A. Routray

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.

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007‐11. The analyses are carried out with respectto1)basinsofformation,2)straight-movingandrecurvingTCs,3)TCintensityatmodelinitialization, and 4) season of occurrence. The impact of high resolution (18 and 9km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375km (7%‐51%) for a 12‐72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9km) predictions yield an improvement in mean track error for the NIO Basin by about 4%‐10% and 8%‐24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ;13%‐28% and 5%‐15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%‐40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean

In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model. The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. The landfall prediction is significantly improved in 11 cases by about 37%. The intensity prediction also improves by 10–20%. Kinematic and thermodynamic structures of TCs are also better explained, as it could simulate heat and momentum exchange between sea surface and upper air. Due to better simulation of structure, intensity and track, the 24-hour accumulated rainfall intensity and distribution are also well predicted with the assimilation of satellite-derived winds.

Impact of Land Surface Processes on the South Asian Monsoon Simulations Using WRF Modeling System

The Weather Research and Forecasting model has been used to examine the role of land surface processes on Indian summer monsoon simulations. Isolated experiments have been carried out with physical parameterization schemes (land surface and planetary boundary layer) and data assimilation to examine their relative roles in the representation of regional hydroclimate in model simulations. The impact of vegetation green fraction on the model simulations has been extensively studied by replacing the default United States Geological Survey (USGS) vegetation cover data with that of Indian Space Research Organisation (ISRO) data. Results indicate that differences in the treatment of surface processes in the model lead to large differences in precipitation simulation over the Indian domain. Several hydroclimate parameters from the simulations using ISRO and USGS vegetation green fractions were examined. It is seen that the role of vegetation green fraction in these experiments has been to increase latent heat flux to the atmosphere. Two sets of data assimilation experiments were also carried out for an entire year using the same set of observed data but with different land surface parameterization schemes. It is found that evenwhen using the same observed data, the differences in land surface schemes reduce the impact and contribution of observed data being assimilated into the model. The hydroclimate over the region becomes a function of the land surface scheme. This study highlights the importance of vegetation green fraction and land surface schemes in the context of the regional hydroclimate over South Asia.

Impact of Satellite Radiance Data on Simulations of Bay of Bengal Tropical Cyclones Using the WRF-3DVAR Modeling System

This study attempts to evaluate whether assimilating radiance observations in the Weather Research and Forecasting (WRF) model could improve track, intensity, and precipitation forecasts of tropical cyclones (TCs) that occurred over the Bay of Bengal. The bias correction coefficients obtained from offline statistics, along with the quality control for radiances, were computed in the variational assimilation system. For this study, three numerical experiments named CNTL (no assimilation), GTS (with Global Telecommunication System observations), and RAD (radiance data along with GTS observations) were carried out with ten different model initial conditions for two TCs. The averaged root-mean-square errors of the analysis were relatively lower in the RAD experiments in comparison to the GTS experiments for all assimilation cycles of the meteorological variables. The mean initial position errors of TCs were reduced by 30%-47% in the RAD runs over the other runs. The results indicate that the assimilation of radiance data has a positive impact on the prediction of track, intensity, thermodynamic structures, and reflectivity associated with the storms. Improvements in mean landfall position errors were shown to range from 40% to 70% in the RAD experiments as compared to the CNTL and GTS simulations. This is because the RAD analyses are able to successfully reproduce the initial vortex and vertical structures as well as the prominent synoptic features associated with TC storms; therefore, the performance of the WRF modeling system is enhanced for simulations of track, structures, and intensity of TCs.

Improved Prediction of Bay of Bengal Tropical Cyclones through Assimilation of Doppler Weather Radar Observations

AbstractThe impact on tropical cyclone (TC) prediction from assimilating Doppler weather radar (DWR) observations obtained from the TC inner core and environment over the Bay of Bengal (BoB) is studied. A set of three operationally relevant numerical experiments were conducted for 24 forecast cases involving 5 unique severe/very severe BoB cyclones: Sidr (2007), Aila (2009), Laila (2010), Jal (2010), and Thane (2011). The first experiment (CNTL) used the NCEP FNL analyses for model initial and boundary conditions. In the second experiment [Global Telecommunication System (GTS)], the GTS observations were assimilated into the model initial condition while the third experiment (DWR) used DWR with GTS observations. Assimilation of the TC environment from DWR improved track prediction by 32%–53% for the 12–72-h forecast over the CNTL run and by 5%–25% over GTS and was consistently skillful. More gains were seen in intensity, track, and structure by assimilating inner-core DWR observations as they provided mor...

A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

The present study focuses on the performance-based comparison of simulations carried out using nudging (NUD) technique and three-dimensional variational (3DVAR) data assimilation system (3DV) of a heavy rainfall event occurred during 25–28 June 2005 along the west coast of India. The Indian conventional and nonconventional observations are used in the 3DV experiment. Three numerical experiments are conducted using WRF modeling system, the model is integrated upto 54 hours from the initial time 0000 UTC of 25 June 2005. It is noticed that the meteorological parameters are improved in the resulting high-resolution analyses prepared by NUD and 3DV compared to without data assimilation experiment (i.e., called CNTL experiment). However, after the successful inclusion of observations using the 3DVAR data assimilation technique, the model is able to simulate better structure of the convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC) than the NUD experiment and well correlated with the observations. The simulated location and intensity of rainfall is also improved in 3DV simulation as compared with other experiments. Similar results are noticed in the root mean squar errors, correlation coefficients, and Equitable Threat Scores between TRMM and model simulated rainfall for all the three experiments.

Simulation of Tropical Cyclones over Bay of Bengal with NCMRWF Regional Unified Model

This study delineates the relative performance of the 12-km resolution NCMRWF regional Unified Model (NCUM-R) over the operational global NCUM (NCUM-G) model. Forecasts of four Bay of Bengal (BoB) landfalling tropical cyclones (TCs) using several different initial conditions (ICs) are used to compare the performance of two models. The position and intensity errors of the TCs are estimated with respect to the India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) best-track datasets and an inter-comparison study is also carried out between IMD and JTWC. The overall results suggest that the NCUM-R simulates the position and intensity of TCs more accurately compared to the NCUM-G. A majority of the TC tracks in the NCUM-G diverge more from the IMD track when compared to NCUM-R simulated tracks. It is also clearly noticed that both the models are more skillful in track prediction when initialized at intensity stages greater than “cyclone” category. However, the mean position errors at different forecast hours and landfall errors of TCs are reduced by approximately 31 and 47% in the NCUM-R simulations compared to NCUM-G simulations, respectively. The mean gain in skill of the NCUM-R in cross track (CT) and along track (AT) error is around 29 and 24% over NCUM-G, respectively. The intensity errors are less in the NCUM-R simulations. The mean rainfall skill scores are considerably improved in the NCUM-R simulations in day-1 and day-2 as compared to the NCUM-G simulations. It is noticed that the mean position errors of the TCs are approximately 8% lower when compared against the JTWC tracks than the IMD tracks. However, the intensity errors are higher against the JTWC than that of IMD most likely due to the averaging period of the wind speed.

A Qualitative study of Some Meteorological Features During tropical Cyclone PHET Using Satellite Observations and WRF Modeling System

The satellite derived meteorological parameters are quite useful for understanding the genesis of a tropical cyclone. This paper analyses some of the characteristic features of the tropical cyclone (TC) PHET using satellite derived meteorological observations, and numerical model simulations while investigating the performance of various cumulus parameterization schemes using Weather Research and Forecasting (WRF) modeling system. The genesis of the TC is primarily discussed using the observed meteorological parameters including the outgoing long-wave radiation, quantitative precipitation estimate (or rainfall), sea surface temperature, relative vorticity and upper tropospheric humidity. These satellite derived parameters show suitable meteorological condition for the development and propagation of the TC. The qualitative analysis of WRF simulated results indicates that Kain-Fritsch cumulus scheme (Kain and Fritsch, 1990 and 1993; Kain, 2004) performs relatively better in predicting various parameters in relation to the genesis and propagation of PHET.

Impact of Radiance Data Assimilation on Simulation of Tropical Cyclone Thane Using WRF-3DVAR Modelling System

Tropical cyclones (TCs) are well known for their devastation, mainly due to torrential rains, strong winds and associated storm surges, which cause flooding, soil erosion and landslides, even far away from the landfall location, resulting in numerous human casualties and enormous property damage. These disasters are particularly severe over the North Indian Ocean (NIO), comprising both the Bay of Bengal (BoB) and Arabian Sea (AS), as their coastal areas are heavily populated. In the past 300 years, out of all recorded cases of very heavy loss of life (ranging from about 5000 to well over 300,000) in the world due to TCs, more than 75% cases have occurred in the BoB and AS (WMO Technical Report, 2008).