Articles | Volume 23, issue 11
https://doi.org/10.5194/acp-23-6339-2023
© 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/acp-23-6339-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jun 2023
Research article |
| 09 Jun 2023
| 09 Jun 2023
Antarctic atmospheric Richardson number from radiosonde measurements and AMPS
Qike Yang
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Xiaoqing Wu
CORRESPONDING AUTHOR
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Xiaodan Hu
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Zhiyuan Wang
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Chun Qing
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Pengfei Wu
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Xianmei Qian
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Yiming Guo
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
Related authors
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev., 18, 5373–5396, https://doi.org/10.5194/gmd-18-5373-2025, https://doi.org/10.5194/gmd-18-5373-2025, 2025
Short summary
Short summary
This study provides a comprehensive evaluation of unstructured meshes using the integrated Atmospheric Model Across Scales (iAMAS) over Antarctica, encompassing both surface and upper-level meteorological fields. Comparisons with the fifth-generation reanalysis (ERA5) from the European Centre for Medium-Range Weather Forecasts and observational data indicate that iAMAS performs well in simulating the Antarctic atmosphere.
Zining Yang, Qiuyan Du, Qike Yang, Chun Zhao, Gudongze Li, Zihan Xia, Mingyue Xu, Renmin Yuan, Yubin Li, Kaihui Xia, Jun Gu, and Jiawang Feng
Atmos. Chem. Phys., 25, 8831–8857, https://doi.org/10.5194/acp-25-8831-2025, https://doi.org/10.5194/acp-25-8831-2025, 2025
Short summary
Short summary
This study investigates the impact of turbulent mixing on black carbon (BC) concentrations in urban areas simulated at 25, 5, and 1 km resolutions. Significant variations in BC and turbulent mixing occur mainly at night. Higher resolutions reduce BC overestimation due to enhanced mixing coefficients and vertical wind fluxes. Small-scale eddies at higher resolutions increase the BC lifetime and column concentrations. Land use and terrain variations across multiple resolutions affect turbulent mixing.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev., 18, 5373–5396, https://doi.org/10.5194/gmd-18-5373-2025, https://doi.org/10.5194/gmd-18-5373-2025, 2025
Short summary
Short summary
This study provides a comprehensive evaluation of unstructured meshes using the integrated Atmospheric Model Across Scales (iAMAS) over Antarctica, encompassing both surface and upper-level meteorological fields. Comparisons with the fifth-generation reanalysis (ERA5) from the European Centre for Medium-Range Weather Forecasts and observational data indicate that iAMAS performs well in simulating the Antarctic atmosphere.
Zining Yang, Qiuyan Du, Qike Yang, Chun Zhao, Gudongze Li, Zihan Xia, Mingyue Xu, Renmin Yuan, Yubin Li, Kaihui Xia, Jun Gu, and Jiawang Feng
Atmos. Chem. Phys., 25, 8831–8857, https://doi.org/10.5194/acp-25-8831-2025, https://doi.org/10.5194/acp-25-8831-2025, 2025
Short summary
Short summary
This study investigates the impact of turbulent mixing on black carbon (BC) concentrations in urban areas simulated at 25, 5, and 1 km resolutions. Significant variations in BC and turbulent mixing occur mainly at night. Higher resolutions reduce BC overestimation due to enhanced mixing coefficients and vertical wind fluxes. Small-scale eddies at higher resolutions increase the BC lifetime and column concentrations. Land use and terrain variations across multiple resolutions affect turbulent mixing.
Zhi Qiao, Shengcheng Cui, Huiqiang Xu, Xiaoqing Wu, Xiaodan Liu, Zihan Zhang, Mengying Zhai, Yue Pan, Tao Luo, and Xuebin Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-1463, https://doi.org/10.5194/egusphere-2025-1463, 2025
Short summary
Short summary
We gave first insight into the diel characteristics of the marine aerosol, especially during the day night transition period, to better understand which and how meteorological elements affect the marine aerosol. Overall, wind speeds and sea surface temperature indeed play critical roles in the regulation of aerosol production and diffusion processes, while the sea-air temperature difference is found to be the most vital factor related to the variations of marine aerosol distributions.
Kun Zhang, Tao Luo, Xuebin Li, Shengcheng Cui, Ningquan Weng, Yinbo Huang, and Yingjian Wang
Atmos. Chem. Phys., 24, 11157–11173, https://doi.org/10.5194/acp-24-11157-2024, https://doi.org/10.5194/acp-24-11157-2024, 2024
Short summary
Short summary
In order to deeply understand the formation mechanisms and evolution processes associated with vertical tropopause structures, this study proposes a new method for identifying the multiple characteristic parameters of vertical tropopause structures by fitting temperature profiles using the bi-Gaussian function. The identification results from the bi-Gaussian method are more reasonable and more consistent with the evolution process of atmospheric thermal stratifications.
Mingxuan Wu, Xiaohong Liu, Hongbin Yu, Hailong Wang, Yang Shi, Kang Yang, Anton Darmenov, Chenglai Wu, Zhien Wang, Tao Luo, Yan Feng, and Ziming Ke
Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020, https://doi.org/10.5194/acp-20-13835-2020, 2020
Short summary
Short summary
The spatiotemporal distributions of dust aerosol simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate dust extinction profiles, optical depth, and surface concentrations simulated in three GCMs and one reanalysis against multiple satellite retrievals and surface observations to gain process-level understanding. Our results highlight the importance of correctly representing dust emission, dry/wet deposition, and size distribution in GCMs.
Cited articles
Agabi, A., Aristidi, E., Azouit, M., Fossat, E., Martin, F., Sadibekova, T., Vernin, J., and Ziad, A.:
First Whole Atmosphere Nighttime Seeing Measurements at Dome C, Antarctica, Publ. Astron. Soc. Pac., 118, 344–348, https://doi.org/10.1086/498728, 2006. a
AMPS: AMPS full model (WRF) output files in NetCDF format, https://www2.mmm.ucar.edu/rt/amps/information/amps_esg_data_info.html, last access: 1 March 2022. a
AMRC, SSEC, and UW-Madison: Antarctic Meteorological Research Center data sets, ftp://amrc.ssec.wisc.edu/pub, last access: 1 March 2022. a
Argentini, S., Pietroni, I., Mastrantonio, G., Viola, A. P., Dargaud, G., and Petenko, I.:
Observations of near surface wind speed, temperature and radiative budget at Dome C, Antarctic Plateau during 2005, Antarct. Sci., 26, 104–112, https://doi.org/10.1017/s0954102013000382, 2013. a
Aristidi, E., Agabi, K., Azouit, M., Fossat, E., Vernin, J., Travouillon, T., Lawrence, J. S., Meyer, C., Storey, J. W. V., Halter, B., Roth, W. L., and Walden, V.:
An analysis of temperatures and wind speeds above Dome C, Antarctica, Astron. Astrophys., 430, 739–746, https://doi.org/10.1051/0004-6361:20041876, 2005. a
Aristidi, E., Vernin, J., Fossat, E., Schmider, F. X., Travouillon, T., Pouzenc, C., Traullé, O., Genthon, C., Agabi, A., Bondoux, E., Challita, Z., Mékarnia, D., Jeanneaux, F., and Bouchez, G.:
Monitoring the optical turbulence in the surface layer at Dome C, Antarctica, with sonic anemometers, Mon. Not. R. Astron. Soc., 454, 4304–4315, https://doi.org/10.1093/mnras/stv2273, 2015. a, b
Bonner, C. S., Ashley, M. C. B., Cui, X., Feng, L., Gong, X., Lawrence, J. S., Luong-Van, D. M., Shang, Z., Storey, J. W. V., Wang, L., Yang, H., Yang, J., Zhou, X., and Zhu, Z.:
Thickness of the Atmospheric Boundary Layer Above Dome A, Antarctica, during 2009, Publ. Astron. Soc. Pac., 122, 1122–1131, https://doi.org/10.1086/656250, 2010. a, b, c
Boville, B. A., Kiehl, J. T., and Briegleb, B. P.:
Evolution of the Antarctic polar vortex in spring: Response of a GCM to a prescribed Antarctic ozone hole, Report, NASA, Goddard Space Flight Center, Polar Ozone Workshop, https://ntrs.nasa.gov/citations/19890005218 (last access: 1 March 2022), 1988. a
Bromwich, D. H., Otieno, F. O., Hines, K. M., Manning, K. W., and Shilo, E.:
Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic, J. Geophys. Res.-Atmos., 118, 274–292, https://doi.org/10.1029/2012jd018139, 2013. a, b
Burton, M. G.:
Astronomy in Antarctica, Astron. Astrophys. Rev., 18, 417–469, https://doi.org/10.1007/s00159-010-0032-2, 2010. a, b
Chan, P. W.:
Determination of Richardson number profile from remote sensing data and its aviation application, IOP. C. Ser. Earth Env., 1, 012043, https://doi.org/10.1088/1755-1307/1/1/012043, 2008. a
Deardorff, J. W.:
On the entrainment rate of a stratocumulus-topped mixed layer, Q. J. Roy. Meteor. Soc., 102, 563–582, https://doi.org/10.1002/qj.49710243306, 1976. a
Gallée, H., Preunkert, S., Argentini, S., Frey, M. M., Genthon, C., Jourdain, B., Pietroni, I., Casasanta, G., Barral, H., Vignon, E., Amory, C., and Legrand, M.:
Characterization of the boundary layer at Dome C (East Antarctica) during the OPALE summer campaign, Atmos. Chem. Phys., 15, 6225–6236, https://doi.org/10.5194/acp-15-6225-2015, 2015. a
Geissler, K. and Masciadri, E.:
Meteorological Parameter Analysis above Dome C Using Data from the European Centre for Medium-Range Weather Forecasts, Publ. Astron. Soc. Pac., 118, 1048–1065, https://doi.org/10.1086/505891, 2006. a, b
Gultepe, I. and Feltz, W. F.:
Aviation Meteorology: Observations and Models. Introduction, Pure Appl. Geophys., 176, 1863–1867, https://doi.org/10.1007/s00024-019-02188-2, 2019. a, b
Han, Y., Yang, Q., Liu, N., Zhang, K., Qing, C., Li, X., Wu, X., and Luo, T.:
Analysis of wind-speed profiles and optical turbulence above Gaomeigu and the Tibetan Plateau using ERA5 data, Mon. Not. R. Astron. Soc., 501, 4692–4702, https://doi.org/10.1093/mnras/staa2960, 2021. a
He, Y., Sheng, Z., and He, M.:
The First Observation of Turbulence in Northwestern China by a Near-Space High-Resolution Balloon Sensor, Sensors, 20, 677, https://doi.org/10.3390/s20030677, 2020. a
Hines, K. M. and Bromwich, D. H.:
Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology, Mon. Weather Rev., 136, 1971–1989, https://doi.org/10.1175/2007mwr2112.1, 2008. a, b
Hines, K. M., Bromwich, D. H., Bai, L., Bitz, C. M., Powers, J. G., and Manning, K. W.:
Sea Ice Enhancements to Polar WRF, Mon. Weather Rev., 143, 2363–2385, https://doi.org/10.1175/mwr-d-14-00344.1, 2015. a, b
Hines, K. M., Bromwich, D. H., Wang, S.-H., Silber, I., Verlinde, J., and Lubin, D.:
Microphysics of summer clouds in central West Antarctica simulated by the Polar Weather Research and Forecasting Model (WRF) and the Antarctic Mesoscale Prediction System (AMPS), Atmos. Chem. Phys., 19, 12431–12454, https://doi.org/10.5194/acp-19-12431-2019, 2019. a, b, c, d
Hu, Y., Hu, K., Shang, Z., Ashley, M. C. B., Ma, B., Du, F., Li, Z., Liu, Q., Wang, W., Yang, S., Yu, C., and Zeng, Z.:
Meteorological Data from KLAWS-2G for an Astronomical Site Survey of Dome A, Antarctica, Publ. Astron. Soc. Pac., 131, 015001, https://doi.org/10.1088/1538-3873/aae916, 2019. a
Huang, T., Yang, Y., O'Connor, E. J., Lolli, S., Haywood, J., Osborne, M., Cheng, J. C.-H., Guo, J., and Yim, S. H.-L.:
Influence of a weak typhoon on the vertical distribution of air pollution in Hong Kong: A perspective from a Doppler LiDAR network, Environ. Pollut., 276, 116534, https://doi.org/10.1016/j.envpol.2021.116534, 2021. a
Hudson, S. R. and Brandt, R. E.:
A Look at the Surface-Based Temperature Inversion on the Antarctic Plateau, J. Climate, 18, 1673–1696, https://doi.org/10.1175/jcli3360.1, 2005. a
IPEV/PNRA Project: Routin Meteorological Observation at Station Concordia, http://www.climantartide.it, last access: 1 March 2022. a
Janjić, Z. I.:
The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:Tsmecm>2.0.Co;2, 1994. a
Kantha, L., Luce, H., and Hashiguchi, H.:
Midlevel Cloud-Base Turbulence: Radar Observations and Models, J. Geophys. Res.-Atmos., 124, 3223–3245, https://doi.org/10.1029/2018JD029479, 2019. a
Karpetchko, A., Kyrö, E., and Knudsen, B. M.:
Arctic and Antarctic polar vortices 1957–2002 as seen from the ERA-40 reanalyses, J. Geophys. Res., 110, D21109, https://doi.org/10.1029/2005jd006113, 2005. a
Kudo, A.:
The Generation of Turbulence below Midlevel Cloud Bases: The Effect of Cooling due to Sublimation of Snow, J. Appl. Meteorol. Clim., 52, 819–833, https://doi.org/10.1175/jamc-d-12-0232.1, 2013. a
Lascaux, F., Masciadri, E., Hagelin, S., and Stoesz, J.:
Mesoscale optical turbulence simulations at Dome C, Mon. Not. R. Astron. Soc., 398, 1093–1104, https://doi.org/10.1111/j.1365-2966.2009.15151.x, 2009. a
Listowski, C. and Lachlan-Cope, T.:
The microphysics of clouds over the Antarctic Peninsula – Part 2: modelling aspects within Polar WRF, Atmos. Chem. Phys., 17, 10195–10221, https://doi.org/10.5194/acp-17-10195-2017, 2017. a
Lv, Y., Guo, J., Li, J., Cao, L., Chen, T., Wang, D., Chen, D., Han, Y., Guo, X., Xu, H., Liu, L., Solanki, R., and Huang, G.:
Spatiotemporal characteristics of atmospheric turbulence over China estimated using operational high-resolution soundings, Environ. Res. Lett., 16, 054050, https://doi.org/10.1088/1748-9326/abf461, 2021. a
Ma, B., Shang, Z., Hu, Y., Hu, K., Wang, Y., Yang, X., Ashley, M. C. B., Hickson, P., and Jiang, P.:
Night-time measurements of astronomical seeing at Dome A in Antarctica, Nature, 583, 771–774, https://doi.org/10.1038/s41586-020-2489-0, 2020. a
Ma, Y., Bian, L., Xiao, C., Allison, I., and Zhou, X.:
Near surface climate of the traverse route from Zhongshan Station to Dome A, East Antarctica, Antarct. Sci., 22, 443–459, https://doi.org/10.1017/s0954102010000209, 2010. a, b
Marks, R. D., Vernin, J., Azouit, M., Manigault, J. F., and Clevelin, C.:
Measurement of optical seeing on the high antarctic plateau, Astron. Astrophys. Supplement Series, 134, 161–172, https://doi.org/10.1051/aas:1999100, 1999. a
Mihalikova, M., Kirkwood, S., Arnault, J., and Mikhaylova, D.:
Observation of a tropopause fold by MARA VHF wind-profiler radar and ozonesonde at Wasa, Antarctica: comparison with ECMWF analysis and a WRF model simulation, Ann. Geophys., 30, 1411–1421, https://doi.org/10.5194/angeo-30-1411-2012, 2012. a
Monaghan, A. J., Bromwich, D. H., Powers, J. G., and Manning, K. W.:
The Climate of the McMurdo, Antarctica, Region as Represented by One Year of Forecasts from the Antarctic Mesoscale Prediction System, J. Climate, 18, 1174–1189, https://doi.org/10.1175/JCLI3336.1, 2005. a
Nigro, M. A., Cassano, J. J., Wille, J., Bromwich, D. H., and Lazzara, M. A.:
A Self-Organizing-Map-Based Evaluation of the Antarctic Mesoscale Prediction System Using Observations from a 30-m Instrumented Tower on the Ross Ice Shelf, Antarctica, Weather Forecast., 32, 223–242, https://doi.org/10.1175/waf-d-16-0084.1, 2017. a
Lönnberg, P. and Shaw, D. B.: ECMWF Data Assimilation – scientific documentation, 3rd ed., Report, ECMWF, https://www.ecmwf.int/en/elibrary/75427-ecmwf-data-assimilation-scientific-documentation-3rd-ed (last access: 1 March 2022), 1992. a
Petenko, I., Argentini, S., Pietroni, I., Viola, A., Mastrantonio, G., Casasanta, G., Aristidi, E., Bouchez, G., Agabi, A., and Bondoux, E.:
Observations of optically active turbulence in the planetary boundary layer by sodar at the Concordia astronomical observatory, Dome C, Antarctica, Astron. Astrophys., 568, https://doi.org/10.1051/0004-6361/201323299, 2014. a, b
Petenko, I., Argentini, S., Casasanta, G., Genthon, C., and Kallistratova, M.:
Stable Surface-Based Turbulent Layer During the Polar Winter at Dome C, Antarctica: Sodar and In Situ Observations, Bound.-Lay. Meteorol., 171, 101–128, https://doi.org/10.1007/s10546-018-0419-6, 2019. a, b
Pietroni, I., Argentini, S., Petenko, I., and Sozzi, R.:
Measurements and Parametrizations of the Atmospheric Boundary-Layer Height at Dome C, Antarctica, Bound.-Lay. Meteorol., 143, 189–206, https://doi.org/10.1007/s10546-011-9675-4, 2012. a, b, c
Powers, J. G., Manning, K. W., Bromwich, D. H., Cassano, J. J., and Cayette, A. M.:
A Decade of Antarctic Science Support Through Amps, B. Am. Meteorol. Soc., 93, 1699–1712, https://doi.org/10.1175/BAMS-D-11-00186.1, 2012. a
Richardson, L. F. and Shaw, W. N.:
The supply of energy from and to atmospheric eddies, P. R. Soc. Lond. A-Conta., 97, 354–373, https://doi.org/10.1098/rspa.1920.0039, 1920. a
Rinke, A., Ma, Y., Bian, L., Xin, Y., Dethloff, K., Persson, P. O. G., Lüpkes, C., and Xiao, C.:
Evaluation of atmospheric boundary layer-surface process relationships in a regional climate model along an East Antarctic traverse, J. Geophys. Res.-Atmos., 117, D09121, https://doi.org/10.1029/2011jd016441, 2012. a, b
Saunders, W., Lawrence, J. S., Storey, J. W. V., Ashley, M. C. B., Kato, S., Minnis, P., Winker, D. M., Liu, G., and Kulesa, C.:
Where Is the Best Site on Earth? Domes A, B, C, and F, and Ridges A and B, Publ. Astron. Soc. Pac., 121, 976–992, https://doi.org/10.1086/605780, 2009. a
Seefeldt, M. W., Cassano, J. J., and Nigro, M. A.:
A Weather-Pattern-Based Approach to Evaluate the Antarctic Mesoscale Prediction System (AMPS) Forecasts: Comparison to Automatic Weather Station Observations, Weather Forecast., 26, 184–198, https://doi.org/10.1175/2010waf2222444.1, 2011. a
Solanki, R., Guo, J., Lv, Y., Zhang, J., Wu, J., Tong, B., and Li, J.:
Elucidating the atmospheric boundary layer turbulence by combining UHF radar wind profiler and radiosonde measurements over urban area of Beijing, Urban Climate, 43, 101151, https://doi.org/10.1016/j.uclim.2022.101151, 2022. a
Spinhirne, J. D., Palm, S. P., and Hart, W. D.:
Antarctica cloud cover for October 2003 from GLAS satellite lidar profiling, Geophys. Res. Lett., 32, L22S05, https://doi.org/10.1029/2005GL023782, 2005. a
Swain, M. and Gallée, H.:
Antarctic Boundary Layer Seeing, Publ. Astron. Soc. Pac., 118, 1190–1197, https://doi.org/10.1086/507153, 2006. a, b
Town, M. S. and Walden, V. P.:
Surface energy budget over the South Pole and turbulent heat fluxes as a function of an empirical bulk Richardson number, J. Geophys. Res., 114, D22107, https://doi.org/10.1029/2009jd011888, 2009. a
Travouillon, T., Ashley, M. C. B., Burton, M. G., Storey, J. W. V., and Loewenstein, R. F.:
Atmospheric turbulence at the South Pole and its implications for astronomy, Astron. Astrophys., 400, 1163–1172, https://doi.org/10.1051/0004-6361:20021814, 2003. a, b
Trinquet, H., Agabi, A., Vernin, J., Azouit, M., Aristidi, E., and Fossat, E.:
Nighttime Optical Turbulence Vertical Structure above Dome C in Antarctica, Publ. Astron. Soc. Pac., 120, 203–211, https://doi.org/10.1086/528808, 2008. a
Verma, M. K., Kumar, A., and Pandey, A.:
Phenomenology of buoyancy-driven turbulence: recent results, New J. Phys., 19, 025012, https://doi.org/10.1088/1367-2630/aa5d63, 2017. a
Vernin, J., Chadid, M., Aristidi, E., Agabi, A., Trinquet, H., and Van der Swaelmen, M.:
First single star scidar measurements at Dome C, Antarctica, Astron. Astrophys., 500, 1271–1276, https://doi.org/10.1051/0004-6361/200811119, 2009. a
Vázquez B, G. E. and Grejner-Brzezinska, D. A.:
GPS-PWV estimation and validation with radiosonde data and numerical weather prediction model in Antarctica, GPS Solut., 17, 29–39, https://doi.org/10.1007/s10291-012-0258-8, 2012. a
Wille, J. D., Bromwich, D. H., Nigro, M. A., Cassano, J. J., Mateling, M., Lazzara, M. A., and Wang, S.-H.:
Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf with Tower Observations, J. Appl. Meteorol. Clim., 55, 2349–2367, https://doi.org/10.1175/jamc-d-16-0032.1, 2016. a
Wille, J. D., Bromwich, D. H., Cassano, J. J., Nigro, M. A., Mateling, M. E., and Lazzara, M. A.:
Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations, J. Appl. Meteorol. Clim., 56, 2239–2258, https://doi.org/10.1175/jamc-d-16-0339.1, 2017. a
Xie, B., Fung, J. C. H., Chan, A., and Lau, A.:
Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model, J. Geophys. Res.-Atmos., 117, D12103, https://doi.org/10.1029/2011jd017080, 2012. a
Xue, H., Giorgetta, M. A., and Guo, J.:
The daytime trapped lee wave pattern and evolution induced by two small-scale mountains of different heights, Q. J. Roy. Meteor. Soc., 148, 1300–1318, https://doi.org/10.1002/qj.4262, 2022. a
Yagüe, C., Maqueda, G., and Rees, J. M.:
Characteristics of turbulence in the lower atmosphere at Halley IV station, Antarctica, Dynam. Atmos. Oceans, 34, 205–223, https://doi.org/10.1016/S0377-0265(01)00068-9, 2001. a
Yang, Q.: Temporal evolution of the logarithmic Richardson number vertical cross-sections (through Dome A and McMurdo in Antarctica) at the annual time scale, TIB AV-Portal [video], https://doi.org/10.5446/60760, 2023a. a, b, c
Yang, Q.: Temporal evolution of the logarithmic Richardson number vertical cross-sections (through the South Pole and Dome C in Antarctica) at the annual time scale, TIB AV-Portal [video], https://doi.org/10.5446/60761, 2023b. a
Yang, Q., Wu, X., Han, Y., Qing, C., Wu, S., Su, C., Wu, P., Luo, T., and Zhang, S.:
Estimating the astronomical seeing above Dome A using Polar WRF based on the Tatarskii equation, Opt. Express, 29, 44000–44011, https://doi.org/10.1364/oe.439819, 2021.
a, b, c
Yang, Q., Wu, X., Wang, Z., Hu, X., Guo, Y., and Qing, C.:
Simulating the night-time astronomical seeing at Dome A using Polar WRF, Mon. Not. R. Astron. Soc., 515, 1788–1794, https://doi.org/10.1093/mnras/stac1930, 2022. a, b
Yin, J., Liu, H., Huang, R., Gao, Z., and Wei, Z.:
Performance of a PPM hard decision-based ARQ-FSO system in a weak turbulence channel, Chin. Opt. Lett., 15, 060101–60106, https://doi.org/10.3788/col201715.060101, 2017. a
Zhang, J., Guo, J., Xue, H., Zhang, S., Huang, K., Dong, W., Shao, J., Yi, M., and Zhang, Y.:
Tropospheric Gravity Waves as Observed by the High-Resolution China Radiosonde Network and Their Potential Sources, J. Geophys. Res.-Atmos., 127, e2022JD037174, https://doi.org/10.1029/2022JD037174, 2022a. a
Zhang, J., Guo, J., Zhang, S., and Shao, J.:
Inertia-gravity wave energy and instability drive turbulence: evidence from a near-global high-resolution radiosonde dataset, Clim. Dynam., 58, 2927–2939, https://doi.org/10.1007/s00382-021-06075-2, 2022b. a, b
Zuev, V. V. and Savelieva, E.:
The cause of the strengthening of the Antarctic polar vortex during October–November periods, J. Atmos. Sol.-Terr. Phys., 190, 1–5, https://doi.org/10.1016/j.jastp.2019.04.016, 2019a. a
Zuev, V. V. and Savelieva, E.:
The cause of the spring strengthening of the Antarctic polar vortex, Dyn. Atmos. Oceans, 87, https://doi.org/10.1016/j.dynatmoce.2019.101097, 2019b. a
Short summary
The AMPS-forecasted Richardson number was first comprehensively validated over the Antarctic continent. Some potential underlying reasons for the discrepancies between the forecasts and observations were analyzed. The underlying physical processes of triggering atmospheric turbulence in Antarctica were investigated. Our results suggest that the estimated Richardson number by the AMPS is reasonable and the turbulence conditions in Antarctica are well revealed.
The AMPS-forecasted Richardson number was first comprehensively validated over the Antarctic...
Altmetrics
Final-revised paper
Preprint