Preprints
https://doi.org/10.5194/egusphere-2025-5017
https://doi.org/10.5194/egusphere-2025-5017
16 Feb 2026
 | 16 Feb 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Development and validation of ARMS-gb v2.0: Extending fast radiative transfer modeling capability to all-sky conditions for ground-based microwave radiometer retrievals

Ziyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang

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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Ziyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang

Status: open (until 13 Apr 2026)

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Ziyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang

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

Ziyue Huang, Yi-Ning Shi, Fuzhong Weng, and Jun Yang
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Short summary
We present an updated version of the Advanced Radiative Transfer Modeling System for ground-based sensors to better use microwave instruments in all weather. We added realistic cloud and rain effects and compared the results with six months of observations at two stations. The model accurately simulates observations in cloudy conditions. This advance can effectively improve the use of observational data and enhance weather forecasting capability.
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