Differences in the Ice Particle Shattering Impact on the CIP Measurements in the Stratiform Cloud Region and the Embedded Convection Region

Stratiform clouds with embedded convective cells is an important precipitation system. Precise knowledge of the cloud’s microphysical structure can be useful for the development of a numerical weather prediction model and precipitation enhancement. Airborne measurement is one of the important ways for determining the microphysical structure of clouds. However, cloud particle shattering during measurement poses a serious problem to the measured microphysical characterization of clouds. In order to study the different influences of the shattered ice particles on the standard cloud imaging probe (CIP) measurement in the stratiform cloud region and the convective cloud region, a time-variant threshold method to identify the shattered fragments is presented. After application of this algorithm, the shattered fragments were recognized and their impacts on the particle size distribution (PSD), particle number concentration and ice water content measurement were analyzed. It was found that the shattering effect on the PSD decreases with the increasing size of less than 400 μm, fluctuates between 400 μm and 1000 μm and slightly increases with the increasing size of larger than 1000 μm on average in a stratiform region and a convective region. However, the average ratio of PSD uncorrected to that corrected for shattering events using the presented algorithm in convective clouds is larger than that in the stratiform regions in the whole size, and nearly twice that in the size of less than 1000 μm. The measured number concentration can be overestimated by up to a factor of 3.9 on average in a stratiform region, while in a convective region, it is 7.7, nearly twice that of a stratiform region. The ice water content in a stratiform region can be overestimated by 29.5% on average, but by 60.7% in a convective region. These findings can be helpful for the cloud physics community to use the airborne CIP measurement data for numerical weather and climate models.

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Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

10.5194/amt-2021-168 ◽

2021 ◽

Author(s):

Nicholas J. Kedzuf ◽

J. Christine Chiu ◽

Venkatachalam Chandrasekar ◽

Sounak Biswas ◽

Shashank S. Joshil ◽

...

Keyword(s):

Water Content ◽

Number Concentration ◽

Radiation Budget ◽

Radar Signal ◽

Polarimetric Radar ◽

Radar Observations ◽

Microphysical Properties ◽

Uncertainty Estimates ◽

Ice Water Content ◽

Ice Water

Abstract. Ice and mixed phase clouds play a key role in our climate system, because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates, because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 %, and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in-situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in total ice number concentration and 44 % in total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

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The Evaluation of CloudSat and CALIPSO Ice Microphysical Products Using Ground-Based Cloud Radar and Lidar Observations

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecha1397.1 ◽

2010 ◽

Vol 27 (5) ◽

pp. 793-810 ◽

Cited By ~ 45

Author(s):

A. Protat ◽

J. Delanoë ◽

E. J. O’Connor ◽

T. S. L’Ecuyer

Keyword(s):

Water Content ◽

Statistical Method ◽

Air Temperature ◽

Number Concentration ◽

Effective Radius ◽

Vertical Profiles ◽

Statistical Relationship ◽

Ice Clouds ◽

Ice Water Content ◽

Ice Water

Abstract In this paper, the statistical properties of tropical ice clouds (ice water content, visible extinction, effective radius, and total number concentration) derived from 3 yr of ground-based radar–lidar retrievals from the U.S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility in Darwin, Australia, are compared with the same properties derived using the official CloudSat microphysical retrieval methods and from a simpler statistical method using radar reflectivity and air temperature. It is shown that the two official CloudSat microphysical products (2B-CWC-RO and 2B-CWC-RVOD) are statistically virtually identical. The comparison with the ground-based radar–lidar retrievals shows that all satellite methods produce ice water contents and extinctions in a much narrower range than the ground-based method and overestimate the mean vertical profiles of microphysical parameters below 10-km height by over a factor of 2. Better agreements are obtained above 10-km height. Ways to improve these estimates are suggested in this study. Effective radii retrievals from the standard CloudSat algorithms are characterized by a large positive bias of 8–12 μm. A sensitivity test shows that in response to such a bias the cloud longwave forcing is increased from 44.6 to 46.9 W m−2 (implying an error of about 5%), whereas the negative cloud shortwave forcing is increased from −81.6 to −82.8 W m−2. Further analysis reveals that these modest effects (although not insignificant) can be much larger for optically thick clouds. The statistical method using CloudSat reflectivities and air temperature was found to produce inaccurate mean vertical profiles and probability distribution functions of effective radius. This study also shows that the retrieval of the total number concentration needs to be improved in the official CloudSat microphysical methods prior to a quantitative use for the characterization of tropical ice clouds. Finally, the statistical relationship used to produce ice water content from extinction and air temperature obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is evaluated for tropical ice clouds. It is suggested that the CALIPSO ice water content retrieval is robust for tropical ice clouds, but that the temperature dependence of the statistical relationship used should be slightly refined to better reproduce the radar–lidar retrievals.

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Development of a Method to Detect High Ice Water Content Environments Using Machine Learning

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0179.1 ◽

2020 ◽

Vol 37 (4) ◽

pp. 641-663

Author(s):

Julie A. Haggerty ◽

Allyson Rugg ◽

Rodney Potts ◽

Alain Protat ◽

J. Walter Strapp ◽

...

Keyword(s):

Machine Learning ◽

False Alarm ◽

Water Content ◽

Field Experiments ◽

Weather Prediction ◽

In Situ Measurements ◽

Probability Of Detection ◽

Ice Water Content ◽

Ice Water

AbstractThis paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC.

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The Characterization of Ice Cloud Properties from Doppler Radar Measurements

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2543.1 ◽

2007 ◽

Vol 46 (10) ◽

pp. 1682-1698 ◽

Cited By ~ 40

Author(s):

Julien Delanoë ◽

A. Protat ◽

D. Bouniol ◽

Andrew Heymsfield ◽

Aaron Bansemer ◽

...

Keyword(s):

Water Content ◽

Weather Prediction ◽

Doppler Velocity ◽

Fall Velocity ◽

Ice Cloud ◽

Cloud Radar ◽

Cloud Properties ◽

Ice Water Content ◽

Radar Measurements ◽

Ice Water

Abstract The paper describes an original method that is complementary to the radar–lidar algorithm method to characterize ice cloud properties. The method makes use of two measurements from a Doppler cloud radar (35 or 95 GHz), namely, the radar reflectivity and the Doppler velocity, to recover the effective radius of crystals, the terminal fall velocity of hydrometeors, the ice water content, and the visible extinction from which the optical depth can be estimated. This radar method relies on the concept of scaling the ice particle size distribution. An error analysis using an extensive in situ airborne microphysical database shows that the expected errors on ice water content and extinction are around 30%–40% and 60%, respectively, including both a calibration error and a bias on the terminal fall velocity of the particles, which all translate into errors in the retrieval of the density–diameter and area–diameter relationships. Comparisons with the radar–lidar method in areas sampled by the two instruments also demonstrate the accuracy of this new method for retrieval of the cloud properties, with a roughly unbiased estimate of all cloud properties with respect to the radar–lidar method. This method is being systematically applied to the cloud radar measurements collected over the three-instrumented sites of the European Cloudnet project to validate the representation of ice clouds in numerical weather prediction models and to build a cloud climatology.

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Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Atmospheric Measurement Techniques ◽

10.5194/amt-14-6885-2021 ◽

2021 ◽

Vol 14 (10) ◽

pp. 6885-6904

Author(s):

Nicholas J. Kedzuf ◽

J. Christine Chiu ◽

V. Chandrasekar ◽

Sounak Biswas ◽

Shashank S. Joshil ◽

...

Keyword(s):

Water Content ◽

Number Concentration ◽

Radiation Budget ◽

Radar Signal ◽

Polarimetric Radar ◽

Radar Observations ◽

Microphysical Properties ◽

Uncertainty Estimates ◽

Ice Water Content ◽

Ice Water

Abstract. Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

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A Parameterization of the Visible Extinction Coefficient of Ice Clouds in Terms of the Ice/Water Content

Journal of the Atmospheric Sciences ◽

10.1175/1520-0469(1997)054<2083:apotve>2.0.co;2 ◽

1997 ◽

Vol 54 (16) ◽

pp. 2083-2098 ◽

Cited By ~ 49

Author(s):

C. Martin R. Platt

Keyword(s):

Water Content ◽

Extinction Coefficient ◽

Ice Clouds ◽

Ice Water Content ◽

Ice Water

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Ice water content assessment in the single-, dual-, and triple-frequency radar scenarios

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112242 ◽

2021 ◽

Vol 254 ◽

pp. 112242

Author(s):

Eugenio Gorgucci ◽

Luca Baldini ◽

Elisa Adirosi ◽

Mario Montopoli

Keyword(s):

Water Content ◽

Content Assessment ◽

Triple Frequency ◽

Ice Water Content ◽

Ice Water

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Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-10609-2016 ◽

2016 ◽

Vol 16 (16) ◽

pp. 10609-10620 ◽

Cited By ~ 30

Author(s):

Johannes Bühl ◽

Patric Seifert ◽

Alexander Myagkov ◽

Albert Ansmann

Keyword(s):

Water Content ◽

Liquid Water ◽

Mass Flux ◽

Mixed Phase ◽

Cloud Base ◽

Doppler Velocity ◽

Special Focus ◽

Data Set ◽

Ice Water Content ◽

Ice Water

Abstract. An analysis of the Cloudnet data set collected at Leipzig, Germany, with special focus on mixed-phase layered clouds is presented. We derive liquid- and ice-water content together with vertical motions of ice particles falling through cloud base. The ice mass flux is calculated by combining measurements of ice-water content and particle Doppler velocity. The efficiency of heterogeneous ice formation and its impact on cloud lifetime is estimated for different cloud-top temperatures by relating the ice mass flux and the liquid-water content at cloud top. Cloud radar measurements of polarization and Doppler velocity indicate that ice crystals formed in mixed-phase cloud layers with a geometrical thickness of less than 350 m are mostly pristine when they fall out of the cloud.

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