Assimilation of 3D Polarimetric Microphysical Retrievals in a Convective-Scale NWP System

Reimann, Lucas; Simmer, Clemens; Trömel, Silke

doi:https://doi.org/10.5194/egusphere-2023-1132

Preprints

https://doi.org/10.5194/egusphere-2023-1132

Preprints

19 Jun 2023

| 19 Jun 2023

Assimilation of 3D Polarimetric Microphysical Retrievals in a Convective-Scale NWP System

Lucas Reimann, Clemens Simmer, and Silke Trömel

Abstract. This study assimilates for the first time polarimetric C-band radar observations from the German meteorological service (DWD) into DWD’s convective-scale model ICON-D2 using DWD’s ensemble-based KENDA assimilation framework. We compare the assimilation of conventional observations (CNV) with the additional assimilation of radar reflectivity Z (CNV+Z), with the additional assimilation of liquid or ice water content (LWC or IWC) estimates below or above the melting layer instead of Z where available (CNV+LWC/Z or CNV+IWC/Z, respectively). Hourly quantitative precipitation forecasts (QPF) are evaluated for two stratiform and one convective rainfall event in the summers of 2017 and 2021.

With optimized data assimilation settings (e.g., observation errors), the assimilation of LWC mostly improves first guess QPF compared to the assimilation of Z alone (CNV+Z), while the assimilation of IWC does not, especially for convective cases, probably because of the lower quality of the IWC retrieval in these situations. Improvements are, however, notable for stratiform rainfall in 2021, for which the IWC estimator profits from better specific differential phase estimates due to a higher radial radar resolution compared to the other cases. The assimilation of all radar data sets together (CNV+LWC+IWC+Z) yields the best first guesses.

All assimilation configurations with radar information consistently improve deterministic nine-hour QPF compared to the assimilation of only conventional data (CNV). Forecasts based on the assimilation of LWC and IWC retrievals on average slightly improve FSS and FBI compared to the assimilation of Z alone (CNV+Z), especially when LWC is assimilated for the 2017 convective case and when IWC is assimilated for the high-resolution 2021 stratiform case. However, IWC assimilation again degrades forecast FSS for the convective cases. Forecasts initiated using all radar data sets together (CNV+LWC+IWC+Z) yield the best FSS. The development of IWC retrievals more adequat for convection constitutes one next step to further improve the exploitation of ice microphysical retrievals for radar data assimilation.

Received: 27 May 2023 – Discussion started: 19 Jun 2023

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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.

Journal article(s) based on this preprint

15 Nov 2023

Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system

Lucas Reimann, Clemens Simmer, and Silke Trömel

Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023,https://doi.org/10.5194/acp-23-14219-2023, 2023

Short summary

Lucas Reimann et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1132', Anonymous Referee #1, 09 Jul 2023
General comments:
This manuscript is a novel attempt to assimilate C-band polarimetric microphysical retrievals into a convection-resolving model in Germany compared to the legacy assimilation approach using Z. The authors find generally positive benefits, particularly for LWC, with IWC neutral/beneficial in stratiform cases but with harmful effects in a convective case. This is not particularly surprising, as the retrievals used are not formulated for rimed ice/hail, but it is nonetheless a good demonstration of what can happen if the retrieval equations are not applied appropriately/selectively to how they were formulated. The results overall indicate that the addition of LWC and IWC retrievals, with Z, results in the best forecasts, and encourages further exploration. The manuscript is very well written, with a thorough introduction section that clearly establishes the state-of-the-art of polarimetric radar DA and its challenges. The basis and results of the study are novel and timely as polarimetric radars are increasingly implemented and their information content explored. There are a number of improvements I’d like to see to the manuscript primarily regarding clarity or requests for additional context/information. While I don’t think it is necessary for publication, I did feel the manuscript could benefit from one or two plots actually depicting the results of the different assimilation experiments (e.g., actual post-assimilation hydrometeor and/or moisture fields, or QPF fields, vs. the observed QPE; the distribution/statistics of the IWC/LWC fields, etc.), so that readers can get a visual sense of the effects these are having rather than solely relying on only statistics. Despite this, I believe the manuscript will be ready for publication once the following minor comments are addressed.

Specific comments:
L26: Please define FSS and FBI for the abstract.

L103: It may be worth mentioning that beyond the rudimentary treatment of PSDs, hydrometeor shapes and orientations are rarely (if ever?) taken into account at all within model microphysics schemes.

L160: Which microphysics scheme is used in the ICON-D2? A citation is needed here.

L180: In this study, when Z is assimilated (either in CNV+Z, CNV+[LWC + IWC]/Z, or CNV + LWC + IWC + Z) does this mean it is both directly assimilated and assimilated through LHN? It is not explicitly clear to me how Z is being used as the reference assimilation study. (Edit: I see now on L338 it is stated that LHN is excluded in this case, but this wasn’t clear at first when mentioned in the context of the operational Z assimilation scheme).

L204 and elsewhere: Is there a specific reason the variables are included as ZDR, PHIDP, KDP, and RHOHV rather than Z_DR, Φ_DP, K_dp, and ρ_hv? It isn’t critical, but I think use of the variables are more standard notation, would clean up the equations/discussion, and not suggest that ZDR, RHOHV, PHIDP, and KDP might be acronyms to unfamiliar readers.

L206: Strictly speaking, isotropic scatterers will have an intrinsic ZDR of exactly 0 dB rather than one close to 0 dB.

L217: This value of ρ_hvseems much lower than what I would have expected. At S band, ρ_hvin dry snow/ice (rather than melting particles, which are neglected in these retrievals) is usually nowhere near 0.85 unless it is very large hail experiencing resonance scattering, in which case the use of the IWC retrieval equations would be inappropriate anyway (both due to it being hail rather than snow and due to the non-Rayleigh scattering). With all the tests done to find the optimal DAP, were any tests done to examine the impact of these thresholds and find optimal values? What did the typical distribution of ρ_hvvalues actually look like above the ML? I am concerned that using such a low ρ_hvthreshold aloft will necessarily retain data that is going to have very noisy Φ_DP/K_dpand thus noisy/erroneous IWC retrievals that should not be assimilated. I would be curious if a much more stringent ρ_hthreshold (perhaps something like 0.92 or greater) would result in better retrieval/assimilation results.

L221: How is the bottom of the melting layer determined, as shown in Fig. 2? I assume some sort of ρ_hv threshold when using a QVP, but for the convective case (for which I presume model data was relied upon) it is less clear.

L225: I appreciate why such a large window is needed for consistency, but depending on how small and isolated the convection is, could 9 km be too large a window and end up heavily incorporating data boundary regions into the Kdp calculation for precipitation regions?

L246 and elsewhere: I assume log here is log₁₀ and not ln? It may be helpful just to clarify.

L256: It may also be worth mentioning in addition to just hail that R(A) at C band struggles from the resonance scattering of medium-large-sized raindrops, which causes R(Kdp) to outperform R(A) for moderate to heavy rainrates.

L207: I am a bit confused by this. The Figure 2 caption says LS = 10 km, which would make it equivalent to LC, but here it makes it sound like LS != LC.

L305: While the results of these tests are not shown here, previous studies such as Liu et al. (2020) have demonstrated the same thing for hydrometeor mixing ratios and could be cited here, if desired.

Liu, C., M. Xue, and R. Kong, 2020: Direct variational assimilation of radar reflectivity and radial velocity data: Issues with nonlinear reflectivity operator and solutions. Mon. Wea. Rev. 148.
L331: I may be misunderstanding something simple, but if the assimilation process during this period is what is done operationally (CONV + Z + LHN), what is the reason the data was obtained 24-35 hours beforehand and further spun up rather than just using the operational data from the model initial time (e.g, 00 UTC 13 July 2021 instead of 00 UTC 12 July 2021)?

L336: I am curious about the assimilation of Z data only within the melting layer, where the relationship between Z and the microphysical state variables becomes most complicated and obfuscated. If anything the data in the melting layer is often neglected because it can result in some large errors.

L512: I apologize if I’m misunderstanding, but these two sentences seem contradictory to me. It is stated that the best forecasts are achieved when there is limited influence from the radar-based retrievals (I assume in terms of impact to the analysis by way of a larger lower threshold, rather than spatial localization?). But subsequently it is stated that a smaller observation error standard deviation, which I believe would enhance the weighting toward observations, is also beneficial.

L548: This, to me, is an absolutely crucial piece of information for interpreting the results of assimilating LWC/IWC in this study and needs to be discussed earlier in the paper in the assimilation section. While I think the adjustment of the moisture variables (rather than precipitation variables) is in general an important aspect of radar data assimilation, I also feel the impacts of assimilating a “bad” retrieval (say above the ML in what is actually graupel/hail) on moisture may be even more deleterious than if it were acting on qs, which could precipitate out relatively quickly and lead to little harm to the forecast. This seems like an important follow-up study since it is likely not what readers would have expected at first.

L565: Could additional studies be done that only assimilate the IWC retrievals in areas identified as snow in convective cases, thus potentially limiting the presumed harmful impacts of assimilating poor/inappropriate retrievals of IWC? With benefits coming from Z alone, I am curious if it could still be wise to assimilate IWC only selectively, in addition to LWC and Z (and V and CNV).

Technical corrections:
L31: “adequat” should be “adequate”

L330: “and including LHN” should be “including LHN”

L538: “adjusted” should be “developed”
Citation: https://doi.org/10.5194/egusphere-2023-1132-RC1
- AC1: 'Reply on RC1', Lucas Reimann, 03 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1132/egusphere-2023-1132-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1132-AC1
RC2:
'Comment on egusphere-2023-1132', Anonymous Referee #2, 12 Jul 2023

see attached file

Citation: https://doi.org/10.5194/egusphere-2023-1132-RC2
- AC2: 'Reply on RC2', Lucas Reimann, 03 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1132/egusphere-2023-1132-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1132-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1132', Anonymous Referee #1, 09 Jul 2023
General comments:
This manuscript is a novel attempt to assimilate C-band polarimetric microphysical retrievals into a convection-resolving model in Germany compared to the legacy assimilation approach using Z. The authors find generally positive benefits, particularly for LWC, with IWC neutral/beneficial in stratiform cases but with harmful effects in a convective case. This is not particularly surprising, as the retrievals used are not formulated for rimed ice/hail, but it is nonetheless a good demonstration of what can happen if the retrieval equations are not applied appropriately/selectively to how they were formulated. The results overall indicate that the addition of LWC and IWC retrievals, with Z, results in the best forecasts, and encourages further exploration. The manuscript is very well written, with a thorough introduction section that clearly establishes the state-of-the-art of polarimetric radar DA and its challenges. The basis and results of the study are novel and timely as polarimetric radars are increasingly implemented and their information content explored. There are a number of improvements I’d like to see to the manuscript primarily regarding clarity or requests for additional context/information. While I don’t think it is necessary for publication, I did feel the manuscript could benefit from one or two plots actually depicting the results of the different assimilation experiments (e.g., actual post-assimilation hydrometeor and/or moisture fields, or QPF fields, vs. the observed QPE; the distribution/statistics of the IWC/LWC fields, etc.), so that readers can get a visual sense of the effects these are having rather than solely relying on only statistics. Despite this, I believe the manuscript will be ready for publication once the following minor comments are addressed.

Specific comments:
L26: Please define FSS and FBI for the abstract.

L103: It may be worth mentioning that beyond the rudimentary treatment of PSDs, hydrometeor shapes and orientations are rarely (if ever?) taken into account at all within model microphysics schemes.

L160: Which microphysics scheme is used in the ICON-D2? A citation is needed here.

L180: In this study, when Z is assimilated (either in CNV+Z, CNV+[LWC + IWC]/Z, or CNV + LWC + IWC + Z) does this mean it is both directly assimilated and assimilated through LHN? It is not explicitly clear to me how Z is being used as the reference assimilation study. (Edit: I see now on L338 it is stated that LHN is excluded in this case, but this wasn’t clear at first when mentioned in the context of the operational Z assimilation scheme).

L204 and elsewhere: Is there a specific reason the variables are included as ZDR, PHIDP, KDP, and RHOHV rather than Z_DR, Φ_DP, K_dp, and ρ_hv? It isn’t critical, but I think use of the variables are more standard notation, would clean up the equations/discussion, and not suggest that ZDR, RHOHV, PHIDP, and KDP might be acronyms to unfamiliar readers.

L206: Strictly speaking, isotropic scatterers will have an intrinsic ZDR of exactly 0 dB rather than one close to 0 dB.

L217: This value of ρ_hvseems much lower than what I would have expected. At S band, ρ_hvin dry snow/ice (rather than melting particles, which are neglected in these retrievals) is usually nowhere near 0.85 unless it is very large hail experiencing resonance scattering, in which case the use of the IWC retrieval equations would be inappropriate anyway (both due to it being hail rather than snow and due to the non-Rayleigh scattering). With all the tests done to find the optimal DAP, were any tests done to examine the impact of these thresholds and find optimal values? What did the typical distribution of ρ_hvvalues actually look like above the ML? I am concerned that using such a low ρ_hvthreshold aloft will necessarily retain data that is going to have very noisy Φ_DP/K_dpand thus noisy/erroneous IWC retrievals that should not be assimilated. I would be curious if a much more stringent ρ_hthreshold (perhaps something like 0.92 or greater) would result in better retrieval/assimilation results.

L221: How is the bottom of the melting layer determined, as shown in Fig. 2? I assume some sort of ρ_hv threshold when using a QVP, but for the convective case (for which I presume model data was relied upon) it is less clear.

L225: I appreciate why such a large window is needed for consistency, but depending on how small and isolated the convection is, could 9 km be too large a window and end up heavily incorporating data boundary regions into the Kdp calculation for precipitation regions?

L246 and elsewhere: I assume log here is log₁₀ and not ln? It may be helpful just to clarify.

L256: It may also be worth mentioning in addition to just hail that R(A) at C band struggles from the resonance scattering of medium-large-sized raindrops, which causes R(Kdp) to outperform R(A) for moderate to heavy rainrates.

L207: I am a bit confused by this. The Figure 2 caption says LS = 10 km, which would make it equivalent to LC, but here it makes it sound like LS != LC.

L305: While the results of these tests are not shown here, previous studies such as Liu et al. (2020) have demonstrated the same thing for hydrometeor mixing ratios and could be cited here, if desired.

Liu, C., M. Xue, and R. Kong, 2020: Direct variational assimilation of radar reflectivity and radial velocity data: Issues with nonlinear reflectivity operator and solutions. Mon. Wea. Rev. 148.
L331: I may be misunderstanding something simple, but if the assimilation process during this period is what is done operationally (CONV + Z + LHN), what is the reason the data was obtained 24-35 hours beforehand and further spun up rather than just using the operational data from the model initial time (e.g, 00 UTC 13 July 2021 instead of 00 UTC 12 July 2021)?

L336: I am curious about the assimilation of Z data only within the melting layer, where the relationship between Z and the microphysical state variables becomes most complicated and obfuscated. If anything the data in the melting layer is often neglected because it can result in some large errors.

L512: I apologize if I’m misunderstanding, but these two sentences seem contradictory to me. It is stated that the best forecasts are achieved when there is limited influence from the radar-based retrievals (I assume in terms of impact to the analysis by way of a larger lower threshold, rather than spatial localization?). But subsequently it is stated that a smaller observation error standard deviation, which I believe would enhance the weighting toward observations, is also beneficial.

L548: This, to me, is an absolutely crucial piece of information for interpreting the results of assimilating LWC/IWC in this study and needs to be discussed earlier in the paper in the assimilation section. While I think the adjustment of the moisture variables (rather than precipitation variables) is in general an important aspect of radar data assimilation, I also feel the impacts of assimilating a “bad” retrieval (say above the ML in what is actually graupel/hail) on moisture may be even more deleterious than if it were acting on qs, which could precipitate out relatively quickly and lead to little harm to the forecast. This seems like an important follow-up study since it is likely not what readers would have expected at first.

L565: Could additional studies be done that only assimilate the IWC retrievals in areas identified as snow in convective cases, thus potentially limiting the presumed harmful impacts of assimilating poor/inappropriate retrievals of IWC? With benefits coming from Z alone, I am curious if it could still be wise to assimilate IWC only selectively, in addition to LWC and Z (and V and CNV).

Technical corrections:
L31: “adequat” should be “adequate”

L330: “and including LHN” should be “including LHN”

L538: “adjusted” should be “developed”
Citation: https://doi.org/10.5194/egusphere-2023-1132-RC1
- AC1: 'Reply on RC1', Lucas Reimann, 03 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1132/egusphere-2023-1132-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1132-AC1
RC2:
'Comment on egusphere-2023-1132', Anonymous Referee #2, 12 Jul 2023

see attached file

Citation: https://doi.org/10.5194/egusphere-2023-1132-RC2
- AC2: 'Reply on RC2', Lucas Reimann, 03 Sep 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1132/egusphere-2023-1132-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1132-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Lucas Reimann on behalf of the Authors (03 Sep 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (15 Sep 2023) by Matthew Lebsock

RR by Anonymous Referee #1 (25 Sep 2023)

ED: Publish as is (25 Sep 2023) by Matthew Lebsock

AR by Lucas Reimann on behalf of the Authors (04 Oct 2023)

Journal article(s) based on this preprint

15 Nov 2023

Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system

Lucas Reimann, Clemens Simmer, and Silke Trömel

Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023,https://doi.org/10.5194/acp-23-14219-2023, 2023

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Polarimetric radar observations were assimilated for the first time in a convective-scale numerical weather prediction system in Germany and their impact on short-term precipitation forecasts was evaluated. The assimilation was performed using microphysical retrievals of liquid and ice water content and yielded slightly improved deterministic nine-hour precipitation forecasts for three intense summer precipitation cases with respect to the assimilation of radar reflectivity alone.


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