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The northeastern depressed region of Bangladesh is highly susceptible to recurrent flash flooding due to excessive rainfall over these areas and in the upstream hilly regions. Two such severe pre-monsoon flash flood events occurred in 2016 and 2017. This research attempts to forecast both flash flood events using a coupled atmospheric-hydrological numerical weather prediction (NWP) model, namely the weather research and forecasting (WRF) model. The ARW (Advanced Research WRF) model is able to predict the rainfall over these areas with a lead time of 91 h. However, the discharge and water level are overestimated by the WRF-Hydro model. The model predicts a flash flood with a lag of approximately 12 h with respect to the highest amount of rainfall. The overall performances of the models were satisfactory. The two parameters, rainfall and subsequent discharge, which are required for delineation of lag time, were almost precisely simulated. Simulated values also had fewer errors, justified by the root mean squared error (RMSE) and mean absolute error values. The Nash–Sutcliffe efficiency criterion scores for model-derived discharge were close to 1.0, and the RMSE-observation standard deviation ratio scores were less than 0.5. This finding proves that the NWP models could be considered for forecasting flash flood events over selected areas of Bangladesh.

The physical and dynamical characteristics of pre-monsoon (March–May) thunderstorms (TS) are investigated over Bangladesh using the Weather Research and Forecasting (WRF) model. The model was run for 24-h using 6-hourly data sets as initial and lateral boundary conditions. The Milbrandt cloud microphysics scheme, Krain–Fritsch cumulus scheme, and Yonsei University planetary boundary layer are found to be the best combination by comparing the root mean square error of rainfall from 18 sensitivity experiments. The synoptic condition and atmospheric instability associated with three TS cases have been analyzed based on mean sea level pressure, convective available potential energy (CAPE), and thermodynamic indices. CAPE is found between 500 and 2700 J kg−1 at 0000 UTC for the TS cases. The presence of low-level southerly wind from the Bay of Bengal and upper-level westerly or north-westerly wind with speeds ranging from 5 to 30 m s−1 is found during the storms. The cloud characteristics and intensity were investigated based on Doppler Weather Radar and INSAT 3DR satellite observations. Cloud micro-physical hydrometeors and other composite features (e.g., cloud top and core precipitation altitude, length of squall line) were also studied by the model and then compared with available observed values. For 24-h rainfall, 2 × 2 contingency tables are computed using Model Evaluation Tools. Categorical skill scores such as proportion correct, frequency bias index, probability of false detection, false alarm ratio, and Gilbert skill score were calculated to evaluate the model's performance. The forecast goodness is found to be reasonably satisfactory after analyzing the significance of the skill scores.

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.

Heavy rainfall events frequently occur over the western and central Himalayas during the monsoon season causing losses of lives and damages to properties over the fragile mountain environment. A heavy rainfall event that occurred over Uttarkashi (30.73°N, 78.45°E) in the Western Himalayas on 3rd August 2012 is investigated. The formation of the storm, its evolution and the initial physical mechanisms responsible for this event are analyzed by using a double nested Weather Research and Forecasting (WRF) model. The model skill is evaluated against available observations and analysis fields. The impact of assimilation of Global Telecommunication System (GTS) data in the model initial condition is also studied. The model simulated rainfall is compared with daily rainfall data from satellite-gauge merged rainfall and 3 hourly Tropical Rainfall Measuring Mission (TRMM) estimated rainfall. The double nested configuration of the model successfully simulates this heavy rainfall event. 3DVAR assimilation of GTS data in the model initial condition improves prediction of precipitation amount and location of heavy rainfall. The results of double nested WRF model simulation with GTS data assimilation are compared with the results of global model forecasts of National Centre for Medium Range Weather Forecasting (NCMRWF), India. Forecast skill of the model with and without data assimilation is computed with respect to TRMM estimated daily rainfall.

Petrography and mineralogy of four calc-alkaline granitoid plutons Agarpur, Sindurpur, Raghunathpur and Sarpahari located from west to east of northern Purulia of Chhotanagpur Gneissic Complex, eastern India, are investigated. The plutons, as a whole, are composed of varying proportions of Qtz–Pl–Kfs–Bt–Hbl±Px–Ttn–Mag–Ap–Zrn±Ep. The composition of biotite is consistent with those of calc-alkaline granitoids. Hornblende–plagioclase thermometry, aluminium-in-hornblende barometry and the assemblage sphene–magnetite–quartz were used to determine the P, T and $$f_{\mathrm{O}_2}$$ during the crystallisation of the parent magmas in different plutons. The plutons are crystallised under varying pressures (6.2–2.4 kbar) and a wide range of temperatures (896– $$718{^{\circ }}\hbox {C}$$ ) from highly oxidised magmas (log $$f_{\mathrm{O}_2}$$ $$-11.2$$ to $$-15.4$$ bar). The water content of the magma of different plutons varied from 5.0 to 6.5 wt%, consistent with the calc-alkaline nature of the magma. Calc-alkaline nature, high oxygen fugacity and high $$\hbox {H}_{2}\hbox {O}_{{\mathrm{melt}}}$$ suggest that these plutons were emplaced in subduction zone environment. The depths of emplacement of these plutons seem to increase from west to east. Petrologic compositions of these granitoids continuously change from enderbite (opx-tonalite: Sarpahari) in the east to monzogranite (Raghunathpur) to syenogranite (Sindurpur) to alkali feldspar granite (Agarpur) in the west. The water contents of the parental magmas of different plutons also increase systematically from east to west. No substantial increase in the depth of emplacement is found in these plutons lying south and north of the major shear zone passing through the study area suggesting the strike-slip nature of the east–west shear zone.

A recent heavy precipitation event on 13 September 2012 and the associated landslide on 14 September 2012 is one of the most severe calamities that occurred over the Rudraprayag region in Uttarakhand, India. This heavy precipitation event is also emblematic of the natural hazards occuring in the Himalayan region. Study objectives are to present dynamical fields associated with this event, and understand the processes related to the severe storm event, using the Weather Research and Forecasting (WRF ver 3.4) model. A triple-nested WRF model is configured over the Uttarakhand region centered over Ukhimath (30∘30′N; 79 ∘15′E), where the heavy precipitation event is reported. Model simulation of the intense storm on 13 September 2012 is with parameterized and then with explicit convection are examined for the 3 km grid spacing domain. The event was better simulated without the consideration of convection parameterization for the innermost domain. The role of steep orography forcings is notable in rapid dynamical lifting as revealed by the positive vorticity and high reflectivity values and the intensification of the monsoonal storm. Incursion of moist air, in the lower levels, converges at the foothills of the mountains and rise along the orography to form the updraft zone of the storm. Such rapid unstable ascent leads to deep convection and increases the condensation rate of the water vapour forming clouds at a swift rate. This culminates into high intensity precipitation which leads to high amount of surface runoff over regions of susceptible geomorphology causing the landslide. Even for this intense and potentially unsual rainfall event, the processes involved appear to be the ‘classic’ enhanced convective activity by orographic lifting of the moist air, as an important driver of the event.

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.

The Advanced Research WRF (ARW) model is used to simulate Very Severe Cyclonic Storms (VSCS) Hudhud (7–13 October, 2014), Phailin (8–14 October, 2013) and Lehar (24–29 November, 2013) to investigate the sensitivity to microphysical schemes on the skill of forecasting track and intensity of the tropical cyclones for high-resolution (9 and 3 km) 120-hr model integration. For cloud resolving grid scale (

In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30 km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7 mm) event than light rainfall (for thresholds of 1 and 3 mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.

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.

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.

Re-analysis, using surface, upper-air, and satellite observations specially collected during the Arabian Sea Monsoon Experiment-I (ARMEX-I), has been performed with a global data assimilation system at T-80/L18 resolution. Re-analysis was performed for the entire ARMEX-I period (15th June-16th August 2002). In this paper we discuss the results based on re-analysis and subsequent forecasts for two successive intensive observation periods associated with heavy rainfall along the west coast of India during 2-12 August, 2002. Results indicate that the re-analysed fields can bring out better synoptic features, for example troughs along the west coast and mid tropospheric circulation over the Arabian Sea. Simulated rainfall distribution using re-analysis as initial condition also matches observed rainfall better than data from the initial analysis.

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

A global weather analysis-forecast system is used to produce six hourly analysis of meteorological fields at roughly 150 km × 150 km resolution at the National Center for Medium Range Weather Forecast (NCMRWF). In this paper, we have studied the Total Precipitable Water Content (TPWC) and Cloud Liquid Water Path (CLWP) derived from the Indian Remote Sensing (IRS-P4) Satellite over the Indian Ocean region in relation to operational numerical weather prediction (NWP) model analysis and short-range forecasts. An objective analysis was carried out by introducing the observations of CLWP, TPWC and their values (six hour forecasts) from the T80 model as the first guess, for a 20 days period of August 1999 using the standard Cressman’s technique. The reanalysis could capture the signature of TPWC and CLWP data from IRS-P4 satellite. In general the observed values of TPWC and CLWP from IRS-P4 have a positive bias compared to NCMRWF analysis over the region where the satellite passed. The CLWP values have been compared with Special Sensor Microwave/Imager (SSM/I) products from the Defense Meteorological Satellite Program (DMSP) satellites. Results indicate that the model derived CLWP values were within acceptable limits, whereas the observations from the Multi-channel Scanning Microwave Radiometer (MSMR) showed slightly larger values.

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.

The global spectral model of NCMRWF at T80 horizontal resolution and 18 vertical levels has been integrated for the summer season (July) using different cumulus parameterization schemes namely, the Simplified Arakawa-Schubert scheme (SAS), the Relaxed Arakawa-Schubert Scheme (RAS), and the Kuo-type cumulus parameterization scheme (KUO). The results have been compared with mean analysis of the operational NCMRWF model (ANA) and other available observations. Results indicate that, while the global distributions of basic fields such as the wind, temperature and moisture are fairly well simulated by all the three schemes, there are many differences seen in the simulation of the typical features of the Indian summer monsoon. The strength of the Low Level Westerly Jet (LLWJ), the Cross Equatorial Flow (CEF), and the Tropical Easterly Jet (TEJ) are better simulated by RAS and SAS as compared to ANA than the KUO scheme. RAS and SAS produce strong rising motion owing to strong intensity of convection produced by these two schemes. This in turn produces stronger Hadley cell by RAS and SAS than compared to the KUO scheme. Simulation of the 200 mb velocity potential and divergent wind by RAS and SAS produced two prominent centers, one in the Bay of Bengal and another in the Western Pacific, which correspond to the intense latent heating by cumulus convection during the active monsoon phase. The velocity potential and divergent winds were weaker in KUO, than compared to RAS and SAS. The simulation of OLR is improved by RAS as compared to observations. The cold bias produced by KUO at 200 mb is reduced by RAS and is substantially improved by SAS. Study of observed and simulated rainfall indicated that RAS and SAS produced better distribution of precipitation over the Western Ghat Mountains and the Arakan coast, where deep cumulus convection is produced due to orographic forcing of the warm moist air. The KUO scheme underestimated the rainfall over these two regions, but produced slightly better distribution of rainfall over the northwest and central India, where the intensity of convection is relatively weaker. Evaluation of overall dynamics, thermal structure and rainfall indicates that in general, SAS is able to provide relatively better results compared to other two schemes.