Development of the TCWA2 Bulk Cloud Microphysics Scheme and Its Integration with a Dual-Polarization Radar Operator for Forecasting Applications

Tsai, Tzu-Chin; Chen, Jen-Ping; Liu, Zhiquan; Jiang, Siou-Ying; Kong, Rong; Wu, Ying-Jhang; Ban, Junmei; Hsiao, Ling-Feng; Tang, Yu-Shuang; Chang, Pao-Liang; Hong, Jing-Shan

doi:10.22541/essoar.177248713.30739498/v1

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https://doi.org/10.22541/essoar.177248713.30739498/v1

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12 Mar 2026

| 12 Mar 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Development of the TCWA2 Bulk Cloud Microphysics Scheme and Its Integration with a Dual-Polarization Radar Operator for Forecasting Applications

Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong

Abstract. This study presents the development and evaluation of TCWA2, a double-moment bulk cloud microphysics scheme designed for weather forecasting that incorporates radar observations at the Taiwan Central Weather Administration. By simplifying the triple-moment NTU microphysics scheme, TCWA2 retains a gamma-type particle size distribution with variable spectral parameters, diagnoses hydrometeor-associated physical properties, revises number sinks due to evaporation loss, and implements theoretically based fall-speed formulations that account for particle density and aspect ratio. To connect bulk microphysics parameterizations with radar-based diagnostics, TCWA2 is coupled with a customized bulk dual-polarization radar operator derived from offline bin-based scattering calculations under the Rayleigh approximation. This integrated microphysics–radar system provides an internally consistent representation linking particle-size distribution characteristics, hydrometeor morphology, sedimentation processes, and bulk radar observables. The intrinsic behavior of TCWA2 is first examined through two-dimensional idealized squall-line simulations in the WRF model, which reveal realistic microphysical structures and coherent polarimetric radar signatures. The scheme is further assessed through a real-case simulation of an afternoon convective event using the MPAS model, with validation against observed dual-polarization radar data. The joint distributions of radar reflectivity and polarimetric variables show strong agreement with observations, with pattern correlations exceeding 0.9 across three altitude layers, indicating that TCWA2 effectively captures the dominant microphysical features in radar signatures. Therefore, TCWA2 offers a physically consistent and computationally efficient framework for integrating bulk cloud microphysics with dual-polarization radar operators across platforms, with potential for future radar-based forecasting.

Received: 06 Mar 2026 – Discussion started: 12 Mar 2026

Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong

Status: open (until 08 May 2026)

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RC1:
'Comment on egusphere-2026-1257', Anonymous Referee #1, 20 Apr 2026 reply
Review of EGUSphere-2026-1257

This paper describes the development and evaluation of the new TCWA2 bulk microphysics scheme (BMS), which is based on the more detailed 3-moment NTU scheme. The new scheme is described, along with descriptions of how aspects of TCWA2 are “parameterized” based on NTU. The scheme also has a dual-polarization radar simulator component. The scheme and the forward model (radar operator) are described. Detailed illustrations of how the scheme works are shown through idealized 2D WRF simulations of a squall line. Finally, a real case convective system over Taiwan is performed using the MPAS model with comparisons to observations (precipitation and dual-pol radar) and to the two existing BMPs in MPAS.

Overall this is a solid paper. It is clearly written and presented and scientifically sound. The authors do a nice job in illustrating how the new scheme works in the simple idealized 2D framework before proceeding to show its behavior for a real-case 3D simulation. I do not have any fundamental problems or major comments, just a few minor comments. I will recommend minor revisions, but these should be straightforward.

SPECIFIC COMMENTS:

The Abstract could probably be broken up into two paragraphs, the second starting at “The intrinsic behavior…”. WRF and MPAS should probably be defined in the abstract.

Line 120: Is there are reference for the MPAS model?

Line 227 “predicting the second moment (M2)”. This is potentially confusing since the intended meaning, I think, is that M2 would be predicted for a 3-moment scheme (i.e. “second moment” suggested 2-moment scheme).

Line 234-235 “Thus, traditional BMPs usually treat ice particles as spheres with fixed, prescribed densities.” This is legally true as worded (with “usually”) but misleading to the point of being incorrect – since after 2005 several BMPs started using m-D relationships for “snow” with an exponent of 2, not 3; thus not treated as spheres and not with constant densities. This should be acknowledged.

Table 1: The values of the coefficients appear to have way too many significant digits.

Line 402: Which idealized case is this? Please either show the initial sounding or add a reference.

For the vertical cross-section plots (Figs. 3-7), you could consider plotting the altitude axis labels for only the left-most panels. This would allow you to widen each of the panels. I also think panel labels [a), b), etc.] should be added and appropriately referenced in the main text when referring to a specific panel.

Section 3 gives a nice overview of how the BMP scheme and dual-pol simulator work. By definition of an idealized set-up, no “verification” is needed. However, would it be possible to provide some descriptions, and even figures, of how some of these fields look for a typical squall line? For example, I have an idea what Z_hor should look like, so my sense from Fig. 9”a” is that the general structure and values are reasonable though it appears to be missing a bright band and the values above the melting layer in the stratiform region look too high (though maybe this is just an artifact of the simple model set-up). However, I don’t have a sense of what the other dual-pol “total” values should look like (e.g. Figs. 10a, 11a). A plot of these from a real squall line would be really great for highly qualitative “verification”.

Line 651 “…with a peak of 242 mm, indicating it overestimates precipitation efficiency.” In my opinion, a single point value should not be used to make this claim. My suggestion would be to take an average value over the highest 20 (?) points – or something like that – and the same for the QPESUMS observations.

Line 751 “Figure 18 further examines…”. This type of phrase appears is several places and should be improved. A figure does not examine anything, it *illustrates* or depicts something.

Figure 14: I would recommend making this a larger 2 x 2 panel figure.

Given that there is an operational NWP direction of this work, it would be useful in section 4 to include some information about computational time of, e.g., Thompson vs. TCWA2 in MPAS. If TCWA2 is significantly more expense, I don’t think you need to worry about it at this point – the results are impressive and the proof-of-concept is clear. But people are always interested in computational cost.

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Citation: https://doi.org/10.5194/egusphere-2026-1257-RC1

Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong

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Short summary

Weather radars observe rain and ice inside clouds, but models often cannot fully use this information because their cloud physics schemes do not describe the particle properties needed to simulate radar signals. This study develops a new cloud microphysics scheme directly linked to a radar operator that uses the same particle information. Tests using an idealized and a real case show that TCWA2 can reproduce observed radar features, supporting improved radar-based weather forecasting.


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