A comparative study on performance of MM5 and WRF models in simulation of tropical cyclones over Indian seas

Tropical cyclone is one of the most devastating and deadly weather phenomenon worldwide. It is a result of organized intense convective activities over warm tropical oceans. In recent years mesoscale models are extensively used for simulation of genesis, intensification and movement of tropical cyclones. In this study, the recent three very severe cyclonic storms generated over Indian seas (Bay of Bengal and Arabian Sea) are considered. During 26-29 April 2006, a very severe tropical cyclone, known as Mala, developed over the Bay of Bengal and crossed the Arakan coast of Myanmar on 29 April 2006. During 2-7 June 2007, a super cyclonic storm, known as Gonu, developed over the Arabian sea and crossed the Makran coast on 7 June 2007. During 11-16 November 2007, a very severe cyclonic storm, known as Sidr, developed over the Bay of Bengal and crossed the Khulna-Barisal coast of Bangladesh on 15 November 2007. In the present study, two state-ofthe- art mesoscale models, MM5 and WRF, developed at the National Center for Atmospheric Research (NCAR), USA, have been used to evaluate the performances of both the models in the simulation of the above-mentioned tropical cyclones. The performances of both the models have been calculated by integrating with 15 different initial conditions, i.e. each case with five different initial conditions. A number of meteorological fields, viz. central pressure, wind and precipitation have been verified against observations/ verification analyses. The vector displacement error in track forecast has also been calculated using the best track provided by the India Meteorological Department. The results indicate that the WRF model has better performance in respect of track and intensity prediction than the MM5 model

Location-specific forecast at Sriharikota during the launch of GSLV-F01

The present study was carried out to examine the performance of two high-resolution mesoscale/regional atmospheric models to provide real time short range forecast during the GSLV-F01 launch on 20 September 2004. The main objective was to provide vertical shear of horizontal wind, which is very important for launch operations. The models are integrated to provide forecasts 36 h in advance. The model predictions are compared with observations and their performances are evaluated in terms of statistical skill scores. The mesoscale model of Pennsylvania State University (PSU)/National Center for Atmospheric Research (NCAR) MM5 was found to perform better than the High-resolution Regional Model (HRM) though marginally. Performance of MM5 model was further investigated after improvement of model initial condition with insertion of conventional observations into the large-scale global analysis to perform reanalysis at high resolution (horizontal resolution of 9 km). Results indicate significant improvement in model performance with improvement in initial condition

Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system

Tropical cyclone is one of the most devastating weather phenomena all over the world. The Environmental Modeling Center (EMC) of the National Center for Environmental Prediction (NCEP) has developed a sophisticated mesoscale model known as Hurricane Weather Research and Forecasting (HWRF) system for tropical cyclone studies. The state-of-the-art HWRF model (atmospheric component) has been used in simulating most of the features our present study of a very severe tropical cyclone “Mala”, which developed on April 26 over the Bay of Bengal and crossed the Arakan coast of Myanmar on April 29, 2006. The initial and lateral boundary conditions are obtained from Global Forecast System (GFS) analysis and forecast fields of the NCEP, respectively. The performance of the model is evaluated with simulation of cyclone Mala with six different initial conditions at an interval of 12 h each from 00 UTC 25 April 2006 to 12 UTC 27 April 2006. The best result in terms of track and intensity forecast as obtained from different initial conditions is further investigated for large-scale fields and structure of the cyclone. For this purpose, a number of important predicted fields’ viz. central pressure/pressure drop, winds, precipitation, etc. are verified against observations/verification analysis. Also, some of the simulated diagnostic fields such as relative vorticity, pressure vertical velocity, heat fluxes, precipitation rate, and moisture convergences are investigated for understanding of the characteristics of the cyclone in more detail. The vector displacement errors in track forecasts are calculated with the estimated best track provided by the India Meteorological Department (IMD). The results indicate that the model is able to capture most of the features of cyclone Mala with reasonable accuracy

Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error

RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model

Simulation of Bay of Bengal tropical cyclones with WRF model: impact of initial and boundary conditions

An attempt is made to delineate the relative performances and credentials of GFS, FNL, and NCMRWF global analyses/forecast products as initial and boundary conditions (IBCs) to the WRF-ARW model in the simulation of four Bay of Bengal tropical cyclones (TCs). The results suggest that FNL could simulate horizontal advection of vorticity maxima at 850 hPa; hence, the tracks are more realistic with least errors as compared to GFS and NCMRWF. The mean landfall errors for 24-, 48-, and 72-hour forecasts are 73, 41, and 72 km, respectively. The TC intensity is well captured by NCMRWF IBCs, as it could predict 850 hPa vorticity maxima. The 24-hour accumulated rainfall is well simulated with FNL, and equitable threat score is more than 0.2 up to 100 mm with minimum bias