level Arctic mixed-phase clouds. In this study, a 9-year record of aerosol, cloud and atmospheric state properties is used to quantify aerosol influence on ice production in mixed-phase clouds. It is found that mixed-phase clouds present in a clean aerosol state have higher ice water content (IWC) by a factor of 1.22 to 1.63 at cloud base than do similar clouds in cases with higher aerosol loading. We additionally analyze radar-derived mean Doppler velocities to better understand the drivers behind this relationship, and we conclude that aerosol induced reduction of the ice crystal nucleation rate, together with decreased riming rates in polluted clouds, are likely influences on the observed reductions in IWC.


2021 ◽  
Vol 13 (24) ◽  
pp. 5001
Author(s):  
Eleni Marinou ◽  
Kalliopi Artemis Voudouri ◽  
Ioanna Tsikoudi ◽  
Eleni Drakaki ◽  
Alexandra Tsekeri ◽  
...  

In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in 38% of the cases, high or convective clouds in 58% of the cases, and low-level clouds in 2% of the cases. From the observations of cloudy profiles, pure ice phase occurred in 94% of the cases, mixed-phase clouds were observed in 27% of the cases, and liquid clouds were observed in 8.7% of the cases, while Drizzle or rain occurred in 12% of the cases. The significant presence of Mixed-Phase Clouds was observed in all the clouds formed at the top of a dust layer, with three times higher abundance than the mean conditions (26% abundance at −15 °C). The low-level clouds were formed in the presence of sea salt and continental particles with ice abundance below 30%. The derived statistics on clouds’ high-resolution vertical distributions and thermodynamic phase can be combined with Cloudnet cloud products and lidar-retrieved aerosol properties to study aerosol-cloud interactions in this understudied region and evaluate microphysics parameterizations in numerical weather prediction and global climate models.


2020 ◽  
Author(s):  
Jasper R. Lewis ◽  
James R. Campbell ◽  
Simone Lolli ◽  
Sebastian A. Stewart ◽  
Ivy Tan ◽  
...  

Abstract. A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5) assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (−37 °C–0 °C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.


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