Synergistic radar and radiometer retrievals of ice hydrometeors

Pfreundschuh, Simon; Eriksson, Patrick; Buehler, Stefan A.; Brath, Manfred; Duncan, David; Larsson, Richard; Ekelund, Robin

doi:https://doi.org/10.5194/amt-13-4219-2020

Articles | Volume 13, issue 8

https://doi.org/10.5194/amt-13-4219-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-4219-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 8

Research article

|

12 Aug 2020

Research article |

| 12 Aug 2020

Synergistic radar and radiometer retrievals of ice hydrometeors

Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund

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Final revised paper (published on 12 Aug 2020)
Supplement to the final revised paper
Preprint (discussion started on 21 Oct 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review of “Synergistic radar and radiometer retrievals of ice hydrometeors” By Simon Pfreundschuh et al.', Anonymous Referee #1, 19 Nov 2019
- AC1: 'Response to Anonymous Referee #1', Simon Pfreundschuh, 10 Feb 2020
RC2: 'Review Pfreundschuh et al.', Anonymous Referee #2, 02 Dec 2019
- AC2: 'Response to Anonymous Referee #2', Simon Pfreundschuh, 10 Feb 2020
RC3: 'Review', Anonymous Referee #3, 04 Dec 2019
- AC3: 'Response to Anonymous Referee #3', Simon Pfreundschuh, 10 Feb 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Simon Pfreundschuh on behalf of the Authors (12 Mar 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Apr 2020) by Pavlos Kollias

RR by Anonymous Referee #1 (18 Apr 2020)

RR by Anonymous Referee #2 (23 Apr 2020)

RR by Anonymous Referee #3 (11 May 2020)

Suggestions for revision or reasons for rejection

The authors have only partially addressed my concerns. The paper is still not acceptable for publication as it is.
Major revisions are still needed. There are still mistakes (even for things that were highlighted in the first round) and very vague and generic sentences
that are unacceptable in a peer-review paper; sloppiness tends to distract the reader from properly judging the merit of the science.

I just make some examples here below.

1) The abstract from line 13 onwards is way too generic. There is not a single statement there that
the reader can remember as specific for this work.
2) In the introduction some statements are still quite inaccurate in my opinion.
E.g.
``Microwave sensors employ wavelengths ranging down to about 1 mm. ...
At the same time, they provide the advantage of penetrating even thick clouds.'' Well if this may be true for
wavelength in the cm-region is certainly not true for frequency in the G-band for instance. What are ``thick ice clouds''?

``Compared to the sizes of ice particles, the wavelengths are very long and therefore sensitive only to very large ice particles.'', again this is way too generic (what is very large?), it seems to give the idea that
radars (even cloud radars) are not sensitive to 100-200 micron size particles, which is erroneous (see your Fig.5!)

``Although radars and lidars allow detection of lower ice water contents than their passive counterparts,
they are ultimately limited by the same principles.''
I am not sure what the authors are alluding to here. Radars in the G-band are now a reality (e.g. see recent work
by Roy et al.,) and the technology for active systems for even higher frequencies (>300 GHz) has already been demonstrated.
Anyhow if a radar cannot penetrate a certain cloud the radiometer at the same frequency will suffer from the same issue;
actually the radar via PIA technique can provide estimates of optical thicknesses up to 10 and more (where the radiometers
are already saturated).
``Prominent examples of
satellite missions that exploit both of these synergies.....''
Again I do not see how the ``non-local synergy which uses the vertically resolved
radar observations to support passive-only retrievals across the wide swath of the passive sensor``.
What GPM does is use synergistic radar-radiometer retrievals to build a database for a
Bayesian inversion, whih is a different thing.
``and hence provide only limited
sensitivity to frozen hydrometeors'' but later on you say that MWi ``will provide additional sensitivity to
liquid and frozen precipitation''. Again very generic statements.
3) Fig.2: I do not see any change to the figure (despite what the authors say). Units are still the same and wrong.
4) Units of standard deviation of Z. The authors are reiterating common mistakes/typos present in literature,
even when errors are highlighted. dBZ-dBZ is dB, that's the nature of logarithmic units!
5) Eq.3 : another wrong equation. Not sure what the authors are doing here but it is simple algebra, the equation is clarly wrong because
there is no factor pi involved (see
https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018JD028603).
6) Eq.6 is another example of very confused terminology (the numerator is not a cross section!! efficiency is usually used for other scattering quantities)
7) Fig.4 also does not make much sense to me (if you are normalising by IWC you should not plot it in the x-axis).
8) Fig.7 y-label panel b) That is not IWP but a ratio of IWP_ret/IWP/true. Re-label.
9) Units of IWC in Fig.14 are wrong.
Proper scrutiny must be paid by the authors in the third round (and I would specifically
urge the more expert co-authors for providing such scrutiny that was clearly missing in the previous two rounds! ). This means not simply addressing
the issues mentioned above but properly perusing thoroughly the whole paper!
Please avoid generic sentences.

Concerning the science:
1) I am still convinced about some major issues related to the selection of the scene.
The key strength of the combination should be in ice clouds and mixed-phase but no clear message are coming out of this study on this (i.e. what king
of cloud-LWP can we retrieve with confidence? how important is to know the location of the SLWC layer?).
Profiles with high density ice and rain underneath are much more complicated and certainly should not be the
target of such a suite of instruments (but they occupy a large fraction of your scene). The only semi-quantitative statements is indeed present in the
conclusions ``While observations at currently available microwave frequencies provide information complementary to that from a
radar only for thick clouds with very large particles ...'' and is actually based on your Fig.5 (no retrieval involved).

2) Fig.3 and retrieval grid: the definition of the retrieval grid must be different if you consider a radiometer only or a
retrieval including the radar. The advantage of a radar is indeed to produce a cloud mask first (actually with the
sensitivity you have used all clouds are practically detected by the radar). So I really do not understand how it is possible to see
IWC in Fig,.7 panel e) and f) where there are no clouds (this magically disappear in Fig.10???). On the other hand I am also
curious to know how with a radiometer-only retrieval you can constrain the cloud top like you are doing.

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ED: Reconsider after major revisions (13 May 2020) by Pavlos Kollias

AR by Simon Pfreundschuh on behalf of the Authors (11 Jun 2020) Author's response Manuscript

ED: Publish as is (03 Jul 2020) by Pavlos Kollias

AR by Simon Pfreundschuh on behalf of the Authors (07 Jul 2020)

Short summary

The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.