the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Sep 2024
Assessment and application of melting layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
Abstract. Because of their high sensitivity to hydrometeors and their high vertical resolutions, space-borne radar observations are emerging as an undeniable asset for Numerical Weather Prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TOVS) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. This study provides an in-depth description of the radar simulator available within this software. In addition, this study proposes a revised version of the existing melting layer parametrization scheme of Bauer (2001) within the RTTOV-SCATT v13.1 model to provide a better fit to observations below the freezing level. Simulations are performed with and without melting layer schemes for the Dual precipitation radar (DPR) instrument onboard GPM using the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) global NWP model running operationally at Météo-France for two different one-month periods (June, 2020 and January, 2021). Results for a case study over the Atlantic ocean show that the revised melting scheme produces more realistic simulations as compared to the default scheme both at Ku (13.5 GHz) and Ka (35.5 GHz) frequencies and these simulations are much closer to observations. A statistical assessment using more samples show significant improvement of the first-guess departure statistics with the revised scheme compared to the existing melting scheme. As a step further, this study showcases the use of melting layer simulations for the classification of precipitation (stratiform, convective and transition) using the Dual Frequency Ratio algorithm (DFR). The classification results also reveal a significant overestimation of the rain reflectivities in all hemispheres, which can either be due to a tendency of the ARPEGE model to produce a too large amount of convective precipitation, or to a mis-representation of the convective precipitation fraction within the forward operator.
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RC1: 'Comment on amt-2024-131', Anonymous Referee #1, 17 Oct 2024
Summary
In their manuscript "Assessment and application of melting layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model", Mangla et al. briefly introduce the radar simulator recently implemented in the widely known and used radiative transfer model RTTOV v13.1. In addition, they present a revised version of the melting layer model by Bauer (2001). The new radar simulator and the melting layer model are validated and applied in a case study for a single location and time step over the Atlantic Ocean and in a statistical approach to observations of GPM/DPR in different climatic regions. The RTTOV radar simulator is used with the ARPEGE global NWP model as atmospheric input.
The results show that the radar simulator, in combination with the new melting scheme, is able to realistically reproduce the reflectivities even in and below the melting layer for the case study at Ku- and Ka-band in comparison to GPM/DPR observations. For the statistical comparison, they show a significant improvement in terms of first-guess departures of the simulation from the observations.
Scientific contribution and general comments
The radar simulator in RTTOV is a very important extension and makes the model even more usable for the community. Having a bright-band scheme included in the model is very good as well. Therefore, the manuscript gives a substantial contribution to the scientific progress.
Technically, I would have appreciated an additional proofreading, maybe even by a native speaker, before submitting the manuscript to AMT for discussion. Some parts or sentences are not very easy to read. In addition, there are many inconsistencies:
- the use of commas before and in enumerations
- whether bright-band is written "bright-band" or "bright band"
- same for Ku- and Ka-band
- using in units in situations like "15 dBZ and 10 dBZ" or "15 and 10 dBZ"
- acronyms they are sometimes introduced like "Global Precipitation Mission (GPM)" and sometimes "global precipitation mission (GPM)"
In the specific comments, I'm not mentioning all typos and, in my opinion, falsely set commas.
Specific comments
p.1 l.4: TOVS is not explained here
p.1 l.9: GPM is not explained here
p.2 l.24: CloudSat should be written with capital S throughout the manuscript
p.2 l.45-47: The sentence should be rewritten to "In the melting layer, the maximum size snowflakes are first transformed into wet flakes and then to raindrops of smaller sizes of equivalent mass and less number density as compared to the original flakes (Galligani et al., 2013).". Thereby, I'm not sure about "less number density". Why should that change? You do not change the number of particles within the volume.
p.3 l.63: change "retains" to "retain". Geer and Bardo are two people.
p.3 l.79: Why are graupel and hail written capitals? Same later on in the manuscript.
p.3 l.87: "whether if" remove "if" p.6 l.139: Remove "." in "mm6.m-3"
p.6 l.149: "radar range gate"
p.7 l.181: "The Marshall and Palmer (1948)(hereafter MP)" what?
p.8 Table1: Remove "s" from hydrometeors
p.9 l.223: "density and mass ... are"
p.9 l.226: "...melted hydrometeor fraction f_m..."
p.9 l.231: "...has also been used..."
p.10 l.243: "...melting layer model" or "scheme"
p.12/13 figure 3/4: Why do the lines at different temperatures cross without following any rules.
p.20 l.345pp: "this can.." and "This can..."
p.21 l.361/362: I do not understand this sentence.
p.22 figure 9: "...less (larger)..." less and larger do not belong together. It is either "less and more" or "smaller and larger"
p.24 l.384: There is always a profile of DFR. Sometimes the values are just equal 0.
p.25: First define V1 and V2 before talking about them.
p.25 l.405: "(given in the methodology diagram)" Please mention the threshold values here.
p.26 l.418: Mention here what the markers I, II, and III denote.
p.26 l.426/427: This sentence needs to be revised by including some articles.
p.27 figure 12: labels on the colorbar need to be adjusted better
p.29 l.449: I do not see the bright-band peak in the observations
p.29: In line 133 you mention the averaging of the profiles and promise to discuss it later. I would have expected this here.
p.36 l.487: Please give a reference where the melting layer scheme is validated for the passive simulations.
p.38 l.544: Why are there only five temperature bins but n_levels level?
Citation: https://doi.org/10.5194/amt-2024-131-RC1 -
AC2: 'Reply on RC1', Mary Borderies, 31 Jan 2025
The authors would like to thank the reviewer for its careful review of the manuscript and for providing valuable suggestions which significantly improve the quality of the work. In accordance with the suggestions, the authors have thoroughly checked the manuscript and shared the point by point responses to the comments by Reviewer 1 herewith. In the revised pdf file, the scientific changes are highlighted in orange. As requested, the authors corrected the English in some sections. Thes corrections are displayed in blue in the revised pdf file.
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RC2: 'Comment on amt-2024-131', Anonymous Referee #2, 17 Oct 2024
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AC1: 'Reply on RC2', Mary Borderies, 31 Jan 2025
The authors are grateful to Reviewer 2 for his/her in-depth review of the manuscript and his/her constructive comments which significantly improved the manuscript. We appreciate the concern regarding the input profiles taken from the global NWP model ARPEGE, which has been operational at Meteo-France since 1992. The authors recognize that this concern is particularly true in the context of a global NWP model in which the convection is parametrized and for which the effective resolution of the model is larger than the observations. Therefore, the differences between the model and the observations can indeed arise from several sources such as the modeling of the radiative transfer within the forward operator (e.g. Particle Size Distribution, precipitation fraction, shapes, etc...), as well as modelling of the clouds and precipitation within the forecast used as input. One alternative to reduce the latter source of error in the comparison would be to use cloud and precipitation retrievals as inputs of the radiative transfer (Johnson et al. 2016). This is kept in mind for future work and the authors added some sentences in the manuscript to highlight this particular point. Nonetheless, the authors think that the present work and analysis between observations and simulations is important as it is a preliminary step before the assimilation of these observations in a global NWP model, which has proven to be useful by several NWP centers (Ikuta et al. 2021; Fielding et al. 2020).
Please, you can find attached our point-to-point responses to all the comments. You can also the track changes related to your suggestions in red in the attached revised manuscript.
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AC1: 'Reply on RC2', Mary Borderies, 31 Jan 2025
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