Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Jabbari, Aida; So, Jae-Min; Bae, Deg‐Hyo

doi:10.3390/atmos11010034

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…The WRF computation domain extended over Korea with 400 × 400 × 40 grid points and a high spatial resolution of 1 km × 1 km. Moreover, the temporal resolution was 10 min, which was forecasted for a lead-time of 72 h that repeated every 6 h [18,23]. During the summer, South and North Korea experience torrential rainfall and storms, as has been reported in previous studies [22].…”

Section: Meteorological Model: Weather Research and Forecasting (Wrf)mentioning

confidence: 87%

Section: Meteorological Model: Weather Research and Forecasting (Wrf)mentioning

confidence: 99%

“…The quality control (QC) is applied to the observed data including precipitation, wind speed, temperature, solar radiation and relative humidity to ensure the quality of the data. The QC filled the missing values using interpolation from nearby stations [18,23].…”

Section: Introductionmentioning

confidence: 99%

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

Jabbari

Bae

2020

Atmosphere

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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...

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Section: Meteorological Model: Weather Research and Forecasting (Wrf)mentioning

confidence: 87%

Section: Meteorological Model: Weather Research and Forecasting (Wrf)mentioning

confidence: 99%

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

Jabbari

Bae

2020

Atmosphere

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“…In the Korean Peninsula, flood season normally lasts from the beginning of June through to the end of September, and in this season, high river discharge is naturally caused by a large amount of precipitation over the basin. Annual precipitation of the Imjin river basin is 1100 mm, with the highest hourly rainfall recorded at 120 mm [24]. About 74% of the total annual discharge occurs between June and September, roughly parallel to the monsoonal climate, with its wet summers and dry winters.…”

Section: Study Areamentioning

confidence: 99%

“…The physical parameters can be estimated based on the basin and channel characteristics; however, for determining the conceptual model, a calibration and validation process has to be undertaken. There are twelve conceptual parameters in the SURR model, in which only five parameters are sensitive in rainfall runoff simulation [24,25]. The SURR model was manually calibrated and validated using observed discharge at 3 flow stations, namely Gunnam, Jeonkok, and Jeogseong ( Figure 1b).…”

Section: Surr Model Calibration and Validationmentioning

confidence: 99%

Impacts of Upstream Structures on Downstream Discharge in the Transboundary Imjin River Basin, Korean Peninsula

Kim

Bae

2020

Applied Sciences

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The transboundary river basin is a great challenge for water management and disaster reduction due to its specific characteristics. In this study, upstream impacts from natural and artificial sources on the downstream discharge on the Imjin river basin, the well-known transboundary region in the Korean peninsula, were evaluated using a hydrological model integrating a dam operation module at an hourly timescale. The module uses a concept of the AutoROM method as the operational rule to update the dam storage and decide water release. Dam storages were translated into water levels using a water level–storage curve. To quantify the impact of hydraulic structures on the Northern Imjin river basin, change in discharge was analyzed in four flood events (2009, 2010, 2011, and 2012). Dam failure scenarios were developed under conditions of the 2010 flood event, in which the releases of 100%, 80%, 50%, and 20% of water storage of Hwanggang dam were simulated. The results indicate that the amount of water released from upstream dams is the main cause of floods in the downstream region. To reduce the risk of floods in the downstream river basin, an optimal dam operation module and information on upstream dams play an important role and contribute to the effective use of water resources.

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Optimised power generation system by the SWAT and artificial neural network based on improved farmland fertility optimisation (IFFO) algorithm

Zhang

Zafetti

2021

International Journal of Ambient Energy

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Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Cited by 13 publications

References 28 publications

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

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

Impacts of Upstream Structures on Downstream Discharge in the Transboundary Imjin River Basin, Korean Peninsula

Optimised power generation system by the SWAT and artificial neural network based on improved farmland fertility optimisation (IFFO) algorithm

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