Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

Tang, Yehai; Wu, Qiang; Soomro, Shan‐e‐hyder; Li, Xiang; Sun, Yue; Hu, Caihong

doi:10.1007/s11600-022-00877-6

Cited by 2 publications

(1 citation statement)

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“…For instance, in comparison with the power regression model, the values of the NSE and NRMSE metrics were improved on average for the GMDH model by 27% and 16%, and for the WA-WLSR model by 41% and 25%, respectively. These results are consistent with the results of previous studies [29,61,64,65]. Therefore, more accurate results derived from the proposed multi-model ensemble systems represent their efficiency in forecasting observed precipitation.…”

Section: Multi-model Ensemble Forecastssupporting

confidence: 92%

Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System

Tanhapour

Soltani

Malekmohammadi

et al. 2023

Water

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The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw precipitation forecasts because of their significant bias when they feed hydrological models. In this research, ensemble precipitation forecasts (EPFs) of three NWP models (National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) (Exeter, UK), and Korea Meteorological Administration (KMA) (SEOUL, REPUBLIC OF KOREA)) were investigated for six historical storms leading to heavy floods in the Dez basin, Iran. To post-process EPFs, the raw output of every single NWP model was corrected using regression models. Then, two proposed models, the Group Method of Data Handling (GMDH) deep learning model and the Weighted Average–Weighted Least Square Regression (WA-WLSR) model, were employed to construct a multi-model ensemble (MME) system. The ensemble reservoir inflow was simulated using the HBV hydrological model under the two modeling approaches involving deterministic forecasts (simulation using observed precipitation data as input) and ensemble forecasts (simulation using post-processed EPFs as input). The results demonstrated that both GMDH and WA-WLSR models had a positive impact on improving the forecast skill of the NWP models, but more accurate results were obtained by the WA-WLSR model. Ensemble forecasts outperformed coupled atmospheric–hydrological modeling in comparison with deterministic forecasts to simulate inflow hydrographs. Our proposed approach lends itself to quantifying uncertainty of ensemble forecasts in hydrometeorological the models, making it possible to have more reliable strategies for extreme-weather event management.

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Section: Multi-model Ensemble Forecastssupporting

confidence: 92%