Simulation of Bay of Bengal Tropical Cyclones with WRF Model: Impact of Initial and Boundary Conditions

2010 ◽  
Vol 33 (4) ◽  
pp. 294-314 ◽  
Author(s):  
U. C. Mohanty ◽  
Krishna K. Osuri ◽  
A. Routray ◽  
M. Mohapatra ◽  
Sujata Pattanayak
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meenakshi Shenoy ◽  
P. V. S. Raju ◽  
Jagdish Prasad

AbstractEvaluation of appropriate physics parameterization schemes for the Weather Research and Forecasting (WRF) model is vital for accurately forecasting tropical cyclones. Three cyclones Nargis, Titli and Fani have been chosen to investigate the combination of five cloud microphysics (MP), three cumulus convection (CC), and two planetary boundary layer (PBL) schemes of the WRF model (ver. 4.0) with ARW core with respect to track and intensity to determine an optimal combination of these physical schemes. The initial and boundary conditions for sensitivity experiments are drawn from the National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) data. Simulated track and intensity of three cyclonic cases are compared with the India Meteorological Department (IMD) observations. One-way analysis of variance (ANOVA) is applied to check the significance of the data obtained from the model. Further, Tukey’s test is applied for post-hoc analysis in order to identify the cluster of treatments close to IMD observations for all three cyclones. Results are obtained through the statistical analysis; average root means square error (RMSE) of intensity throughout the cyclone period and time error at landfall with the step-by-step elimination method. Through the elimination method, the optimal scheme combination is obtained. The YSU planetary boundary layer with Kain–Fritsch cumulus convection and Ferrier microphysics scheme combination is identified as an optimal combination in this study for the forecasting of tropical cyclones over the Bay of Bengal.


2018 ◽  
Vol 57 (3) ◽  
pp. 733-753 ◽  
Author(s):  
Sergio Fernández-González ◽  
María Luisa Martín ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
Jesús Lorenzana ◽  
...  

AbstractWind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.


2015 ◽  
Vol 30 (6) ◽  
pp. 1749-1761 ◽  
Author(s):  
John Lawson ◽  
John Horel

Abstract A downslope windstorm on 1 December 2011 led to considerable damage along a narrow 50-km swath at the western base of the Wasatch Mountains in northern Utah. Operational forecasts issued by the Salt Lake City National Weather Service Forecast Office provided accurate guidance for the event at 1–2-day lead times, partially based on locally generated high-resolution numerical forecasts. Forecasters highlighted the possibility of the windstorm 4 days in advance. To address the apparent reduced uncertainty for this windstorm, three 11-member three-domain ensemble forecasts were initialized at 0000 UTC 25 November, 0000 UTC 27 November, and 0000 UTC 29 November 2011 using the Weather Research and Forecasting (WRF) Model with initial and boundary conditions supplied by Global Ensemble Forecast System Reforecast, version 2 (GEFS/R2). Eight of the 11 ensemble members from the 29 November 2011 forecast (60 h before the windstorm) generated a strong, localized windstorm with outliers arising from reduced cross-barrier flow. Analysis of kinetic energy error growth suggests that the reduced uncertainty of 60-h forecasts was not primarily a result of the underdispersion of GEFS/R2 initial and boundary conditions but was related to a regional reduction in error growth in midtropospheric flow upstream of northern Utah. The ensemble initialized 2 days earlier (27 November, 108 h before the windstorm) contains fewer members that generate strong windstorms, while no members generate a windstorm in the ensemble initialized on 25 November (156 h prior). This sudden increase in uncertainty with forecast lead time results from the sensitivity of the ensemble solutions to the lateral boundary conditions imposed by the GEFS/R2 between 0000 UTC 29 November and 0000 UTC 30 November.


2016 ◽  
Vol 37 (13) ◽  
pp. 3086-3103 ◽  
Author(s):  
M. Dhanya ◽  
Deepak Gopalakrishnan ◽  
A. Chandrasekar ◽  
Sanjeev Kumar Singh ◽  
V.S. Prasad

2021 ◽  
pp. 1-20
Author(s):  
Shailee Patel ◽  
Manisha Vithalpura ◽  
Subrat Kumar Mallick ◽  
Smitha Ratheesh

2018 ◽  
Vol 66 (1) ◽  
pp. 79-86
Author(s):  
Ashik Imran ◽  
Ishtiaque M Syed ◽  
SM Quamrul Hassan ◽  
Kh Hafizur Rahman

Tropical cyclones (TCs) over Bay of Bengal (BoB) have significant socio-economic impacts on the countries bordering the BoB. In this study, we have examined the structure and thermodynamic features of the TC Hudhud (7th -14th October, 2014) using WRF model. Simulated outputs are in good agreement with the available observations of India Meteorological Department and Joint Typhoon warning Center. At maximum intensity stage, the system’s horizontal size is found around 690 km. Wind and vorticity distributions capture the circulation of the system very well. Most strong winds of 60 ms−1 are extended vertically from 850 hPa to about 700 hPa. Simulation has shown intensification of the system above 200 hPa with wind speed of about 30ms−1. Relative humidity of the order of 90 % is found up to 400 hPa. Dhaka Univ. J. Sci. 66(1): 79-86, 2018 (January)


2018 ◽  
Vol 18 (2) ◽  
pp. 445-454 ◽  
Author(s):  
Jeonghyeon Choi ◽  
Jeonghoon Lee ◽  
Hyeon-gyo Jeong ◽  
Juhyoung Jang ◽  
Sangdan Kim

2021 ◽  
Author(s):  
Harish Baki ◽  
Sandeep Chinta ◽  
C. Balaji ◽  
Balaji Srinivasan

Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation predictions compared to the default set of parameters.


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