Articles | Volume 16, issue 17 
            
                
                    
            
            
            https://doi.org/10.5194/amt-16-4137-2023
                    © Author(s) 2023. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-4137-2023
                    © Author(s) 2023. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Feasibility analysis of optimal terahertz (THz) bands for passive limb sounding of middle and upper atmospheric wind
Wenyu Wang
                                            Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
                                        
                                    
                                            Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
                                        
                                    Zhenzhan Wang
                                            Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
                                        
                                    Related authors
Yifan Yang, Tingfeng Dou, Gaojie Xu, Rui Zhou, Bo Li, Letu Husi, Wenyu Wang, and Cunde Xiao
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-447, https://doi.org/10.5194/essd-2025-447, 2025
                                    Preprint under review for ESSD 
                                    Short summary
                                    Short summary
                                            
                                                We built an AI using China's Fengyun satellites (2009–2024) to map global atmospheric ice vital for climate. It processes tough data, making 3 public sets: orbital ice scans, monthly global maps, cloud masks. First long-term ice records over land/ocean from Chinese satellite. Offers unmatched coverage for decade climate studies despite precision limits.
                                            
                                            
                                        Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
                                    Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
                                            
                                            
                                        Yifan Yang, Tingfeng Dou, Gaojie Xu, Rui Zhou, Bo Li, Letu Husi, Wenyu Wang, and Cunde Xiao
                                        Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-447, https://doi.org/10.5194/essd-2025-447, 2025
                                    Preprint under review for ESSD 
                                    Short summary
                                    Short summary
                                            
                                                We built an AI using China's Fengyun satellites (2009–2024) to map global atmospheric ice vital for climate. It processes tough data, making 3 public sets: orbital ice scans, monthly global maps, cloud masks. First long-term ice records over land/ocean from Chinese satellite. Offers unmatched coverage for decade climate studies despite precision limits.
                                            
                                            
                                        Bohai Li, Shanshan Wang, Zhiwen Jiang, Yuhao Yan, Sanbao Zhang, Ruibin Xue, Yuhan Shi, Chuanqi Gu, Jian Xu, and Bin Zhou
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-2588, https://doi.org/10.5194/egusphere-2025-2588, 2025
                                    Short summary
                                    Short summary
                                            
                                                Based on ground-based remote sensing and sea-land breeze identification algorithms, researchers found that sea breezes and typhoons along Hainan Island's coast suppress photochemical reactions but transport ozone precursors to the area. Sea breezes largely confine pollutants below 300 m, while typhoons elevate pollution levels at mid-upper altitudes. These findings highlight that tropical coastal sea breezes and typhoons threaten air quality, necessitating targeted pollution mitigation policies.
                                            
                                            
                                        Yutong Wang, Huazhe Shang, Chenqian Tang, Jian Xu, Tianyang Ji, Wenwu Wang, Lesi Wei, Yonghui Lei, Jiancheng Shi, and Husi Letu
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-2471, https://doi.org/10.5194/egusphere-2025-2471, 2025
                                    Short summary
                                    Short summary
                                            
                                                By analyzing global CloudSat data, we identified that most liquid cloud profiles have triangle-shaped or steadily decreasing structures, and we developed a new method using pattern recognition, fitting techniques, and machine learning to accurately estimate these profiles. This research advances our understanding of cloud life cycle and improves the ability to characterize cloud profiles, which is crucial for enhancing weather forecast and climate change research.
                                            
                                            
                                        Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
                                    Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
                                            
                                            
                                        Song Liu, Pieter Valks, Gaia Pinardi, Jian Xu, Ka Lok Chan, Athina Argyrouli, Ronny Lutz, Steffen Beirle, Ehsan Khorsandi, Frank Baier, Vincent Huijnen, Alkiviadis Bais, Sebastian Donner, Steffen Dörner, Myrto Gratsea, François Hendrick, Dimitris Karagkiozidis, Kezia Lange, Ankie J. M. Piters, Julia Remmers, Andreas Richter, Michel Van Roozendael, Thomas Wagner, Mark Wenig, and Diego G. Loyola
                                    Atmos. Meas. Tech., 14, 7297–7327, https://doi.org/10.5194/amt-14-7297-2021, https://doi.org/10.5194/amt-14-7297-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                In this work, an improved tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented with correction for the dependency of the stratospheric NO2 on the viewing geometry. The AMF calculation is implemented using improved surface albedo, a priori NO2 profiles, and cloud correction. The improved tropospheric NO2 data show good correlations with ground-based MAX-DOAS measurements.
                                            
                                            
                                        Mareike Heckl, Andreas Fix, Matthias Jirousek, Franz Schreier, Jian Xu, and Markus Rapp
                                    Atmos. Meas. Tech., 14, 1689–1713, https://doi.org/10.5194/amt-14-1689-2021, https://doi.org/10.5194/amt-14-1689-2021, 2021
                            Seidai Nara, Tomohiro O. Sato, Takayoshi Yamada, Tamaki Fujinawa, Kota Kuribayashi, Takeshi Manabe, Lucien Froidevaux, Nathaniel J. Livesey, Kaley A. Walker, Jian Xu, Franz Schreier, Yvan J. Orsolini, Varavut Limpasuvan, Nario Kuno, and Yasuko Kasai
                                    Atmos. Meas. Tech., 13, 6837–6852, https://doi.org/10.5194/amt-13-6837-2020, https://doi.org/10.5194/amt-13-6837-2020, 2020
                                    Short summary
                                    Short summary
                                            
                                                In the atmosphere, more than 80 % of chlorine compounds are anthropogenic. Hydrogen chloride (HCl), the main stratospheric chlorine reservoir, is useful to estimate the total budget of the atmospheric chlorine compounds. We report, for the first time, the HCl vertical distribution from the middle troposphere to the lower thermosphere using a high-sensitivity SMILES measurement; the data quality is quantified by comparisons with other measurements and via theoretical error analysis.
                                            
                                            
                                        Cited articles
                        
                        Anderson, G. P., Chetwynd, J. H., and She, E. P.:
AFGL Atmospheric Constituent Profiles (0–120 km), Air Force Geophys. Lab., Hanscom AFB, MA, afgl-tr-86-0110 edn., 1986. a
                    
                
                        
                        Baldwin, M. P. and Dunkerton, T. J.:
Stratospheric Harbingers of Anomalous Weather Regimes, Science, 294, 581–584, https://doi.org/10.1126/science.1063315, 2001. a
                    
                
                        
                        Baldwin, M. P., Stephenson, D. B., Thompson, D. W. J., Dunkerton, T. J., Charlton, A. J., and O'Neill, A.:
Stratospheric Memory and Skill of Extended-Range Weather Forecasts, Science, 301, 636–640, https://doi.org/10.1126/science.1087143, 2003. a
                    
                
                        
                        Baron, P., Murtagh, D. P., Urban, J., Sagawa, H., Ochiai, S., Kasai, Y., Kikuchi, K., Khosrawi, F., Körnich, H., Mizobuchi, S., Sagi, K., and Yasui, M.:
Observation of horizontal winds in the middle-atmosphere between 30∘ S and 55∘ N during the northern winter 2009–2010, Atmos. Chem. Phys., 13, 6049–6064, https://doi.org/10.5194/acp-13-6049-2013, 2013b. a
                    
                
                        
                        Baron, P., Manago, N., Ozeki, H., Irimajiri, Y., Murtagh, D., Uzawa, Y., Ochiai, S., Shiotani, M., and Suzuki, M.:
Measurement of stratospheric and mesospheric winds with a submillimeter wave limb sounder: results from JEM/SMILES and simulation study for SMILES-2, in: Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX, Toulouse, 21–24 September 2015, 9639, p. 96390N, 2015.  a, b, c
                    
                
                        
                        Baron, P., Murtagh, D., Eriksson, P., Mendrok, J., Ochiai, S., Pérot, K., Sagawa, H., and Suzuki, M.:
Simulation study for the Stratospheric Inferred Winds (SIW) sub-millimeter limb sounder, Atmos. Meas. Tech., 11, 4545–4566, https://doi.org/10.5194/amt-11-4545-2018, 2018. a
                    
                
                        
                        Baron, P., Ochiai, S., Dupuy, E., Larsson, R., Liu, H., Manago, N., Murtagh, D., Oyama, S., Sagawa, H., Saito, A., Sakazaki, T., Shiotani, M., and Suzuki, M.:
Potential for the measurement of mesosphere and lower thermosphere (MLT) wind, temperature, density and geomagnetic field with Superconducting Submillimeter-Wave Limb-Emission Sounder 2 (SMILES-2), Atmos. Meas. Tech., 13, 219–237, https://doi.org/10.5194/amt-13-219-2020, 2020. a
                    
                
                        
                        Blanc, E., Ceranna, L., Hauchecorne, A., Charlton-Perez, A., Marchetti, E., Evers, L. G., Kvaerna, T., Lastovicka, J., Eliasson, L., Crosby, N. B., Blanc-Benon, P., Le Pichon, A., Brachet, N., Pilger, C., Keckhut, P., Assink, J. D., Smets, P. S. M., Lee, C. F., Kero, J., Sindelarova, T., Kämpfer, N., Rüfenacht, R., Farges, T., Millet, C., Näsholm, S. P., Gibbons, S. J., Espy, P. J., Hibbins, R. E., Heinrich, P., Ripepe, M., Khaykin, S., Mze, N., and Chum, J.:
Toward an Improved Representation of Middle Atmospheric Dynamics Thanks to the ARISE Project, Surv. Geophys., 39, 171–225, https://doi.org/10.1007/s10712-017-9444-0, 2018. a
                    
                
                        
                        Buehler, S. A., Mendrok, J., Eriksson, P., Perrin, A., Larsson, R., and Lemke, O.:
ARTS, the Atmospheric Radiative Transfer Simulator – version 2.2, the planetary toolbox edition, Geosci. Model Dev., 11, 1537–1556, https://doi.org/10.5194/gmd-11-1537-2018, 2018. a
                    
                
                        
                        Burrage, M. D., Skinner, W. R., Gell, D. A., Hays, P. B., Marshall, A. R., Ortland, D. A., Manson, A. H., Franke, S. J., Fritts, D. C., Hoffman, P., McLandress, C., Niciejewski, R., Schmidlin, F. J., Shepherd, G. G., Singer, W., Tsuda, T., and Vincent, R. A.:
Validation of mesosphere and lower thermosphere winds from the high resolution Doppler imager on UARS, J. Geophys. Res., 101, 10365–10392, https://doi.org/10.1029/95JD01700, 1996. a
                    
                
                        
                        Burrows, S. M., Martin, C. L., and Roberts, E. A.:
High-latitude remote sensing of mesospheric wind speeds and carbon monoxide, J. Geophys. Res.-Atmos., 112, D17109, https://doi.org/10.1029/2006JD007993, 2007. a
                    
                
                        
                        Clancy, R. T. and Muhleman, D. O.:
Groundbased Microwave Spectroscopy of the Earth's Stratosphere and Mesosphere, chap. 7 in: Atmospheric remote sensing by microwave radiometry, edited by: Janssen, M. A., Wiley-Interscience, New York, USA, 372–374, ISBN-10: 0471628913, ISBN-13: 978-0471628910, 1993. a
                    
                
                        
                        Drob, D. P., Emmert, J. T., Meriwether, J. W., Makela, J. J., Doornbos, E., Conde, M., Hernandez, G., Noto, J., Zawdie, K. A., McDonald, S. E., Huba, J. D., and Klenzing, J. H.:
An update to the Horizontal Wind Model (HWM): The quiet time thermosphere, Earth Space Sci., 2, 301–319, https://doi.org/10.1002/2014EA000089, 2015. a
                    
                
                        
                        Eriksson, P.: Atmlab, version 2.4.1, Universität Hamburg [code], https://arts.mi.uni-hamburg.de/svn/rt/atmlab/branches/atmlab-2.4/, last access: 12 March 2022. a
                    
                
                        
                        Eriksson, P., Jimenez, C., and Buehler, S. A.:
Qpack, a general tool for instrument simulation and retrieval work, J. Quant. Spectrosc. Ra., 91, 47–64, https://doi.org/10.1016/j.jqsrt.2004.05.050, 2005. a
                    
                
                        
                        Gault, W. A., Thuillier, G., Shepherd, G. G., Zhang, S. P., Wiens, R. H., Ward, W. E., Tai, C., Solheim, B. H., Rochon, Y. J., McLandress, C., Lathuillere, C., Fauliot, V., Hersé, M., Hersom, C. H., Gattinger, R., Bourg, L., Burrage, M. D., Franke, S. J., Hernandez, G., Manson, A., Niciejewski, R., and Vincent, R. A.:
Validation of O(1S) wind measurements by WINDII: the WIND Imaging Interferometer on UARS, J. Geophys. Res.-Atmos., 101, 10405–10430, https://doi.org/10.1029/95JD03352, 1996. a
                    
                
                        
                        Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath, P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., Drouin, B. J., Flaud, J. M., Gamache, R. R., Hodges, J. T., Jacquemart, D., Perevalov, V. I., Perrin, A., Shine, K. P., Smith, M. A. H., Tennyson, J., Toon, G. C., Tran, H., Tyuterev, V. G., Barbe, A., Csaszar, A. G., Devi, V. M., Furtenbacher, T., Harrison, J. J., Hartmann, J. M., Jolly, A., Johnson, T. J., Karman, T., Kleiner, I., Kyuberis, A. A., Loos, J., Lyulin, O. M., Massie, S. T., Mikhailenko, S. N., Moazzen-Ahmadi, N., Mueller, H. S. P., Naumenko, O. V., Nikitin, A. V., Polyansky, O. L., Rey, M., Rotger, M., Sharpe, S. W., Sung, K., Starikova, E., Tashkun, S. A., Vander Auwera, J., Wagner, G., Wilzewski, J., Wcislo, P., Yu, S., and Zak, E. J.:
The HITRAN2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203, 3–69, https://doi.org/10.1016/j.jqsrt.2017.06.038, 2017. a
                    
                
                        
                        Hagen, J., Murk, A., Rüfenacht, R., Khaykin, S., Hauchecorne, A., and Kämpfer, N.:
WIRA-C: a compact 142-GHz-radiometer for continuous middle-atmospheric wind measurements, Atmos. Meas. Tech., 11, 5007–5024, https://doi.org/10.5194/amt-11-5007-2018, 2018. a
                    
                
                        
                        Hardiman, S. C., Butchart, N., Charlton-Perez, A. J., Shaw, T. A., Akiyoshi, H., Baumgaertner, A., Bekki, S., Braesicke, P., Chipperfield, M., Dameris, M., Garcia, R. R., Michou, M., Pawson, S., Rozanov, E., and Shibata, K.:
Improved predictability of the troposphere using stratospheric final warmings, J. Geophys. Res., 116, D18113, https://doi.org/10.1029/2011JD015914, 2011. a
                    
                
                        
                        Hubers, H.-W.:
Terahertz Heterodyne Receivers, IEEE J. Sel. Top. Quant., 14, 378–391, https://doi.org/10.1109/JSTQE.2007.913964, 2008. a
                    
                
                        
                        Hysell, D. L., Chau, J. L., Coles, W. A., Milla, M. A., Obenberger, K., and Vierinen, J.:
The Case for Combining a Large Low-Band Very High Frequency Transmitter With Multiple Receiving Arrays for Geospace Research: A Geospace Radar, Radio Sci., 54, 533–551, https://doi.org/10.1029/2018RS006688, 2019. a
                    
                
                        
                        Ilma, R.: rilma/pyHWM14: Official release of the HWM14 wrapper in Python (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.240890, 2017. a
                    
                
                        
                        Killeen, T. L., Wu, Q., Solomon, S. C., Ortland, D. A., Skinner, W. R., Niciejewski, R. J., and Gell, D. A.:
TIMED Doppler interferometer: Overview and recent results, J. Geophys. Res.-Space, 111, A10S01, https://doi.org/10.1029/2005ja011484, 2006. a
                    
                
                        
                        Lemke, O.: ARTS XML Data, version 2.4.0, Universität Hamburg [data set], https://arts.mi.uni-hamburg.de/svn/rt/arts-xml-data/branches/arts-xml-data-2.4/, last access: 12 March 2022. a
                    
                
                        
                        Liu, X., Xu, J., Yue, J., and Andrioli, V. F.:
Variations in global zonal wind from 18 to 100 km due to solar activity and the quasi-biennial oscillation and El Niño–Southern Oscillation during 2002–2019, Atmos. Chem. Phys., 23, 6145–6167, https://doi.org/10.5194/acp-23-6145-2023, 2023. a
                    
                
                        
                        Newnham, D. A., Ford, G. P., Moffat-Griffin, T., and Pumphrey, H. C.:
Simulation study for measurement of horizontal wind profiles in the polar stratosphere and mesosphere using ground-based observations of ozone and carbon monoxide lines in the 230–250 GHz region, Atmos. Meas. Tech., 9, 3309–3323, https://doi.org/10.5194/amt-9-3309-2016, 2016. a
                    
                
                        
                        Ochiai, S., Baron, P., Nishibori, T., Irimajiri, Y., Uzawa, Y., Manabe, T., Maezawa, H., Mizuno, A., Nagahama, T., Sagawa, H., Suzuki, M., and Shiotani, M.:
SMILES-2 Mission for Temperature, Wind, and Composition in the Whole Atmosphere, Sola, 13A, 13–18, https://doi.org/10.2151/sola.13A-003, 2017. a
                    
                
                        
                        Rodgers, C. D.:
Inverse methods for atmospheric sounding: theory and practice, Vol. 2, World scientific, Singapore, ISBN-10 981022740X, ISBN-13 978-9810227401, 2000. a
                    
                
                        
                        Rüfenacht, R., Kämpfer, N., and Murk, A.:
First middle-atmospheric zonal wind profile measurements with a new ground-based microwave Doppler-spectro-radiometer, Atmos. Meas. Tech., 5, 2647–2659, https://doi.org/10.5194/amt-5-2647-2012, 2012. a
                    
                
                        
                        Rüfenacht, R., Murk, A., Kämpfer, N., Eriksson, P., and Buehler, S. A.:
Middle-atmospheric zonal and meridional wind profiles from polar, tropical and midlatitudes with the ground-based microwave Doppler wind radiometer WIRA, Atmos. Meas. Tech., 7, 4491–4505, https://doi.org/10.5194/amt-7-4491-2014, 2014. a
                    
                
                        
                        Shepherd, G. G.:
Development of wind measurement systems for future space missions, Acta Astronaut., 115, 206–217, https://doi.org/10.1016/j.actaastro.2015.05.015, 2015. a
                    
                
                        
                        Urban, J.:
Optimal sub-millimeter bands for passive limb observations of stratospheric HBr, BrO, HOCl, and HO2 from space, J. Quant. Spectrosc. Ra., 76, 145–178, https://doi.org/10.1016/s0022-4073(02)00051-1, 2003. a
                    
                
                        
                        Wang, W., Wang, Z., and Duan, Y.:
Performance evaluation of THz Atmospheric Limb Sounder (TALIS) of China, Atmos. Meas. Tech., 13, 13–38, https://doi.org/10.5194/amt-13-13-2020, 2020. a
                    
                
                        
                        Witschas, B., Lemmerz, C., Geiß, A., Lux, O., Marksteiner, U., Rahm, S., Reitebuch, O., and Weiler, F.:
First validation of Aeolus wind observations by airborne Doppler wind lidar measurements, Atmos. Meas. Tech., 13, 2381–2396, https://doi.org/10.5194/amt-13-2381-2020, 2020. a
                    
                
                        
                        Wu, D. L., Schwartz, M. J., Waters, J. W., Limpasuvan, V., Wu, Q., and Killeen, T. L.:
Mesospheric doppler wind measurements from Aura Microwave Limb Sounder (MLS), Adv. Space Res., 42, 1246–1252, https://doi.org/10.1016/j.asr.2007.06.014, 2008. a
                    
                
                        
                        Wu, D. L., Yee, J.-H., Schlecht, E., Mehdi, I., Siles, J., and Drouin, B. J.:
THz limb sounder (TLS) for lower thermospheric wind, oxygen density, and temperature, J. Geophys. Res.-Space, 121, 7301–7315, https://doi.org/10.1002/2015ja022314, 2016. a
                    
                
                        
                        Xu, H., Lu, H., Wang, Z., Liu, J., and Wang, W.:
The System Design and Preliminary Tests of the THz Atmospheric Limb Sounder (TALIS), IEEE T. Instrum. Meas., 71, 1–12, https://doi.org/10.1109/TIM.2021.3135008, 2022. a
                    
                
                        
                        Yan, Z., Hu, X., Guo, W., Guo, S., Cheng, Y., Gong, J., and Yue, J.:
Development of a mobile Doppler lidar system for wind and temperature measurements at 30–70 km, J. Quant. Spectrosc. Ra., 188, 52–59, https://doi.org/10.1016/j.jqsrt.2016.04.024, 2017. a
                    
                
                        
                        Yee, J.-H., Mehdi, I., Hayton, D., Siles, J., and Wu, D.:
Remote Sensing of Global Lower Thermospheric Winds: Sensing Techniques and Sensor Design, chap. 22 in: Space Physics and Aeronomy, Upper Atmosphere Dynamics and Energetics, Vol. 4, edited by: Wang, W., Zhang, Y., and Paxton, L. J., Wiley, Hoboken, NJ, USA, 469–486, ISBN-13 978-1119507567, 2021. a
                    
                Short summary
            This article presents a study for feasibility analysis of atmospheric wind measurement using a terahertz (THz) passive limb radiometer with high spectral resolution. The simulations show that line-of-sight wind from 40 to 120 km can be obtained better than 10 m s−1 (at most altitudes it is better than 5 m s−1) using the O3, O2, H2O, and OI bands. This study will provide reference for future payload design.
            This article presents a study for feasibility analysis of atmospheric wind measurement using a...
            
         
 
                        
                                         
                        
                                         
                        
                                         
                        
                                         
             
             
            