scholarly journals Thermodynamic phase retrieval of convective clouds: impact of sensor viewing geometry and vertical distribution of cloud properties

2013 ◽  
Vol 6 (3) ◽  
pp. 539-547 ◽  
Author(s):  
E. Jäkel ◽  
J. Walter ◽  
M. Wendisch
Keyword(s):  
Phase Retrieval ◽  
Near Infrared ◽  
Three Dimensional ◽  
Ice Clouds ◽  
Convective Clouds ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Reflectivity Spectra ◽  
Vertical Distributions ◽  
Thermodynamic Phase

Abstract. The sensitivity of passive remote sensing measurements to retrieve microphysical parameters of convective clouds, in particular their thermodynamic phase, is investigated by three-dimensional (3-D) radiative transfer simulations. The effects of different viewing geometries and vertical distributions of the cloud microphysical properties are investigated. Measurement examples of spectral solar radiance reflected by cloud sides (passive) in the near-infrared (NIR) spectral range are performed together with collocated lidar observations (active). The retrieval method to distinguish the cloud thermodynamic phase (liquid water or ice) exploits different slopes of cloud side reflectivity spectra of water and ice clouds in the NIR. The concurrent depolarization backscattering lidar provides geometry information about the cloud distance and height as well as the depolarization.

scholarly journals Thermodynamic phase retrieval of convective clouds: impact of sensor viewing geometry and vertical distribution of cloud properties

2012 ◽  
Vol 5 (5) ◽  
pp. 7729-7752
Author(s):  
E. Jäkel ◽  
J. Walter ◽  
M. Wendisch
Keyword(s):  
Phase Retrieval ◽  
Near Infrared ◽  
Three Dimensional ◽  
Ice Clouds ◽  
Convective Clouds ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Index Measurement ◽  
Microphysical Parameters ◽  
Thermodynamic Phase

Abstract. The potential to use combined passive and active remote sensing measurements to retrieve microphysical parameters of convective clouds in particular thermodynamic phase, is investigated by three-dimensional (3-D) radiative transfer simulations. The 3-D simulations are used to quantify the effect of different viewing geometries and distributions of the cloud microphysical properties on the derived ice index. Measurement examples of spectral solar and radiance reflected by cloud sides (passive) in the near-infrared (NIR) spectral range are synchronized with collocated Lidar observations (active). A retrieval method to distinguish the cloud thermodynamic phase (liquid water or ice) using the reported reflectivity measurements is applied which uses the different spectral slopes of water and ice clouds in the NIR. The concurrent depolarization backscattering Lidar provides geometry information about the cloud distance and height as well as the depolarization.

scholarly journals Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

2010 ◽  
Vol 10 (9) ◽  
pp. 21931-21988 ◽  
Author(s):  
L. Bugliaro ◽  
T. Zinner ◽  
C. Keil ◽  
B. Mayer ◽  
R. Hollmann ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Ice Clouds ◽  
Cloud Property ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Weather Model

Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 79%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and underestimated by the CMSAF code (−16.4 K with a standard deviation of 37.3 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation for the DLR and to overestimation for the CMSAF algorithm. Cloud effective radii are most difficult to evaluate and not always the algorithms are able to produce realistic values. The CMSAF cloud water path, which is a combination of the last two quantities, is particularly accurate for ice clouds, while water clouds are overestimated, mainly because of the effective radius overestimation for water clouds.

scholarly journals The variability of tropical ice cloud properties as a function of the large-scale context from ground-based radar-lidar observations over Darwin, Australia

2011 ◽  
Vol 11 (16) ◽  
pp. 8363-8384 ◽  
Author(s):  
A. Protat ◽  
J. Delanoë ◽  
P. T. May ◽  
J. Haynes ◽  
C. Jakob ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Radius ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Ice Water Content ◽  
Mean Differences ◽  
Ice Water ◽  
Lidar Observations

Abstract. The high complexity of cloud parameterizations now held in models puts more pressure on observational studies to provide useful means to evaluate them. One approach to the problem put forth in the modelling community is to evaluate under what atmospheric conditions the parameterizations fail to simulate the cloud properties and under what conditions they do a good job. It is the ambition of this paper to characterize the variability of the statistical properties of tropical ice clouds in different tropical "regimes" recently identified in the literature to aid the development of better process-oriented parameterizations in models. For this purpose, the statistical properties of non-precipitating tropical ice clouds over Darwin, Australia are characterized using ground-based radar-lidar observations from the Atmospheric Radiation Measurement (ARM) Program. The ice cloud properties analysed are the frequency of ice cloud occurrence, the morphological properties (cloud top height and thickness), and the microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration). The variability of these tropical ice cloud properties is then studied as a function of the large-scale cloud regimes derived from the International Satellite Cloud Climatology Project (ISCCP), the amplitude and phase of the Madden-Julian Oscillation (MJO), and the large-scale atmospheric regime as derived from a long-term record of radiosonde observations over Darwin. The vertical variability of ice cloud occurrence and microphysical properties is largest in all regimes (1.5 order of magnitude for ice water content and extinction, a factor 3 in effective radius, and three orders of magnitude in concentration, typically). 98 % of ice clouds in our dataset are characterized by either a small cloud fraction (smaller than 0.3) or a very large cloud fraction (larger than 0.9). In the ice part of the troposphere three distinct layers characterized by different statistically-dominant microphysical processes are identified. The variability of the ice cloud properties as a function of the large-scale atmospheric regime, cloud regime, and MJO phase is large, producing mean differences of up to a factor 8 in the frequency of ice cloud occurrence between large-scale atmospheric regimes and mean differences of a factor 2 typically in all microphysical properties. Finally, the diurnal cycle of the frequency of occurrence of ice clouds is also very different between regimes and MJO phases, with diurnal amplitudes of the vertically-integrated frequency of ice cloud occurrence ranging from as low as 0.2 (weak diurnal amplitude) to values in excess of 2.0 (very large diurnal amplitude). Modellers should now use these results to check if their model cloud parameterizations are capable of translating a given atmospheric forcing into the correct statistical ice cloud properties.

scholarly journals Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

2017 ◽  
Vol 17 (7) ◽  
pp. 4731-4749 ◽  
Author(s):  
Chenglai Wu ◽  
Xiaohong Liu ◽  
Minghui Diao ◽  
Kai Zhang ◽  
Andrew Gettelman ◽  
...  
Keyword(s):  
Nucleation And Growth ◽  
Ice Nucleation ◽  
Ice Crystal ◽  
Ice Clouds ◽  
Model Version ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Ice Water

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

scholarly journals Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

2007 ◽  
Vol 46 (3) ◽  
pp. 249-272 ◽  
Author(s):  
M. Chiriaco ◽  
H. Chepfer ◽  
P. Minnis ◽  
M. Haeffelin ◽  
S. Platnick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiant Energy ◽  
Energy System ◽  
Effective Radius ◽  
Cloud Detection ◽  
Regional Study ◽  
Crystal Face ◽  
Ice Clouds ◽  
Microphysical Properties ◽  
Cloud Properties

Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

Cirrus Microphysical Properties and Air Motion Statistics Using Cloud Radar Doppler Moments. Part II: Climatology

2008 ◽  
Vol 47 (12) ◽  
pp. 3221-3235 ◽  
Author(s):  
Min Deng ◽  
Gerald G. Mace
Keyword(s):  
Great Plains ◽  
Seasonal Variations ◽  
Radiative Heating ◽  
Ice Clouds ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Early Afternoon ◽  
Seasonal And Diurnal Variations ◽  
Air Motion

Abstract The algorithm described in Part I has been applied to the millimeter cloud radar observations from January 1999 to December 2005 at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (including Manus and Nauru) sites. Approximately 10 000 cirrus hours from each of these sites were analyzed. Retrieved cloud properties including condensed mass, particle size, optical depth, and in-cloud vertical air motions were analyzed in terms of their geographical, seasonal, and diurnal variations. The analysis shows that tropical ice clouds observed by millimeter radar are very different from ice clouds at SGP, with the tropical clouds having slightly larger particle sizes and greater ice masses and being more likely to be associated with ascending air motions, in addition to being colder and higher in altitude. A positive residual of derived in-cloud air motion found in the tropical data likely provides evidence for lofting of air into the tropopause transition layer as a result of radiative heating. The midlatitude cirrus demonstrate strong seasonal variations with more frequent, thicker clouds occurring during the summer than during the winter. Very subtle seasonal variations are found for tropical ice clouds, and evidence is presented that cirrus properties vary interannually and are correlated with El Niño oscillations. In addition, it is found that tropical cirrus demonstrate a stronger diurnal cycle than cirrus of the midlatitudes, with the in-cloud updrafts peaking in the early afternoon.

scholarly journals Subvisible cirrus clouds – a dynamical system approach

2016 ◽  
Author(s):  
E. J. Spreitzer ◽  
M. P. Marschalik ◽  
P. Spichtinger
Keyword(s):  
Cloud Model ◽  
Three Dimensional ◽  
Complex Model ◽  
Cirrus Clouds ◽  
Diffusional Growth ◽  
Ice Clouds ◽  
Formation Pathway ◽  
Microphysical Properties ◽  
Tropopause Region ◽  
Homogeneous Freezing

Abstract. Ice clouds, so-called cirrus clouds, occur very frequently in the tropopause region. A special class are subvisible cirrus clouds with an optical depth lower than 0.03. Obviously, the ice crystal number concentration of these clouds is very low. The dominant pathway for these clouds is not known well. It is often assumed that heterogeneous nucleation at solid aerosol particles is the preferred mechanism although homogeneous freezing of aqueous solution droplets might be possible. For investigating subvisible cirrus clouds as formed by homogeneous freezing we develop a simple analytical cloud model from first principles; the model consists of a three dimensional set of ordinary differential equations, including the relevant processes as ice nucleation, diffusional growth and sedimentation, respectively. The model is integrated numerically and is investigated using theory of dynamical systems. We found two different states for the long-term behaviour of subvisible cirrus clouds, i.e. an attractor case and a limit cycle scenario. The transition between the states constitutes a Hopf bifurcation and is determined by environmental conditions as vertical updraughts and temperature. In both cases, the microphysical properties of the simulated clouds agree reasonably well with simulations using a complex model, with former analytical studies and with observations of subvisible cirrus. In addition, the model can also be used for explaining complex model simulations close to the bifurcation qualitatively. Finally, the results indicate that homogeneous nucleation might be a possible formation pathway for subvisible cirrus clouds.

scholarly journals Thermodynamic Phase and Ice Cloud Properties in Northern Hemisphere Winter Extratropical Cyclones Observed by Aqua AIRS

2015 ◽  
Vol 54 (11) ◽  
pp. 2283-2303 ◽  
Author(s):  
Catherine M. Naud ◽  
Brian H. Kahn
Keyword(s):  
Northern Hemisphere ◽  
Cloud Cover ◽  
Frontal Region ◽  
Extratropical Cyclones ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Hemisphere Winter ◽  
Thermodynamic Phase ◽  
Northern Hemisphere Winter

AbstractIce cloud properties in Northern Hemisphere winter extratropical cyclones are examined using the Atmospheric Infrared Sounder (AIRS), version 6, cloud products. The cloud thermodynamic phase product indicates that warm frontal clouds are dominated by ice, liquid-phase clouds occur outside of the warm frontal region, and supercooled or mixed-phase clouds are found in the southwestern quadrant of the cyclones. Stratiform ice clouds populate the warm frontal region and portions of the cold sector while convective ice clouds populate southeastern portions of the warm front and the southeastern quadrant. Total cloud cover is smaller in land cyclones than in ocean cyclones, especially in the southwestern quadrant and the warm frontal region. Ice cloud cover is less over land in the warm frontal region, because land cyclones are generally weaker and drier than ocean cyclones. The impact of cyclone average precipitable water (PW) and the magnitude of 850-hPa vertical ascent ω850 on the thermodynamic phase, occurrence of stratiform or convective ice cloud, ice particle effective diameter, optical thickness, and cloud-top temperature are discussed. When comparing land and ocean cyclones with similar PW and ω850, ice cloud coverage is found to be greater over land. Convective ice cloud occurs more often and is deeper over land. Supercooled cloud appears to persist to colder temperatures over ocean than over land, especially in the warm frontal region. These results suggest that, over land, ice cloud formation in warm fronts is possibly more efficient because of a greater aerosol amount from local or regional sources.

scholarly journals High Cloud Properties from Three Years of MODIS Terra and Aqua Collection-4 Data over the Tropics

2007 ◽  
Vol 46 (11) ◽  
pp. 1840-1856 ◽  
Author(s):  
Gang Hong ◽  
Ping Yang ◽  
Bo-Cai Gao ◽  
Bryan A. Baum ◽  
Yong X. Hu ◽  
...  
Keyword(s):  
Cloud Fraction ◽  
South Pacific Convergence Zone ◽  
Convergence Zone ◽  
Ice Clouds ◽  
High Cloud ◽  
Convective Clouds ◽  
Cloud Properties ◽  
Deep Convective Clouds ◽  
The Tropics ◽  
High Clouds

Abstract This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S–30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning–afternoon, and summer–winter variations of high cloud properties are also observed.

scholarly journals Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Shan Zeng ◽  
Ali Omar ◽  
Mark Vaughan ◽  
Macarena Ortiz ◽  
Charles Trepte ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semantic Segmentation ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Ice Clouds ◽  
Microphysical Properties ◽  
Texture Information ◽  
Vertical Distributions ◽  
Aerosol Classification ◽  
Aerosol Types

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.

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