Wu Qishu, Han Mei, Liu Ming, et al. A comparison of optimal-score-based correction algorithms of model precipitation prediction. J Appl Meteor Sci, 2017, 28(3): 306-317. DOI:  10.11898/1001-7313.20170305.
Citation: Wu Qishu, Han Mei, Liu Ming, et al. A comparison of optimal-score-based correction algorithms of model precipitation prediction. J Appl Meteor Sci, 2017, 28(3): 306-317. DOI:  10.11898/1001-7313.20170305.

A Comparison of Optimal-score-based Correction Algorithms of Model Precipitation Prediction

DOI: 10.11898/1001-7313.20170305
  • Received Date: 2016-10-13
  • Rev Recd Date: 2017-02-20
  • Publish Date: 2017-05-31
  • Based on data from national meteorological stations, one year quasi-symmetrical mixed running training period (QSRTP), and precipitation prediction from CMA (T639), ECMWF, NCEP, JMA, both optimal threat score (OTS) method and optimal equitable threat score (OETS) method are designed to conduct a comparison experiment on correction algorithms for model precipitation with frequency matching (FM) method. Through classification correction, three methods are used merely to calibrate model precipitation amount with the predicted rain-belt location and shape kept unchanged. The OTS method figures out correction coefficients of different precipitation classes by optimizing threat score (TS) of corrected precipitation within training period. OETS is similar to OTS but achieved by optimizing ETS. Correction experiments are conducted twice a day with forecast time at 0000 UTC and 1200 UTC, respectively. To consider seasonal background, 20 days before the forecast day and 20 days after the same day in the previous year are adopted to constitute training period. For each national meteorological station, there are 80 samples in total. The correction experiment shows that for either precipitation products of ECMWF, JMA, NCEP, CMA, or their ensemble mean, both OTS and OETS show much better performance than FM in 24 h accumulated precipitation classification calibration with different lead time according to traditional verification methods like TS and ETS. In particular, OTS is the best and can improve precipitation prediction in all lead times. After correction, both OTS and OETS incline to forecast larger precipitation area than observation for most classes but less precipitation amounts. Compared to FM, both methods tend to produce a little higher false alarm rates in middle and low classes, which is much less than the reduced missing rate, thereby leading to a higher threat score. In terms of ECMWF correction, OTS and OETS have a relatively stable Bias score of 1.1, although there are much fewer samples in high class. By contrast, FM produces an unstable Bias score, especially in maximum class with score over 2.2, indicating an excessively high missing rate. As for stable equitable error in probability space (SEEPS), OTS has superiorities over all lead times, all single models and multi-model mean. Furthermore, TS of corrected ECMWF precipitation using OTS method in 2015 are also better than subjective forecast from all aspects, with national averaged threat score of 1 d rainstorm forecast reaching 0.194.
  • Fig. 1  qIllustration of different conditions of TS or ETS performance improvement

    (a) the number of forecast stations is in consistent with the observation, (b) the number of forecast stations is larger than the observation, (c) the number of forecast stations is less than the observation

    Fig. 2  Illustration of threshold F solution

    Fig. 3  TS (a), ETS (b), false alarm rate (c), miss rate (d), Bias (e) and HSS (f) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time of 24 h during 2014-2015

    Fig. 4  TS of 0.1 mm (a), 10 mm (b), 25 mm (c) 和50 mm (d) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015

    Fig. 5  SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015

    Fig. 6  TS of 24 h accumulative precipitation by FM, OTS and OETS based on JMA (a), NCEP (b) and T639(c) with lead time of 24 h during 2014-2015

    Fig. 7  TS of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time of 24 h during 2014-2015

    Fig. 8  SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time from 24 h to 72 h during 2014-2015

    Table  1  SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on JMA, NCEP and T639 with lead time from 24 h to 72 h during 2014-2015

    模式 算法 24 h预报 48 h预报 72 h预报
    JMA 未订正 0.562 0.506 0.466
    FM 0.626 0.562 0.504
    OTS 0.640 0.572 0.513
    OETS 0.632 0.567 0.511
    NCEP 未订正 0.525 0.493 0.474
    FM 0.572 0.533 0.495
    OTS 0.594 0.556 0.526
    OETS 0.588 0.550 0.519
    T639 未订正 0.512 0.472 0.405
    FM 0.567 0.518 0.425
    OTS 0.602 0.546 0.453
    OETS 0.599 0.541 0.451
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    Table  2  TS comparisons of 24 h accumulative precipitation between China NMC forecasters and ECMWF_OTS all over China stations in 2015

    起报时间 预报时效/h 小雨 中雨 大雨 暴雨
    预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS
    24 0.594 0.608 0.395 0.414 0.281 0.302 0.175 0.189
    48 0.572 0.590 0.359 0.376 0.250 0.263 0.147 0.153
    72 0.552 0.565 0.328 0.335 0.225 0.233 0.118 0.136
    00:00 96 0.522 0.538 0.298 0.305 0.198 0.206 0.094 0.118
    120 0.500 0.514 0.271 0.276 0.168 0.178 0.073 0.093
    144 0.473 0.486 0.242 0.244 0.145 0.148 0.080 0.083
    168 0.442 0.448 0.208 0.214 0.124 0.133 0.050 0.062
    24 0.590 0.609 0.392 0.414 0.285 0.304 0.186 0.199
    12:00 48 0.574 0.584 0.354 0.372 0.255 0.264 0.158 0.163
    72 0.550 0.559 0.323 0.337 0.222 0.236 0.125 0.150
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    • Received : 2016-10-13
    • Accepted : 2017-02-20
    • Published : 2017-05-31

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