Evaluation on the applicability of ERA5 reanalysis dataset to tropical cyclones affecting Shanghai

Zhihui HAN , Caijun YUE , Changhai LIU , Wen GU , Yuqi TANG , Yongyu LI

Front. Earth Sci. ›› 2022, Vol. 16 ›› Issue (4) : 1025 -1039.

PDF (9281KB)
Front. Earth Sci. ›› 2022, Vol. 16 ›› Issue (4) : 1025 -1039. DOI: 10.1007/s11707-022-0972-7
RESEARCH ARTICLE
RESEARCH ARTICLE

Evaluation on the applicability of ERA5 reanalysis dataset to tropical cyclones affecting Shanghai

Author information +
History +
PDF (9281KB)

Abstract

Based on the 16 historical tropical cyclones (TCs) affecting Shanghai from 2007 to 2019, the suitability of ERA5 for studying TCs affecting Shanghai is systematically evaluated from the perspective of TC track, intensity, 10-m and upper-level wind, using TC best-track data of China Meteorological Administration and surface observations and sounding data. Corresponding to tropical storm (TS), strong tropical storm (STS), typhoon (TY), strong typhoon (STY) and super typhoon (SuperTY), the median TC track bias is 68.1, 52.9, 42.5, 25.4, and 18.2 km, respectively, the median maximum 10-m wind speed (VMAX10m) bias is –3.7, –6.5, –11.4, –21.7, and –32.2 m·s–1, respectively, and the median minimum mean sea level pressure (MSLPmin) bias is 2.2, 5.6, 8.1, 28.2, and 48.7 hPa, respectively. With the increase of TC intensity, the median TC track bias decreases, while the median VMAX10m and MSLPmin bias increase. In general, VMAX10m in ERA5 is lower than observed, and MSLPmin is larger than observed. Under influence of TS, STS, TY and STY, the median 10-m wind speed (V10m) bias in the city is 3.2, 4.2, 4.7, and 5.4 m·s–1, respectively, and is 4.4–5.2 m·s–1 near the east coast, respectively. V10m is mostly biased high, showing an “M” type pattern with the distance between TC and Shanghai. The median 10 m wind direction (WD10m) bias is in a range of –7º to +7º. The median upper-level wind speed (Vupper) bias decreases with height, with a maximum of ~5 m·s–1 at 975 hPa. Below 900 hPa Vupper in ERA5 is typically larger than the radiosonde observation, and its mean bias error (MBE) increases with TC intensity. The upper-level wind direction (WDupper) matches the sounding data well, with a maximum bias of a few degrees only. The results provide a reference for the application of ERA5 to coastal cities affected by TCs.

Keywords

ERA5 reanalysis / tropical cyclone / wind field / urban

Cite this article

Download citation ▾
Zhihui HAN, Caijun YUE, Changhai LIU, Wen GU, Yuqi TANG, Yongyu LI. Evaluation on the applicability of ERA5 reanalysis dataset to tropical cyclones affecting Shanghai. Front. Earth Sci., 2022, 16(4): 1025-1039 DOI:10.1007/s11707-022-0972-7

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

An Y, Quan Y, Gu M ( 2013). Turbulence characteristics analysis of typhoon ‘Mufia’ near 500 m above ground in Lujiazui District of Shanghai. China Civil Eng J, 46(7): 21– 27 (in Chinese)

[2]

Bian G,, Nie G,, Qiu X. ( 2021). How well is outer tropical cyclone size represented in the ERA5 reanalysis dataset. Atmos Res, 249: 105339

[3]

Bosilovich M G ,, Santha A,, Lawrence C,, Richard C,, Clara D,, Ronald Ge,, Robin K,, Qing L,, Andrea M,, Peter N,, Krzysztof W,, Winston C,, Rolf R,, Lawrence T,, Yury V,, Steve B,, Allison C,, Stacey F,, Gordon L,, Gary P,, Steven P,, Oreste R,, Siegfried D S,, Max S. ( 2015). MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. Series on Global Modeling and Data Assimilation NASA/TM-2015-104606, 43

[4]

Chen Y, Han G, Jiao S, Wang Q, Yuan C ( 2008). Application of ECMWF products to typhoon track forecasting. J Meteor Sci, 28( 2): 91– 97 (in Chinese)

[5]

Chen Z ( 2010). Characteristics of the overall sounding data drift in China. Meteor Mon, 36(2): 22– 27 (in Chinese)

[6]

Dee D P,, Uppala S M,, Simmons A J,, Berrisford P,, Poli P,, Kobayashi S,, Andrae U,, Balmaseda M A,, Balsamo G,, Bauer P,, Bechtold P,, Beljaars A C M,, van de Berg L,, Bidlot J,, Bormann N,, Delsol C,, Dragani R,, Fuentes M,, Geer A J,, Haimberger L,, Healy S B,, Hersbach H,, Hólm E V,, Isaksen L,, Kållberg P,, Köhler M,, Matricardi M,, McNally A P,, Monge-Sanz B M,, Morcrette J J,, Park B K,, Peubey C,, de Rosnay P,, Tavolato C,, Thépaut J N,, Vitart F. ( 2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc, 137( 656): 553– 597

[7]

Geetha B,, Balachandran S. ( 2020). Development and rapid intensification of tropical cyclone OCKHI (2017) over the north Indian Ocean. J Atmosph Sci Res, 3( 3): 13– 22

[8]

Hart R E,, Maue R N,, Watson M C. ( 2007). Estimating local memory of tropical cyclones through MPI anomaly evolution. Mon Weather Rev, 135( 12): 3990– 4005

[9]

Hatsushika H,, Tsutsui J,, Fiorino M,, Onogi K. ( 2006). Impact of wind profile retrievals on the analysis of tropical cyclones in the JRA-25 reanalysis. J Meteorol Soc Jpn, 84( 5): 891– 905

[10]

He L, Chen S, Guo Y ( 2020). Observation characteristics and synoptic mechanisms of typhoon Lekima extreme rainfall in 2019. J App Meteor Sci, 31( 5): 513– 526 (in Chinese)

[11]

Hersbach H. ( 2019). ECMWF’s ERA5 reanalysis extends back to 1979. ECMWF Newsl, No: 158

[12]

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,, 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,, Rosnay P,, Rozum I,, Vamborg F,, Villaume S,, Thépaut J N. ( 2020). The ERA5 global reanalysis. Q J R Meteorol Soc, 146( 730): 1999– 2049

[13]

Hodges K,, Cobb A,, Vidale P L. ( 2017). How well are tropical cyclones represented in reanalysis datasets?. J Clim, 30( 14): 5243– 5264

[14]

Kobayashi S,, Ota Y,, Harada Y,, Ebita A,, Moriya M,, Onoda H,, Onogi K,, Kamahori H,, Kobayashi C,, Endo H,, Miyaoka K,, Takahashi K. ( 2015). The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn, 93( 1): 5– 48

[15]

Knutson T R,, Sirutis J J,, Garner S T,, Vecchi G A,, Held I M. ( 2008). Simulated reduction in Atlantic hurricane frequency under twenty first-century warming condition. Nat Geosci, 1( 6): 359– 364

[16]

Lam C C, Lai S T ( 1994). Use of ECMWF 850-hPa vorticity fields in the forecasting of tropical cyclones and intense lows in June-July 1994. In: Proceeding of 9th Guangdong-Hong Kong-Macau Joint Seminar on Hazardous Weather, Hong Kong Observatory, Hong Kong, China, 137– 156

[17]

Liu H, Xue J, Shen T, Zhuang S, Zhu G ( 2005). Study on sounding balloon drifting and its impact on numerical predictions. J App Meteor Sci, 16( 4): 518– 526 (in Chinese)

[18]

Malakar P,, Kesarkar A P,, Bhate J N,, Singh V,, Deshamukhya A. ( 2020). Comparison of reanalysis datasets to comprehend the evolution of tropical cyclones over North Indian Ocean. Earth Space Sci, 7( 2): e2019EA000978

[19]

Maloney E,, Hartmann D. ( 2000a). Modulation of eastern North Pacific hurricanes by the Madden-Julian oscillation. J Clim, 13( 9): 1451– 1460

[20]

Maloney E,, Hartmann D. ( 2000b). Modulation of hurricane activity in the Gulf of Mexico by the Madden-Julian oscillation. Science, 287( 5460): 2002– 2004

[21]

Manning D M,, Hart R E. ( 2007). Evolution of North Atlantic ERA-40 tropical cyclone representation. Geophys Res Lett, 34( 5): L05705

[22]

Murakami H. ( 2014). Tropical cyclones in reanalysis data sets. Geophys Res Lett, 41( 6): 2133– 2141

[23]

Onogi K,, Tsutsui J,, Koide H,, Sakamoto M,, Kobayashi S,, Hatsushika H,, Matsumoto T,, Yamazaki N,, Kamahori H,, Takahashi K,, Kadokura S,, Wada K,, Kato K,, Oyama R,, Ose T,, Mannoji N,, Taira R. ( 2007). The JRA-25 reanalysis. J Meteorol Soc Jpn, 85( 3): 369– 432

[24]

Rienecker M M,, Suarez M J,, Gelaro R,, Todling R,, Bacmeister J,, Liu E,, Bosilovich M G,, Schubert S D,, Takacs L,, Kim G K,, Bloom S,, Chen J,, Collins D,, Conaty A,, da Silva A,, Gu W,, Joiner J,, Koster R D,, Lucchesi R,, Molod A,, Owens T,, Pawson S,, Pegion P,, Redder C R,, Reichle R,, Robertson F R,, Ruddick A G,, Sienkiewicz M,, Woollen J. ( 2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim, 24( 14): 3624– 2648

[25]

Saha S,, Moorthi S,, Pan H L,, Wu X,, Wang J,, Nadiga S,, Tripp P,, Kistler R,, Woollen J,, Behringer D,, Liu H,, Stokes D,, Grumbine R,, Gayno G,, Wang J,, Hou Y T,, Chuang H,, Juang H M H,, Sela J,, Iredell M,, Treadon R,, Kleist D,, Van Delst P,, Keyser D,, Derber J,, Ek M,, Meng J,, Wei H,, Yang R,, Lord S,, van den Dool H,, Kumar A,, Wang W,, Long C,, Chelliah M,, Xue Y,, Huang B,, Schemm J K,, Ebisuzaki W,, Lin R,, Xie P,, Chen M,, Zhou S,, Higgins W,, Zou C Z,, Liu Q,, Chen Y,, Han Y,, Cucurull L,, Reynolds R W,, Rutledge G,, Goldberg M. ( 2010). The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc, 91( 8): 1015– 1058

[26]

Schenkel B A,, Hart R E. ( 2012). An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets. J Clim, 25( 10): 3453– 3475

[27]

Schenkel B A,, Lin N,, Chavas D,, Oppenheimer M,, Brammer A. ( 2017). Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT data. J Clim, 30( 21): 8745– 8762

[28]

Scoccimarro E,, Gualdi S,, Navarra A. ( 2012). Tropical cyclone effects on Arctic Sea ice variability. Geophys Res Lett, 39( 17): L17704

[29]

Thorne P W,, Vose R S. ( 2010). Reanalyses suitable for characterizing long-term trends: Are they really achievable?. Bull Am Meteorol Soc, 91( 3): 353– 362

[30]

Tu S,, Xu J,, Chan J C L,, Huang K,, Xu F,, Chiu L S. ( 2021). Recent global decrease in the inner-core rain rate of tropical cyclones. Nat Commun, 12( 1): 1948

[31]

Tu X, Yao R ( 2010). Analysis on ECMWF NWP products in track forecasting. J Trop Meteorol, 26( 6): 759– 764 (in Chinese)

[32]

Uppala S,, KÅllberg P W,, Simmons A J,, Andrae U,, Bechtold V D C,, Fiorino M,, Gibson J K,, Haseler J,, Hernandez A,, Kelly G A,, Li X,, Onogi K,, Saarinen S,, Sokka N,, Allan R P,, Andersson E,, Arpe K,, Balmaseda M A,, Beljaars A C M,, Berg L V D,, Bidlot J,, Bormann N,, Caires S,, Chevallier F,, Dethof A,, Dragosavac M,, Fisher M,, Fuentes M,, Hagemann S,, Hólm E,, Hoskins B J,, Isaksen L,, Janssen P A E M,, Jenne R,, Mcnally A P,, Mahfouf J F,, Morcrette J J,, Rayner N A,, Saunders R W,, Simon P,, Sterl A,, Trenberth K E,, Untch A,, Vasiljevic D,, Viterbo P,, Woollen J. ( 2005). The ERA-40 re-analysis. Q J R Meteorol Soc, 131( 612): 2961– 3012

[33]

Wentz F J, Scott J, Hoffman R, Leidner M, Atlas R, Ardizzone J ( 2015). Remote sensing systems cross-calibrated multi-platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0. Remote Sensing Systems, Santa Rosa, CA

[34]

Xi D, Han G, Yin X, Li Y, Liu Y, Tang Y ( 2020). Study on application of CPS method to typhoons affecting Jiangsu Province. Meteor Mon, 46( 6): 765– 775 (in Chinese)

[35]

Xu J ( 2005). Distribution of wind speed and direction when typhoons influencing Shanghai. Meteoro Mon, 31( 8): 66– 70 (in Chinese)

[36]

Ying M,, Zhang W,, Yu H,, Lu X,, Feng J,, Fan Y,, Zhu Y,, Chen D. ( 2014). An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol, 31( 2): 287– 301

[37]

Yue C, Tang Y, Gu W, Han Z, Wang X ( 2019). Study of city barrier effect on local typhoon precipitation. Meteor Mon, 45(11): 1611– 1620 (in Chinese)

[38]

Zarzycki C M,, Ullrich P A,, Reed K A. ( 2021). Metrics for evaluating tropical cyclones in climate data. J Appl Meteorol Climatol, 60( 5): 643– 660

[39]

Zick S E,, Matyas C J. ( 2015). Tropical cyclones in the North American regional reanalysis: an assessment of spatial biases in location, intensity, and structure. J Geophys Res D Atmospheres, 120( 5): 1651– 1669

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (9281KB)

789

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/