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The impact of soil moisture (SM) in the regional climate model RegCM4.6 is investigated for the simulation of Indian summer monsoon seasonal rainfall and its spatiotemporal variability. For this purpose, the model is initialized with soil moisture from five different sources such as the (1) European Space Agency Climate Change Initiative (ESACCI), (2) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), (3) Climate Prediction Center (CPC), (4) ECMWF's reanalysis (ERA) of the twentieth century (ERA-20C), and (5) corrected ERA (CERA). There is a notable discrepancy in the magnitude and distribution of SM among the different sources. In the first stage of the experiment, the model is integrated for two deficit (1982 and 1987), two excess (1983 and 1988), and two normal (1984 and 1989) monsoon seasons to identify the best suitable SM source. Soil moisture tends to have a positive relationship with precipitation and evapotranspiration and a negative relation with sensible heat flux. The CPC SM having a correlation coefficient of 0.6 performs better than the other sources, and it is hence used for long-term simulations of the recent 18 monsoon seasons. We suggest that the initialization of RegCM using CPC data is suitable since the model skill is higher than the other sources of SM. Comprehensive statistical analyses also confirm that the CPC is a better choice over the other sources, and the skill of the model, while using CPC improves over the control experiment. The performance index is higher with the CPC data than the control experiment for a longer-period simulations. The number of phase-synchronizing events based on the standardized anomaly index suggest that the CPC anomaly is aligned in the same direction to the observed rainfall anomaly in 13 of the 18 seasons, whereas the control experiment is aligned for nine of the 18 monsoon seasons. Also, the skill of the model increases from 0.48 with the control experiment to 0.56 with the CPC-initialized experiment.

This study assessed two different vortex initialization (VI) and relocation methods for improved prediction of tropical cyclones (TCs) over the Bay of Bengal (BoB) using the triply nested (27/9/3 km) state-of-the-art Hurricane Weather Research and Forecasting (HWRF) model. The first VI method, “cold-start,” obtained the initial TC vortex from the global analysis. The second one, “cyclic-start,” received the initial vortex from the 6-h forecast of the previous forecast cycle of the same model. In both the strategies, the vortex was corrected to the position, strength, and structure defined by the India Meteorological Department. A total of 32 forecast cases (from five cyclones) over the BoB were considered. The cyclic-start experiments yielded better initial structure and asymmetry as compared to the cold-start experiments. The average statistics indicated that the cyclic initialization improved the 24-h track prediction (by 29%), while the cold initialization was better for the 72-h prediction (by ~ 28%). The intensity was consistently improved in the cyclic-start experiment by up to 68%. The number of cyclic initializations depended on the TC duration. On average, the cyclic initialization improved the representation (strength and size) of the initial vortex up to nine cycles after the first cold start and exhibited an improved skill of 25%; beyond nine cycles, the skill improvement was only 12%. Diagnostic analyses of very severe cyclonic storm (VSCS) Phailin (rapidly intensified) and VSCS Lehar (rapidly weakening) revealed that the cyclic initialization realistically represented equivalent potential temperature, upper-level cloud condensate, and moisture intrusion, which improved the model performance. This study brought out the benefit of the (cyclic) VI for improved TC prediction capabilities in the BoB basin.

In the present study, local insitu observations are utilized in the Land Data Assimilation System (LDAS) to generate high-resolution (4 km and hourly) soil moisture (SM) and soil temperature (ST) data over India. Further, the impact of the LDAS-derived SM and ST initialization on simulation of pre-monsoon (March–May) thunderstorms over the Gangetic West Bengal region are assessed. The high-resolution (4 km and hourly) land surface conditions such as SM and ST data are prepared for the period from 2010 to 2013 using the LDAS forced with various insitu observations from Automatic Weather Stations (AWS), Meteorological Aviation Reports (METAR), and micro-meteorological tower observations over India. Four thunderstorm (TS) events during the pre-monsoon season of 2010 are considered for the numerical experiments using climatological SM and ST at coarser resolution (CNTR) and LDAS-generated high-resolution SM and ST (WLDAS) for initializing the Weather Research and Forecasting (WRF) Model. The efficacy of the LDAS-generated SM and ST data are verified against micro-meteorological tower observations at Kharagpur, West Bengal, and the results indicate the magnitude and variation in the data product are close to observations. The initialization of high-resolution LDAS-generated SM and ST in the WRF improves the simulation of near-surface air temperature, humidity, and pressure at Kharagapur. Most importantly, the location and timing of the storm are relatively better simulated in the WLDAS than CNTR over the Kolkata region. The study encourages the use of more local observations in the generation of high-resolution SM and ST data for application in the simulation of pre-monsoon TSs.

The increased temperature and humidity in the atmosphere under global warming is the primary cause of the upsurge of heat waves in the tropical belt. The central east coast of India (CECI; Odisha, Andhra Pradesh, and Telangana) is one of the most heavily affected areas in terms of casualties due to heat waves during pre-monsoon (March–May; MAM). Thus, there is a need to analyze the characteristics of pre-monsoon weekly maximum temperature (Tmax) and associated heat waves over the CECI. In the present study, characteristics of weekly Tmax from 23 March to 31 May over the CECI associated with heat waves have been analyzed using the India Meteorological Department gridded (1o × 1o) analysis data set of daily maximum temperature for the period 1980–2015. The recent changes in the weekly Tmax and frequency of various heat-wave spells (1-, 2-, 3-, and 5-day) were also evaluated. The results suggest that the climatological weekly Tmax along the coastal region is less than that in the interior parts for all 10 weeks, and the inter-annual variability and coefficient of variation exhibit similar patterns. The continuous increase in Tmax and its variability is observed as the season progresses, leading to increased intensity and frequency of heat waves in most parts of the CECI. In the recent period, a notable increase in the weekly Tmax and its variability has been observed over most parts of the CECI that has resulted in more heat waves. This study is very beneficial for determining the effects on various sectors for the planning of adaptation methodologies through appropriate strategies for a tolerable future over the CECI in the context of global warming.

The present study evaluates the skill of a High-Resolution Land Data Assimilation System (HRLDAS) in simulating soil moisture (SM), soil temperature (ST) and sensible heat flux (SHF) for the Indian region (5°–39°N; 60°–100°E). The HRLDAS framework uses uncoupled Noah Land Surface Model (LSM) that integrates near-surface atmospheric parameters and land surface parameters from observations and analysis for the period January 2001–October 2013 at 20 km spatial resolution. The HRLDAS takes about 1 year to reach its quasi-equilibrium state for clay soil. The HRLDAS simulated ST and SM reasonably agree with the in situ observations. The simulated ST shows a negative bias in the monsoon season over the Gujarat, Mandla, and Kharagpur. The SM is under-estimated and the under-estimation increases with soil depth at Kharagpur, India. The negative bias in TRMM precipitation forcing causes under-estimation of SM. The simulated SM shown higher saturation point than observations. The daytime SHF has positive bias during the pre-monsoon, monsoon seasons and agrees well with observations in the post-monsoon season at Ranchi, India. The Noah 1D sensitivity experiments revealed that there is a need to revisit soil field capacity and porosity parameter for improving the skill of the HRLDAS.

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill.

The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .

Three land surface schemes available in the regional climate model RegCM4 have been examined to understand the coupling between land and atmosphere for simulation of the Indian summer monsoon rainfall. The RegCM4 is coupled with biosphere–atmosphere transfer scheme (BATS) and the National Center for Atmospheric Research (NCAR) Community Land Model versions 3.5, and 4.5 (CLM3.5 and CLM4.5, respectively) and model performance is evaluated for recent drought (2009) and normal (2011) monsoon years. The CLM4.5 has a more distinct category of surface and it is capable of representing better the land surface characteristics. National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) reanalysis version 2 (NNRP2) datasets are considered as driving force to conduct the experiments for the Indian monsoon region (30°E–120°E; 30°S–50°N). The NNRP2 and India Meteorological Department (IMD) gridded precipitation data are used for verification analysis. The results indicate that RegCM4 simulations with CLM4.5 (RegCM4–CLM4.5) and CLM3.5 (RegCM4–CLM3.5) surface temperature (at 2 ms) have very low warm biases (~1 °C), while with BATS (RegCM4–BATS) has a cold bias of about 1–3 °C in peninsular India and some parts of central India. Warm bias in the RegCM4–BATS is observed over the Indo-Gangetic plain and northwest India and the bias is more for the deficit year as compared to the normal year. However, the warm (cold) bias is less in RegCM4–CLM4.5 than other schemes for both the deficit and normal years. The model-simulated maximum (minimum) surface temperature and sensible heat flux at the surface are positively (negatively) biased in all the schemes; however, the bias is higher in RegCM4–BATS and lower in RegCM4–CLM4.5 over India. All the land surface schemes overestimated the precipitation in peninsular India and underestimated in central parts of India for both the years; however, the biases are less in RegCM4–CLM4.5 and more in RegCM4–CLM3.5 and RegCM4–BATS. During both the years, BATS scheme in RegCM4 failed to represent low precipitation over the leeward than windward side of the Western Ghats, while CLM schemes (both versions) in the RegCM4 are able to depict this feature. In the topographic regions, such as the Western Ghats, northeast India and state of Jammu and Kashmir, RegCM4–BATS overestimates the rainfall amount with higher bias. Statistical analysis using anomaly correlation coefficient, root mean square error, equitable threat score, and critical success index confirms that RegCM4–CLM performs better than RegCM4–BATS in the simulation of the Indian summer monsoon.

The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model’s precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP–NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model’s inability to simulate the observed remote response of sea surface temperatures to all India winter precipitation. Of the two, CFSv1 is appreciable in capturing year-to-year variations in observed winter precipitation while CFSv2 failed in simulating the same. CFSv1 has accounted for less mean bias and RMSE errors along with good correlations and index of agreements than CFSv2 for predicting winter precipitation over India. In addition, the CFSv1 is also having a high probability of detection in predicting different categories (normal, excess and deficit) of observed winter precipitation over India.

This document is the Accepted Manuscript version of the following article: Tiwari, P.R., Kar, S.C., Mohanty, U.C. et al. Pure Appl. Geophys. (2016) 173: 657. https://doi.org/10.1007/s00024-015-1102-1. The final publication is available at Springer via: http://dx.doi.org/10.1007/s00024-015-1102-1. © Springer Basel 2015.

The present study examines the performance of convective parameterization schemes at two different horizontal resolutions (90 and 30 km) in simulating winter (December–February; DJF) circulation and associated precipitation over the Western Himalayas using the regional climate model RegCM4. The model integrations are carried out in a one-way nested mode for three distinct precipitation years (excess, normal and deficit) using four combinations of cumulus schemes. The National Center for Environment Prediction—Department of Energy Reanalysis-2 project utilized gridded data, observed precipitation data from the India Meteorological Department and station data from the Snow and Avalanche Study Establishment were used to evaluate model performance. The seasonal mean circulation patterns and precipitation distribution are well demonstrated by all of the cumulus convection schemes. However, model performance varies using different schemes. Statistical analysis confirms that the root mean square error is reduced by about 2–3 times and the correlation coefficient (CC) increases in the fine resolution (30 km) simulations compared to coarse resolution (90 km) simulations. A statistically significant CC (at a 10 % significance level) is found only in the fine resolution simulations. The Grell cumulus model with a Fritsch–Chappell closure (Grell-FC) is better at simulating seasonal mean patterns and inter-annual variability of two contrasting winter seasons than the other scheme combinations.

There is a statistical linkage between tropical outgoing longwave radiation (OLR) and all-India summer monsoon rainfall (AISMR). A positive and significant correlation is observed over the surrounding areas of northeast Australia and the Arafura Sea during the months of January and February (J&F) which drops down as the lead month decreases. The OLR index as an area average over the surrounding areas of northeast Australia and the Arafura Sea is found to have 0.4 correlation with AISMR. The index is also found to be strongly correlated with the Indian monsoon index. In view of the teleconnection pattern, the OLR index is used for the development of statistical models using the concept of linear regression (LR) and canonical correlation analysis (CCA). Potential of CCA over LR is observed for the prediction of seasonal rainfall over the northwest, west central and over the whole country as well. The seasonal rainfall predictability basically comes from the months of June and September.

In this study, sensitivity of the Indian summer monsoon simulation to the Himalayan orography representation in a regional climate model (RegCM) is examined. The prescribed height of the Himalayan orography is less in the RegCM model than the actual height of the Himalayas. Therefore, in order to understand the impact of the Himalayan orography representation on the Indian summer monsoon, the height of the Himalayan orography is increased (decreased) by 10 % from its control height in the RegCM model. Three distinct monsoon years such as deficit (1987), excess (1988) and normal rainfall years are considered for this study. The performance of the RegCM model is tested with the use of a driving force from the reanalysis data and a global model output. IMD gridded rainfall and the reanalysis-2 data are used as verification analysis to validate the model results. The RegCM model has the potential to represent mean rainfall distribution over India as well as the upper air circulation patterns and some of the semi-permanent features during the Indian summer monsoon season. The skill of RegCM is reasonable in representing the variation in circulation and precipitation pattern and intensity during two contrasting rainfall years. The simulated seasonal mean rainfall over many parts of India especially, the foothills of the Himalaya, west coast of India and over the north east India along with the whole of India are more when the orography height is increased. The low level southwesterly wind including the Somali jet stream as well as upper air circulation associated with the tropical easterly jet stream become stronger with the enhancement of the Himalayan orography. Statistical analysis suggests that the distribution and intensity of rainfall is represented better with the increased orography of RegCM by 10 % from its control height. Thus, representation of the Himalayan orography in the model is close to actual and may enhance the skill in seasonal scale simulation of the Indian summer monsoon.

An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track.

In this study the predictability of northeast monsoon (Oct–Nov–Dec) rainfall over peninsular India by eight general circulation model (GCM) outputs was analyzed. These GCM outputs (forecasts for the whole season issued in September) were compared with high-resolution observed gridded rainfall data obtained from the India Meteorological Department for the period 1982–2010. Rainfall, interannual variability (IAV), correlation coefficients, and index of agreement were examined for the outputs of eight GCMs and compared with observation. It was found that the models are able to reproduce rainfall and IAV to different extents. The predictive power of GCMs was also judged by determining the signal-to-noise ratio and the external error variance; it was noted that the predictive power of the models was usually very low. To examine dominant modes of interannual variability, empirical orthogonal function (EOF) analysis was also conducted. EOF analysis of the models revealed they were capable of representing the observed precipitation variability to some extent. The teleconnection between the sea surface temperature (SST) and northeast monsoon rainfall was also investigated and results suggest that during OND the SST over the equatorial Indian Ocean, the Bay of Bengal, the central Pacific Ocean (over Nino3 region), and the north and south Atlantic Ocean enhances northeast monsoon rainfall. This observed phenomenon is only predicted by the CCM3v6 model.

The Regional Integrated Multi-Hazard Early Warning System (RIMES), an international, intergovernmental organization based in Thailand is engaged in disaster risk reduction over the Asia–Pacific region through early warning information. In this paper, RIMES’ customized Weather Research Forecast (WRF) model has been used to evaluate the simulations of cyclone Nargis which hit Myanmar on 2 May 2008, the most deadly severe weather event in the history of Myanmar. The model covers a domain of 35oE to 145oE in the east—west direction and 12oS to 40oN in the north—south direction in order to cover Asia and east Africa with a resolution of 9 km in the horizontal and 28 vertical levels. The initial and boundary conditions for the simulations were provided by the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1o lon/lat resolution. An attempt is being made to critically evaluate the simulation of cyclone Nargis by seven set of simulations in terms of track, intensity and landfall time of the cyclone. The seven sets of model simulations were initialized every 12 h starting from 0000 UTC 28 April to 01 May 2008. Tropical Rainfall Measurement Mission (TRMM) precipitation (mm) is used to evaluate the performance of the simulations of heavy rainfall associated with the tropical cyclone. The track and intensity of the simulated cyclone are compared by making use of Joint Typhoon Warning Center (JTWC) data sets. The results indicate that the landfall time, the distribution and intensity of the rainfall, pressure and wind field are well simulated as compared with the JTWC estimates. The average landfall track error for all seven simulations was 64 km with an average time error of about 5 h. The average intensity error of central pressure in all the simulations were found out to be approximately 6 hPa more than the JTWC estimates and in the case of wind, the simulations under predicted it by an average of 12 m s−1.

An attempt is made to evaluate the impact of the three dimensional variational (3DVAR) data assimilation within the Weather Research Forecasting (WRF) modeling system to simulate two heavy rainfall events which occured on 26–27 July 2005 and 27–30 July 2006. During the 26–27 July 2005 event, the unprecedented localized intense rainfall 90–100 cm was recorded over the northeast parts of Mumbai city; however, southern parts received only 10 cm. Model simulation with the data assimilation experiment is reasonably well predicted for the rainfall intensity (800 mm) in 24 h and with accurate location over Mumbai agreeing with observation. Divergence, vorticity, vertical velocity and moisture parameters are evaluated during the various stages of the event. It is noticed that maximum convergence and vorticity during the mature stage; at the same time the vertical velocity also follows a similar trend during the period in the assimilation experiment. Vorticity budget terms over the location of heavy rainfall revealed that the contribution of the positive tilting term produced positive vorticity which triggered the convection and negative contribution to vorticity from the tilting term to precede the dissipation of the system. Model simulations from the second rain event, the off-shore trough at sea level along the west coast of India, is well represented after assimilation of observations during day-1 and day-2 as compared to the control simulations; the orientation of the off-shore trough is well matched with that of the observed. The intensity and spatial distribution of the rainfall has considerably improved in the assimilation simulation. The statistical skill scores also revealed that the precipitation forecast during the period has appreciably improved due to assimilation of observations. The results of this study indicate a positive impact of the 3DVAR assimilation on the simulation of heavy rainfall events.

Orissa State, a meteorological subdivision of India, lies on the east coast of India close to north Bay of Bengal and to the south of the normal position of the monsoon trough. The monsoon disturbances such as depressions and cyclonic storms mostly develop to the north of 15° N over the Bay of Bengal and move along the monsoon trough. As Orissa lies in the southwest sector of such disturbances, it experiences very heavy rainfall due to the interaction of these systems with mesoscale convection sometimes leading to flood. The orography due to the Eastern Ghat and other hill peaks in Orissa and environs play a significant role in this interaction. The objective of this study is to develop an objective statistical model to predict the occurrence and quantity of precipitation during the next 24 hours over specific locations of Orissa, due to monsoon disturbances over north Bay and adjoining west central Bay of Bengal based on observations to up 0300 UTC of the day. A probability of precipitation (PoP) model has been developed by applying forward stepwise regression with available surface and upper air meteorological parameters observed in and around Orissa in association with monsoon disturbances during the summer monsoon season (June-September). The PoP forecast has been converted into the deterministic occurrence/non-occurrence of precipitation forecast using the critical value of PoP. The parameters selected through stepwise regression have been considered to develop quantitative precipitation forecast (QPF) model using multiple discriminant analysis (MDA) for categorical prediction of precipitation in different ranges such as 0.1–10, 11–25, 26–50, 51–100 and >100 mm if the occurrence of precipitation is predicted by PoP model. All the above models have been developed based on data of summer monsoon seasons of 1980–1994, and data during 1995–1998 have been used for testing the skill of the models. Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 96% and 88%, respectively for developmental and independent data. The skill of the QPF model, though relatively less, is reasonable for lower ranges of precipitation. The skill of the model is limited for higher ranges of precipitation.

A continuing goal in the diagnostic studies of the atmospheric general circulation is to estimate various quantities that cannot be directly observed. Evaluation of all the dynamical terms in the budget equations for kinetic energy, vorticity, heat and moisture provide estimates of kinetic energy and vorticity generation, diabatic heating and source/sinks of moisture. All these are important forcing factors to the climate system. In this paper, diagnostic aspects of the dynamics and energetics of the Asian summer monsoon and its spatial variability in terms of contrasting features of surplus and deficient summer monsoon seasons over India are studied with reanalysis data sets. The daily reanalysis data sets from the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) are used for a fifty-two year (1948–1999) period to investigate the large-scale budget of kinetic energy, vorticity, heat and moisture. The primary objectives of the study are to comprehend the climate diagnostics of the Asian summer monsoon and the role of equatorial convection of the summer monsoon activity over India.It is observed that the entrance/exit regions of the Tropical Easterly Jet (TEJ) are characterized by the production/destruction of the kinetic energy, which is essential to maintain outflow/inflow prevailing at the respective location of the TEJ. Both zonal and meridional components contribute to the production of kinetic energy over the monsoon domain, though the significant contribution to the adiabatic generation of kinetic energy originates from the meridional component over the Bay of Bengal in the upper level and over the Somali Coast in the low level. The results indicate that the entire Indian peninsula including the Bay of Bengal is quite unstable during the summer monsoon associated with the production of vorticity within the domain itself and maintain the circulation. The summer monsoon evinces strong convergence of heat and moisture over the monsoon domain. Also, considerable heat energy is generated through the action of the adiabatic process. The combined effect of these processes leads to the formation of a strong diabatic heat source in the region to maintain the monsoon circulation. The interesting aspect noted in this study is that the large-scale budgets of heat and moisture indicate excess magnitudes over the Arabian Sea and the western equatorial Indian Ocean during surplus monsoon. On the other hand, the east equatorial Indian Ocean and the Bay of Bengal region show stronger activity during deficient monsoon. This is reflected in various budget terms considered in this study.

— The hydrostatic Naval Research Laboratory/North Carolina State University (NRL/NCSU) model was used to study the mesoscale dynamics and diurnal variability of the Intertropical Convergence Zone (ITCZ) over the Indian Ocean in the short-range period. To achieve this objective the initial conditions from two northeast monsoon episodes (29 January, 1997 and 29 January, 1998) were run for 48-hour simulations using a triple-nested grid version of the model with 1.5° × 1.5°, 0.5° × 0.5° and 0.17° × 0.17° resolutions. The 1997 case represents a typical northeast monsoon episode, while the 1998 case depicts an abnormal monsoon episode during an El Nino event.¶Comparisons between the model-produced and analyzed mean circulation, wind speed, and associated rainfall for different spatial scales are presented. During the active northeast monsoon season in 1997, the major low-level westerly winds and associated high rainfall rates between 0° and 15°S were simulated reasonably well up to 24 hours. During the 1998 El Nino event, the model was capable of simulating weak anomalous easterly winds (between 0° and 15°S) with much lower rainfall rates up to 48 hours. In both simulations, the finest grid size resulted in largest rainfall rates consistent with Outgoing Longwave Radiation data.¶The model performance was further evaluated using the vertical profiles of the vertical velocity, the specific humidity and temperature differences between the model outputs and the analyses. It is found that during a typical northeast monsoon year, 1997, the water vapor content in the middle troposphere was largely controlled by the low-level convergence determined by strong oceanic heat flux gradient. In contrast, during the 1998 El Nino year moisture was present only in the lower troposphere. Due to strong subsidence associated with Walker circulation over the central and eastern Indian Ocean, deep convection was not present. Finally, the diurnal variations of the maximum rainfall, vertical velocity and total heat flux were noticeable only during the 1997 northeast monsoon year.

The present study emphasizes the importance of proper representation of boundary layer physics in a general circulation model. The Turbulent Kinetic Energy (TKE) closure scheme incorporates important processes of the Planetary Boundary Layer (PBL) compared to a simplistic first-order closure model. Hence the model which has the TKE closure scheme is capable of simulating important weather systems associated with summer monsoon, such as monsoon depressions and lows that form over the Indian subcontinent quite well compared to the first-order closure model. The present study indicates better performance of the global model with the TKE scheme in the prediction of the monsoon circulation, including the tracks of the depressions over the Indian subcontinent. Medium-range weather prediction has also improved with the use of the TKE closure. However further studies are necessary to improve the forecast, with emphasis on boundary layer processes.

—The thermodynamic characteristics of the Asian summer monsoon are examined with a global analysis-forecast system. In this study, we investigated the large-scale balances of heat and moisture by making use of operational analyses as well as forecast fields for June, July and August (JJA), 1994. Apart from elucidating systematic errors in the temperature and moisture fields, the study expounds the influence of these errors on the large-scale budgets of heat and moisture over the monsoon region. The temperature forecasts of the model delineate predominant cooling in the middle and lower tropospheres over the monsoon region. Similarly, the moisture forecasts evince a drying tendency in the lower troposphere. However, certain sectors of moderate moistening exist over the peninsular India and adjoining oceanic sectors of the Arabian Sea and Bay of Bengal.¶The broad features of the large-scale heat and moisture budgets represented by the analysis/forecast fields indicate good agreement with the observed aspects of the summer monsoon circulation. The model forecasts fail to retain the analyzed atmospheric variability in terms of the mean circulation, which is indicated by underestimation of various terms of heat and moisture budgets with an increase in the forecast period. Further, the forecasts depict an anomalous diabatic cooling layer in the lower middle troposphere of the monsoon region which inhibits vertical transfer of heat and moisture from the mixed layer of the atmospheric boundary layer to the middle troposphere. In effect, the monsoon circulation is considerably weakened with an increase in the forecast period. The treatment of shallow convection and the use of interactive clouds in the model can reduce the cooling bias considerably.

The influence of soil moisture and vegetation variation on simulation of monsoon circulation and rainfall is investigated. For this purpose a simple land surface parameterization scheme is incorporated in a three-dimensional regional high resolution nested grid atmospheric model. Based on the land surface parameterization scheme, latent heat and sensible heat fluxes are explicitly estimated over the entire domain of the model. Two sensitivity studies are conducted; one with bare dry soil conditions (no latent heat flux from land surface) and the other with realistic representation of the land surface parameters such as soil moisture, vegetation cover and landuse patterns in the numerical simulation. The sensitivity of main monsoon features such as Somali jet, monsoon trough and tropical easterly jet to land surface processes are discussed. Results suggest the necessity of including a detailed land surface parameterization in the realistic short-range weather numerical predictions. An enhanced short-range prediction of hydrological cycle including precipitation was produced by the model, with land surface processes parameterized. This parameterization appears to simulate all the main circulation features associated with the summer monsoon in a realistic manner.