The evolution and retreat features of the summer monsoon over India

The motivation for this study stems from the fact that there are no uniform criteria to identify the onset of the summer monsoon at a particular location. Furthermore, proper understanding of the events that culminate in the onset of the monsoon is crucial to allow its prediction over various time scales. An attempt is made to elucidate the characteristics of the onset and retreat of the monsoon using a global data assimilation and forecast system. For this purpose, the time series of net tropospheric large-scale budgets of kinetic energy, heat and moisture are examined over the Arabian Sea, Bay of Bengal and some land locations in India. The study makes use of daily data comprising operational analysis (0000 UTC) and forecasts (day1 through day5) produced by the National Centre for Medium Range Weather Forecasting, India, for the summer season of 1994. The sequence of events associated with the advent of the monsoon is noticeably different at various locations. The horizontal fluxes of heat and moisture produce a divergence regime prior to the evolution and change to convergence during the onset of monsoon over the Arabian Sea. Similarly, intense diabatic cooling is noticed prior to the onset of mansoon and changes to heating subsequently. On the other hand, heat and moisture fluxes remain in the convergence regime well before the arrival of the monsoon over the Bay of Bengal. In turn, diabatic heating is noticed prior to the onset here. Onset characteristics at Bombay and Nagpur are similar to those of the Arabian Sea. However, features at Calcutta are identical to those over the Bay of Bengal. Further, the budgets of kinetic energy, heat and moisture depict monotonic decrease at various land locations corresponding to the retreat. Interestingly, the signature of retreat is very similar at the various locations considered here. These changes are observed in the analysis and further corroborated by the model forecasts. Despite the systematic biases, the model captures the essential signature of the onset at various forecast ranges

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

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

Seasonal prediction skill of winter temperature over North India

This document is the Accepted Manuscript version of the following article: Tiwari, P.R., Kar, S.C., Mohanty, U.C. et al. Theor Appl Climatol (2016) 124: 15. The final publication is available at Springer via https://doi.org/10.1007/s00704-015-1397-y. © Springer-Verlag Wien 2015.The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December–January– February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982–2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.Peer reviewedFinal Accepted Versio

Influence of the Planetary Boundary Layer physics on medium-range prediction of monsoon over India

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

Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW model

The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007-11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375km (7-51) for a 12-72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4-10 and 8-24, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13-28 and 5-15 when compared with the 27-and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15-40 over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models

The Effectiveness of a Hip Abduction Orthosis for Perthes Disease

Perthes disease is a rare childhood disorder of femoral head affecting 5-10 per 100,000 children. Prognostic factors remain uncertain after age five which requires careful evaluation of subject, planning for treatment always associated with a slow recovery. Orthoses are provided as a conservative treatment to reduce weight bearing stress across hip joint, maintenance of joint congruity, allowing safe and pain free ambulation for school growing children by keeping the limb in abduction and internal rotation. Though there are different number of orthoses used earlier, the present literature does not provide sufficient evidence to support its use in Perthes disease. We report a case of 8 years old active school going subject with Perthes disease of right hip who was fitted with an ambulatory trilateral hip abduction orthosis and assessed by radiographic examination with satisfactory resul

Seasonal prediction of the Indian summer monsoon rainfall using canonical correlation analysis of the NCMRWF global model products

In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981-2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave-three-out cross-validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981-2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006-2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east- and northeast India as well as many parts of peninsular region especially over west coast of India