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An attempt is made to determine skill of a mesoscale model through calculation of distance, intensity and time errors with respect to observed maximum reflectivity data (maxdBZ) of Doppler Weather Radar (DWR). A new approach is adopted to calculate distance, intensity and time errors of the model simulated thunderstorm events. Five cases of pre-monsoon thunderstorms over the eastern part of India are simulated by Advanced Research WRF (ARW) model and the model simulated maxdBZ is validated against DWR data. In all the cases the model is run from two different initial conditions. The global model forecast of National Centre for Medium Range Weather Forecasting (NCMRWF), India is directly used as the initial condition for the control run (Exp1) while in the other run (Exp2) initial condition is prepared by assimilating local synoptic data with 6 h model forecast taken as background. The spatial distributions of model simulated and observed maxdBZ show that the newly adopted method is capable of capturing the model skill through computation of distance error. This newly adopted method is used to investigate whether the model simulation with data assimilation is more skillful in determining distance, intensity and time errors. In all the cases, except one, experiments with data assimilations have shown comparatively less distance error than control experiments in the initial hours of the days of the events. Intensity errors of experiments with data assimilations are marginally better than those of control experiment for two cases. Experiments with data assimilation show less time errors than that of control experiment in some cases but it is not always true.

The quantitative data from Doppler Weather Radar (DWR) such as the radial winds and reflectivity are useful for improving the numerical prediction of weather events like squalls. Mesoscale convective systems are responsible for majority of the squall and hail events and related natural hazards that occur over Bangladesh and surrounding region in pre-monsoon season. In this study, DWR observations (radial winds and reflectivity) of Bangladesh Meteorological Department are used for simulating the squall events during May 2011 with a view to update the initial and boundary conditions through three-dimensional variational assimilation technique within the Advanced Research Weather Research and Forecasting model. The simulated sea-level pressure, thermodynamic indices, wind fields at 850 hPa, and cloud hydrometeors from eight experiments are presented in this study for analyzing the observed and simulated features of the squall events which occurred in the month of May 2011. The model results are also compared with the Kalpana-1 satellite imagery and the observations of India Meteorological Department. Further, the intensity of the events generated from the simulations is also compared with the in situ meteorological observations in order to evaluate the model performance.

A common feature of the weather during the pre-monsoon season (March–May) over the east and northeast India is the outburst of severe local storms which have significant socio-economic impact due to loss of lives and properties. Forecasting thunderstorms is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal scales and the inherent non-linearity of their dynamics and physics. In the present study, an attempt has been made to simulate severe local storms that occurred over east India during STORM field experiments 2007, 2009 and 2010, using Non-hydrostatic Mesoscale Model (NMM) and validate the model results with observation. This study shows that the NMM model holds better promise for prediction of thunderstorm with reasonable accuracy. The intensity of rainfall rates is in good agreement with the observation. The model has well captured the stability indices, which act as indicators of severe convective activity. The surface temperature and relative humidity over Kolkata are reasonably well simulated by the NMM model even though one hour time lag or lead exists. The model simulated well the updraft and downdraft over Kolkata, which is an important phenomenon related to thunderstorm life cycle. From the model simulated spatial plots of composite radar reflectivity and cloud top temperature, we can see that the model has also been able to capture the movement of thunder squall. The results of these analyses determined that the 3 km WRF-NMM model has good skill when it comes to the thunderstorm simulation.

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS.

A global model (T80L18; Triangular Truncation at wave number 80 with 18 vertical layers) and a mesoscale model MM5 (nested at 90 and 30 km resolutions) are integrated for 5 monsoon years 1998-2002. The impact of dynamical downscaling from global to mesoscale in the simulations of Indian summer monsoon rainfall is studied. Comparisons between the global and the mesoscale models show that, though the global model has an edge over the mesoscale model in simulating the all-India mean rainfall closer to the observation, the T80L18 model lacks in simulating the spatial variations in rainfall. The effect of downscaling is better represented in the rainfall variations produced by MM5 both quantitatively and qualitatively over the foothills of the Himalayas and along Nepal to North-eastern India. It is also seen that the mesoscale model is able to represent the dispersion (standard deviation) present in the observed rainfall over India. In the five monsoon seasons, RMSE of mean rainfall (monthly and seasonal) of T80L18 forecasts are mostly lower than that of MM5 forecasts. However, synoptic features like the Somali Jet and Tibetan anticyclone are better represented by MM5. This model has also simulated the regions of convection better than the T80L18 model. However, the MM5 simulations produced an anomalous circulation over the Saudi Arabian region (15-200 N and 45-500 E) in many cases. The mesoscale model simulates better wind fields than the global model in general. Over peninsular India T80L18 model showed higher temperature gradient but, over Central India this model has better temperature field as compared to MM5. Over southern and north-eastern India, the temperature field of T80L18 and MM5 are very similar.

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.

Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, ‘cloudbursts’, which are devastating convective phenomena producing sudden high-intensity rainfall (∼10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km grid-resolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometers

Severe weather has a more calamitous effect in the mountainous region because the terrain is complex and the economy is poorly developed and fragile. Such weather systems occurring on a small spatiotemporal scale invite application of models with fine-grid resolution and observations from radars and satellites besides the conventional observations for forecasting and disaster mitigation.

Thunderstorms of pre-monsoon season (March–May), locally known as “Kal Boishakhi” or nor’westers, develop from a variety of mesoscale convective structures as they mature to meso α scale (200–1,000 km) systems. These systems develop mainly due to merging of cold dry northwesterly winds aloft and southerly low level warm moist winds from the Bay of Bengal.

[1] We examine a deep precipitating system that formed over the west coast of India during 26–28 June 2002 producing heavy rainfall of 2–61 cm day−1. The system developed into a well-marked low pressure area due to interaction between an eastward moving westerly trough and a westward moving monsoon low. We used the PSU/NCAR Mesoscale Model (MM5) to make 10-day interactively nested simulations at 90, 30, and 10 km grid-resolutions. We used observations from a special data set collected during an Arabian Sea Monsoon Experiment (ARMEX) conducted June–August, 2002. Nudging the observations produced a balanced atmosphere which, in turn, matched the location of vortex with cloud clusters observed from satellite. The simulated rainfall corresponded well with observations. We then use the simulated fields as forcing and boundary conditions for MM5 run in cloud-system resolving mode at 2 km grid-resolution to detail cloud-clusters embedded within the monsoon disturbance. We examined the sensitivity to different physical parameterizations and also the effect of continuous nudging four dimensional data assimilation (FDDA) on the rainfall forecasts. While the simulation of the convective event improved with certain combinations of physical parameterizations, the rainfall was not forecasted at the correct location, no matter which parameterization was used, unless continuous FDDA was performed in all domains throughout the integrations. Finally, cloud-cluster properties of the cloud-system resolving simulations were compared with observations.

Severe thunderstorms form over the Eastern and Northeastern parts of India, i.e., Gangetic West Bengal, Jharkhand, Orissa, Assam and parts of Bihar during the pre-monsoon months (April-May). These storms are known as "Nor'wester" as they move from Northwest to Southeast. In this study we have made numerical simulations of 10 thunderstorms that formed over the West Bengal region during April-May of 2005 and 2006. Numerical simulations have been carried out using MM5 mesoscale model (at 10 km resolution) using conventional and non-conventional observations from Doppler Weather Radar (DWR) and satellites. Composite characteristics of the Nor'wester have been made based upon the simulations. Results indicate that the Nor'westers occur generally when the CAPE increases above 1500 J Kg -1 . They have updraft speeds up to 3-4 m s -1 , while the downdrafts have magnitudes of about 0.4 - 0.5 m s -1 . The updrafts can extend up to 8-9 km altitudes. The total amount of hydrometeors simulated inside the Nor'westers is up to 600-800 mg kg -1 . Large amount of ice and snow exist at upper levels, while liquid water is present in the lower levels. The magnitudes of the ice, snow and liquid water depend on the stage of their life cycle.

Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.