scholarly journals Assessment of the Weather Research and Forecasting (WRF) Model for Extreme Rainfall Event Simulations in the Upper Ganga Basin

2017 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Simulated Rainfall ◽  
Grid Spacing ◽  
Rainfall Events ◽  
Arw Model

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model, are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multiscale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF-ARW model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganges basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layer (PBL), and two land surface physics options; and different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it is noted that the selection of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influence the magnitude of rainfall in the model simulations. Further, WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic' CU scheme is found to perform best in simulating this heavy rain event. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme as compared to the simple Slab model. To analyze the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, Global to Regional (G2R) scale; and (ii) 1 : 9, Global to Convection-permitting scale (G2C) are employed. Results indicate that higher downscaling ratio (G2C) causes higher variability and consequently, large errors in the simulations. Therefore, G2R is opted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF simulated rainfall is found to exhibit least bias when compared with that of the Coordinated Regional Climate Downscaling Experiment (CORDEX) data and the NCEP FiNaL (FNL) reanalysis data.

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

2018 ◽  
Vol 22 (2) ◽  
pp. 1095-1117 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Ganga Basin ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

scholarly journals Evaluation of the WRF-ARW model during an extreme rainfall event: subtropical storm Guará

2021 ◽  
Author(s):  
Yasmin Kaore Lago Kitagawa ◽  
Erick Giovani Sperandio Nascimento ◽  
Noéle Bissoli Perini Souza ◽  
Pedro Junior Zucatelli ◽  
Prashant Kumar ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Temporal Evolution ◽  
Extreme Event ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Extreme Rainfall Event ◽  
Arw Model

This study simulates an unusual extreme rainfall event that occurred in Salvador City, Bahia, Brazil, on December 9, 2017, which was the subtropical storm Guará and had precipitation of approximately 24 mm within less than 1 h. Numerical simulations were conducted using the weather research and forecasting (WRF) model over three domains with horizontal resolutions of 9, 3, and 1 km. Different combinations of seven microphysics, three cumulus, and three planetary boundary layer schemes were evaluated based on their ability to simulate the hourly precipitation during this rainfall event. The results were compared with the data measured at the Brazilian National Institute of Meteorology (INMET) meteorological stations. The best configuration for the planetary boundary layer, cumulus, and microphysics schemes were Mellor-Yamada-Janjić, Grell-Devenyi, and Lin, respectively. The WRF model could depict the daily variations on the hourly parameters well, along with the spatial and temporal evolution of the extreme event.

scholarly journals Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 304 ◽  
Author(s):  
Gonzalo Yáñez-Morroni ◽  
Jorge Gironás ◽  
Marta Caneo ◽  
Rodrigo Delgado ◽  
René Garreaud
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Complex Topography ◽  
Front Range ◽  
Meteorological Forcing ◽  
Rainfall Events ◽  
Weather Research

The Weather Research and Forecasting (WRF) model has been successfully used in weather prediction, but its ability to simulate precipitation over areas with complex topography is not optimal. Consequently, WRF has problems forecasting rainfall events over Chilean mountainous terrain and foothills, where some of the main cities are located, and where intense rainfall occurs due to cutoff lows. This work analyzes an ensemble of microphysics schemes to enhance initial forecasts made by the Chilean Weather Agency in the front range of Santiago. We first tested different vertical levels resolution, land use and land surface models, as well as meteorological forcing (GFS/FNL). The final ensemble configuration considered three microphysics schemes and lead times over three rainfall events between 2015 and 2017. Cutoff low complex meteorological characteristics impede the temporal simulation of rainfall properties. With three days of lead time, WRF properly forecasts the rainiest N-hours and temperatures during the event, although more accuracy is obtained when the rainfall is caused by a meteorological frontal system. Finally, the WSM6 microphysics option had the best performance, although further analysis using other storms and locations in the area are needed to strengthen this result.

scholarly journals Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gamal El Afandi ◽  
Mostafa Morsy ◽  
Fathy El Hussieny
Keyword(s):  
Heavy Rainfall ◽  
Flash Flood ◽  
Wrf Model ◽  
Rain Gauge ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Sinai Peninsula ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Weather Research

Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

Numerical Experiments on Spatially Averaged Precipitation in Heavy Rainfall Event Using the WRF Model

2015 ◽  
Vol 10 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Yuji Sugihara ◽  
◽  
Sho Imagama ◽  
Nobuhiro Matsunaga ◽  
Yukiko Hisada ◽  
...  
Keyword(s):  
Numerical Experiments ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Spatial Averaging ◽  
Hourly Rainfall ◽  
Precipitation Analysis

It is difficult to forecast hourly rainfall locally even using the latest meteorological models, although hourly rainfall averaged spatially to some extent can be used for calculating practical rainfall. This study conducts numerical experiments with triple nesting on the 2012 heavy rainfall event in northern Kyushu using the weather research and forecasting (WRF) model and examines the features of hourly rainfall averaged spatially. The dependence of rainfall is averaged spatially on a spatial averaging scale and clarified by comparing rainfall calculated by simulation using the WRF model with radar/AMeDAS precipitation analysis data. This study’s findings indicate the effective spatial averaging scale making relative error of calculated values to the observed ones minimum.

scholarly journals Evaluation of the ability of the Weather Research and Forecasting model to reproduce a sub-daily extreme rainfall event in Beijing, China using different domain configurations and spin-up times

2018 ◽  
Vol 22 (6) ◽  
pp. 3391-3407 ◽  
Author(s):  
Qi Chu ◽  
Zongxue Xu ◽  
Yiheng Chen ◽  
Dawei Han
Keyword(s):  
Mean Squared Error ◽  
Effective Means ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Extreme Rainfall Event ◽  
Verification Methods ◽  
Beijing China ◽  
Weather Research

Abstract. The rainfall outputs from the latest convection-scale Weather Research and Forecasting (WRF) model are shown to provide an effective means of extending prediction lead times in flood forecasting. In this study, the performance of the WRF model in simulating a regional sub-daily extreme rainfall event centred over Beijing, China is evaluated at high temporal (sub-daily) and spatial (convective-resolving) scales using different domain configurations and spin-up times. Seven objective verification metrics that are calculated against the gridded ground observations and the ERA-Interim reanalysis are analysed jointly using subjective verification methods to identify the likely best WRF configurations. The rainfall simulations are found to be highly sensitive to the choice of domain size and spin-up time at the convective scale. A model run covering northern China with a 1 : 5 : 5 horizontal downscaling ratio (1.62 km), 57 vertical layers (less than 0.5 km), and a 60 h spin-up time exhibits the best performance in terms of the accuracy of rainfall intensity and the spatial correlation coefficient (R′). A comparison of the optimal run and the initial run performed using the most common settings reveals clear improvements in the verification metrics. Specifically, R′ increases from 0.226 to 0.67, the relative error of the maximum precipitation at a point rises from −56 to −11.7 %, and the root mean squared error decreases by 33.65 %. In summary, re-evaluation of the domain configuration options and spin-up times used in WRF is crucial for improving the accuracy and reliability of rainfall outputs used in applications related to regional sub-daily heavy rainfall (SDHR).

scholarly journals Evaluation of the WRF model with different domain configurations and spin-up time in reproducing a sub-daily extreme rainfall event in Beijing, China

2017 ◽  
Author(s):  
Qi Chu ◽  
Zongxue Xu ◽  
Yiheng Chen ◽  
Dawei Han
Keyword(s):  
Wrf Model ◽  
Evaluation Process ◽  
Domain Size ◽  
Extreme Rainfall ◽  
Northern China ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Extreme Rainfall Event ◽  
Convective Scale ◽  
Beijing China

Abstract. The use of rainfall outputs from the latest convection-scale Weather Research and Forecasting (WRF) model is proven to be an effective way to extend the prediction lead time for flood forecasting. In this study, the effects of WRF domain configurations and spin-up time on rainfall simulations were evaluated at high temporal (sub-daily) and spatial (convective-permitting) scales for simulating a regional sub-daily extreme rainfall event occurred in Beijing, China. Seven objective verification metrics calculated against the ground precipitation observations and the ERA-Interim reanalysis, were analyzed jointly by the subjective verification to explore the likely best set of domain configurations and spin-up time. It was found that the rainfall simulations were quite sensitive to the change of the WRF domain size and spin-up time when evaluated at the convective scale. A model run with 1 : 5 : 5 horizontal downscaling ratio (1.6 km), 57 vertical layers (0.5 km), and 60-hour spin-up time covering Northern China exhibited the best skill in terms of the accuracy of rainfall intensity and the spatial correlation coefficient (R). Comparison made between the optimal run with the above set of the configurations and the initial run of the comparative test setup based on the most common settings revealed an evidential increase in each verification metric after the evaluation process, with R increased from 0.49 to 0.678, the relative error of point maximum precipitation rose from 0.41 to 0.881, and the spatial accumulated error fell by 43.22 %. In summary, the reevaluation of the domain configurations and spin-up time is of great importance and worthwhile in improving the accuracy and reliability of the rainfall simulations in the regional sub-daily heavy rainfall (SDHR) applications.

scholarly journals Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Mary-Jane M. Bopape ◽  
David Waitolo ◽  
Robert S. Plant ◽  
Elelwani Phaduli ◽  
Edson Nkonde ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one.

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