the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and validation of ARMS-gb v2.0: Extending fast radiative transfer modeling capability to all-sky conditions for ground-based microwave radiometer retrievals
Abstract. Ground-based microwave radiometers provide continuous, all-weather observations of boundary-layer temperature and humidity, closing a critical near-surface observation gap. The Advanced Radiative Transfer Modeling System – ground-based (ARMS-gb) is a fast radiative transfer model specifically designed to simulate the brightness temperatures these instruments observe. This paper presents ARMS-gb v2.0, which introduces modules to calculate absorption and scattering from hydrometeors, and a multi-scattering solver using the discrete ordinate addition method (ADOM). The model now simulates cloud water, rain, ice, snow, and graupel using optical-property look-up tables computed with Mie theory and the discrete dipole approximation (DDA). Other new aspects are the extension of the existing tangent-linear and adjoint (TL/AD) modules to include hydrometeor processes, enabling all-sky retrieval and variational data assimilation. Validation against field measurements from 14- and 22-channel ground-based microwave radiometers indicates that ARMS-gb v2.0 can effectively simulate brightness temperatures under all-sky conditions, with the mean observed minus simulated brightness temperature across all channels kept within 1 K in cloudy cases. Compared with ARMS-gb v1.0, which neglects cloud effects, the root mean square error (RMSE) under cloudy conditions decreases by 1–2 K in the strong water-vapor channels, most notably at 30 GHz, where the correlation improves from 0.34 to 0.71. In the weak oxygen band, the O-B decreases by 3–4 K, particularly at 51 GHz, where the correlation increases from 0.43 to 0.85. Moreover, the results indicate that the DDA model slightly outperforms the Mie model in characterizing frozen hydrometeors at these channels. However, simulation errors increase significantly during precipitation events, and the RMSE in the water-vapor absorption band can reach 30–40 K, which remains a challenge for assimilation and retrieval in such conditions.
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RC1: 'Comment on egusphere-2025-5017', Anonymous Referee #1, 24 Mar 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5017/egusphere-2025-5017-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-5017-RC1 -
RC2: 'Comment on egusphere-2025-5017', Anonymous Referee #2, 20 Jun 2026
Review for „Development and validation of ARMS-gb v2.0: Extending fast radiative transfer modeling capability to all-sky conditions for ground-based microwave radiometer retrievals” by Ziyue Huang et al.
Synopsis:
This manuscript presents a fast radiative transfer model for ground-based microwave radiances, and its potential under cloudy and rainy conditions.
General comments:
The manuscript is generally well written, the figures and tables are presented in a comprehensive and clear way.
However, I have one methodological problem with the evaluation in section 4: The new model (ARMS-gb v2.0) is compared to observations and other fast radiative transfer models (RTTOV-gb and ARMS-gb v1.0 without clouds). What is missing to my opinion is the comparison to a high-accuracy radiative transfer tool, such as MonoRTM or others. Only with that it can be assessed whether the observed biases (see Fig. 6) are due to instrumental issues or due to deficiencies of the model. A further uncertainty source might be biases in ERA-5 data, especially regarding cloud occurrence. As the behavior of YKW-1 and YKW-2 are quite different, I would be very much interested in this comparison, and it would make it possible to attribute biases to either model or observation.
To my mind this is crucial to assess the performance of the new radiative transfer model.
Detailed comments:
Line 29: especially the vertical structure of the PBL is important for extreme weather
Line 41-42: better: “profiles with moderate vertical resolution, and decreasing with height"
Line 45: better: “local” than “regional”
Line 47: Absorption is dominated by water-vapor absorption only in K-Band (not V-Band)
Line 120: Please provide more information about the instruments (YKW-1 and YKW-2) From which manufacturer were they built? Are there references to these instruments?
Lines 194-195: Please rephrase – I would rather say, “….plays a fundamental role in the microwave radiative transfer and thus in the resulting brightness temperature.
Lines 247-248: How many profiles did you use? Daily? Hourly?
Line 368: Here you write about instrument calibration issues. Can you specify the calibration accuracy for both instruments? How often were these radiometers calibrated and using which method? This should be part of the instrument section.
Figure 5: Do you average over all cases? How would the difference look for only cloudy cases, excluding all clear sky scenes?
Data availability: Please provide the datasets from GMRs on an openly accessible platform.
Citation: https://doi.org/10.5194/egusphere-2025-5017-RC2
Model code and software
Development and validation of ARMS-gb v2.0: Extending fast radiative transfer modeling capability to all-sky conditions for ground-based microwave radiometers Ziyue Huang, Yining Shi, Fuzhong Weng, and Jun Yang https://zenodo.org/records/17318670?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjE0NTY4MDYzLTEyNjUtNDM5ZC1iMjNjLWFlNTAwYzg4MTJmMCIsImRhdGEiOnt9LCJyYW5kb20iOiIwOTY2YjE0ZTg3OWFlMjA2Njk2YTM3MmMzZGMzMmJjZCJ9.qqV5bjm8DjH4UDewf25Gnu80kuDHYD7SIAx9Yr07fDo2d1jInz656chBciEgyN--MabLZyzpzOjf4LtR4TGPwg
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