the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 May 2023
Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions
Abstract. The cloud particle size distribution (PSD) is a key parameter for the retrieval of micro-physical and optical properties from remote sensing instruments, which in turn are necessary for determining the radiative effect of clouds. Current representations of PSDs for ice clouds rely on parameterizations that were largely based on in situ measurements where the distribution of small ice crystals were uncertain. This makes current parameterizations deficient to simulate remote sensing observations sensitive to small ice, such as from lidar and thermal infrared instruments. In this study we fit the in situ PSDs of ice crystals from the JULIA (JÜLich In situ Aircraft data set) database, which consists of 11 campaigns covering the tropics, mid-latitudes and the Arctic, consistently processes and considered more robust in their measurements of small ice. For the fitting, we implement an established approach to PSD parameterizations, which consists in finding an adequate set of parameters for a modified gamma function after normalization of both PSD axes. These parameters are constrained to match in-situ mea surements when predicting microphysical properties from the PSDs, via a cost function minimization method. We selected the ice water content and the ice crystal number concentration, which are currently key parameters for modern satellite retrievals and model microphysics schemes. We found that a bimodal parameterization yields better results than a monomodal one. The bimodal parameterization has a lower spread for almost all ice crystal sizes over the entire range of analyzed temperatures and fits better the observations, especially for particles between 20 and about 110 μm at temperatures between −60 and −20 °C. For this temperature range, the root mean square error for the retrieved Nice is reduced from 0.36 to 0.20. This demonstrates a clear advantage to considering the bimodality of PSDs, e.g. for satellite retrievals.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-754', Anonymous Referee #1, 03 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-RC1-supplement.pdf
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AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC1-supplement.pdf
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AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
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RC2: 'Second review of Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions', Anonymous Referee #2, 19 Jun 2023
Review of Tecnical note: Bimodal Parameterizations of in-situ Ice Clouds Particle Size Distributions
Authors: Irene Bartolomé Garcia et al.
The authors are proposing a new technic for the parameterization of ice particle size distributions with gamma normalized size distributions as in Delanoë et al 2014. But, they are using two normalized distributions, one for Diameters smaller than 50µm and one for Diameters larger than 50µm, instead of one for all spectrum of size of measured ice crystals. They are comparing their retrieved ice PSD with the ones of retrieved with the former methods i.e Delanoë et al., (2014 and 2005) and applied to their dataset. Globally, overall their dataset (Figure 4 and 6) the new method seems to be more accurate to retrieve small ice crystals concentration. They motivate their study, on the fact that concentrations of small ice crystals are too often neglected or not considered, impacting accuracy of retrieval methods for clouds properties. The main reason being the measurment uncertainty of small ice crystals.
This is not the first study that offers a parameterization of ice PSD with two modes (two gamma distributions cf. Field et al., 2007). However, this is the first in my knowledge that includes ice particles since 3 µm.
Major Comments:
Bimodality:
Are you assuming that all ice PSD in your ice clouds are bimodal ?
Line 53: You are introducing frequencies of bimodality in the discussion, would it be consistent to divide the distribution in two modes if there is only one mode ?
Hu et al 2022 have developped a methode to estimate the number of modes in ice PSD. They, showed that at coldest temperature (-50°C to -40°C) ice PSD are 60% of the time one mode; except for IWC> 1.5 g.m3.
Why there should be bimodality ?
In the introduction you are linking the shape of the ice PSD and the growth process. Then, it is shortly discussed in section 4.3. You are assuming that it is the difference of newly formed ice particle against sedimenting sizes. I encourage you to improve the discussion on this topic. Because, if the evolution of the size of the hydrometeors is linked to the growth rate: vapor diffusion, aggregation and riming. Then, If there is more than one growth process (without counting secondary ice production) there should be more than one mode in ice PSD !?
Then, you choose a cutting diameter of 50 µm, do you mean that the division of the growth processes such growth by vapor diffusion against growth by aggregation (or sedimentation) is here. Can you give a reference or an argument, assumpltion maybe, for this cutting diameter ? If I observe one column of few hundreds of micron wasn’t it a monocrystal of few microns in its past ?
You are citing Field et al., (2007) that also proposed a bimodal normalized parameterization, but as function of optical maximum length and effective radius. However, they did not use concentrations of small ice under 100 microns. What would be the impact by taking the concentration from 3µm (this would be maybe to consider for a second part publication).
Melting diameter and mass-size relations:
To retrieve the metling diameter you are using a mass-dimension relationship used in Krämer et al., (2016). In this later study, it is justified for temperature less than -38°C (235.15K) in cirrus cloud and based on former studies. Is it consistent to use it for T> 235K, knowing that few studies with direct measurment of IWC have shown an impact of the temperature on the m-D coefficients in ice clouds.
I would like to see IWC retrieved with this m-D and original ice PSD, compared with the measured IWC available in your dataset; and also as function of temperature. Why not use, your own retrieved m-D from the dataset you are using and see the impact on the Nice. And also with Brown and Francis as in the original version (see first review comment).
Figure 4 and 5, I would consider plotting the error in percent regarding original concentrations instead of pure concentration, with a recall of your measurment uncertainties especially for smaller size. Small crystals and large ones do not have the same order of concentrations ; this is important.
Figure 5 only, AS your study is questioning the retrieval of small ice concentration, I would summarize it, for small and large ice particles i.e. below and above the cutting diameter, instead of showing it as function of size bins.
Line 243 : I do not think that IWC and ice PSD can be dissociated. Can you be more clear on your description of the error of IWC, dimension you are using instead of log, , rate of underestimation and overestimation. It does not talk for someone who is not a specialist.
“IWC is sensitive to large particles” : it is more complicated than that. Where do you define large ice particles hundred of microns, millimeter … The spectrum of all ice cristals goes from few microns to centimeter in some case. Then, C, S and X band radars would be enougth to retrieve IWC in cloud. For a fact, W band and Ka band do a better job i.e Delanoë et al., (2005 & 2014) which are less sensitive to very large ice crystals.
Remarks on the conclusion ;
The methods of Delanoë et al ., is developped for all ice clouds, while I understand that the dataset used in this study is mainly made with sampling in cirrus clouds (except for ACRIDION campaign). What about the temperature below -20°C ? Can we generalize your conclusions to all ice clouds and to all range of temperature ? If yes Why ?
Maybe you can recall the definition of cirrus clouds you are using, does it agree with the one in Heymsfield et al., (2017) and the AMS glossary for example ?
I suggest these references to help the discussion :
Korolev, A., Heckman, I., Wolde, M., Ackerman, A.S., Fridlind, A.M., Ladino, L.A., Lawson, R.P., Milbrandt, J., Williams, E., 2020. A new look at the environmental conditions favorable to secondary ice production. Atmospheric Chemistry and Physics 20, 1391–1429. https://doi.org/10.5194/acp-20-1391-2020
Heymsfield, A.J., Schmitt, C., Bansemer, A., 2013. Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86°C. J. Atmos. Sci. 70, 4123–4154. https://doi.org/10.1175/JAS-D-12-0124.1
Schmitt, C.G., Heymsfield, A.J., 2010. The Dimensional Characteristics of Ice Crystal Aggregates from Fractal Geometry. Journal of the Atmospheric Sciences 67, 1605–1616. https://doi.org/10.1175/2009JAS3187.1
Heymsfield, A.J., Krämer, M., Luebke, A., Brown, P., Cziczo, D.J., Franklin, C., Lawson, P., Lohmann, U., McFarquhar, G., Ulanowski, Z., Tricht, K.V., 2017. Cirrus Clouds. Meteorological Monographs 58, 2.1-2.26. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1
Citation: https://doi.org/10.5194/egusphere-2023-754-RC2 -
AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-754', Anonymous Referee #1, 03 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Irene Bartolome Garcia, 28 Sep 2023
-
RC2: 'Second review of Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions', Anonymous Referee #2, 19 Jun 2023
Review of Tecnical note: Bimodal Parameterizations of in-situ Ice Clouds Particle Size Distributions
Authors: Irene Bartolomé Garcia et al.
The authors are proposing a new technic for the parameterization of ice particle size distributions with gamma normalized size distributions as in Delanoë et al 2014. But, they are using two normalized distributions, one for Diameters smaller than 50µm and one for Diameters larger than 50µm, instead of one for all spectrum of size of measured ice crystals. They are comparing their retrieved ice PSD with the ones of retrieved with the former methods i.e Delanoë et al., (2014 and 2005) and applied to their dataset. Globally, overall their dataset (Figure 4 and 6) the new method seems to be more accurate to retrieve small ice crystals concentration. They motivate their study, on the fact that concentrations of small ice crystals are too often neglected or not considered, impacting accuracy of retrieval methods for clouds properties. The main reason being the measurment uncertainty of small ice crystals.
This is not the first study that offers a parameterization of ice PSD with two modes (two gamma distributions cf. Field et al., 2007). However, this is the first in my knowledge that includes ice particles since 3 µm.
Major Comments:
Bimodality:
Are you assuming that all ice PSD in your ice clouds are bimodal ?
Line 53: You are introducing frequencies of bimodality in the discussion, would it be consistent to divide the distribution in two modes if there is only one mode ?
Hu et al 2022 have developped a methode to estimate the number of modes in ice PSD. They, showed that at coldest temperature (-50°C to -40°C) ice PSD are 60% of the time one mode; except for IWC> 1.5 g.m3.
Why there should be bimodality ?
In the introduction you are linking the shape of the ice PSD and the growth process. Then, it is shortly discussed in section 4.3. You are assuming that it is the difference of newly formed ice particle against sedimenting sizes. I encourage you to improve the discussion on this topic. Because, if the evolution of the size of the hydrometeors is linked to the growth rate: vapor diffusion, aggregation and riming. Then, If there is more than one growth process (without counting secondary ice production) there should be more than one mode in ice PSD !?
Then, you choose a cutting diameter of 50 µm, do you mean that the division of the growth processes such growth by vapor diffusion against growth by aggregation (or sedimentation) is here. Can you give a reference or an argument, assumpltion maybe, for this cutting diameter ? If I observe one column of few hundreds of micron wasn’t it a monocrystal of few microns in its past ?
You are citing Field et al., (2007) that also proposed a bimodal normalized parameterization, but as function of optical maximum length and effective radius. However, they did not use concentrations of small ice under 100 microns. What would be the impact by taking the concentration from 3µm (this would be maybe to consider for a second part publication).
Melting diameter and mass-size relations:
To retrieve the metling diameter you are using a mass-dimension relationship used in Krämer et al., (2016). In this later study, it is justified for temperature less than -38°C (235.15K) in cirrus cloud and based on former studies. Is it consistent to use it for T> 235K, knowing that few studies with direct measurment of IWC have shown an impact of the temperature on the m-D coefficients in ice clouds.
I would like to see IWC retrieved with this m-D and original ice PSD, compared with the measured IWC available in your dataset; and also as function of temperature. Why not use, your own retrieved m-D from the dataset you are using and see the impact on the Nice. And also with Brown and Francis as in the original version (see first review comment).
Figure 4 and 5, I would consider plotting the error in percent regarding original concentrations instead of pure concentration, with a recall of your measurment uncertainties especially for smaller size. Small crystals and large ones do not have the same order of concentrations ; this is important.
Figure 5 only, AS your study is questioning the retrieval of small ice concentration, I would summarize it, for small and large ice particles i.e. below and above the cutting diameter, instead of showing it as function of size bins.
Line 243 : I do not think that IWC and ice PSD can be dissociated. Can you be more clear on your description of the error of IWC, dimension you are using instead of log, , rate of underestimation and overestimation. It does not talk for someone who is not a specialist.
“IWC is sensitive to large particles” : it is more complicated than that. Where do you define large ice particles hundred of microns, millimeter … The spectrum of all ice cristals goes from few microns to centimeter in some case. Then, C, S and X band radars would be enougth to retrieve IWC in cloud. For a fact, W band and Ka band do a better job i.e Delanoë et al., (2005 & 2014) which are less sensitive to very large ice crystals.
Remarks on the conclusion ;
The methods of Delanoë et al ., is developped for all ice clouds, while I understand that the dataset used in this study is mainly made with sampling in cirrus clouds (except for ACRIDION campaign). What about the temperature below -20°C ? Can we generalize your conclusions to all ice clouds and to all range of temperature ? If yes Why ?
Maybe you can recall the definition of cirrus clouds you are using, does it agree with the one in Heymsfield et al., (2017) and the AMS glossary for example ?
I suggest these references to help the discussion :
Korolev, A., Heckman, I., Wolde, M., Ackerman, A.S., Fridlind, A.M., Ladino, L.A., Lawson, R.P., Milbrandt, J., Williams, E., 2020. A new look at the environmental conditions favorable to secondary ice production. Atmospheric Chemistry and Physics 20, 1391–1429. https://doi.org/10.5194/acp-20-1391-2020
Heymsfield, A.J., Schmitt, C., Bansemer, A., 2013. Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86°C. J. Atmos. Sci. 70, 4123–4154. https://doi.org/10.1175/JAS-D-12-0124.1
Schmitt, C.G., Heymsfield, A.J., 2010. The Dimensional Characteristics of Ice Crystal Aggregates from Fractal Geometry. Journal of the Atmospheric Sciences 67, 1605–1616. https://doi.org/10.1175/2009JAS3187.1
Heymsfield, A.J., Krämer, M., Luebke, A., Brown, P., Cziczo, D.J., Franklin, C., Lawson, P., Lohmann, U., McFarquhar, G., Ulanowski, Z., Tricht, K.V., 2017. Cirrus Clouds. Meteorological Monographs 58, 2.1-2.26. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1
Citation: https://doi.org/10.5194/egusphere-2023-754-RC2 -
AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-754/egusphere-2023-754-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Irene Bartolome Garcia, 28 Sep 2023
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Irene Bartolomé García
Odran Sourdeval
Reinhold Spang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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