Evaluation of Short-Range Precipitation Reforecasts from East Asia Regional Reanalysis

Eun-Gyeong Yang Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Science, Yonsei University, Seoul, South Korea

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Hyun Mee Kim Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Science, Yonsei University, Seoul, South Korea

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Abstract

As the need for regional reanalyses emerged around the world, a short period of the East Asia Regional Reanalysis (EARR) system was recently developed based on the Unified Model (UM). In this study, the quality of the EARR is evaluated by comparing the short-range precipitation reforecasts against reforecasts of ERA-Interim (ERA-I) reanalysis and operational forecasts of the Korea Meteorological Administration (OPER). For the verification, two different periods are selected for 14 days in the summer (July 2013, denoted as 201307) and winter (February 2014, denoted as 201402). The equitable threat score (ETS) of EARR and OPER is generally greater than that of ERA-I. The frequency bias index (FBI) of EARR and OPER is overall closer to 1 than that of ERA-I for all thresholds, which indicates that EARR and OPER are much closer to the observation compared to ERA-I. For the period 201307, the ERA-I FBI is greater than 1 for lower thresholds and the probability of detection (POD) and false alarm ratio (FAR) of ERA-I are high, implying that ERA-I tends to overforecast light precipitation. In addition, using the same Weather Research and Forecasting (WRF) Model, the 6-h precipitation forecasts are integrated every 12 h (initialized from 0000/1200 UTC) for 4 months for the summer and winter season. Although the differences of ETS and FBI between EARR and ERA-I are not distinct for the summer season, overall EARR ETS is higher than ERA-I ETS, and EARR FBI is closer to 1 than ERA-I FBI. Based on several evaluations, the precipitation reforecasts of EARR are confirmed to be more accurate than those of OPER and ERA-I in East Asia.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hyun Mee Kim, khm@yonsei.ac.kr

Abstract

As the need for regional reanalyses emerged around the world, a short period of the East Asia Regional Reanalysis (EARR) system was recently developed based on the Unified Model (UM). In this study, the quality of the EARR is evaluated by comparing the short-range precipitation reforecasts against reforecasts of ERA-Interim (ERA-I) reanalysis and operational forecasts of the Korea Meteorological Administration (OPER). For the verification, two different periods are selected for 14 days in the summer (July 2013, denoted as 201307) and winter (February 2014, denoted as 201402). The equitable threat score (ETS) of EARR and OPER is generally greater than that of ERA-I. The frequency bias index (FBI) of EARR and OPER is overall closer to 1 than that of ERA-I for all thresholds, which indicates that EARR and OPER are much closer to the observation compared to ERA-I. For the period 201307, the ERA-I FBI is greater than 1 for lower thresholds and the probability of detection (POD) and false alarm ratio (FAR) of ERA-I are high, implying that ERA-I tends to overforecast light precipitation. In addition, using the same Weather Research and Forecasting (WRF) Model, the 6-h precipitation forecasts are integrated every 12 h (initialized from 0000/1200 UTC) for 4 months for the summer and winter season. Although the differences of ETS and FBI between EARR and ERA-I are not distinct for the summer season, overall EARR ETS is higher than ERA-I ETS, and EARR FBI is closer to 1 than ERA-I FBI. Based on several evaluations, the precipitation reforecasts of EARR are confirmed to be more accurate than those of OPER and ERA-I in East Asia.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hyun Mee Kim, khm@yonsei.ac.kr
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  • Barker, D., R. Renshaw, and P. Jermey, 2013: Regional reanalysis. MOSAC and SRG Meetings 2013, Exeter, United Kingdom, Met Office, MOSAC PAPER 18.12, https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/library/mosac/mosac_18.12_barker.pdf.

  • Barker, D., R. Renshaw, and P. Jermey, 2015: Towards a UM-based regional reanalysis for Europe. 9th Annual CAWCR Workshop, Melbourne, Australia, Bureau of Meteorology, 24 pp., http://www.cawcr.gov.au/events/AWS9/PDF/Barker_Wed_CAWCR.pdf.

  • Borsche, M., A. K. Kaiser-Weiss, P. Undén, and F. Kaspar, 2015: Methodologies to characterize uncertainties in regional reanalyses. Adv. Sci. Res., 12, 207218, https://doi.org/10.5194/asr-12-207-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., 2008: NASA’s Modern Era Retrospective-Analysis for Research and Applications: Integrating Earth observations. Earthzine, https://earthzine.org/nasas-modern-era-retrospective-analysis/.

  • Bosilovich, M. G., J. Chen, F. R. Robertson, and R. F. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 22792299, https://doi.org/10.1175/2008JAMC1921.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D., Y.-H. Kuo, M. Serreze, J. Walsh, L. S. Bai, M. Barlage, K. Hines, and A. Slater, 2010: Arctic System Reanalysis: Call for community involvement. Eos, Trans. Amer. Geophys. Union, 91, 1314, https://doi.org/10.1029/2010EO020001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bukovsky, M. S., and D. J. Karoly, 2007: A brief evaluation of precipitation from the North American Regional Reanalysis. J. Hydrometeor., 8, 837846, https://doi.org/10.1175/JHM595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., T. Iwasaki, H. Qin, and W. Sha, 2014: Evaluation of the warm-season diurnal variability over East Asia in recent reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA. J. Climate, 27, 55175537, https://doi.org/10.1175/JCLI-D-14-00005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Compo, G. P., J. S. Whitaker, and P. D. Sardeshmukh, 2008: The 20th Century Reanalysis Project. Third WCRP Int. Conf. on Reanalysis, Tokyo, Japan, University of Tokyo, Japan, 5 pp., http://wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/Documents/V5-511_ea.pdf.

  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, https://doi.org/10.1002/qj.776.

  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387, https://doi.org/10.1002/qj.49712051912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahlgren, P., T. Landelius, P. Kållberg, and S. Gollvik, 2016: A high-resolution regional reanalysis for Europe. Part 1: Three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM). Quart. J. Roy. Meteor. Soc., 142, 21192131, https://doi.org/10.1002/qj.2807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Quart. J. Roy. Meteor. Soc., 131, 17591782, https://doi.org/10.1256/qj.04.101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., M. Balmaseda, G. Balsamo, R. Engelen, A. J. Simmons, and J.-N. Thépaut, 2014: Toward a consistent reanalysis of the climate system. Bull. Amer. Meteor. Soc., 95, 12351248, https://doi.org/10.1175/BAMS-D-13-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., U. Damrath, W. Wergen, and M. E. Baldwin, 2003: The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc., 84, 481492, https://doi.org/10.1175/BAMS-84-4-481.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebita, A., P. Kållberg, and Coauthors, 2011: The Japanese 55-year Reanalysis “JRA-55”: An interim report. SOLA, 7, 149152, https://doi.org/10.2151/sola.2011-038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gibson, J. K., P. Kållberg, S. Uppala, A. Nomura, A. Hernandez, and E. Serrano, 1997: ERA description. ECMWF Re-Analysis Final Rep. Series 1, 71 pp.

  • Gilbert, G. F., 1884: Finley’s tornado predictions. Amer. Meteor. J., 1, 166172.

  • Hersbach, H., C. Peubey, A. Simmons, P. Berrisford, P. Poli, and D. Dee, 2015: ERA-20CM: A twentieth-century atmospheric model ensemble. Quart. J. Roy. Meteor. Soc., 141, 23502375, https://doi.org/10.1002/qj.2528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ho, C.-H., and Coauthors, 2011: A projection of extreme climate events in the 21st century over East Asia using the Community Climate System Model 3. Asia-Pac. J. Atmos. Sci., 47, 329344, https://doi.org/10.1007/s13143-011-0020-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y., S. Maskey, and S. Uhlenbrook, 2012: Trends in temperature and rainfall extremes in the Yellow River source region, China. Climatic Change, 110, 403429, https://doi.org/10.1007/s10584-011-0056-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2007: Climate Change 2007: Impacts, Adaptation and Vulnerability. M. L. Parry et al., Eds., Cambridge University Press, 976 pp.

  • IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, 582 pp.

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jermey, P. M., and R. J. Renshaw, 2016: Precipitation representation over a two-year period in regional reanalysis. Quart. J. Roy. Meteor. Soc., 142, 13001310, https://doi.org/10.1002/qj.2733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and D. R. Easterling, 1999: Climate extremes: Selected review and future research directions. Climatic Change, 42, 309325, https://doi.org/10.1023/A:1005436904097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247267, https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lader, R., U. S. Bhatt, J. E. Walsh, T. S. Rupp, and P. A. Bieniek, 2016: Two-meter temperature and precipitation from atmospheric reanalysis evaluated for Alaska. J. Appl. Meteor. Climatol., 55, 901922, https://doi.org/10.1175/JAMC-D-15-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, C., and H. Kunstmann, 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 13971420, https://doi.org/10.1175/JHM-D-11-088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, S., P. Jermey, and R. Renshaw, 2014: Methods for evaluating model performance of IMDAA. Forecasting Research Tech. Rep. 594, Met Office, 24 pp., https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/migrated/frtr594.compressed.pdf.

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, https://doi.org/10.1175/BAMS-87-3-343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MoES and NOAA, 2010: South Asian Regional Reanalysis (SARR). SARR Scoping Workshop Rep., Ministry of Earth Sciences, Government of India, and NOAA, 17 pp., http://www.ncmrwf.gov.in/SARR-workshop-report-final.pdf.

  • Nkiaka, E., N. R. Nawaz, and J. C. Lovett, 2017: Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano-Sahel region: Case study of the Logone catchment, Lake Chad Basin. Meteor. Appl., 24, 918, https://doi.org/10.1002/met.1600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Onogi, K., and Coauthors, 2007: The JRA-25 reanalysis. J. Meteor. Soc. Japan, 85, 369432, https://doi.org/10.2151/jmsj.85.369.

  • Peña-Arancibia, J. L., A. I. van Dijk, L. J. Renzullo, and M. Mulligan, 2013: Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and South and East Asia. J. Hydrometeor., 14, 13231333, https://doi.org/10.1175/JHM-D-12-0132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., X. Zhang, M. Brunet-India, and J. L. Vázquez-Aguirre, 2008: Changes in North American extremes derived from daily weather data. J. Geophys. Res., 113, D07113, https://doi.org/10.1029/2007JD009453.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in Fortran 77: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 933 pp.

  • Rawlins, F., S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133, 347362, https://doi.org/10.1002/qj.32.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Renshaw, R., P. Jermey, D. Barker, A. Maycock, and S. Oxley, 2013: EURO4M regional reanalysis system. Forecasting Research Tech. Rep. 583, Met Office, 30 pp., https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/o/4/frtr583.pdf.

  • Rienecker, M. M., J. Pfaendtner, and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rood, R. B., and M. G. Bosilovich, 2010: Reanalysis: Data assimilation for scientific investigation of climate. Data Assimilation, W. Lahoz, B. Khattatov, and R. Menard, Eds., Springer, 623–646, https://doi.org/10.1007/978-3-540-74703-1_23.

    • Crossref
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, S., J. Pfaendtner, and R. Rood, 1993: An assimilated dataset for earth science applications. Bull. Amer. Meteor. Soc., 74, 23312342, https://doi.org/10.1175/1520-0477(1993)074<2331:AADFES>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, H.-C., H.-M. Noh, D.-J. Kim, T.-K. Jang, E. H. Lee, and S. Joo, 2015: Improvement of the numerical prediction system of the Korea Meteorological Administration to improve the forecasting performance of high-impact weather events in East Asia (in Korean). Proc. Autumn Meeting of Korean Meteorological Society, Daegu, South Korea, Korean Meteorological Society, 244–245.

  • Smith, G. C., F. Roy, P. Mann, F. Dupont, B. Brasnett, J.-F. Lemieux, S. Laroche, and S. Bélair, 2014: A new atmospheric dataset for forcing ice–ocean models: Evaluation of reforecasts using the Canadian global deterministic prediction system. Quart. J. Roy. Meteor. Soc., 140, 881894, https://doi.org/10.1002/qj.2194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, https://doi.org/10.1256/qj.04.176.

  • Wang, S. Y., T. C. Chen, and S. E. Taylor, 2009: Evaluations of NAM forecasts on midtropospheric perturbation-induced convective storms over the U.S. northern plains. Wea. Forecasting, 24, 13091333, https://doi.org/10.1175/2009WAF2222185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q. X., X. H. Fan, Z. D. Qin, and M. B. Wang, 2012: Change trends of temperature and precipitation in the Loess Plateau region of China, 1961–2010. Global Planet. Change, 92–93, 138147, https://doi.org/10.1016/j.gloplacha.2012.05.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 100, Academic Press, 648 pp.

  • Wilson, L., 2010: Verification of severe weather forecasts in support of the “SWFDP Southern Africa” project. Rep. for the WMO, 21 pp., www.wmo.int/pages/prog/www/BAS/documents/Doc-7-Verification.doc.

  • WMO, 2013: The global climate 2001–2010: A decade of climate extremes—Summary report. WMO-1119, World Meteorological Organization, 15 pp., https://library.wmo.int/pmb_ged/wmo_1119_en.pdf.

  • Xie, P., M. Chen, S. Yang, A. Yatagai, T. Hayasaka, Y. Fukushima, and C. Liu, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607626, https://doi.org/10.1175/JHM583.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, E.-G., and H. M. Kim, 2017: Evaluation of a regional reanalysis and ERA-Interim over East Asia using in situ observations during 2013–14. J. Appl. Meteor. Climatol., 56, 28212844, https://doi.org/10.1175/JAMC-D-16-0227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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