Probabilistic forecasting based on ensemble forecasts and EMOS method for TGR inflow

doi:10.1007/s11707-019-0773-9

Front. Earth Sci.

2020, Vol. 14

Issue (1) : 188-200 https://doi.org/10.1007/s11707-019-0773-9

RESEARCH ARTICLE

Probabilistic forecasting based on ensemble forecasts and EMOS method for TGR inflow

Yixuan ZHONG^1,², Shenglian GUO¹(

), Feng XIONG¹, Dedi LIU¹, Huanhuan BA¹, Xushu WU¹

¹. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
². China Water Resources Pearl River Planning Surveying & Designing Co, Ltd., Guangzhou 510610, China

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Abstract

Probabilistic inflow forecasts can quantify the uncertainty involved in the forecasting process and provide useful risk information for reservoir management. This study proposed a probabilistic inflow forecasting scheme for the Three Gorges Reservoir (TGR) at 1–3 d lead times. The post-processing method Ensemble Model Output Statistics (EMOS) is used to derive probabilistic inflow forecasts from ensemble inflow forecasts. Considering the inherent skew feature of the inflow series, lognormal and gamma distributions are used as EMOS predictive distributions in addition to conventional normal distribution. Results show that TGR’s ensemble inflow forecasts at 1–3 d lead times perform well with high model efficiency and small mean absolute error. Underestimation of forecasting uncertainty is observed for the raw ensemble inflow forecasts with biased probability integral transform (PIT) histograms. The three EMOS probabilistic forecasts outperform the raw ensemble forecasts in terms of both deterministic and probabilistic performance at 1–3 d lead times. The EMOS results are more reliable with much flatter PIT histograms, coverage rates approximate to the nominal coverage 89.47% and satisfactory sharpness. Results also show that EMOS with gamma distribution is superior to normal and lognormal distributions. This research can provide reliable probabilistic inflow forecasts without much variation of TGR’s operational inflow forecasting procedure.

Keywords ensemble forecast probabilistic forecast numeric weather prediction EMOS Three Gorges Reservoir

Corresponding Author(s): Shenglian GUO

Online First Date: 27 December 2019 Issue Date: 24 March 2020

Cite this article:

Yixuan ZHONG,Shenglian GUO,Feng XIONG, et al. Probabilistic forecasting based on ensemble forecasts and EMOS method for TGR inflow[J]. Front. Earth Sci., 2020, 14(1): 188-200.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0773-9
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I1/188

Fig.1 Sketch map of the TGR interval basin.

Fig.2 Flowchart of probabilistic inflow forecasting scheme based on ensemble NWP and EMOS method.

Fig.3 Schematic of the TGR ensemble inflow forecasting scheme

Fig.4 Illustration of sliding window method for adaptive parameter optimization.

Tab.1 Performances of the inflow forecasting schemes during calibration period

Fig.5 Box-whisker plots for the evaluation metrics of TGR ensemble inflow forecasts at 1–3 d lead time.

Fig.6 CRPS values with different window length n_w for 1–3 d EMOS probabilistic inflow forecasts.

Tab.2 Comparison of raw ensemble and EMOS probabilistic forecasts at 1–3 d lead time in terms of deterministic and probabilistic performance

Fig.7 Calibration evaluation using Verification Ranked Histogram for (a) raw ensemble forecasts and PIT histograms for (b) NOR-EMOS, (c) LN-EMOS and (d) GM-EMOS probabilistic forecasts at 1–3 d lead times. The horizontal line indicates the ideal uniform distribution. The corresponding CD values are displayed in the legends.

Fig.8 Probabilistic PDFs issued at 1–3 d lead time for daily inflow on 2012, July, 25^th. The vertical solid lines indicate the inflow observation (64700 m³/s).

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