Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties

Abstract. In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train mission. Based on a large dataset of in situ ice cloud measurements, the parameterizations used in the microphysical model of the algorithm – i.e. the normalized particle size distribution, the mass–size relationship, and the parameterization of the a priori value of the normalized number concentration as a function of temperature – were assessed and refined to better fit the measurements, keeping the same formalism as proposed in DARDAR basis papers. Additionally, in regions where lidar measurements are available, the lidar ratio retrieved for ice clouds is shown to be well constrained by the lidar–radar synergy. Using this information, the parameterization of the lidar ratio was also refined, and the new retrieval equals on average 35±10 sr in the temperature range between −60 and −20 ∘C. The impact of those changes on the retrieved ice cloud properties is presented in terms of ice water content (IWC) and effective radius. Overall, IWC values from the new DARDAR-CLOUD product are on average 16 % smaller than the previous version, leading to a 24 % reduction in the ice water path. In parallel, the retrieved effective radii increase by 5 % to 40 %, depending on temperature and the availability of the instruments, with an average difference of +15 %. Modifications of the microphysical model strongly affect the ice water content retrievals with differences that were found to range from −50 % to +40 %, depending on temperature and the availability of the instruments. The largest differences are found for the warmest temperatures (between −20 and 0 ∘C) in regions where the cloud microphysical processes are more complex and where the retrieval is almost exclusively based on radar-only measurements. The new lidar ratio values lead to a reduction of IWC at cold temperatures, the difference between the two versions increasing from around 0 % at −30 ∘C to 70 % below −80 ∘C, whereas effective radii are not impacted.

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Evolution of DARDAR-CLOUD ice cloud cloud retrieval: new parameters and impacts on the retrieved microphysical properties

10.5194/amt-2018-397 ◽

2018 ◽

Cited By ~ 1

Author(s):

Quitterie Cazenave ◽

Marie Ceccaldi ◽

Julien Delanoë ◽

Jacques Pelon ◽

Silke Groß ◽

...

Keyword(s):

Water Content ◽

Ice Clouds ◽

Ice Cloud ◽

Microphysical Properties ◽

Lidar Measurements ◽

Ice Water Content ◽

Microphysical Processes ◽

Pm 10 ◽

Cloud Ice ◽

Ice Water

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Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0230.1 ◽

2017 ◽

Vol 56 (1) ◽

pp. 189-215 ◽

Cited By ~ 16

Author(s):

Andrew Heymsfield ◽

Martina Krämer ◽

Norman B. Wood ◽

Andrew Gettelman ◽

Paul R. Field ◽

...

Keyword(s):

Water Content ◽

Global Climate ◽

Global Climate Model ◽

Ice Cloud ◽

In Situ Data ◽

Microphysical Properties ◽

Ice Water Content ◽

In Situ Observations ◽

Ice Water

AbstractCloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading to model snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.

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A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS

Journal of Applied Meteorology and Climatology ◽

10.1175/2011jamc2646.1 ◽

2011 ◽

Vol 50 (9) ◽

pp. 1952-1969 ◽

Cited By ~ 47

Author(s):

Thorwald H. M. Stein ◽

Julien Delanoë ◽

Robin J. Hogan

Keyword(s):

Water Content ◽

Effective Radius ◽

Retrieval Algorithm ◽

Empirical Formulas ◽

Mean Values ◽

Ice Cloud ◽

Microphysical Properties ◽

Ice Water Content ◽

The Mean ◽

Ice Water

AbstractThe A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.

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The impact of ice cloud particle microphysics on the uncertainty of ice water content retrievals

Journal of Quantitative Spectroscopy and Radiative Transfer ◽

10.1016/j.jqsrt.2010.04.003 ◽

2011 ◽

Vol 112 (2) ◽

pp. 189-196 ◽

Cited By ~ 12

Author(s):

Wenbo Sun ◽

Yongxiang Hu ◽

Bing Lin ◽

Zhaoyan Liu ◽

Gorden Videen

Keyword(s):

Water Content ◽

Cloud Particle ◽

Ice Cloud ◽

Ice Water Content ◽

Ice Water ◽

The Impact

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-8363-2011 ◽

2011 ◽

Vol 11 (16) ◽

pp. 8363-8384 ◽

Cited By ~ 22

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.

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An Inverse Problem Approach for the Retrieval of Ice Particle Mass from In Situ Measurements

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0013.1 ◽

2017 ◽

Vol 34 (11) ◽

pp. 2457-2473 ◽

Cited By ~ 2

Author(s):

Pierre Coutris ◽

Delphine Leroy ◽

Emmanuel Fontaine ◽

Alfons Schwarzenboeck

Keyword(s):

Inverse Problem ◽

Particle Size ◽

Power Law ◽

In Situ Measurements ◽

Ice Cloud ◽

Size Dependent ◽

Microphysical Properties ◽

Ice Water Content ◽

Ice Water

AbstractMass–dimensional relationships have been published for decades to characterize the microphysical properties of ice cloud particles. Classical retrieval methods employ a simplifying assumption that restricts the form of the mass–dimensional relationship to a power law, an assumption that was proved inaccurate in recent studies. In this paper, a nonstandard approach that leverages optimal use of in situ measurements to remove the power-law constraint is presented. A model formulated as a linear system of equations relating ice particle mass to particle size distribution (PSD) and ice water content (IWC) is established, and the mass retrieval process consists of solving the inverse problem with numerical optimization algorithms. First, the method is applied to a synthetic crystal dataset in order to validate the selected algorithms and to tune the regularization strategy. Subsequently, the method is applied to in situ measurements collected during the High Altitude Ice Crystal–High Ice Water Content field campaigns. Preliminary results confirm the method is efficient at retrieving size-dependent masses from real data despite a significant amount of noise: the IWC values calculated from the retrieved masses are in good agreement with reference IWC measurements (errors on the order of 10%–15%). The possibility to retrieve ice particle size–dependent masses combined with the flexibility left for sorting datasets as a function of parameters such as cloud temperature, cloud type, or convective index makes this approach well suited for studying ice cloud microphysical properties.

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Ice water content of arctic, midlatitude, and tropical cirrus – Part 2: Extension of the database and new statistical analysis

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-29443-2012 ◽

2012 ◽

Vol 12 (11) ◽

pp. 29443-29474 ◽

Cited By ~ 1

Author(s):

A. E. Luebke ◽

L. M. Avallone ◽

C. Schiller ◽

C. Rolf ◽

M. Krämer

Keyword(s):

Water Content ◽

Radiative Forcing ◽

Climate Models ◽

Distribution Functions ◽

Formation Mechanisms ◽

Mean Values ◽

Microphysical Properties ◽

Ice Water Content ◽

Ground Based Lidar ◽

Ice Water

Abstract. Ice clouds are known to be major contributors to radiative forcing in the Earth's atmosphere, yet describing their microphysical properties in climate models remains challenging. Among these properties, the ice water content (IWC) of cirrus clouds is of particular interest both because it is measurable and because it can be directly related to a number of other radiatively important variables such as extinction and effective radius. This study expands upon the work of Schiller et al. (2008), extending a climatology of IWC by combining datasets from several European and US airborne campaigns and ground-based lidar measurements over Jülich, Germany. The relationship between IWC and temperature is further investigated using the new merged dataset and probability distribution functions (PDFs). A PDF-based formulation allows for representation of not only the mean values of IWC, but also the variability of IWC within a temperature band. The IWC-PDFs are found to be bimodal over the whole cirrus temperature range, which might be attributed to different cirrus formation mechanisms such as heterogeneous and homogeneous freezing. The PDFs of IWC are further compared to distributions of cirrus ice crystal number and mass mean radius, which show that the general relationship between IWC and temperature appears to be influenced much more by particle number than by particle size.

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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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Ice Microphysical Properties near the Tops of Deep Convective Cores Implied by the GPM Dual-Frequency Radar Observations

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-18-0243.1 ◽

2019 ◽

Vol 76 (9) ◽

pp. 2899-2917 ◽

Cited By ~ 2

Author(s):

Xiang Ni ◽

Chuntao Liu ◽

Edward Zipser

Keyword(s):

Particle Size ◽

Water Content ◽

Deep Convection ◽

Radar Reflectivity ◽

Lookup Table ◽

Dual Frequency ◽

Ka Band ◽

Microphysical Properties ◽

Ice Water Content ◽

Ice Water

Abstract Using three years of observations from the Dual-Frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory, properties of the cores of deep convection are examined. First, deep convective systems are selected, defined as GPM precipitation features with maximum 20-dBZ echo-top heights above 10 km. The cores of deep convection are described by the profiles of Ku- and Ka-band radar reflectivity at the location of the highest echo top in each deep convective system. Then the dual-frequency ratio (DFR) profile is derived by subtracting Ka-band from Ku-band radar reflectivity. It is found that values of DFR are larger over land than over ocean in general near the top of the convection, which is consistent with larger ice particles in stronger updrafts in continental convection. The magnitude of DFR at 12 km is positively correlated with the convection intensity indicated by 20- and 30-dBZ echo tops. The microphysical properties including volume-weighted mean diameter, ice water content, and total ice particle number concentration are derived using a simple lookup table approach. Under the same particle size distribution assumption, the cores of deep convection over land have larger ice particle size, higher ice water content, and lower particle concentration than those over ocean at levels above 10 km, but with some distinct regional variations.

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Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics

10.5194/amt-2020-448 ◽

2021 ◽

Author(s):

Florian Ewald ◽

Silke Groß ◽

Martin Wirth ◽

Julien Delanoë ◽

Stuart Fox ◽

...

Keyword(s):

Water Content ◽

Cloud Microphysics ◽

Accurate Estimation ◽

Crystal Shape ◽

Radiative Effect ◽

Ice Crystal ◽

Ice Clouds ◽

Ice Cloud ◽

Ice Water Content ◽

Ice Water

Abstract. Ice clouds and their effect on Earth's radiation budget are one of the largest sources of uncertainty in climate change predictions. The uncertainty in predicting ice cloud feedbacks in a warming climate arises due to uncertainties in measuring and explaining their current optical and microphysical properties as well as from insufficient knowledge about their spatial and temporal distribution. This knowledge can be significantly improved by active remote sensing, which can help to explore the vertical profile of ice cloud microphysics, such as ice particle size and ice water content. This study focuses on the well-established variational approach VarCloud to retrieve ice cloud microphysics from radar-lidar measurements. While active backscatter retrieval techniques surpass the information content of most passive, vertically integrated retrieval techniques, their accuracy is limited by essential assumptions about the ice crystal shape. Since most radar-lidar retrieval algorithms rely heavily on universal mass-size relationships to parameterize the prevalent ice particle shape, biases in ice water content and ice water path can be expected in individual cloud regimes. In turn, these biases can lead to an erroneous estimation of the radiative effect of ice clouds. In many cases, these biases could be spotted and corrected by the simultaneous exploitation of measured solar radiances. The agreement with measured solar radiances is a logical prerequisite for an accurate estimation of the radiative effect of ice clouds. To this end, this study exploits simultaneous radar, lidar, and passive measurements made on board the German High Altitude and Long Range Research Aircraft. By using the ice clouds derived with VarCloud as an input to radiative transfer calculations, simulated solar radiances are compared to measured solar radiances made above the actual clouds. This radiative closure study is done using different ice crystal models to improve the knowledge of the prevalent ice crystal shape. While in one case aggregates were capable of reconciling radar, lidar, and solar radiance measurements, this study also analyses a more problematic case for which no radiative closure could be achieved. In this case, simultaneously acquired in-situ measurements could narrow this inability to an unexpected high ice crystal number concentration.

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