Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model

Raju, P. V. S.; Potty, Jayaraman; Mohanty, U. C.

doi:10.1007/s00703-011-0151-y

Cited by 101 publications

(64 citation statements)

References 17 publications

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“…To start with, the WSM6 scheme for microphysics, YSU for PBL and NOAH for surface physics are chosen because this combination has achieved the best performance in previous studies (e.g. Krishna et al, 2012;Mukhopadhyay, 2011;Raju et al, 2011a). The 6 h time variation of mean errors (i.e.…”

Section: Sensitivity Experiments With Convection Schemesmentioning

confidence: 99%

“…(e.g. Mohanty et al, 2004;Bhaskar Rao et al, 2006, 2007Prasad and Rama Rao, 2003;Trivedi et al, 2006;Srinivas et al, 2007Srinivas et al, , 2010Deshpande et al, 2010;Krishna et al, 2010;Mukhopadhyay et al, 2011;Singh et al, 2008Singh et al, , 2011Singh et al, , 2012Pattanik andMohanty, 2008, 2010;Raju et al, 2011a). Very few studies have reported the performance of numerical models statistically for the cyclones over the NIO.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

Srinivas¹,

Rao²,

Yesubabu³

et al. 2012

Quart J Royal Meteoro Soc

123

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Section: Sensitivity Experiments With Convection Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

Srinivas¹,

Rao²,

Yesubabu³

et al. 2012

Quart J Royal Meteoro Soc

123

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“…The advanced research version of the Weather Research and Forecasting model (hereafter referred to as the WRF model) is a regional popular community model that is widely used for both studying as well as forecasting a variety of high-impact meteorological events, such as rainfall (Vaidya and Kulkarni, 2007;Deb et al, 2008;Kumar et al, 2008;Chang et al, 2009;Routray et al, 2010;Mohanty et al, 2012), tropical cyclones (Raju et al, 2011;Routray et al, 2016;Osuri et al, 2017b) and thunderstorms (Madala et al, 2014;Osuri et al, 2017a). Several works are reported in the literature which have considered the WRF model over the Himalayan region.…”

Section: Introductionmentioning

confidence: 99%

“…However, the influence of the interaction between model parameterization schemes on mesoscale rainfall simulations over India is still an understudied issue. In particular, heavy rain simulation studies have reviewed the impact of individual parameterization options such as the microphysics (MP) scheme (Rajeevan et al, 2010;Raju et al, 2011;, cumulus (CU) parameterization scheme (Deb et al, 2008;Mukhopadhyay et al, 2010;Srinivas et al, 2013;Madala et al, 2014), planetary boundary layer (PBL) scheme (Li and Pu, 2008;Hu et al, 2010;Hariprasad et al, 2014) and land surface model (LSM) options (Chang et al, 2009). However, the ensemble analysis that reviews the relative impact of different configurations and the associated variability (uncertainty) is lacking.…”

Section: Introductionmentioning

confidence: 99%

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

Chawla¹,

Osuri²,

Mujumdar³

et al. 2018

Hydrol. Earth Syst. Sci.

118

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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-MillerJanjic 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.

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“…Thus, the PBL and microphysics processes play an important role in the development of tropical cyclone and require to be represented in the WRF model for realistic predictions. Several studies revealed that the track and intensity prediction of tropical cyclone is sensitive to the cumulus convection, planetary boundary layer (PBL), and cloud microphysics parameterization schemes [6,[13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Microphysics Schemes and Topography on the Prediction of the Heavy Rainfall in Western Myanmar Associated with Tropical Cyclone ROANU (2016)

Maw

Min

2017

Advances in Meteorology

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The impacts of different microphysics and boundary schemes and terrain settings on the heavy rainfall over western Myanmar associated with the tropical cyclone (TC) ROANU (2016) are investigated using the Weather Research and Forecasting (WRF) model. The results show that the microphysics scheme of Purdue Lin (LIN) scheme produces the strongest cyclone. Six experiments with various combinations of microphysics and boundary schemes indicated that a combination of WRF Single-Moment 6-class (WSM6) scheme and Mellor-Yamada-Janjic (MYJ) best fits to the Joint Typhoon Warning Center (JTWC) data. WSM6-MYJ also performs the best for the track and intensity of rainfall and obtains the best statistics skill scores in the range of maximum rainfall intensity for 48-h. Sensitivity experiments on different terrain settings with Normal Rakhine Mountain (NRM), with Half of Rakhine Mountain (HRM), and Without Rakhine Mountain (WoRM) are designed with the use of WSM6-MYJ scheme. The track of TC ROANU moved northwestward in WoRM and HRM. Due to the presence of Rakhine Mountain, TC track moved into Myanmar and the peak rainfall occurred on the leeward side of the Mountain. In the absence of Rakhine Mountain, a shift in peak rainfall was observed in north side of the Mountain.

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Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model

Cited by 101 publications

References 17 publications

Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

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

Impacts of Microphysics Schemes and Topography on the Prediction of the Heavy Rainfall in Western Myanmar Associated with Tropical Cyclone ROANU (2016)

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