Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Chawla, Ila; Osuri, Krishna K.; Mujumdar, Pradeep P.; Niyogi, Dev

doi:10.5194/hess-22-1095-2018

Cited by 122 publications

(63 citation statements)

References 88 publications

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“…The 72 h real-time forecast data of the WRF model underestimated the rainfall for all the events, which led to the underestimation of the real-time flood forecast in the studied area. The underestimation of the WRF model has also been reported in previous studies [28], which is in agreement with our current study. The application of the bias correction methods resulted in a more accurate rainfall forecast.…”

Section: Discussionsupporting

confidence: 94%

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: Discussionsupporting

confidence: 94%

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

Jabbari

Bae

2020

Atmosphere

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“…The WRF model, a popular numerical weather prediction system, was used and applied in both atmospheric research and forecast. It has been an extensively used model recently, especially in heavy rainfall research over the Indian region (Fadnavis et al ., ; Chawla et al ., ; Karki et al ., ; Madhulatha and Rajeevan, ; Reshmi et al ., ). A recent study by Chawla et al .…”

Section: Methodsmentioning

confidence: 99%

“…A recent study by Chawla et al . () found that the WRF‐simulated rainfall exhibit less bias compared with national centers for environmental prediction final (NCEP‐FNL) reanalysis data. The WRF model is a highly customizable model with several radiation, planetary boundary layer (PBL), cloud microphysics and convection physics options.…”

Section: Methodsmentioning

confidence: 99%

“…It has been an extensively used model recently, especially in heavy rainfall research over the Indian region (Fadnavis et al, 2014;Chawla et al, 2018;Karki et al, 2018;Madhulatha and Rajeevan, 2018;Reshmi et al, 2018). A recent study by Chawla et al (2018) found that the WRF-simulated rainfall exhibit less bias compared with national centers for environmental prediction final (NCEP-FNL) reanalysis data. The WRF model is a highly customizable model with several radiation, planetary boundary layer (PBL), cloud microphysics and convection physics options.…”

Section: Model Configuration and Experiments Designmentioning

confidence: 99%

“…The selection of appropriate physics schemes is crucial for the dependable prediction of intensive rainfall (Ratnam et al, 2017;Karki et al, 2018). The influence of various combinations of parameterization schemes on rainfall simulation is still an active area of research in India (Chawla et al, 2018).…”

Section: Model Configuration and Experiments Designmentioning

confidence: 99%

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Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India

Ajilesh

Rakesh

Sahoo

et al. 2019

Meteorological Applications

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A total of 32 rainfall events spanning the period from 2012 to 2014 over the urban Indian city of Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli level (Bangalore has 34 hobli divisions with an area of each hobli of the order of about 10 km2) was evaluated in terms of its intensity and pattern of spatial distribution by comparing it with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing root mean square error, bias and mean absolute error. Thermodynamic variables such as equivalent potential temperature, convective available potential energy (CAPE), convective inhibition (CIN), K index (KI), lifted index (LI) and total totals index (TTI) were also derived from the simulated model parameters for all the events and then verified against corresponding observations. The results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were > 100%. The forecast errors were relatively lower for cases during the southwest monsoon season compared with other seasons. It was found that the model underestimated thermodynamic indices such as CAPE, dew point depression and the simulated LI were positive; these were indicative of the model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.

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Evaluation of the effects of a multiphysics ensemble on the simulation of an extremely hot summer in 2003 over the CORDEX‐EA‐II region

Yang

Tang

2019

Intl Journal of Climatology

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In this paper, we evaluate a 48‐member multiphysics ensemble using the Weather Research and Forecasting (WRF) model for the JJA extreme precipitation and temperature in 2003 over the CORDEX‐EA‐II domain. The simulated precipitation and temperature are reasonable in the subregions controlled by a large‐scale circulation, yet the biases for both precipitation and temperature are evident over the subregions where the effects of mesoscale processes are important. The performance of various combinations of WRF physical schemes for simulating the JJA precipitation is dependent on the region. Meanwhile, the cumulus and microphysical schemes have substantial influences on the simulation of precipitation, and the land surface models and cumulus schemes play crucial roles in the surface temperature. Our analysis shows that the combination of Noah for the land surface process, Lin for the microphysics, G3D for the cumulus parameterization, and CAM for the radiation scheme can provide the most reliable reproduction of both precipitation and temperature extremes over China. Ensemble analysis shows that the simulated climate extremes are usually accompanied by a large ensemble spread, implying sensitivities to the model physical processes in some subregions. The simulated wind fields at low‐to‐middle atmospheric levels display responses to the options of the land surface models and cumulus schemes. The essential impact of the land–atmospheric interaction on simulating the extremes can be largely attributed to the active convective processes. WRF has difficulties in reproducing the observed temporal evolution of the rainfall process, which consists of continuous large rainfall episodes in the observation data set during the simulation period.

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Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Cited by 122 publications

References 88 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

Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India

Evaluation of the effects of a multiphysics ensemble on the simulation of an extremely hot summer in 2003 over the CORDEX‐EA‐II region

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