scholarly journals Assessing Weather Research and Forecasting (WRF) Model Parameterization Schemes Skill to Simulate Extreme Rainfall Events over Dar es Salaam on 21 December 2011

2018 ◽  
Vol 06 (01) ◽  
pp. 36-54 ◽  
Author(s):  
Triphonia Jacob Ngailo ◽  
Nyimvua Shaban ◽  
Joachim Reuder ◽  
Michel D. S. Mesquita ◽  
Edwin Rutalebwa ◽  
...  
Keyword(s):  
Wrf Model ◽  
Extreme Rainfall ◽  
Dar Es Salaam ◽  
Weather Research And Forecasting ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Parameterization Schemes ◽  
Model Parameterization ◽  
Weather Research

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 Characterizing past and future trend and frequency of extreme rainfall in urban catchments: a case study

2020 ◽  
Vol 3 (1) ◽  
pp. 288-305
Author(s):  
Philip Mzava ◽  
Patrick Valimba ◽  
Joel Nobert
Keyword(s):  
Trend Analysis ◽  
Rainfall Variability ◽  
Extreme Rainfall ◽  
Dar Es Salaam ◽  
Extreme Rainfall Events ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
The Past ◽  
The Future ◽  
Urban Catchments

Abstract Urban communities in developing countries are one of the most vulnerable areas to extreme rainfall events. The availability of local information on extreme rainfall is therefore critical for proper planning and management of urban flooding impacts. This study examined the past and future characteristics of extreme rainfall in the urban catchments of Dar es Salaam, Tanzania. Investigation of trends and frequency of annual, seasonal and extreme rainfall was conducted, with the period 1967–2017 taken as the past scenario and 2018–2050 as the future scenario; using data from four key ground-based weather stations and RCM data respectively. Mann–Kendall trend analysis and Sen's slope estimator were used in studying changes in rainfall variability. Frequencies of extreme rainfall events were modeled using the Generalized Pareto model. Overall, the results of trend analysis provided evidence of a significant increase in annual and seasonal maximum rainfall and intensification of extreme rainfall in the future under the RCP4.5 CO2 concentration scenario. It was determined that extreme rainfall will become more frequent in the future, and their intensities were observed to increase approximately between 20 and 25% relative to the past. The findings of this study may help to develop adaptation strategies for urban flood control in Dar es Salaam.

 Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations 

2021 ◽  
Author(s):  
Ajay Bankar ◽  
Rakesh Vasudevan
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Distinct Advantage ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Synoptic Conditions

<p><span>Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE’s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.</span></p>

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.

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 Evaluating Weather Research and Forecasting (WRF) Model Predictions of Turbulent Flow Parameters in a Dry Convective Boundary Layer

2011 ◽  
Vol 50 (12) ◽  
pp. 2429-2444 ◽  
Author(s):  
Jeremy A. Gibbs ◽  
Evgeni Fedorovich ◽  
Alexander M. J. van Eijk
Keyword(s):  
Boundary Layer ◽  
Convective Boundary Layer ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Convective Boundary ◽  
Parameterization Schemes ◽  
Model Predictions ◽  
Horizontal Grid ◽  
Weather Research

AbstractWeather Research and Forecasting (WRF) model predictions using different boundary layer schemes and horizontal grid spacings were compared with observational and numerical large-eddy simulation data for conditions corresponding to a dry atmospheric convective boundary layer (CBL) over the southern Great Plains (SGP). The first studied case exhibited a dryline passage during the simulation window, and the second studied case was used to examine the CBL in a post-cold-frontal environment. The model runs were conducted with three boundary layer parameterization schemes (Yonsei University, Mellor–Yamada–Janjić, and asymmetrical convective) commonly employed within the WRF model environment to represent effects of small-scale turbulent transport. A study domain was centered over the Atmospheric Radiation Measurement Program SGP site in Lamont, Oklahoma. Results show that near-surface flow and turbulence parameters are predicted reasonably well with all tested horizontal grid spacings (1, 2, and 4 km) and that value added through refining grid spacing was minimal at best for conditions considered in this study. In accord with this result, it was suggested that the 16-fold increase in computing overhead associated with changing from 4- to 1-km grid spacing was not justified. Therefore, only differences among schemes at 4-km spacing were presented in detail. WRF model predictions generally overestimated the contribution to turbulence generation by mechanical forcing over buoyancy forcing in both studied CBL cases. Nonlocal parameterization schemes were found to match observational data more closely than did the local scheme, although differences among the predictions with all three schemes were relatively small.

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