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Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Department of Civil & Environmental Engineering, Sejong University, Seoul 05006, Korea
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Atmosphere 2020, 11(3), 300; https://doi.org/10.3390/atmos11030300
Received: 13 January 2020 / Revised: 15 March 2020 / Accepted: 18 March 2020 / Published: 19 March 2020
(This article belongs to the Section Meteorology)
Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively. View Full-Text
Keywords: bias correction; dynamic weighting; lead-time; quantitative precipitation forecast; real-time; total least squares; weather research and forecasting bias correction; dynamic weighting; lead-time; quantitative precipitation forecast; real-time; total least squares; weather research and forecasting
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MDPI and ACS Style

Jabbari, A.; Bae, D.-H. Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction. Atmosphere 2020, 11, 300. https://doi.org/10.3390/atmos11030300

AMA Style

Jabbari A, Bae D-H. Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction. Atmosphere. 2020; 11(3):300. https://doi.org/10.3390/atmos11030300

Chicago/Turabian Style

Jabbari, Aida, and Deg-Hyo Bae. 2020. "Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction" Atmosphere 11, no. 3: 300. https://doi.org/10.3390/atmos11030300

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