Applying the Betts–Miller–Janjic Scheme of Convection in Prediction of the Indian Monsoon

Vaidya, Shubhangi; Singh, S. S.

doi:10.1175/1520-0434(2000)015<0349:atbmjs>2.0.co;2

Cited by 25 publications

(16 citation statements)

References 12 publications

(3 reference statements)

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“…In addition, the latest and most exhaustive study discussing the performance of WRF for tropical cyclone prediction in the Bay of Bengal [Srinivas et al, 2013b] shows that, at 9 km resolution, the KF scheme provides the best simulations for intensity and track prediction, giving higher convective warming with stronger vertical motions relative to the other tested cumulus schemes. Vaidya and Singh [2000], Mukhopadhyay et al [2010], and Srinivas et al [2013a] on the other hand illustrated that the BMJ scheme produces the most reasonable mean monsoon pattern, with realistic atmospheric flow, surface pressure, heating profiles, and moist instability.…”

Section: Model Experiments and Data Setsmentioning

confidence: 95%

The NOW regional coupled model: Application to the tropical Indian Ocean climate and tropical cyclone activity

Samson

Masson

Lengaigne³

et al. 2014

J Adv Model Earth Syst

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This paper presents the NOW regional coupled ocean-atmosphere model built from the NEMO ocean and WRF atmospheric numerical models. This model is applied to the tropical Indian Ocean, with the oceanic and atmospheric components sharing a common 1 =4 horizontal grid. Long experiments are performed over the 1990-2009 period using the Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) cumulus parameterizations. Both simulations produce a realistic distribution of seasonal rainfall and a realistic northward seasonal migration of monsoon rainfall over the Indian subcontinent. At subseasonal time scales, the model reasonably reproduces summer monsoon active and break phases, although with underestimated rainfall and surface wind signals. Its relatively high resolution results in realistic spatial and seasonal distributions of tropical cyclones, but it fails to reproduce the strongest observed cyclone categories. At interannual time scales, the model reproduces the observed variability associated with the Indian Ocean Dipole (IOD) and the delayed basin-wide warming/cooling induced by the El Niño Southern Oscillation (ENSO). The timing of IOD occurrence in the model generally matches that of the observed events, confirming the influence of ENSO on the IOD development (through the effect of lateral boundary conditions in our simulations). Although the KF and BMJ simulations share a lot in common, KF strongly overestimates rainfall at all time scales. KF also overestimates the number of simulated cyclones by a factor two, while simulating stronger events (up to 55 m s 21) compared to BMJ (up to 40 m s 21). These results could be related to an overly active cumulus parameterization in KF.

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Section: Model Experiments and Data Setsmentioning

confidence: 95%

The NOW regional coupled model: Application to the tropical Indian Ocean climate and tropical cyclone activity

Samson

Masson

Lengaigne³

et al. 2014

J Adv Model Earth Syst

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“…Some possible factors that could contribute would be that local boundary formulation in MYJ may be more appropriately capturing the vertical environment in the complex terrain compared to the non-local YSU scheme which seeks to simulate vertical mixing and boundary layer evolution using averaged and grid representative fields. With regard to the BMJ CU emerging in the top configuration, there are a number of studies for the ISMR where it has emerged as performing "overall best" (Vaidya and Singh, 2000;Ratnam and Kumar, 2005;Vaidya, 2006;Rao et al, 2007;Kumar et al, 2010;Mukhopadhyay et al, 2010;Srinivas et al, 2013;Sikder and Hossain, 2016). As for the MP scheme, there are limited studies in comparison to those that have studied the CU configuration for the ISMR.…”

Section: Model Configuration and Experimental Setupmentioning

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.

119

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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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“…The Betts-Miller-Janjic Cumulus parameterization scheme was used because it is well tested for regional application and precipitation forecasting (Vaidya and Singh, 2000). Gilliland and Rowe (2007) found that it considers a sophisticated cloud mixing scheme in order to determine entrainment/detrainment which is found to be more suitable for non-tropical convection.…”

Section: Weather Research and Forecasting (Wrf) Modelmentioning

confidence: 99%

WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

Srivastava

Islam

Gupta

et al. 2015

Water Resour Manage

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Rainfall and Reference Evapotranspiration (ETo) are the most fundamental and significant variables in hydrological modelling. However, these variables are generally not available over ungauged catchments. ETo estimation usually needs measurements of weather variables such as wind speed, air temperature, solar radiation and dew point. After the development of reanalysis global datasets such as the National Centre for Environmental Prediction (NCEP) and high performance modelling framework Weather Research and Forecasting (WRF) model, it is now possible to estimate the rainfall and ETo for any coordinates. In this study, the WRF modelling system was employed to downscale the global NCEP reanalysis datasets over the Brue catchment, England, U.K. After downscaling, two statistical bias correction schemes were used, the first was based on sophisticated computing algorithms i.e., Relevance Vector Machine (RVM), while the second was based on the more simple Generalized Linear Model (GLM). The statistical performance indices for bias correction such as %Bias, index of agreement (d), Root Mean Square Error (RMSE), and Correlation (r) indicated that the RVM model, on the whole, displayed a more accomplished bias correction of the variability of rainfall and ETo in comparison to the GLM. The study provides important information on the performance of WRF derived hydro-meteorological variables using NCEP global reanalysis datasets and statistical bias correction schemes which can be used in numerous hydro-meteorological applications

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Applying the Betts–Miller–Janjic Scheme of Convection in Prediction of the Indian Monsoon

Cited by 25 publications

References 12 publications

The NOW regional coupled model: Application to the tropical Indian Ocean climate and tropical cyclone activity

The NOW regional coupled model: Application to the tropical Indian Ocean climate and tropical cyclone activity

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

WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables

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