Simulation of Flash-Flood-Producing Storm Events in Saudi Arabia Using the Weather Research and Forecasting Model*

Abstract The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite, in situ, and reanalysis data. Here, we focus on characterizing the initial synoptic features and examining the impact of model parameterization and resolution on the reproduction of a number of flood-producing rainfall events that occurred over the western Saudi Arabian city of Jeddah. Analysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data suggests that mesoscale convective systems associated with strong moisture convergence ahead of a trough were the major initial features for the occurrence of these intense rain events. The WRF Model was able to simulate the heavy rainfall, with driving convective processes well characterized by a high-resolution cloud-resolving model. The use of higher (1 km vs 5 km) resolution along the Jeddah coastline favors the simulation of local convective systems and adds value to the simulation of heavy rainfall, especially for deep-convection-related extreme values. At the 5-km resolution, corresponding to an intermediate study domain, simulation without a cumulus scheme led to the formation of deeper convective systems and enhanced rainfall around Jeddah, illustrating the need for careful model scheme selection in this transition resolution. In analysis of multiple nested WRF simulations (25, 5, and 1 km), localized volume and intensity of heavy rainfall together with the duration of rainstorms within the Jeddah catchment area were captured reasonably well, although there was evidence of some displacements of rainstorm events.

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

International Journal of Atmospheric Sciences ◽

10.1155/2013/241050 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 14

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.

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Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

Geoscientific Model Development ◽

10.5194/gmd-10-4229-2017 ◽

2017 ◽

Vol 10 (11) ◽

pp. 4229-4244 ◽

Cited By ~ 20

Author(s):

Joseph C. Y. Lee ◽

Julie K. Lundquist

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Statistical Significance ◽

Wrf Model ◽

Grid Cell ◽

Power Production ◽

Weather Research And Forecasting ◽

Model Version ◽

The Impact ◽

Weather Research

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

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Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

10.5194/gmd-2017-128 ◽

2017 ◽

Cited By ~ 1

Author(s):

Joseph C. Y. Lee ◽

Julie K. Lundquist

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Statistical Significance ◽

Atmospheric Stability ◽

Wrf Model ◽

Power Production ◽

Weather Research And Forecasting ◽

Model Version ◽

The Impact ◽

Weather Research

Abstract. Forecasts of wind power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, the number of wind turbines per model grid cell, and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

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Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations

10.5194/gmd-2016-151 ◽

2016 ◽

Author(s):

Stephen D. Nicholls ◽

Steven G. Decker ◽

Wei-Kuo Tao ◽

Stephen E. Lang ◽

Jainn J. Shi ◽

...

Keyword(s):

Wrf Model ◽

Weather Research And Forecasting ◽

Large Variability ◽

Mixing Ratios ◽

Simulated Precipitation ◽

Single Moment ◽

Double Moment ◽

Order Of Magnitude ◽

The Impact ◽

Weather Research

Abstract. This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPS) on Weather Research and Forecasting (WRF) model (version 3.6.1) winter storm simulations. Model simulations were integrated for 180 hours, starting 72 hours prior to the first measurable precipitation in the highly populated Mid-Atlantic U.S. Simulated precipitation fields were well-matched to precipitation products. However, total accumulations tended to be over biased (1.10–2.10) and exhibited low-to-moderate threat scores (0.27–0.59). Non-frozen hydrometeor species from single-moment BMPS produced similar mixing ratio profiles and maximum saturation levels due to a common parameterization heritage. Greater variability occurred with frozen microphysical species due to varying assumptions among BMPSs regarding ice supersaturation amounts, the dry collection of snow by graupel, various ice collection efficiencies, snow and graupel density and size mappings/intercept parameters, and hydrometeor terminal velocities. The addition of double-moment rain and cloud water resulted in minimal change to species spatial extent or maximum saturation level, however rain mixing ratios tended higher. Although hydrometeor differences varied by up to an order of magnitude among the BMPSs, similarly large variability was not upscaled to mesoscale and synoptic scales.

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An Experiment for Assimilating Different Type of Data Observations in Forecasting Heavy Rainfall over Central Highlands Region Due to the Impact of Hurricane Damrey

VNU Journal of Science Earth and Environmental Sciences ◽

10.25073/2588-1094/vnuees.4478 ◽

2019 ◽

Vol 35 (4) ◽

Author(s):

Le Lan Phuong ◽

Pham Quang Nam ◽

Tran Quang Duc ◽

Phan Van Tan

Keyword(s):

Lead Time ◽

Heavy Rainfall ◽

Three Dimensional ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Data Types ◽

Warm Start ◽

Central Highlands ◽

Atmospheric Motion Vectors ◽

The Impact

This study investigates and assesses the impact of assimilating data types (observed data surface, sounding, and satellite-derived atmospheric motion vectors – AMVs) for the Weather Research and Forecasting (WRF) in forecasting heavy rainfall over Central Highlands region, due to the impact of hurricane Damrey. The WRF model combined with the Gridpoint Statistical Interpolation (GSI) system, was started running at 12Z 03/11/2017, and 84h forecasts in advance, with two kinds for running assimilation "cold start" and "warm start", and with the three-dimensional variational data assimilation (3D-Var) method. The results showed that assimilated cases have improved forecasting about spatial distribution and amount of rainfall at a 24-hour lead time, in which, the "warm start" for better forecasting. Notably, the assimilation of AMVs data with the "warm start" run has improved forecasting quality of heavy rainfall, the POD, FAR, and CSI indicators are the best at the 24-hour lead time, for rainfall thresholds greater than 80mm.

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Moisture Recycling over the Iberian Peninsula: The Impact of 3DVAR Data Assimilation

Atmosphere ◽

10.3390/atmos11010019 ◽

2019 ◽

Vol 11 (1) ◽

pp. 19

Author(s):

Santos J. González-Rojí ◽

Jon Sáenz ◽

Javier Díaz de Argandoña ◽

Gabriel Ibarra-Berastegi

Keyword(s):

Data Assimilation ◽

Iberian Peninsula ◽

Wrf Model ◽

Horizontal Resolution ◽

Spatiotemporal Distribution ◽

Weather Research And Forecasting ◽

Atmospheric Moisture ◽

Moisture Recycling ◽

The Impact ◽

Weather Research

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture.

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WRF Model Simulation of Two Alberta Flooding Events and the Impact of Topography

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-035.1 ◽

2012 ◽

Vol 13 (2) ◽

pp. 695-708 ◽

Cited By ~ 32

Author(s):

Thomas K. Flesch ◽

Gerhard W. Reuter

Keyword(s):

Spatial Resolution ◽

Rocky Mountains ◽

Model Simulation ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Maximum Precipitation ◽

Flooding Events ◽

The Impact ◽

Weather Research

Abstract This study examines simulations of two flooding events in Alberta, Canada, during June 2005, made using the Weather Research and Forecasting Model (WRF). The model was used in a manner readily accessible to nonmeteorologists (e.g., accepting default choices and parameters) and with a relatively large spatial resolution for rapid model runs. The simulations were skillful: strong storms were developed having the correct timing and location, generating precipitation rates close to observations, and with precipitation amounts near that observed. The model was then used to examine the sensitivity of the two storms to the topography of the Rocky Mountains. Comparing model results using the actual topographic grid with those of a reduced-mountain grid, it is concluded that a reduction in mountain elevation decreases maximum precipitation by roughly 50% over the mountains and foothills. There was little sensitivity to topography in the precipitation outside the mountains.

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Numerical Study of the Impact of Complex Terrain and Soil Moisture on Convective Initiation

Atmosphere ◽

10.3390/atmos11080871 ◽

2020 ◽

Vol 11 (8) ◽

pp. 871

Author(s):

Beilei Zan ◽

Ye Yu ◽

Longxiang Dong ◽

Jianglin Li ◽

Guo Zhao ◽

...

Keyword(s):

Soil Moisture ◽

Complex Terrain ◽

Numerical Study ◽

Dominant Role ◽

Deep Convection ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Local Circulation ◽

Convective Initiation ◽

The Impact

The relative importance of topography and soil moisture on the initiation of an afternoon deep convection under weak synoptic-scale forcing was investigated using the weather research and forecasting (WRF) model with high resolution (1.33 km). The convection occurred on 29 June 2017, over the Liupan Mountains, west of the Loess Plateau. The timing and location of the convective initiation (CI) simulated by the WRF model compared well with the radar observations. It showed that the warm and humid southerly airflow under 700 hPa was divided into east and west flows due to the blockage of the Liupan Mountains. The warm and humid air on the west side was forced to climb along the slope and enhanced the humidity near the ridge. The accumulation of unstable energy in the middle and north of the ridge led to a strong vertical convergence and triggered the convection. Sensitivity experiments showed that terrain played a dominant role in triggering the convection, while the spatial heterogeneity of soil moisture played an indirect role by affecting the local circulation and the partition of surface energy.

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