Predictability analysis based on ensemble forecasting of the “7·20” extreme rainstorm in Henan, China
Sai TAN, Qiuping WANG, Xulin MA, Lu SUN, Xin ZHANG, Xinlu LV, Xin SUN
Predictability analysis based on ensemble forecasting of the “7·20” extreme rainstorm in Henan, China
A heavy rainstorm occurred in Henan Province, China, between 19 and 21 July, 2021, with a record-breaking 201.9 mm of precipitation in 1 h. To explore the key factors that led to forecasting errors for this extreme rainstorm, as well as the dominant contributor affecting its predictability, we employed the Global/Regional Assimilation and Prediction System-Regional Ensemble Prediction System (GRAPES-REPS) to investigate the impact of the upper tropospheric cold vortex, middle-low vortex, and low-level jet on predictability and forecasting errors. The results showed that heavy rainfall was influenced by the following stable atmospheric circulation systems: subtropical highs, continental highs, and Typhoon In-Fa. Severe convection was caused by abundant water vapor, orographic uplift, and mesoscale vortices. Multiscale weather systems contributed to maintaining extreme rainfall in Henan for a long duration. The prediction ability of the optimal member of GRAPES-REPS was attributed to effective prediction of the intensity and evolution characteristics of the upper tropospheric cold vortex, middle-low vortex, and low-level jet. Conversely, the prediction deviation of unstable and dynamic conditions in the lower level of the worst member led to a decline in the forecast quality of rainfall intensity and its rainfall area. This indicates that heavy rainfall was strongly related to the short-wave throughput, upper tropospheric cold vortex, vortex, and boundary layer jet. Moreover, we observed severe uncertainty in GRAPES-REPS forecasts for rainfall caused by strong convection, whereas the predictability of rainfall caused by topography was high. Compared with the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System, GRAPES-REPS exhibits a better forecast ability for heavy rainfall, with some ensemble members able to better predict extreme precipitation.
numerical weather prediction / ensemble forecast / ensemble sensitivity / predictability / extreme rainfall
[1] |
Buizza R, Miller M, Palmer T N (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system.Q J R Meteorol Soc, 125(560): 2887–2908
CrossRef
Google scholar
|
[2] |
Chen G, Zhao K, Lu Y, Zheng Y, Xue M, Tan Z M, Xu X, Huang H, Chen H, Xu F, Yang J, Zhang S, Fan X (2022). Variability of microphysical characteristics in the “21·7” Henan extremely heavy rainfall event.Sci China Earth Sci, 65(10): 1861–1878
CrossRef
Google scholar
|
[3] |
Chen J, Li X L (2020). The review of 10 years development of the GRAPES global/regional ensemble prediction.Adv Meteorol Sci Technol, 10(2): 9–18
|
[4] |
Chen T, Sun J, Chen Y, Guo Y Q, Xu J (2019). Study on the numerical predictivity of localized severe mesoscale rainstorm in Guangzhou on 7 May 2017.Meteor Mon, 45(9): 1199–1212
|
[5] |
Cholaw B, Zhuge A R, Xie Z W, Gao Z T, Lin D W (2022). Water vapor transportation features and key synoptic-scale systems of the “7·20” rainstorm in Henan province in 2021.Chin J Atmos Sci, 46(3): 725–744
|
[6] |
Du J (2002). Present situation and prospects of ensemble numerical prediction.J Appl Meteorol Sci, 13(1): 16–28
|
[7] |
Du Y, Chen G X (2018). Heavy rainfall associated with double low-level jets over Southern China. Part I: ensemble-based analysis.Mon Weather Rev, 146(11): 3827–3844
CrossRef
Google scholar
|
[8] |
Gao L, Chen J, Zheng J W, Chen Q L (2019). Progress in researches on ensemble forecasting of extreme weather based on numerical models.Adv Earth Sci, 34(7): 706–716
|
[9] |
Ji X D, Qi L B (2018). Evaluation and application of ECMWF model precipitation and extreme weather forecast index of precipitation on heavy rainfall forecast.Torrential Rain and Disasters, 37(6): 566–573
|
[10] |
Jiang X M, Yuan H L, Xue M, Chen X, Tan X G (2014). Analysis of a torrential rainfall event over Beijing on 21–22 July 2012 based on high resolution model analyses and forecasts.Acta Meteorol Sin, 72(2): 207–219
|
[11] |
Leith C S (1974). Theoretical skill of Monte Carlo forecast.Mon Weather Rev, 102(6): 409–418
CrossRef
Google scholar
|
[12] |
Li H, Wang X M, Zhang X, Lv L Y, Xu W M (2018). Analysis on extremity and characteristics of the 19 July 2016 severe torrential rain in the north of Henan province.Meteor Mon, 44(9): 1136–1147
|
[13] |
Lorenz E N (1963). Deterministic nonperiodic flow.J Atmos Sci, 20(2): 130–141
CrossRef
Google scholar
|
[14] |
Lorenz E N (1969). The predictability of a flow which possesses many scales of motion. Tellus, Ser A, Dyn Meterol Oceanogr, 21(3): 289–307
|
[15] |
Ma X L, He P Y, Zhou B Y, He J (2021). Impact of localization of ensemble transform Kalman filter on initial perturbation of regional ensemble forecast.Trans Atmos Sci, 44(2): 314–323
|
[16] |
Ma X L, Ji Y X, Zhou B Y, Shi Y, Li L L, Guo H (2018). A new scheme of blending initial perturbation of the GRAPES regional ensemble prediction system. Trans Atmos Sci 41(2): 248–257 (in Chinese)
|
[17] |
Ma X L, Xue J S, Lu W S (2009). Study on ETKF-based initial perturbation scheme for GRAPES global ensemble prediction.Acta Meteorol Sin, 23(5): 562–574
|
[18] |
Medina H, Tian D, Marin F R, Chirico G B (2019). Comparing GEFS, ECMWF, and postprocessing methods for ensemble precipitation forecasts over Brazil.J Hydrometeorol, 20(4): 773–790
CrossRef
Google scholar
|
[19] |
Melhauser C, Zhang F Q (2012). Practical and intrinsic predictability of severe and convective weather at the mesoscales.J Atmos Sci, 69(11): 3350–3371
CrossRef
Google scholar
|
[20] |
Pan X, Wang Q P, Zhang Y, He P Y, Ma X L (2021). Analysis constraints scheme of initial perturbation of ensemble prediction.Chin J Atmos Sci, 45(6): 1327–1344
|
[21] |
Quandt L A, Keller J H, Martius O, Pinto J G, Jones S C (2019). Ensemble sensitivity analysis of the blocking system over Russia in summer 2010.Mon Weather Rev, 147(2): 657–675
CrossRef
Google scholar
|
[22] |
Schumacher R S (2011). Ensemble-based analysis of factors leading to the development of a multiday warm-season heavy rain event.Mon Weather Rev, 139(9): 3016–3035
CrossRef
Google scholar
|
[23] |
Sun J H, Zhao S X, Fu S M, Wang H J, Zheng L L (2013). Multi-scale characteristics of record heavy rainfall over Beijing area on July 21, 2012.Chin J Atmos Sci, 37(3): 705–718
|
[24] |
Sun J, Chen Y, Yang S N, Dai K, Chen T, Yao R, Xu J (2012). Analysis and thinking on the extremes of the 21 July 2012 torrential rain in Beijing part II: preliminary causation analysis and thinking.Meteor Mon, 38(10): 1267–1277
|
[25] |
Sun L, Chen S Y, Pan X, Wang Q P, He J, Ma X L (2022). Sensitivity analysis of model initial value of a rainstorm in the warm sector of South China.J Meteorol Sci, 42(3): 356–367
|
[26] |
Torn R D, Hakim G J (2008). Ensemble-based sensitivity analysis.Mon Weather Rev, 136(2): 663–677
CrossRef
Google scholar
|
[27] |
Wang J Z, Chen F J, Chen J, Liu X Q, Li H Q, Deng G, Li X L, Wang Y Z (2021). Verification of GRAPES-REPS model precipitation forecasts over China during 2019 flood season.Chin J Atmos Sci, 45(3): 664–682
|
[28] |
Wei P, Xu X, Xue M, Zhang C Y, Wang Y, Zhao K, Zhou A, Zhang S S, Zhu K F (2023). On the key dynamical processes supporting the 21.7 Zhengzhou record-breaking hourly rainfall in China.Adv Atmos Sci, 40(3): 337–349
CrossRef
Google scholar
|
[29] |
Wu M W, Luo Y L, Chen F, Wong W K (2019). Observed link of extreme hourly precipitation changes to urbanization over coastal South China.J Appl Meteorol Climatol, 58(8): 1799–1819
CrossRef
Google scholar
|
[30] |
Xiao L S, Zhang H L, Zhang X B, Feng L, Chen Z G, Dai G F (2021). Predictability analysis of the extremely heavy rainfall in the Pearl River Delta on 22 May 2020 using CMA-TRAMS-based ensemble prediction system.Acta Meteorol Sin, 79(6): 956–976
|
[31] |
Xue J S, Liu Y (2007). Numerical weather prediction in China in the new century-progress, problems and prospects.Adv Atmos Sci, 24(6): 1099–1108
CrossRef
Google scholar
|
[32] |
Yu H Z, Meng Z Y (2016). Key synoptic-scale features influencing the high impact heavy rainfall in Beijing, China, on 21 July 2012.Tellus A: Dyn Meteorol Oceanogr, 68(1): 31045
CrossRef
Google scholar
|
[33] |
Yuan Y, Li X L, Chen J, Xia Y (2016). Stochastic parameterization toward model uncertainty for the GRAPES mesoscale ensemble prediction system.Meteor Mon, 42(10): 1161–1175
|
[34] |
Zhang X B (2018a). A GRAPES-based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance.Q J R Meteorol Soc, 144(711): 478–498
CrossRef
Google scholar
|
[35] |
Zhang X B (2018b). Application of a convection-permitting ensemble prediction system to quantitative precipitation forecasts over southern China: preliminary results during SCMREX.Q J R Meteorol Soc, 144(717): 2842–2862
CrossRef
Google scholar
|
[36] |
Zhang X, Yang H, Wang X M, Shen L, Wang D, Li H (2021). Analysis on characteristic and abnormality of atmospheric circulations of the July 2021 extreme precipitation in Henan.Trans Atmos Sci, 44(5): 672–687
|
[37] |
Zhang Y, Shi Y, Zhou B Y, Ma X L (2022a). Physical structures and evolution characteristics of wind perturbation in ensemble prediction.Trans Atmos Sci, 45(2): 268–279
|
[38] |
Zhang Y, Yu H, Zhang M, Yang Y, Meng Z (2022b). Uncertainties and error growth in forecasting the record-breaking rainfall in Zhengzhou, Henan on 19–20 July 2021.Sci China Earth Sci, 65(10): 1903–1920
CrossRef
Google scholar
|
[39] |
Zhu K F, Xue M (2016). Evaluation of WRF-based convection-permitting multi-physics ensemble forecasts over China for an extreme rainfall event on 21 July 2012 in Beijing.Adv Atmos Sci, 33(11): 1240–1258
CrossRef
Google scholar
|
[40] |
Zhu K F, Zhang C Y, Xue M, Yang N (2022). Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China.Sci China Earth Sci, 65(10): 1879–1902
CrossRef
Google scholar
|
/
〈 | 〉 |