Abstract
Cyclones with strong winds can make the Southern Ocean and the Antarctic a dangerous environment. Accurate weather forecasts are essential for safe shipping in the Southern Ocean and observational and logistical operations at Antarctic research stations. This study investigated the impact of additional radiosonde observations from Research Vessel “Shirase” over the Southern Ocean and Dome Fuji Station in Antarctica on reanalysis data and forecast experiments using an ensemble data assimilation system comprising the Atmospheric General Circulation Model for the Earth Simulator and the Local Ensemble Transform Kalman Filter Experimental Ensemble Reanalysis, version 2. A 63-member ensemble forecast experiment was conducted focusing on an unusually strong Antarctic cyclonic event. Reanalysis data with (observing system experiment) and without (control) additional radiosonde data were used as initial values. The observing system experiment correctly captured the central pressure of the cyclone, which led to the reliable prediction of the strong winds and moisture transport near the coast. Conversely, the control experiment predicted lower wind speeds because it failed to forecast the central pressure of the cyclone adequately. Differences were found in cyclone predictions of operational forecast systems with and without assimilation of radiosonde observations from Dome Fuji Station.
摘要
强风气旋可以使南大洋和南极成为危险的环境。准确的天气预报对南大洋的安全运输以及南极研究站的观测和后勤作业至关重要。本文研究了从南大洋Shirase研究船和南极圆顶富士站获取的补充无线电探空仪观测数据,对再分析数据及预报试验的影响,预报试验使用集合数据同化系统,该系统包括地球模拟器的大气环流模式以及基于局部集合变换卡尔曼滤波方法的试验集合再分析。针对一个异常强的南极气旋事件进行了63组集合预报试验,使用(观测系统试验)和不使用(控制试验)补充无线电探空仪数据的再分析数据作为初始值。观测系统试验正确获取了气旋的中心气压,因此准确预测了南极沿岸的强风和水汽输送。相反,控制试验预测的风速较低,因其未能充分预测出气旋中心气压。在业务预报系统中同化圆顶富士站的无线电探空仪观测数据与否,对气旋的预测存在差异。
Article PDF
Similar content being viewed by others
![](https://media.springernature.com/w215h120/springer-static/image/art%3A10.1007%2Fs10236-017-1064-1/MediaObjects/10236_2017_1064_Fig1_HTML.gif)
Avoid common mistakes on your manuscript.
References
Bracegirdle, T. J., 2013: Climatology and recent increase of westerly winds over the Amundsen Sea derived from six reana-lyses. International Journal of Climatology, 33, 843–851, https://doi.org/10.1002/joc.3473.
Bracegirdle, T. J., and G. J. Marshall, 2012: The reliability of Antarctic tropospheric pressure and temperature in the latest global reanalyses. J. Climate, 25(20), 7138–7146, https://doi.org/10.1175/JCLI-D-ll-00685.l.
Bromwich, D. H., J. P. Nicolas, and A. J. Monaghan, 2011: An Assessment of Precipitation Changes over Antarctica and the Southern Ocean since 1989 in Contemporary Global Reanalyses. J. Climafe, 24, 4189–4209, https://doi.org/10.1175/2011JCLI4074.1.
Dee, D. P., and Coauthors, 2011: The ERA-interimreanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc, 137, 553–597, https://doi.org/10.1002/qj.828.
Enomoto, T., A. Kuwano-Yoshida, N. Komori, and W. Ohfuchi, 2008: Description of AFES 2: Improvements for high-resolution and coupled simulations. High Resolution Numerical Modelling of the Atmosphere and Ocean, K. Hamilton and W. Ohfuchi, Eds., Springer, 77–97, https://doi.org/10.1007/978-0-387-49791-4_5.
Enomoto, T., T. Miyoshi, Q. Moteki, Q., J. Inoue, M. Hattori, S. Kuwano-Yoshida, N. Komori, and S. Yamane, 2013: Observing-system research and ensemble data assimilation at JAMSTEC. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II), S. K. Park and L. Xu, Eds., Springer, 509–526, https://doi.org/10.1007/978-3-642-35088-7_21.
Gelaro, R., and Coauthors, 2017: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1.
Gorodetskaya, I. V., M. Tsukernik, K. Claes, M. F. Ralph, W. D. Neff, and N. P. M. Van Lipzig, 2014: The role of atmo- spheric rivers in anomalous snow accumulation in East Antarctica. Geophys. Res. Lett, 41, 6199–6206, https://doi.org/10.1002/2014GL060881.
Hirasawa, N., H. Nakamura, H. Motoyama, M. Hayashi, and T. Yamanouchi, 2013: The role of synoptic-scale features and advection in prolonged warming and generation of different forms of precipitation atDome Fuji station, Antarctica, following a prominent blocking event. J. Geophys. Res. Atmos., 118(13), 6916–6928, https://doi.org/10.1002/jgrd.50532.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008.
Inoue, J., T. Enomoto, T. Miyoshi, and S. Yamane, 2009: Impact of observations from Arctic drifting buoys on the reanalysis of surface fields. Geophys. Res. Lett., 36, L08501, https://doi.org/10.1029/2009GL037380.
Inoue, J., M. E. Hori, T. Enomoto, and T. Kikuchi, 2011: Intercom-parison of surface heat transfer near the Arctic marginal ice zone for multiple reanalyses: A case study of September 2009. SOLA, 7, 57–60, https://doi.org/10.2151/sola.2011-015.
Inoue, J., T. Enomoto, and M. E. Hori, 2013: The impact of radiosonde data over the ice-free Arctic Ocean on the atmospheric circulation in the Northern Hemisphere. Geophys. Res. Lett, 40, 864–869, https://doi.org/10.1002/grl.5020-7.
Inoue, J., A. Yamazaki, J. Ono, K. Dethloff, M. Maturilli, R. Neuber, R. Edwards, and H. Yamaguchi, 2015: Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route. Scientific Reports, 5, 16868, https://doi.org/10.1038/srepl6868.
Jakobson, E., T. Vihma, T. Palo, L. Jakobson, H. Keernik, and J. Jaagus, 2012: Validation of atmospheric reanalyses over the central Arctic Ocean. Geophys. Res. Lett., 39, L10802, https://doi.org/10.1029/2012GL051591.
Jones, P. D., and D. H. Lister, 2015: Antarctic near-surface air temperatures compared with ERA-Interim values since 1979. International Journal of Climatology, 35(7), 1354–1366, https://doi.org/10.1002/joc.4061.
Jones, R. W., I. A. Renfrew, A. Orr, B. G. M. Webber, D. M. Holland, and M. A. Lazzara, 2016: Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica. J. Geophys. Res. Atmos., 121, 6240–6257, https://doi.org/10.1002/2015JD024680.
Jung, T., and M. Matsueda, 2016: Verification of global numerical weather forecasting systems in polar regions using TIGGE data. Quart. J. Roy. Meteor. Soc, 142, 574–582, https://doi.org/10.1002/qj.2437.
Jung, T., and Coauthors, 2016: Advancing polar prediction capabilities on daily to seasonal time scales. Bull. Amer. Meteor. Soc, 97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1.
Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan. Ser. II, 93(1), 5–48, https://doi.org/10.2151/jmsj.2015-001.
Kristjansson, J. E., and Coauthors, 2011: The Norwegian IPY-THORPEX: Polar lows and Arctic fronts during the 2008 And0ya Campaign. Bull. Amer. Meteor. Soc, 92, 1443–1466, https://doi.org/10.1175/2011BAMS290Ll.
Miyoshi, T., and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841–3861, https://doi.org/10.1175/2007MWR1873.1.
O'Connor, W. P., and D. H. Bromwich, 1988: Surface airflow around Windless Bight, Ross Island, Antarctica. Quart. J. Roy. Meteor. Soc, 114, 917–938, https://doi.org/10.1002/qj.49711448205.
O'Connor, W. P., D. H. Bromwich, and J. F. Carrasco, 1994: Cyc-lonically forced barrier winds along the Transantarctic Mountains near Ross Island. Mon. Wea. Rev., 122(1), 137–150, https://doi.org/10.1175/1520-0493(1994)122<0137:CFB-WAT>2.0.CO;2.
Ohfuchi, W., and Coauthors, 2004: 10-km mesh meso-scale resolving simulations of the global atmosphere on the Earth Simulator-Preliminary outcomes of AFES (AGCM for the Earth Simulator). Journal of the Earth Simulator, 1, 8–34.
Powers, J. G., 2007: Numerical prediction of an Antarctic severe wind event with the weather research and forecasting (WRF) model Mon.Wea.Rev., 135, 3134–3157, https://doi.org/10.1175/MWR3459.1.
Reynolds, R. W., T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.l.
Rinke, A., Y. F. Ma, L. G. Bian, Y. F. Xin, K. Dethloff, P. O. G. Persson, C. Lupkes, and C. D. Xiao, 2012: 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.
Rinke, A., K. Dethloff, W. Dorn, D. Handorf, and J. C. Moore, 2013: Simulated Arctic atmospheric feedbacks associated with late summer sea ice anomalies. J. Geophys. Res. Atmos., 118, 7698–7714, https://doi.org/10.1002/jgrd.50584.
Saha, S., and Coauthors, 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc, 91(8), 1015–1058, https://doi.org/10.1175/2010BAMS300Ll.
Saha, S., and Coauthors, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.l.
Sato, K., J. Inoue, A. Yamazaki, J.-H. Kim, M. Maturilli, K. Dethloff, S. R. Hudson, and M. A. Granskog, 2017: Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations. J. Geophys. Res. Oceans, 122, 775–787, https://doi.org/10.1002/2016JC012197.
Sato, K., J. Inoue, A. Yamazaki, J.-H. Kim, A. Makshtas, V. Kustov, M. Maturilli, and K. Dethloff, 2018a: Impact on predictability of tropical and mid-latitude cyclones by extra Arctic observations. Scientific Reports, 8, 12104, https://doi.org/10.1038/s41598-018-30594-4.
Sato, K., J. Inoue, S. P. Alexander, G. McFarquhar, and A. Yamazaki, 2018b: Improved reanalysis and prediction of atmospheric fields over the Southern Ocean using campaign-based radiosonde observations. Geophys. Res. Lett., 45, 11, https://doi.org/10.1029/2018GL079037.
Semmler, T., M. A. Kasper, T. Jung, and S. Serrar, 2016: Remote impact of the Antarctic atmosphere on the Southern mid-latitudes. Meteor. Z, 25, 71–77, https://doi.org/10.1127/metz/2015/0685.
Soldatenko, S., C. Tingwell, P. Steinle, and B. A. Kelly-Gerreyn, 2018: Assessing the impact of surface and upper-air observations on the forecast skill of the ACCESS numerical weather prediction model over Australia. Atmosphere, 9(1), 23, https://doi.org/10.3390/atmos9010023.
Swinbank, R., and Coauthors, 2016: The TIGGE project and its achievements. Bull. Amer. Meteor. Soc, 97(1), 49–67, https://doi.org/10.1175/BAMS-D-13-0019Ll.
Yamazaki, A., J. Inoue, K. Dethloff, M. Maturilli, and G. Konig-Langlo, 2015: Impact of radiosonde observations on forecast- ing summertime Arctic cyclone formation. J. Geophys. Res. Atmos., 120, 3249–3273, https://doi.org/10.1002/2014JD022925.
Yamagami, A., M. Matsueda, and H. L. Tanaka, 2017: Extreme arctic cyclone in August 2016. Atmospheric Science Letters, 18, 307–314, https://doi.org/10.1002/asl.757.
Acknowledgments
This work was supported by a Japan Society for the Promotion of Science (JSPS) Overseas Research Fellowship, JSPS Grants-in-Aid for Scientific Research (KAKENHI) (Grant Nos. 19K14802 and 18H05053). We would like to thank the anonymous reviewers, whose constructive comments improved the quality of this manuscript. The authors thank the crew of RV “Shirase”. The MODIS dataset received at Syowa Station is archived and provided by the Arctic Data archive System (ADS developed by the National Institute of Polar Research. The ADS transferred radiosonde data from RV “Shirase” and Dome Fuji Station to the JMA. The TIGGE and ERA5 datasets are available via the ECMWF data portal (http://apps.ecmwf.int/datasets/). The ALEDAS2 and AFES integrations were performed on the Earth Simulator with the support of JAMSTEC. PREPBUFR, compiled by the NCEP and archived at the University Corporation for Atmospheric Research, was used as the observations (available from http://rda.ucar.edu). The datasets provided by ALEDAS2 were from JAMSTEC's website (http://www.jamstec.go.jp/alera/alera2. html). We thank James BUXTON MSc and Tin TIN PhD from Edanz Group (www.edanzediting.com./ac) for correcting drafts of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights:
• Assimilation of additional Antarctic radiosonde observations improved skill in forecasting the strong winds associated with an Antarctic cyclone.
• Uncertainty originating from excluding additional Antarctic observations extended across the Southern Ocean, even in reanalysis data.
• Assimilation of additional radiosonde observations improves cyclone forecasts in operational forecast systems.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm-ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Sato, K., Inoue, J., Yamazaki, A. et al. Antarctic Radiosonde Observations Reduce Uncertainties and Errors in Reanalyses and Forecasts over the Southern Ocean: An Extreme Cyclone Case. Adv. Atmos. Sci. 37, 431–440 (2020). https://doi.org/10.1007/s00376-019-8231-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-019-8231-x