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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 respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) 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 375 km (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 9 km) 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.

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 hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions.

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.

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.