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The Indian subcontinent is characterized by complex topography and heterogeneous land use-land cover. The Himalayas and the Tibetan Plateau are spread across the northern part of the continent. Due to its highly variable topography, understanding of the prevailing synoptic weather systems is complex over the region. The present study analyzes the energetics of Indian winter monsoon (IWM) over the Indian subcontinent using outputs of mesoscale model (MM5) forced with National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), US, initial and boundary conditions. MM5 modeling framework, designed to simulate or predict mesoscale atmospheric circulations, is having a limited-area, non-hydrostatic and terrain following 12 sigma levels. The IWM energetics is studied using MM5 model outputs. Prior to this model’s validity and deviation from the corresponding observations (NCEP/NCAR) is assessed. The model’s overestimation/underestimation of wind, temperature and specific humidity at upper troposphere proves that the model has difficulty in picking up corresponding fields at all the model grid points because of terrain complexity over the Himalayas and Tibetan Plateau. Hence, the model fields deviate from the corresponding observations. However, model results match well with the winter global energy budget calculated using reanalysis dataset by Peixoto and Oort (1992). It suggests MM5 model’s fitness in simulating large scale synoptic weather systems. And, thus the model outputs are used for calculation of energetics associated with IWM. It is observed that beyond $$15^{{\circ }}\hbox {N}$$ lower as well as upper level convergence of diabatic heating, which represents continental cooling and sinking of heat from atmosphere to land mass (i.e., surface is cooler than surrounding atmosphere) dominates. The diabatic heating divergence (cooling of continents) is found over ocean/sea and whole of the China region, Tibetan and central Himalayas (because of excess condensation than evaporation). The adiabatic generation of kinetic energy depends on the cross isobaric flow (north to south in winter, i.e., the present study shows strong circulation during IWM). It is found that wind divergence of model concludes lower level convergence over study region (i.e., strong winter circulation in the model fields).

The objective of this study is to evaluate the ability of a European chemistry transport model, ‘CHIMERE’ driven by the US meteorological model MM5, in simulating aerosol concentrations [dust, PM10 and black carbon (BC)] over the Indian region. An evaluation of a meteorological event (dust storm); impact of change in soil related parameters and meteorological input grid resolution on these aerosol concentrations has been performed. Dust storm simulation over Indo-Gangetic basin indicates ability of the model to capture dust storm events. Measured (AERONET data) and simulated parameters such as aerosol optical depth (AOD) and Angstrom exponent are used to evaluate the performance of the model to capture the dust storm event. A sensitivity study is performed to investigate the impact of change in soil characteristics (thickness of the soil layer in contact with air, volumetric water, and air content of the soil) and meteorological input grid resolution on the aerosol (dust, PM10, BC) distribution. Results show that soil parameters and meteorological input grid resolution have an important impact on spatial distribution of aerosol (dust, PM10, BC) concentrations.

The Indian northeast monsoon is inherently chaotic in nature as the rainfall realised in the peninsular India depends substantially on the formation and movement of low-pressure systems in central and southwest Bay of Bengal and on the convective activity which is mainly due to the moist north-easterlies from Bay of Bengal. The objective of this study is to analyse the performance of the PSU-NCAR Mesoscale Model Version 5 (MM5), for northeast monsoon 2008 that includes tropical cyclones – Rashmi, Khai-Muk and Nisha and convective events over Sriharikota region, the rocket launch centre. The impact of objective analysis system using radiosonde observations, surface observations and Kalpana-1 satellite derived Atmospheric Motion Wind Vectors (AMV) is also studied. The performance of the model is analysed by comparing the predicted parameters like mean sea level pressure (MSLP), intensity, track and rainfall with the observations. The results show that the model simulations could capture MSLP and intensity of all the cyclones reasonably well. The dependence of the movement of the system on the environmental flow is clearly observed in all the three cases. The vector displacement error and percentage of improvement is calculated to study the impact of objective data analysis on the movement and intensity of the cyclone.

There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das et al (2008), most of the studies in the literature are either the case studies of tropical cyclones and thunderstorms or the sensitivity studies involving physical parameterization or climate simulation studies. Almost all the studies are based on either National Center for Environmental Prediction (NCEP), USA, final analysis fields (NCEP FNL) or the reanalysis data used as initial and lateral boundary conditions for driving the mesoscale model. Here we present a mesoscale model forecast verification and intercomparison study over India involving three mesoscale models: (i) the Weather Research and Forecast (WRF) model developed at the National Center for Atmospheric Research (NCAR), USA, (ii) the MM5 model developed by NCAR, and (iii) the Eta model of the NCEP, USA. The analysis is carried out for the monsoon season, June to September 2008. This study is unique since it is based entirely on the real time global model forecasts of the National Centre for Medium Range Weather Forecasting (NCMRWF) T254 global analysis and forecast system. Based on the evaluation and intercomparison of the mesoscale model forecasts, we recommend the best model for operational real-time forecasts over the Indian region. Although the forecast mean 850 hPa circulation shows realistic monsoon flow and the monsoon trough, the systematic errors over the Arabian Sea indicate an easterly bias to the north (of mean flow) and westerly bias to the south (of mean flow). This suggests that the forecasts feature a southward shift in the monsoon current. The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over southeast peninsula. The 850 hPa specific humidity forecast errors clearly show that the Eta model features dry bias mostly over the sea, while MM5 features moist bias over large part of domain. The RMSE computed at different levels clearly establish that WRF model forecasts feature least errors in the predicted free atmospheric fields. Detailed rainfall forecast verification further establishes that the WRF model forecast rainfall skill remains more or less same in day-2 and day-3 as in day-1, while the forecast skill in the MM5 and Eta models, deteriorates in day-2 and day-3 forecasts.

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.

Air pollution predictions often require the height of atmospheric mixed layer in time especially in big cities. Here, the variation of the height of this layer is estimated from direct measurements and also from a numerical forecast model with a high resolution boundary layer scheme. The height of the daytime mixed layer for the city of Zanjan (48.5°N, 36.7°E, 1700m above sea level) is measured using a LIDAR (532 nm) system, which works based on aerosols scattering of laser light. The mixed layer height (z i ) for Zanjan city, well above mean sea level compared to other major cities in the world, is found to be between 1.4 km typically in spring and 2.2 km in summer, for synoptic calm conditions. Also, the MM5 forecast model with a proper boundary layer scheme (MRF) is used to estimate z i which shows rather good agreement with direct observations using the LIDAR system. The entrainment zone of the mixed layer was also found to undergo some occasional temporal growth that may be attributed to shear instability that led to more mixed layer growth.

Doppler radar derived wind speed and direction profiles showed a well developed sea breeze circulation over the Chennai, India region on 28 June, 2003. Rainfall totals in excess of 100 mm resulted from convection along the sea breeze front. Inland propagation of the sea breeze front was observed in radar reflectivity imagery. High-resolution MM5 simulations were used to investigate the influence of Chennai urban land use on sea breeze initiated convection and precipitation. A comparison of observed and simulated 10m wind speed and direction over Chennai showed that the model was able to simulate the timing and strength of the sea breeze. Urban effects are shown to increase the near surface air temperature over Chennai by 3.0K during the early morning hours. The larger surface temperature gradient along the coast due to urban effects increased onshore flow by 4.0m s−1. Model sensitivity study revealed that precipitation totals were enhanced by 25mm over a large region 150 km west of Chennai due to urban effects. Deficiency in model physics related to night-time forecasts are addressed.

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.

A simulation study of the sea breeze circulation and thermal internal boundary layer (TIBL) characteristics has been carried out at the tropical site Kalpakkam on the east coast of India, for operational atmospheric dispersion prediction. The community based PSU/NCAR MM5 Meso-scale meteorological model is used for the study. Three cases on typical days in summer (24 May 2003), southwest (SW) monsoon (1 July 2001) winter season (2 February 2003) with different large-scale flow pattern are studied. The MM5 model is used with 3 nested domains with horizontal grid resolutions 18 km, 6 km and 2 km and 26 vertical levels. The model is integrated for 24 hours in the above cases with initial and boundary conditions taken from NCEP-FNL analyses data. Observations of 10 meteorological stations and coastal boundary layer experiments conducted at Kalpakkam are used for comparison and validation of the simulation. The characteristics of simulated sea breeze and TIBL at Kalpakkam are seen to vary in the above cases according to the prevailing large-scale winds and surface fluxes. The sea breeze circulation is seen to develop early with larger strength and inland propagation in the summer case under the influence of moderate synoptic wind and strong heating conditions than in the SW monsoon and winter cases. The horizontal and vertical extents of TIBL are found to be larger in the summer case than in other cases. Although model parameters agree in general with observations, all the fine features are not clearly captured and some slowness in model sea breeze development is also seen. The results indicate the need to improve i) the initial conditions by assimilation of available surface/upper air observations to reduce model bias and ii) surface net radiation parameterisation. The model could predict the essential features of the local circulation and further improvement is expected with better initial condition data and incorporation of more realistic surface data.

An operational atmospheric dispersion prediction system is implemented on a cluster supercomputer for Online Emergency Response at the Kalpakkam nuclear site. This numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48-hour forecast of the local weather and radioactive plume dispersion due to hypothetical airborne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. A 16-node dual Xeon distributed memory gigabit ethernet cluster has been found sufficient for operational applications. The runtime of a triple nested domain MM5 is about 4 h for a 24 h forecast. The system had been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Improvement is noticed in rainfall forecasts that used NCEP data, probably because of its high spatial and temporal resolution

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

The initialization scheme designed to improve the representation of a tropical cyclone in the initial condition is tested during Orissa super cyclone (1999) over Bay of Bengal using the fifth-generation Pennsylvania State University — National Center for Atmospheric Research (Penn State — NCAR) Mesoscale Model (MM5). A series of numerical experiments are conducted to generate initial vortices by assimilating the bogus wind information into MM5. Wind speed and location of the tropical cyclone obtained from best track data are used to define maximum wind speed, and centre of the storm respectively, in the initial vortex. The initialization scheme produced an initial vortex that was well adapted to the forecast model and was much more realistic in size and intensity than the storm structure obtained from the NCEP analysis. Using this scheme, the 24-h, 48-h, and 72-h forecast errors for this case was 63, 58, and 46 km, respectively, compared with 120, 335, and 550 km for the non-vortex initialized case starting from the NCEP global analysis. When bogus vortices are introduced into initial conditions, the significant improvements in the storm intensity predictions are also seen.

The skill and efficiency of a numerical model mostly varies with the quality of initial values, accuracy on parameterization of physical processes and horizontal and vertical resolution of the model. Commonly used low-resolution reanalyses are hardly able to capture the prominent features associated with organized convective processes in a monsoon depression. The objective is to prepare improved high-resolution analysis by the use of MM5 modelling system developed by the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR). It requires the objective comparison of high and low-resolution analysis datasets in assessing the specific convective features of a monsoon depression. For this purpose, reanalysis datasets of NCAR/NCEP (National Center for Atmospheric Research/National Centers for Environmental Prediction) at a horizontal resolution of 2.5‡ (latitude/longitude) have been used as first guess in the objective analysis scheme. The additional asynoptic datasets obtained during BOBMEX-99 are utilized within the assimilation process. Cloud Motion Wind (CMW) data of METEOSAT satellite and SSM/I surface wind data are included for the improvement of derived analysis. The multiquadric (MQD) interpolation technique is selected and applied for meteorological objective analysis at a horizontal resolution of 30 km. After a successful inclusion of additional data, the resulting reanalysis is able to produce the structure of convective organization as well as prominent synoptic features associated with monsoon depression. Comparison and error verifications have been done with the help of available upper-air station data. The objective verification reveals the efficiency of the analysis scheme.

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.