The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China

Wang, Han; Zhong, Ping‐an; Zhu, Feilin; Lü, Qingwen; Ma, Yufei; Xu, Sunyu

doi:10.1007/s00477-020-01923-9

Cited by 4 publications

(5 citation statements)

References 59 publications

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“…Therefore, the daily precipitation forecasts with a lead time more than 7 days almost have no practical meaning, which was also found by H. Wang et al. (2021) in the Huaihe River Basin, China.…”

Section: Resultsmentioning

confidence: 78%

“…Therefore, by comparing the results (a) and (b), we can get the conclusion that the NMI is as good as GS in terms of distinguishing the performance of different forecast products and capturing the changing patterns of uncertainties with lead times; and (c) Both NMI and GS are close to 0 when the lead time is +7 days. Therefore, the daily precipitation forecasts with a lead time more than 7 days almost have no practical meaning, which was also found by H. Wang et al (2021) in the Huaihe River Basin, China.…”

Section: Verification For Comprehensive Uncertainty Of All Categoriesmentioning

confidence: 91%

“…Therefore, the original precipitation forecast data of CMA, ECMWF, NCEP, and UKMO are converted to a 0.1° × 0.1° grid using the bilinear interpolation software provided by the ECMWF TIGGE data portal (Su et al., 2014). The interpolated data grid, the center of which is closest to a specific precipitation station, is used as the source of forecast precipitation (H. Wang et al., 2021).…”

Section: Study Area and Datamentioning

confidence: 99%

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A Mutual Information Theory‐Based Approach for Assessing Uncertainties in Deterministic Multi‐Category Precipitation Forecasts

Ning

Liang

Ding

et al. 2022

Water Resources Research

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The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi‐category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory‐based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi‐category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration, European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and United Kingdom Meteorological Office were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (a) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (b) the proposed approach is resistant to the extreme bias; (c) the proposed approach needs a careful choice of bin width; and (d) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts.

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Section: Resultsmentioning

confidence: 78%

Section: Verification For Comprehensive Uncertainty Of All Categoriesmentioning

confidence: 91%

Section: Study Area and Datamentioning

confidence: 99%

See 1 more Smart Citation

A Mutual Information Theory‐Based Approach for Assessing Uncertainties in Deterministic Multi‐Category Precipitation Forecasts

Ning

Liang

Ding

et al. 2022

Water Resources Research

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“…Among the three components, only the bias ratio had a value of above 1, with the others below 1. The low variability of the ensemble mean of the reforecasts was expected, as it has long been known that precipitation ensemble forecasts significantly underestimate heavy rain [28,29], hence also their ensemble means. The bias ratios of 1 or above, combined with an underestimation of heavy rain suggested by the low variability, were an indication of the ensemble mean systematically overestimating low rainfall or dry episodes.…”

Section: Precipitation Reforecastmentioning

confidence: 91%

“…We chose the ensemble weather forecasts of ECMWF as the forcing data in the hydrological forecast systems because of their proven good performance in China [28][29][30][31]. This study used reforecast data, which are forecasts of past dates reproduced with a global forecast system as close to the operational system as possible.…”

Section: Datasetmentioning

confidence: 99%

Regional Adaptability of Global and Regional Hydrological Forecast System

Wang

Zhong

Zsótér

et al. 2023

Water

Self Cite

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Our paper aims to improve flood forecasting by establishing whether a global hydrological forecast system could be used as an alternative to a regional system, or whether it could provide additional information. This paper was based on the operational Global Flood Awareness System (GloFAS) of the European Commission Copernicus Emergency Management Service, as well as on a regional hydrological forecast system named RHFS, which was created with observations recorded in the Wangjiaba river basin in China. We compared the discharge simulations of the two systems, and tested the influence of input. Then the discharge ensemble forecasts were evaluated for lead times of 1–7 d, and the impact on the forecasts of errors in initialization and modelling were considered. We also used quantile mapping (QM) to post-process the discharge simulations and forecasts. The results showed: (1) GloFAS (KGE of 0.54) had a worse discharge simulation than RHFS (KGE of 0.88), mainly because of the poor quality of the input; (2) the average forecast skill of GloFAS (CRPSS about 0.2) was inferior to that of RHFS (CRPSS about 0.6), because of the errors in the initialization and the model, however, GloFAS had a higher forecast quality than RHFS at high flow with longer lead times; (3) QM performed well at eliminating errors in input, the model, and the initialization.

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