Skip to main content
SpringerLink
Log in

Uncertainties and error growth in forecasting the record-breaking rainfall in Zhengzhou, Henan on 19–20 July 2021

  • Research Paper
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

This study explores the controlling factors of the uncertainties and error growth at different spatial and temporal scales in forecasting the high-impact extremely heavy rainfall event that occurred in Zhengzhou, Henan Province China on 19–20 July 2021 with a record-breaking hourly rainfall exceeding 200 mm and a 24-h rainfall exceeding 600 mm. Results show that the strengths of the mid-level low-pressure system, the upper-level divergence, and the low-level jet determine both the amount of the extreme 24-h accumulated and hourly rainfall at 0800 UTC. The forecast uncertainties of the accumulated rainfall are insensitive to the magnitude and the spatial structure of the tiny, unobservable errors in the initial conditions of the ensemble forecasts generated with Global Ensemble Forecast System (GEFS) or sub-grid-scale perturbations, suggesting that the predictability of this event is intrinsically limited. The dominance of upscale rather than upamplitude error growth is demonstrated under the regime of k−5/3 power spectra by revealing the inability of large-scale errors to grow until the amplitude of small-scale errors has increased to an adequate amplitude, and an apparent transfer of the fastest growing scale from smaller to larger scales with a slower growth rate at larger scales. Moist convective activities play a critical role in enhancing the overall error growth rate with a larger error growth rate at smaller scales. In addition, initial perturbations with different structures have different error growth features at larger scales in different variables in a regime transitioning from the k−5/3 to k−3 power law. Error growth with conditional nonlinear optimal perturbation (CNOP) tends to be more upamplitude relative to the GEFS or sub-grid-scale perturbations possibly owing to the inherited error growth feature of CNOP, the inability of convective parameterization scheme to rebuild the k−5/3 power spectra at the mesoscales, and different error growth characteristics in the k−5/3 and k−3 regimes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bei N, Zhang F. 2007. Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-yu front of China. Q J R Meteorol Soc, 133: 83–99

    Article  Google Scholar 

  • Birgin E G, Martínez J M, Raydan M. 2001. Algorithm 813: SPG—Software for convex-constrained optimization. ACM Trans Math Softw, 27: 340–349

    Article  Google Scholar 

  • Bougeault P, Toth Z, Bishop C, Brown B, Burridge D, Chen D H, Ebert B, Fuentes M, Hamill T M, Mylne K, Nicolau J, Paccagnella T, Park Y Y, Parsons D, Raoult B, Schuster D, Dias P S, Swinbank R, Takeuchi Y, Tennant W, Wilson L, Worley S. 2010. The THORPEX interactive grand global ensemble. Bull Amer Meteorol Soc, 91: 1059–1072

    Article  Google Scholar 

  • Cressman G P. 1959. An operational objective analysis system. Mon Weather Rev, 87: 367–374

    Article  Google Scholar 

  • Durran D R, Gingrich M. 2014. Atmospheric predictability: Why butterflies are not of practical importance. J Atmos Sci, 71: 2476–2488

    Article  Google Scholar 

  • Durran D R, Weyn J A. 2016. Thunderstorms do not get butterflies. Bull Am Meteorol Soc, 97: 237–243

    Article  Google Scholar 

  • Ehrendorfer M, Errico R M, Raeder K D. 1999. Singular-vector perturbation growth in a primitive equation model with moist physics. J Atmos Sci, 56: 1627–1648

    Article  Google Scholar 

  • Grell G A, Dudhia J, Stauffer D R. 1995. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech Note NCAR/TN-398+STR. 121

  • Hakim G J, Torn R D. 2008. Ensemble synoptic analysis. In: Bosart L F, Bluestein H B, eds. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders. Boston: American Meteorological Society

    Google Scholar 

  • Hawblitzel D P, Zhang F, Meng Z, Davis C A. 2007. Probabilistic evaluation of the dynamics and predictability of the mesoscale convective vortex of 10–13 June 2003. Mon Weather Rev, 135: 1544–1563

    Article  Google Scholar 

  • Hong S Y, Noh Y, Dudhia J. 2006. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev, 134: 2318–2341

    Article  Google Scholar 

  • Iacono M J, Delamere J S, Mlawer E J, Shephard M W, Clough S A, Collins W D. 2008. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J Geophys Res, 113: D13103

    Article  Google Scholar 

  • Jiménez P A, Dudhia J, González-Rouco J F, Navarro J, Montávez J P, García-Bustamante E. 2012. A revised scheme for the WRF surface layer formulation. Mon Weather Rev, 140: 898–918

    Article  Google Scholar 

  • Judt F. 2018. Insights into atmospheric predictability through global convection-permitting model simulations. J Atmos Sci, 75: 1477–1497

    Article  Google Scholar 

  • Judt F. 2020. Atmospheric predictability of the tropics, middle latitudes, and polar regions explored through global storm-resolving simulations. J Atmos Sci, 77: 257–276

    Article  Google Scholar 

  • Liang Y, Qiao C, Dong J. 2020. Spatial-temporal distribution and impact analysis of the first rainstorm in Henan Province of the recent 34 years (in Chinese). Meteorol Environ Sci, 43: 26–32

    Google Scholar 

  • Lorenz E N. 1963. Deterministic nonperiodic flow. J Atmos Sci, 20: 130–141

    Article  Google Scholar 

  • Lorenz E N. 1969. The predictability of a flow which possesses many scales of motion. Tellus, 21: 289–307

    Article  Google Scholar 

  • Lorenz E N. 1982. Atmospheric predictability experiments with a large numerical model. Tellus, 34: 505–513

    Article  Google Scholar 

  • Lorenz E N. 1996. Predictability—A problem partly solved. In: Proceedings of Seminar on Predictability. Reading, United Kingdom, ECMWF. 1–18

  • Lynch S L, Schumacher R S. 2014. Ensemble-based analysis of the May 2010 extreme rainfall in Tennessee and Kentucky. Mon Weather Rev, 142: 222–239

    Article  Google Scholar 

  • Melhauser C, Zhang F. 2012. Practical and intrinsic predictability of severe and convective weather at the mesoscales. J Atmos Sci, 69: 3350–3371

    Article  Google Scholar 

  • Mu M, Duan W. 2003. A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation. Chin Sci Bull, 48: 1045–1047

    Article  Google Scholar 

  • Mu M, Xu H, Duan W. 2007. A kind of initial errors related to “spring predictability barrier” for El Niño events in Zebiak-Cane model. Geophys Res Lett, 34: L03709

    Article  Google Scholar 

  • Nielsen E R, Schumacher R S. 2016. Using convection-allowing ensembles to understand the predictability of an extreme rainfall event. Mon Weather Rev, 144: 3651–3676

    Article  Google Scholar 

  • Ran L, Li S, Zhou Y, Yang S, Ma S, Zhou K, Shen D, Jiao B, Li N. 2021. Observational analysis of the dynamic, thermal, and water vapor characteristics of the “7·20” extreme rainstorm event in Henan Province (in Chinese). Chin J Atmos Sci 45: 1366–1383

    Google Scholar 

  • Rotunno R, Snyder C. 2008. A generalization of Lorenz’s model for the predictability of flows with many scales of motion. J Atmos Sci, 65: 1063–1076

    Article  Google Scholar 

  • Selz T. 2019. Estimating the intrinsic limit of predictability using a stochastic convection scheme. J Atmos Sci, 76: 757–765

    Article  Google Scholar 

  • Selz T, Craig G C. 2015. Upscale error growth in a high-resolution simulation of a summertime weather event over Europe. Mon Weather Rev, 143: 813–827

    Article  Google Scholar 

  • Shi W, Li X, Zeng M, Zhang B, Wang H, Zhu K, Zhuge X. 2021. Multi-model comparison and high-resolution regional model forecast analysis for the “7·20” Zhengzhou severe heavy rain. Trans (in Chinese). Atmos Sci, 44: 688–702

    Google Scholar 

  • Skamarock W C. 2004. Evaluating mesoscale NWP models using kinetic energy spectra. Mon Weather Rev, 132: 3019–3032

    Article  Google Scholar 

  • Skamarock W C, Klemp J B, Dudhia J, Gill D O, Liu Z, Berner J, Wang W, Powers J G, Duda M G, Barker D, Huang X Y. 2021. A Description of the Advanced Research WRF Model Version 4.3. NCAR Technical Note NCAR/TN-556+STR

  • Sun Y Q, Zhang F. 2016. Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. J Atmos Sci, 73: 1419–1438

    Article  Google Scholar 

  • Sun Y Q, Zhang F. 2020. A new theoretical framework for understanding multiscale atmospheric predictability. J Atmos Sci, 77: 2297–2309

    Article  Google Scholar 

  • Sun Y Q, Rotunno R, Zhang F. 2017. Contribution of moist convection and internal gravity waves to building the atmospheric −5/3 kinetic energy spectra. J Atmos Sci, 74: 185–201

    Article  Google Scholar 

  • Sun Y, Xiao H, Yang H, Ding J, Fu D, Guo X, Feng L. 2021. Analysis of dynamic conditions and hydrometeor transport of Zhengzhou super heavy rainfall event on 20 July 2021 based on optical flow field of remote sensing data (in Chinese). Chin J Atmos Sci, 45: 1384–1399

    Google Scholar 

  • Thompson G, Eidhammer T. 2014. A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci, 71: 3636–3658

    Article  Google Scholar 

  • Weyn J A, Durran D R. 2017. The dependence of the predictability of mesoscale convective systems on the horizontal scale and amplitude of initial errors in idealized simulations. J Atmos Sci, 74: 2191–2210

    Article  Google Scholar 

  • Weyn J A, Durran D R. 2019. The scale dependence of initial-condition sensitivities in simulations of convective systems over the southeastern United States. Q J R Meteorol Soc, 145: 57–74

    Article  Google Scholar 

  • Wilks D S. 1995. Statistical Methods in the Atmospheric Sciences: An Introduction. San Diego, CA: Academic Press. 467

    Google Scholar 

  • Wu N, Zhuang X, Min J, Meng Z. 2020. Practical and intrinsic predictability of a warm-sector torrential rainfall event in the south China monsoon region. J Geophys Res-Atmos, 125: e31313

    Google Scholar 

  • Yu H, Meng Z. 2016. Key synoptic-scale features influencing the high-impact heavy rainfall in Beijing, China, on 21 July 2012. Tellus A-Dynamic Meteorol Oceanography, 68: 31045

    Article  Google Scholar 

  • Yu H, Meng Z. 2022. The impact of moist physics on the sensitive area identification for heavy rainfall associated weather systems. Adv Atmos Sci, 39: 684–696

    Article  Google Scholar 

  • Zhang C, Wang Y. 2017. Projected future changes of tropical cyclone activity over the western North and South Pacific in a 20-km-mesh regional climate model. J Clim, 30: 5923–5941

    Article  Google Scholar 

  • Zhang F, Bei N, Rotunno R, Snyder C, Epifanio C C. 2007. Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J Atmos Sci, 64: 3579–3594

    Article  Google Scholar 

  • Zhang F, Snyder C, Rotunno R. 2002. Mesoscale predictability of the “surprise” snowstorm of 24–25 January 2000. Mon Weather Rev, 130: 1617–1632

    Article  Google Scholar 

  • Zhang F, Sun Y Q, Magnusson L, Buizza R, Lin S J, Chen J H, Emanuel K. 2019. What is the predictability limit of midlatitude weather? J Atmos Sci, 76: 1077–1091

    Article  Google Scholar 

  • Zhang M, Meng Z. 2018. Impact of synoptic-scale factors on rainfall forecast in different stages of a persistent heavy rainfall event in south China. J Geophys Res-Atmos, 123: 3574–3593

    Article  Google Scholar 

  • Zhang X. 2021. Impacts of different perturbation methods on multiscale interactions between multisource perturbations for convection-permitting ensemble forecasting during SCMREX. Q J R Meteorol Soc, 147: 3899–3921

    Article  Google Scholar 

  • Zhang X, Yang H, Wang X, Shen L, Wang D, Li H. 2021. Analysis on characteristic and abnormality of atmospheric circulations of the July 2021 extreme precipitation in Henan (in Chinese). Trans Atmos Sci, 44: 672–687

    Google Scholar 

  • Zhang Y, Zhang F, Stensrud D J, Meng Z. 2016. Intrinsic predictability of the 20 May 2013 tornadic thunderstorm event in Oklahoma at storm scales. Mon Weather Rev, 144: 1273–1298

    Article  Google Scholar 

  • Zhuang Y, Xing A. 2022. History must not repeat itself-urban geological safety assessment is essential. Nat Hazards, 111: 2141–2145

    Article  Google Scholar 

  • Zou X L, Vandenberghe F, Pondeca M, Kuo Y H. 1997. Introduction to adjoint techniques and the MM5 adjoint modeling system. NCAR Tech. Note NCAR/TN2435+STR. 110

Download references

Acknowledgements

The WRF ensemble forecasts were performed on the Stampede 2 supercomputer of the Texas Advanced Computing Center (TACC) through the Extreme Science and Engineering Discovery Environment (XSEDE) program supported by the NSF. The CNOP simulations were performed on the Tianhe Supercomputer at the National Supercomputing Center of Tianjin, China. This work was supported by the National Natural Science Foundation of China (Grant Nos. 42030604, 41875051), the National Science Foundation (Grant No. AGS-1712290), and China Postdoctoral Science Foundation (Grant No. 2021M702725). Dr. Murong Zhang is sponsored by the MEL Outstanding Postdoctoral Scholarship from Xiamen University. Dr. Huizhen Yu is also supported by the East China Regional Meteorological Science and Technology Collaborative Innovation Fund (Grant No. QYHZ201801) and the project from the Qingdao Meteorological Bureau (Grant No. 2021qdqxz01).

Author information

Authors and Affiliations

  1. Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, Pennsylvania, 16802, USA

    Yunji Zhang

  2. Qingdao Meteorological Bureau, Qingdao, 266003, China

    Huizhen Yu

  3. State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen, 361102, China

    Murong Zhang

  4. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

    Yawen Yang

  5. Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China

    Yawen Yang & Zhiyong Meng

Authors
  1. Yunji Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huizhen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Murong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yawen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyong Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiyong Meng.

Supplement Materials

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Yu, H., Zhang, M. et al. Uncertainties and error growth in forecasting the record-breaking rainfall in Zhengzhou, Henan on 19–20 July 2021. Sci. China Earth Sci. 65, 1903–1920 (2022). https://doi.org/10.1007/s11430-022-9991-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-022-9991-4

Keywords

Search

Navigation