the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Abstract. The established relationships between the size, shape, and terminal velocity of raindrops, along with the spheroidal shape approximation (SSA), are commonly employed for calculating radar observables in rain. This study, however, reveals the SSA's limitations in accurately simulating spectral and integrated backscattering polarimetric variables in rain at the W-band.
Improving existing models is a complex task that demands high-precision data from both laboratory settings and natural rain, enhanced stochastic shape approximation techniques, and comprehensive scattering simulations. To circumvent these challenges, this study introduces a simpler and more straightforward approach – the empirical scattering model (ESM).
The ESM is derived from an analysis of high-quality, low-turbulence Doppler spectra, which were selected from measurements taken with a 94 GHz radar at three different locations between 2021 and 2024. The ESM's primary advantages over the SSA include superior accuracy and the direct incorporation of microphysical effects observed in natural rain.
This study demonstrates that the ESM can potentially clarify issues in existing retrieval and calibration methods that use polarimetric observations at the W-band. The findings of this study are not only valuable for experts in cloud radar polarimetry but also for scattering modelers and laboratory experimenters since explaining the presented observations necessitates a more profound understanding of the microphysical properties and processes in rain.
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Status: open (until 14 Dec 2024)
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RC1: 'Comment on amt-2024-143', Anonymous Referee #1, 16 Oct 2024
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The scope of the paper is clear and the idea of proposing an ESM for raindrops at W-band for polarimetric variables is very original. The consequences of using an ESM instead of the standard spheroidal approximation are also thoroughly discussed. The topic is very relevant for precipitation research and characterization of rain microphysics.
There is room for improving the manuscript according to the following suggestions.
- Unclear what is the take home message in the remark at the end of Sect 4.2
- Line 304: “because of smaller concentration of these drops and attenuation by liquid
and gas” I disagree on the second reason, attenuation acts uniformly across the spectrum.
- Line 310-312: I have never noticed this secondary minimum. I would doubt these is due to non spherical effects, I would be more in favor of considering DSD effects only (unless we really disproof Mie)
- Not sure why Figure 9 comes before figure 8. Anyhow to me Fig9 is repetitive (you could include the red model lines in the left panels of fig.8
- 3: do we really need an artificial neural network? To me it just adds confusion. I would stick with a LUT based on Fig.8. Not sure what you add more than that.
- Sect 6.1: I see the differences between integrated ZDR and delta when using your ESM. Maybe it is worth comparing these differences with typical errors of such variable (you mention errors in delta, maybe it is worth also mentioning errors in zdr).
- 11: what are the blue dots all exactly at 6 dB Z offset?
Minor corrections:
- Line 47 A compactness è The compactness
- Line 48: “A large number of cloud radars are capable of polarimetric measurements” (well there are few in the world, I would attenuate the statement.
- Also statements at line 54-56 ( a bit vague, e.g. what do you mean with strong rain, I would rephrase them)
- “an oscillatory behavior at drop sizes roughly proportional to half of the radar wavelength”, there are multiple oscillations occurring at multiple size, rephrase
- “In real rain measurements, however, we do see Zdr considerably exceeding 0.12 dB”. It looks like a sentence out of the blue, not corroborated by any data or a reference. You need to explain more here or skip it. Also could that signal be caused by differential attenuation?
- Line 118-120. The radar actually provides spectra as a function of the radial velocity (V_k) not of v_k. A different thing is how you reprocess the data.
- Line 124: high è higher and lowè lower
- “still include observations in rain affected by strong attenuation”, not sure how you can do that if Z<5 dBZ are excluded, or you need to specify what you mean with strong attenuation
- “all lines with SNR below 30 dB” è all spectral signal with ….. The term “lines” sounds a little bit ambiguous to me. Check its use.
- Line 339: delete “a”
- Line 413: add spheroidal (before approximations)
Citation: https://doi.org/10.5194/amt-2024-143-RC1
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