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The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995–2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006–16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land–atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high-resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal- to interdecadal-scale interactions, and evaluation of the predictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025–28, whose strategies are the subregional observation platforms and integrated global analysis.

This study investigates the use of various thunderstorm indices in predicting severe thunderstorms events during the monsoon season in four different regions in India. The research evaluates the performance of the prediction model using a model skill score and utilizes the Weather Research and Forecasting (WRF) model with the double moment microphysics scheme to simulate model cases. It also compares fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for predicting severe thunderstorms events. The results of this study show that incorporating thunderstorm indices with model skill scores improves severe thunderstorms forecasting in the monsoon season in India. The result revealed that determining the optimal threshold for each index is crucial in achieving accurate predictions. The study also highlights the importance of considering multiple indices rather than relying on a single index to predict severe thunderstorms events. The advance indices such as Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), mainly works well with extreme severe thunderstorms. The simplistic indices can predict the weak or severe thunderstorm easily. The use of multiple thunderstorm indices can also help meteorologists to make more accurate predictions, which can further enhance public safety. In conclusion, this study demonstrates the potential of incorporating thunderstorm indices with model skill scores like HSS and TSS and combinations of different skill scores in severe thunderstorms forecasting during the monsoon season in India. Future research can build upon the findings of this study to develop more accurate and reliable severe weather forecasting models.

In this study an attempt has been made to inspect the forecasting of thunderstorms based on two cases (1st case: 17th May, 2019 and 2nd case: 31st March, 2019) over Dhaka using WRF Model. The model is run for 72 hours with 03 nested domain of 09 km, 03 km and 01 km horizontal resolutions using 0.25º X 0.25º six hourly global data assimilation system. For model simulation, Milbrandt-Yau Double-Moment 7-class scheme (9) has been used as microphysics scheme in this study. The model performance is evaluated by calculating hourly instability indices (VTI, TTI, KI, CTI, MCAPE, MCIN, BRN, LI, SI, SWI) value and have been compared with the threshold value of indices. Different meteorological parameters such as MSLP, temperature, winds at upper (300 hPa) and lower (925 hPa) level, relative humidity along with vertical cross section are also studied by the model and compared with the favorable conditions for forming of thunderstorms. Area rage rainfall (hourly) value has been also calculated and compared with indices value to comprehend the nature of thunderstorms. Observing the indices value it is seen that all indices value increase sharply 5-6 hours before of thunderstorm occurring and MCAPE is giving more reliable result. Moreover, this study shows that inner two domains (3 and 1 km resolution) are giving better results than outer one and which indices are more probable in forecasting of thunderstorm for our country as well as giving less Root Mean square Error. From the simulated and validated results, it can be concluded that the model performance of instability indices can be used as forecasting of thunderstorms over Bangladesh.

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.

Atmospheric turbulence is a primary meteorological hazard to en-route air traffic. The role of Clear Air Turbulence (CAT) for various processes in the atmosphere is still ambiguous. An Air India flight AI462 encountered severe CAT on 19 April 2018. The present study simulates the CAT event and is focused on understanding and investigating favourable conditions for the occurrence of CAT. Weather Research and Forecasting (WRF) Model V4.0.3 has been used to simulate the turbulence. The 6-hourly NCEP FNL Operational Global Analysis data at 0.25° X 0.25° resolution is taken as input to provide the model's initial and lateral boundary conditions. For simulating the atmospheric environments at the time of the event, Yonsei University Scheme, WSM 3-Class Simple Ice Scheme, Kain-Fritsch (New-Eta) Scheme, Rapid Radiative Transfer Model (RRTM) Scheme, and Revised MM5 Monin-Obukhov Scheme are used. This study shows that Vertical Velocity, Geopotential Thickness, Wind Shear and Bulk Richardson Number Shear are correlated with CAT as the model predicted both upward and downward velocity very close to each other between 400 hPa and 550 hPa levels along with strong geopotential thickness gradient and strong wind shear gradient near the accident location. This could lead to CAT. Model simulated variations in CAT Index with change in time and pressure levels. CAT dissipates as we go higher in the atmosphere above 550 hPa.

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.

A tornado occurred at Brahmanbaria in Bangladesh in the afternoon of 22 March 2013. The tornado event has been studied based on tropical rainfall measuring mission (TRMM) data, radar observations and model simulations. The maximum reflectivity and the vertical extent of the system have been recorded to be about 54.7 dBZ and 15 km, respectively, by the Doppler weather radar (DWR) at Agartala, India. The event has been simulated by using the WRF model at 3- and 1-km horizontal resolution nested domains based on six hourly final (FNL) re-analysis data and boundary conditions of National Centers for Environmental Prediction (NCEP). Results show that, while there are differences of 40 minutes before the observed time of the storm, the distance between observed and simulated locations of the storms is 0.5°. The maximum amount of vorticity transferred by directional shear in the storm updraft (helicity) due to convective motion simulated by the model is found to be 1774 m2 s−2, and the highest value of bulk Richardson number shear that defines the region in which low-level mesocyclogenesis is more likely has been 457.3 m2 s−2, which is generally supposed to produce rotating storms according to the prescribed range. The highest vertical velocity simulated by the model is about −28 to 58 m s−1.

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 Nor'westers (severe thunderstorms) that form over northeast India and adjoining Bangladesh region during the pre-monsoon season of 2008 are studied employing observations from ground based radar, Tropical Rainfall Measuring Mission (TRMM) and synoptic stations. Subsequently, an attempt is made to simulate the storms using Weather Research and Forecasting (WRF) model at 9 km horizontal resolution, and 28 vertical levels. Analyses of Radar data for 15 cases out of 108 during the study period showed that the Nor'westers typically propagate in the form of squall lines (parallel bow shaped bands) having horizontal length of about 200 km, reaching more than 400 km on some occasions. They propagate at typical speeds of about 50 km h − 1 from northwest to southeast directions. The model underestimated the strength of the squall lines in terms of wind speed. The simulated results showed the presence of strong vertical wind shear and an advection of warm moist southerly wind from the Bay of Bengal during the formation of Nor'westers. Low level positive vorticity in combination with moist southerly wind from the Bay of Bengal and strong surface heating resulted in the formations of the Nor'westers in all the cases. Cloud tops reached as high as 18–20 km in some of the cases of the severe storms. The altitude of core of maximum precipitation was located between 3–5 km. Average cloud hydrometeor content of the Nor'westers was estimated to be about 3.5 g m − 3 .

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 (

This article describes a unique field experiment on Severe Thunderstorm Observations and Regional Modeling (STORM) jointly undertaken by eight South Asian countries. Several pilot field experiments have been conducted so far, and the results are analyzed. The field experiments will continue through 2016. The STORM program was originally conceived for understanding the severe thunderstorms known as nor'westers that affect West Bengal and the northeastern parts of India during the pre-monsoon season. The nor'westers cause loss of human lives and damage to properties worth millions of dollars annually. Since the neighboring South Asian countries are also affected by thunderstorms, the STORM program is expanded to cover the South Asian countries under the South Asian Association for Regional Cooperation (SAARC). It covers all the SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) in three phases. Some of the science plans (monitoring the life cycle of nor'wester...

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.

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.

In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.

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.

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.

Blisters and slivers are serious defects that can lead to downgrading and rejection of interstitial free (IF) steels used for automotive applications as they affect the painted appearance, structur...

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

Onset of south west monsoon (SWM) over Kerala is associated with intense convection followed by heavy rainfall over the west-coast of India. The intense rainfall events are usually associated with meso-scale convective systems embedded in large scale synoptic system over the Arabian Sea. Such deep and intense cumulus convection can have an important effect on the dynamics and energetics of large-scale atmospheric systems, because of the large magnitudes of the energy transformations associated with changes of phase of water in precipitating cumulus clouds as well as the strong updrafts and downdrafts in the troposphere. The prime objective of this study is to understand the convective structure (active/suppressed) of the atmosphere over the west-coast of India during ARMEX-I (Arabian Sea Monsoon Experiment). This study uses an approach to obtain the average structure of a cloud cluster and its interaction with the environment that enables in distinguishing the variation of kinematic and convective parameters from suppressed to convectively active process. Upper air observations obtained from four coastal land stations viz., Bombay, Goa, Mangalore and Trivandrum, alongwith that obtained over ORV Sagar Kanya are used to calculate both the convective and the kinematic parameters at the centre of the polygon formed by these observation locations. Time averaged circulation kinematic parameters and vertical velocity during active and suppressed convective phases off the west coast of India were discussed. The apparent heating and the apparent moisture sink are also estimated through residuals of the thermodynamic equations during intense and weak phases of SWM.

During ARMEX-I several special observations were taken over Arabian Sea region and adjoining west coast of India, which gave an opportunity to study this region more thoroughly. To handle such voluminous data, ARMEX data center has been established at National Centre for Medium Range Weather Forecasting (NCMRWF), in collaboration with India Meteorological Department (IMD) and Indian Institute Technology (IIT), Delhi. One of the tasks of this center is to produce reanalysis for ARMEX period, utilizing these observations. It is essential to assimilate every observation from all available sources, especially over the data sparse oceanic regions for accurate determination of the three-dimensional structure of the atmospheric circulation. Before assimilating these data in global data assimilation system of NCMRWF, an attempt has been made to validate some of these data against observational facts from other sources. Upper-air observations (Digicora) from Dabolim, Goa have been compared with RS/RW observation of IMD, at Panjim, Goa. Though the trend in variations of all the meteorological parameters of these two stations is similar but there is a large difference in their absolute values. Reanalysis generated using the specially collected upper-air observations have been compared with the operational analysis of NCMRWF. The impact of these special observations on analysis and forecast system is found to be confined over the Arabian Sea region only in the lower troposphere. However, in the upper troposphere and stratosphere the impact is seen also over the equatorial Indian Ocean region.

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.

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.

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 operational global spectral model of the National Center for Medium Range Weather Forecasting (NCMRWF) at T80 resolution and 18 vertical levels has been used to study the skill of medium-range forecasts using three different parameterizations of deep convection namely, a Kuo–Anthes-type cumulus parameterization scheme referred to as “KUO” scheme, the relaxed Arakawa–Schubert (RAS) scheme, and the simplified Arakawa–Schubert (SAS) scheme, during an active phase of the Indian summer monsoon. Several medium-range forecasts (up to 5 days) have been made using the initial conditions of July and August of 1999, when the monsoon was active over the Indian region. Skill scores of predicted rainfall, rmse of wind and temperature, systematic errors, and genesis and tracks of the monsoon depressions predicted by the three schemes have been studied. Results indicate that, in general, the areas of light (heavy) rainfall are overestimated (underestimated) by KUO, which also fails to predict the rain-shadow...

Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing 7-day integrations of the operational Indian T80 Global Spectral Model every Wednesday during the six southwest monsoon seasons of 1994–99. Objective evaluations of the bias and accuracy of these forecasts during that 6-yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen–Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T80 model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-Himalayan areas. The model exhibited negative bias in rainfall simulations over the...

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.

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.

A prognostic cloud prediction scheme, designed for large-scale models, has been incorporated into a Single Column Model (SCM) and used to simulate the cloud cluster properties observed during the 19-26 December, 1992 Tropical Oceans Global Atmosphere (TOGA)-Coupled Ocean Atmosphere Response Experiment (COARE). Results from the SCM simulations have been compared with simulated profiles obtained from the Goddard Cumulus Ensemble Model (GCEM). Observed large-scale advective temperature, water vapor, and surface fluxes have been used as forcings to run the SCM and the GCEM. Results indicate that the SCM produces mixed profiles of cooling/warming and drying/moistening in the vertical, which are highly sensitive to the prescribed surface fluxes. Errors in the temperature and moisture profiles simulated by the SCM are about ±3 K and ±3 g kg -1 , while those from the GCEM are approximately -2 K and 1 g kg -1 at most levels. The SCM produced -10 % errors in the relative humidity above 700 mb and -30 % at the surface, while in the GCEM, the errors were about 10 to 15 % at most levels. The distributions of precipitation rates are fairly well simulated, but the high cloud fractions are slightly underestimated by the SCM as compared to the GCEM. The cloud liquid water is underestimated, but the ice contents are slightly overestimated by the SCM. Results show that the SCM has been able to simulate the distributions of temperature, moisture, and precipitation rates fairly well as compared to the GCEM and other GEWEX Cloud System Study (GCSS) model intercomparison products. Sensitivity studies have been carried out to investigate the implications of different physical processes in the SCM. Results indicate that the interactions between various physical processes are nonlinear, and a mere substitution of the heating and moistening profiles from the GCEM may not be able to reproduce the observed temperature and moisture values by the SCM. The SCM has also been used to simulate the distributions of large-scale cloud cluster properties and their diurnal variation during the disturbed and suppressed periods of convection. Results show that the diurnal variations of simulated values are in agreement with many observational studies conducted by different authors over the TOGA-COARE.

A prognostic cloud-prediction scheme has been tested with the relaxed Arakawa-Schubert cumulus-parametrization scheme using the GATE phase III data set. Observed profiles of temperature, moisture and wind fields have been used as initial conditions for a semi-prognostic as well as a fully prognostic study. The subsequent profiles of cloud liquid-water/ice, cloud fraction and precipitation rates besides several other fields have been generated by using these schemes. The cloud water substance and the cloud mass fraction are allowed to evolve prognostically along with the temperature and moisture fields. Results indicate that errors in the temperature and moisture profiles are lowest when the cloud scheme is allowed to interact with radiation and downdraughts are included in the model. Sensitivity studies have been carried out to understand the role of cloud-top entrainment instability. Results indicate that the cloud-top entrainment instability leads to a reduction of the cloud mass fraction and liquid-water content by 5–10% due to mixing with the environment.

A spectral cumulus ensemble model has been used to determine the impact of lateral detrainment and convective downdraft on the cloud clusters, during, different phases of the, summer monsoon, the model consists of a population of clouds of different sizes which are In different stages of their life cycle. Entrainment and detrainment is considered to take place at all levels, Each cumulus cell has an updraft and a corresponding down draft which originates at a level below the cloud top depending upon the depth of the cloud, Upper, air observations collected from stationary ships forming polygons over the Arabian Sea and the Bay of Bengal during MONEX- 79 have been used for the study

[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.