Hydrometeorological forecasts provide future flooding estimates to reduce damages. Despite the advances and progresses in Numerical Weather Prediction (NWP) models, they are still subject to many uncertainties, which cause significant errors forecasting precipitation. Statistical postprocessing techniques can improve forecast skills by reducing the systematic biases in NWP models. Artificial Neural Networks (ANNs) can model complex relationships between input and output data. The application of ANN in water-related research is widely studied; however, there is a lack of studies quantifying the improvement of coupled hydrometeorological model accuracy that use ANN for bias correction of real-time rainfall forecasts. The aim of this study is to evaluate the real-time bias correction of precipitation data, and from a hydrometeorological point of view, an assessment of hydrological model improvements in real-time flood forecasting for the Imjin River (South and North Korea) is performed. The comparison of the forecasted rainfall before and after the bias correction indicated a significant improvement in the statistical error measurement and a decrease in the underestimation of WRF model. The error was reduced remarkably over the Imjin catchment for the accumulated Mean Areal Precipitation (MAP). The performance of the real-time flood forecast improved using the ANN bias correction method. of these models is still a concern for hydrometeorological prediction studies [5]. NWP models are subject to many uncertainties, which cause significant errors in the forecasting of real-time precipitation. In coupled hydrometeorological studies, one critical issue before running a rainfall-runoff model is to reduce the input forcing errors that are produced by the meteorological model. Therefore, postprocessing of the model outputs of the real-time forecast data would result in a better match with the observation records. Statistical postprocessing methods improve real-time forecast accuracy by relating the model outputs to the observed values.Several studies have shown that statistical postprocessing improved forecast performance by reducing systematic biases [6,7]. The main purpose of statistical bias correction is to develop a relationship between the modeled and observed data. Commonly used statistical methods are the quantile-based mapping method [8] and regression approaches that include linear relationships [9] and nonlinear relationships [10]. In real-time flood forecasting, a comparison of different postprocessing methods such as Bayesian Model Averaging (BMA), classic poor man ensemble (PME) and Multimodel SuperEnsemble Dressing (MSD) indicated that the MSD approach provided better precipitation data for floods in Italy [11]. Six different bias correction methods (including linear scaling (LS), local intensity scaling (LOCI) scaling, Daily Translation (DT), daily bias correction (DBC), quantile mapping based on an empirical distribution (QME) and quantile mapping based on a gamma distribution (QMG)) were applied in ten No...

A numerical weather prediction and a rainfall-runoff model employed to evaluate precipitation and flood forecast for the Imjin River (South and North Korea). The real-time precipitation at point and catchment scales evaluated to select proper hydrological model to couple with atmospheric model. As a major limitation of previous studies, temporal and spatial resolutions of hydrological model are smaller than those of meteorological model. Here, through high resolution of temporal (10 min) and spatial (1 km × 1 km), the optimal resolution determined. The results showed Weather Research and Forecasting (WRF) model underestimated precipitation in point and catchment assessment and its skill was relatively higher for catchment than point scale, as illustrated by the lower Root Mean Square Error (RMSE) of 59.67, 160.48, 68.49 for the catchment and 84.49, 212.80 and 91.53 for the point scale in the events 2002, 2007 and 2011, respectively. The findings led to choose the semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated accuracy decrease; additionally, the optimal spatial resolution obtained at 8 km and temporal resolution did not affect the inherent inaccuracy of the results. Lead-time variation demonstrated that lead-time dependency was almost negligible below 36 h. With reference to this study, comparisons of model performance provided quantitative knowledge for understanding credibility and restrictions of meteo-hydrological models.

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.Atmosphere 2020, 11, 300 2 of 20 focused on improving the prediction skills of NWP models, they have not been able to eliminate the uncertainties included in the NWP model QPFs [9]. Since the performance of hydrological models is sensitive to the forcing data and NWP models are often subject to uncertainties, the input data can be an important source of errors and deficiencies in streamflow forecasting. Therefore, QPFs need to be post-processed and their biases corrected prior to use as reliable data in hydrological models [10].As discussed in several studies, the QPFs produced by the NWP models are usually biased, and in some cases they are severely biased [11]. In previous studies, the accuracy was improved by combining different sources of precipitation and applying the quantile regression forest (QRF) method in the hydrological evaluations [12]. Others improved the forecast precipitation by using post-processing techniques to produce ensemble precipitation predictions (EPPs), for example, using radar precipitation estimates [13]. Statistical bias adjustment/correction is a way to improve the QPF accuracy by reducing its systematic errors through post-processing. Improving the accuracy of the NWP-based QPF by post-processing techniques can be done using methods such as model output statistics (MOS). In recent years, several post-processing techniques have been proposed to develop a statistical relationship between observations and NWP forecasts [10]. There have been previous attempts to decrease the errors of QPFs in hydrometeorological studies [2,14]. Studies on improving streamflow forecas...

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, 10 the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was 15 relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the temporal resolution did not affect the inherent inaccuracy of the results. Lead time variation demonstrated that lead time dependency was almost negligible below 36 h.With reference to our case study, comparisons of model performance provided quantitative knowledge for understanding the 20 credibility and restrictions of hydro-meteorological models.