Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1947-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Mar 2025
Development and technical paper |
| 25 Mar 2025
| 25 Mar 2025
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Yi-Ning Shi
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
Jun Yang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
Lujie Han
Meteorological Observation Center, China Meteorological Administration, Beijing, 100081, China
Zhejiang Lin'an Atmospheric Background National Observation and Research Station, Hangzhou, 311300, China
Jiajia Mao
Meteorological Observation Center, China Meteorological Administration, Beijing, 100081, China
Wanlin Kan
Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, 210041, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
Fuzhong Weng
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, 100081, China
Related authors
Jing Sun, Yunying Li, Hao Hu, Qian Li, Chengzhi Ye, Yi-Ning Shi, and Zitong Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2939, https://doi.org/10.5194/egusphere-2025-2939, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study develops a new retrieval model called DORF to estimate cloud optical thickness and effective radius using satellite data and a radiative transfer model. Results show cloud vertical structure, especially ice clouds above water clouds, can mask signals and cause errors. Changes in water content at different heights affect reflectance and retrievals, especially for small particles. Understanding these effects improves weather and climate forecasts.
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025, https://doi.org/10.5194/gmd-18-4855-2025, 2025
Short summary
Short summary
In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled aerosol–meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the China Meteorological Administration Global Forecasting System (CMA-GFS) operational global numerical weather model. The results show that assimilating BC (black carbon) observations can generate analysis increments not only for BC but also for atmospheric variables.
Chengli Ji, Qiankai Jin, Feilong Li, Yuyang Liu, Zhicheng Wang, Jiajia Mao, Xiaoyu Ren, Yan Xiang, Wanlin Jian, Zhenyi Chen, and Peitao Zhao
Atmos. Meas. Tech., 18, 3179–3191, https://doi.org/10.5194/amt-18-3179-2025, https://doi.org/10.5194/amt-18-3179-2025, 2025
Short summary
Short summary
This study presents the humidity measurements with a synergistic algorithm combining Raman lidar, microwave radiometer, and satellite. The results from 47 sites in China show the best correlation over 0.9 concerning the radiosonde measurements. This validates the relative humidity (RH) accuracy with various data integrations. Three representative sites present the different seasonal characteristics, indicating the geographic and height influences on the RH vertical distribution.
Jing Sun, Yunying Li, Hao Hu, Qian Li, Chengzhi Ye, Yi-Ning Shi, and Zitong Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2939, https://doi.org/10.5194/egusphere-2025-2939, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study develops a new retrieval model called DORF to estimate cloud optical thickness and effective radius using satellite data and a radiative transfer model. Results show cloud vertical structure, especially ice clouds above water clouds, can mask signals and cause errors. Changes in water content at different heights affect reflectance and retrievals, especially for small particles. Understanding these effects improves weather and climate forecasts.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Xiaojing Shen, Quan Liu, Junying Sun, Wanlin Kong, Qianli Ma, Bing Qi, Lujie Han, Yangmei Zhang, Linlin Liang, Lei Liu, Shuo Liu, Xinyao Hu, Jiayuan Lu, Aoyuan Yu, Huizheng Che, and Xiaoye Zhang
Atmos. Chem. Phys., 25, 5711–5725, https://doi.org/10.5194/acp-25-5711-2025, https://doi.org/10.5194/acp-25-5711-2025, 2025
Short summary
Short summary
In this work, an automatic switched inlet system was developed and employed to investigate the aerosols and cloud droplets at a mountain site with frequent cloud processes. It showed different characteristics of cloud residual and interstitial particles. Stronger particle hygroscopicity reduced liquid water content and smaller cloud droplet diameters. This investigation contributes to understanding aerosol–cloud interactions by assessing the impact of aerosol particles on cloud microphysics.
Xiaoze Xu, Wei Han, Jincheng Wang, Zhiqiu Gao, Fenghui Li, Yan Cheng, and Naifeng Fu
Atmos. Meas. Tech., 18, 1339–1353, https://doi.org/10.5194/amt-18-1339-2025, https://doi.org/10.5194/amt-18-1339-2025, 2025
Short summary
Short summary
Commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellites are generally cheap and can generate a large volume of globally distributed observations in a short period of time. To evaluate the practical application value of these data, we must assess their quality. We evaluate the quality of YUNYAO RO data. By using the “three-cornered hat” method and comparing with data from Metop-C and COSMIC-2, it was found that the YUNYAO GNSS-RO data are of high quality.
Quan Liu, Xiaojing Shen, Junying Sun, Yangmei Zhang, Bing Qi, Qianli Ma, Lujie Han, Honghui Xu, Xinyao Hu, Jiayuan Lu, Shuo Liu, Aoyuan Yu, Linlin Liang, Qian Gao, Hong Wang, Huizheng Che, and Xiaoye Zhang
Atmos. Chem. Phys., 25, 3253–3267, https://doi.org/10.5194/acp-25-3253-2025, https://doi.org/10.5194/acp-25-3253-2025, 2025
Short summary
Short summary
Through simultaneous measurements of aerosol particles and fog droplets, the evolution of droplets size distribution during the eight observed fog events was investigated. The results showed that the concentration and size distribution of pre-fog aerosol had significant impacts on fog microphysical characteristics. The extinction of fog interstitial particles played an important role in visibility degradation for light fogs, especially in polluted regions.
Minghua Liu, Wei Han, Yunfan Yang, Haofei Sun, and Ruoying Yin
EGUsphere, https://doi.org/10.5194/egusphere-2025-680, https://doi.org/10.5194/egusphere-2025-680, 2025
Short summary
Short summary
This research develops a machine learning approach to estimate atmospheric temperature and humidity profiles using satellite and weather data. The results showed that our method could accurately retrieve profiles with a high degree of precision. However, we found some limitations in very humid conditions, suggesting that further improvements to the model are needed. Our findings could help enhance the reliability of atmospheric measurements and contribute to better weather predictions.
Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-12, https://doi.org/10.5194/egusphere-2025-12, 2025
Short summary
Short summary
Ground-based microwave radiometers (MWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from MWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
Short summary
Short summary
The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-175, https://doi.org/10.5194/amt-2024-175, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the FengYun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
Short summary
Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Xiaoyong Zhuge, Xiaolei Zou, Lu Yu, Xin Li, Mingjian Zeng, Yilun Chen, Bing Zhang, Bin Yao, Fei Tang, Fengjiao Chen, and Wanlin Kan
Earth Syst. Sci. Data, 16, 1747–1769, https://doi.org/10.5194/essd-16-1747-2024, https://doi.org/10.5194/essd-16-1747-2024, 2024
Short summary
Short summary
The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022, https://doi.org/10.5194/acp-22-6393-2022, 2022
Short summary
Short summary
Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Cited articles
Cady-Pereira, K. E., Turner, E., and Saunders, R.: Inter-comparison of line-by-line radiative transfer models MonoRTM and AMSUTRAN for microwave frequencies from the Top-Of-Atmosphere, Tech. Rep. NWPSAF-MO-VS-057, NWP SAF, https://nwp-saf.eumetsat.int/publications/vs_reports/nwpsaf-mo-vs-057.pdf (lsat access: 30 January 2024), 2021. a
Cao, Y., Shi, B., Zhao, X., Yang, T., and Min, J.: Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall, Remote Sens., 15, 4314, https://doi.org/10.3390/rs15174314, 2023. a
Caumont, O., Cimini, D., Löhnert, U., Alados-Arboledas, L., Bleisch, R., Buffa, F., Ferrario, M. E., Haefele, A., Huet, T., Madonna, F., and Pace, G.: Assimilation of humidity and temperature observations retrieved from ground-based microwave radiometers into a convective-scale NWP model, Q. J. Roy. Meteor. Soc., 142, 2692–2704, https://doi.org/10.1002/qj.2860, 2016. a
Chen, H., Han, W., Wang, H., Pan, C., An, D., Gu, S., and Zhang, P.: Why and How Does the Actual Spectral Response Matter for Microwave Radiance Assimilation?, Geophys. Res. Lett., 48, e2020GL092306, https://doi.org/10.1029/2020GL092306, 2021. a
Chen, Y., Han, Y., Van Delst, P., and Weng, F.: On water vapor Jacobian in fast radiative transfer model, J. Geophys. Res.-Atmos., 115, D12303, https://doi.org/10.1029/2009JD013379, 2010. a
Chen, Y., Han, Y., and Weng, F.: Comparison of two transmittance algorithms in the community radiative transfer model: Application to AVHRR, J. Geophys. Res.-Atmos., 117, D06206, https://doi.org/10.1029/2011JD016656, 2012. a
China Meteorological Administration: China Meteorological Data Service Center, https://data.cma.cn/en (last access: 2 June 2024), 2024. a
Cimini, D., Hewison, T., Martin, L., Güldner, J., Gaffard, C., and Marzano, F.: Temperature and humidity profile retrievals from ground-based microwave radiometers during TUC, Meteorol. Z., 15, 45–56, https://doi.org/10.1127/0941-2948/2006/0099, 2006. a
Cimini, D., Rosenkranz, P. W., Tretyakov, M. Y., Koshelev, M. A., and Romano, F.: Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals, Atmos. Chem. Phys., 18, 15231–15259, https://doi.org/10.5194/acp-18-15231-2018, 2018. a
Cimini, D., Hocking, J., De Angelis, F., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Nilo, S., Romano, F., Ricciardelli, E., Ripepi, E., Viggiano, M., Luini, L., Riva, C., Marzano, F. S., Martinet, P., Song, Y. Y., Ahn, M. H., and Rosenkranz, P. W.: RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability, Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, 2019. a, b, c, d, e, f
Clough, S., Shephard, M., Mlawer, E., Delamere, J., Iacono, M., Cady-Pereira, K., Boukabara, S., and Brown, P.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a, b, c
De Angelis, F., Cimini, D., Hocking, J., Martinet, P., and Kneifel, S.: RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations, Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, 2016. a, b, c, d, e, f, g, h, i
De Angelis, F., Cimini, D., Löhnert, U., Caumont, O., Haefele, A., Pospichal, B., Martinet, P., Navas-Guzmán, F., Klein-Baltink, H., Dupont, J.-C., and Hocking, J.: Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network, Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, 2017. a
Errico, R. M.: What Is an Adjoint Model?, B. Am. Meteorol. Soc., 78, 2577–2592, https://doi.org/10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2, 1997. a
Gordon, I., Rothman, L., Hargreaves, R., Hashemi, R., Karlovets, E., Skinner, F., Conway, E., Hill, C., Kochanov, R., Tan, Y., Wcisło, P., Finenko, A., Nelson, K., Bernath, P., Birk, M., Boudon, V., Campargue, A., Chance, K., Coustenis, A., Drouin, B., Flaud, J., Gamache, R., Hodges, J., Jacquemart, D., Mlawer, E., Nikitin, A., Perevalov, V., Rotger, M., Tennyson, J., Toon, G., Tran, H., Tyuterev, V., Adkins, E., Baker, A., Barbe, A., Canè, E., Császár, A., Dudaryonok, A., Egorov, O., Fleisher, A., Fleurbaey, H., Foltynowicz, A., Furtenbacher, T., Harrison, J., Hartmann, J., Horneman, V., Huang, X., Karman, T., Karns, J., Kassi, S., Kleiner, I., Kofman, V., Kwabia–Tchana, F., Lavrentieva, N., Lee, T., Long, D., Lukashevskaya, A., Lyulin, O., Makhnev, V., Matt, W., Massie, S., Melosso, M., Mikhailenko, S., Mondelain, D., Müller, H., Naumenko, O., Perrin, A., Polyansky, O., Raddaoui, E., Raston, P., Reed, Z., Rey, M., Richard, C., Tóbiás, R., Sadiek, I., Schwenke, D., Starikova, E., Sung, K., Tamassia, F., Tashkun, S., Vander Auwera, J., Vasilenko, I., Vigasin, A., Villanueva, G., Vispoel, B., Wagner, G., Yachmenev, A., and Yurchenko, S.: The HITRAN2020 molecular spectroscopic database, J. Quant. Spectrosc. Rad., 277, 107949, https://doi.org/10.1016/j.jqsrt.2021.107949, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582c, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hocking, J., Vidot, J., Brunel, P., Roquet, P., Silveira, B., Turner, E., and Lupu, C.: A new gas absorption optical depth parameterisation for RTTOV version 13, Geosci. Model Dev., 14, 2899–2915, https://doi.org/10.5194/gmd-14-2899-2021, 2021. a, b
Illingworth, A. J., Cimini, D., Haefele, A., Haeffelin, M., Hervo, M., Kotthaus, S., Löhnert, U., Martinet, P., Mattis, I., O’Connor, E. J., and Potthast, R.: How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?, B. Am. Meteorol. Soc., 100, 605–619, https://doi.org/10.1175/BAMS-D-17-0231.1, 2019. a
Karpowicz, B. M., Stegmann, P. G., Johnson, B. T., Christophersen, H. W., Hyer, E. J., Lambert, A., and Simon, E.: pyCRTM: A python interface for the community radiative transfer model, J. Quant. Spectrosc. Ra., 288, 108263, https://doi.org/10.1016/j.jqsrt.2022.108263, 2022. a
Larosa, S., Cimini, D., Gallucci, D., Nilo, S. T., and Romano, F.: PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations, Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, 2024. a
Leuenberger, D., Haefele, A., Omanovic, N., Fengler, M., Martucci, G., Calpini, B., Fuhrer, O., and Rossa, A.: Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations, B. Am. Meteorol. Soc., 101, E1036–E1051, https://doi.org/10.1175/BAMS-D-19-0119.1, 2020. a
Li, J. and Fu, Q.: Absorption Approximation with Scattering Effect for Infrared Radiation, J. Atmos. Sci., 57, 2905–2914, https://doi.org/10.1175/1520-0469(2000)057<2905:AAWSEF>2.0.CO;2, 2000. a
Lin, H.-C., Sun, J., Weckwerth, T. M., Joseph, E., and Kay, J.: Assimilation of New York State Mesonet Surface and Profiler Data for the 21 June 2021 Convective Event, Mon. Weather Rev., 151, 485–507, https://doi.org/10.1175/MWR-D-22-0136.1, 2023. a
Liou, K.: Radiation and Cloud Processes in the Atmosphere: Theory, Observation and Modeling, Oxford University, ISBN 9780195049107, 1992. a
Martinet, P., Dabas, A., Donier, J.-M., Douffet, T., Garrouste, O., and Guillit, R.: 1D-Var temperature retrievals from microwave radiometer and convective scale model, Tellus A, 67, 27925, https://doi.org/10.3402/tellusa.v67.27925, 2015. a
Martinet, P., Cimini, D., Burnet, F., Ménétrier, B., Michel, Y., and Unger, V.: Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study, Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, 2020. a
Matricardi, M., Chevallier, F., Kelly, G., and Thépaut, J.-N.: An improved general fast radiative transfer model for the assimilation of radiance observations, Q. J. Roy. Meteor. Soc., 130, 153–173, https://doi.org/10.1256/qj.02.181, 2004. a
McMillin, L. M., Crone, L. J., and Kleespies, T. J.: Atmospheric transmittance of an absorbing gas. 5. Improvements to the OPTRAN approach, Appl. Optics, 34, 8396–8399, https://doi.org/10.1364/AO.34.008396, 1995. a
Mlawer, E. J., Payne, V. H., Moncet, J.-L., Delamere, J. S., Alvarado, M. J., and Tobin, D. C.: Development and recent evaluation of the MT_CKD model of continuum absorption, Philos. T. Roy. Soc. A, 370, 2520–2556, https://doi.org/10.1098/rsta.2011.0295, 2012. a
Moradi, I., Goldberg, M., Brath, M., Ferraro, R., Buehler, S. A., Saunders, R., and Sun, N.: Performance of Radiative Transfer Models in the Microwave Region, J. Geophys. Res.-Atmos., 125, e2019JD031831, https://doi.org/10.1029/2019JD031831, 2020. a, b
Rochon, Y. J., Garand, L., Turner, D. S., and Polavarapu, S.: Jacobian mapping between vertical coordinate systems in data assimilation, Q. J. Roy. Meteor. Soc., 133, 1547–1558, https://doi.org/10.1002/qj.117, 2007. a, b
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison of measurements and models, Radio Sci., 33, 919–928, https://doi.org/10.1029/98RS01182, 1998. a, b
Saunders, R., Matricardi, M., and Brunel, P.: An improved fast radiative transfer model for assimilation of satellite radiance observations, Q. J. Roy. Meteor. Soc., 125, 1407–1425, https://doi.org/10.1002/qj.1999.49712555615, 1999. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Shi, Y.-N., Yang, J., and Weng, F.: Codes and Coefficients for Radiative Transfer for Ground-based Microwave Radiometers (ARMS-gb v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.14032776, 2024. a
Stegmann, P. G., Johnson, B., Moradi, I., Karpowicz, B., and McCarty, W.: A deep learning approach to fast radiative transfer, J. Quant. Spectrosc. Ra., 280, 108088, https://doi.org/10.1016/j.jqsrt.2022.108088, 2022. a
Toon, O. B., McKay, C. P., Ackerman, T. P., and Santhanam, K.: Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res.-Atmos., 94, 16287–16301, https://doi.org/10.1029/JD094iD13p16287, 1989. a
Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady-Pereira, K. E., and Gaustad, K. L.: Retrieving Liquid Water Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers, IEEE T. Geosci. Remote S., 45, 3680–3690, https://doi.org/10.1109/TGRS.2007.903703, 2007. a
Turner, E., Rayer, P., and Saunders, R.: AMSUTRAN: A microwave transmittance code for satellite remote sensing, J. Quant. Spectrosc. Ra., 227, 117–129, https://doi.org/10.1016/j.jqsrt.2019.02.013, 2019. a
Vural, J., Merker, C., Löffler, M., Leuenberger, D., Schraff, C., Stiller, O., Schomburg, A., Knist, C., Haefele, A., and Hervo, M.: Improving the representation of the atmospheric boundary layer by direct assimilation of ground-based microwave radiometer observations, Q. J. Roy. Meteor. Soc., 150, 1012–1028, https://doi.org/10.1002/qj.4634, 2024. a
Wei, J., Shi, Y., Ren, Y., Li, Q., Qiao, Z., Cao, J., Ayantobo, O. O., Yin, J., and Wang, G.: Application of Ground-Based Microwave Radiometer in Retrieving Meteorological Characteristics of Tibet Plateau, Remote Sens., 13, 2527, https://doi.org/10.3390/rs13132527, 2021. a
Wei, Y., Peng, K., Ma, Y., Sun, Y., Zhao, D., Ren, X., Yang, S., Ahmad, M., Pan, X., Wang, Z., and Xin, J.: Validation of ERA5 Boundary Layer Meteorological Variables by Remote-Sensing Measurements in the Southeast China Mountains, Remote Sens., 16, 548, https://doi.org/10.3390/rs16030548, 2024. a
Weng, F. and Liu, Q.: Satellite Data Assimilation in Numerical Weather Prediction Models. Part I: Forward Radiative Transfer and Jacobian Modeling in Cloudy Atmospheres, J. Atmos. Sci., 60, 2633–2646, https://doi.org/10.1175/1520-0469(2003)060<2633:SDAINW>2.0.CO;2, 2003. a
Weng, F., Yu, X., Duan, Y., Yang, J., and Wang, J.: Advanced Radiative transfer Modeling System (ARMS): A New-Generation Satellite Observation Operator Developed for Numerical Weather Prediction and Remote Sensing Applications, Adv. Atmos. Sci., 37, 131–136, https://doi.org/10.1007/s00376-019-9170-2, 2020. a
Wu, J., Guo, J., Yun, Y., Yang, R., Guo, X., Meng, D., Sun, Y., Zhang, Z., Xu, H., and Chen, T.: Can ERA5 reanalysis data characterize the pre-storm environment?, Atmos. Res., 297, 107108, https://doi.org/10.1016/j.atmosres.2023.107108, 2024. a
Yang, J. and Min, Q.: Retrieval of Atmospheric Profiles in the New York State Mesonet Using One-Dimensional Variational Algorithm, J. Geophys. Res.-Atmos., 123, 7563–7575, https://doi.org/10.1029/2018JD028272, 2018. a
Yang, J., Ding, S., Dong, P., Bi, L., and Yi, B.: Advanced Radiative transfer Modeling System developed for satellite data assimilation and remote sensing applications, J. Quant. Spectrosc. Ra., 251, 107043, https://doi.org/10.1016/j.jqsrt.2020.107043, 2020. a
Zhang, F., Wu, K., Li, J., Yang, Q., Zhao, J.-Q., and Li, J.: Analytical Infrared Delta-Four-Stream Adding Method from Invariance Principle, J. Atmos. Sci., 73, 4171–4188, https://doi.org/10.1175/JAS-D-15-0317.1, 2016. a
Zhang, F., Shi, Y.-N., Li, J., Wu, K., and Iwabuchi, H.: Variational Iteration Method for Infrared Radiative Transfer in a Scattering Medium, J. Atmos. Sci., 74, 419–430, https://doi.org/10.1175/JAS-D-16-0172.1, 2017. a
Zhang, F., Wu, K., Li, J., Zhang, H., and Hu, S.: Radiative transfer in the region with solar and infrared spectra overlap, J. Quant. Spectrosc. Ra., 219, 366–378, https://doi.org/10.1016/j.jqsrt.2018.08.025, 2018. a
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds...
