Variability of Convective Activity over the North Indian Ocean and its Associations with Monsoon Rainfall over India

The present study is an attempt to examine the variability of convective activity over the north Indian Ocean (Bay of Bengal and Arabian Sea) on interannual and longer time scale and its association with the rainfall activity over the four different homogeneous regions of India (viz., northeast India, northwest India, central India and south peninsular India) during the monsoon season from June to September (JJAS) for the 26 year period (1979 to 2004). The monthly mean Outgoing Long-wave Radiation (OLR) data obtained from National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft are used in this study and the 26-year period has been divided into two periods of 13 years each with period-i from 1979 to 1991 and period -ii from 1992 to 2004. It is ascertained that the convective activity increases over the Arabian Sea and the Bay of Bengal in the recent period (period -ii; 1992 to 2004) compared to that of the former period (period -i; 1979 to 1991) during JJAS and is associated with a significantly increasing trend (at 95% level) of convective activity over the north Bay of Bengal (NBAY). On a monthly scale, July and August also show increase in convective activity over the Arabian Sea and the Bay of Bengal during the recent period and this is associated with slight changes in the monsoon activity cycle over India. The increase in convective activity particularly over the Arabian Sea during the recent period of June is basically associated with about three days early onset of the monsoon over Delhi and relatively faster progress of the monsoon northward from the southern tip of India. Over the homogeneous regions of India the correlation coefficient (CC) of OLR anomalies over the south Arabian Sea (SARA) is highly significant with the rainfall over central India, south peninsular India and northwest India, and for the north Arabian Sea (NARA), it is significant with northwest India rainfall and south peninsular rainfall. Similarly, the OLR anomalies over the south Bay of Bengal (SBAY) have significant CC with northwest India and south peninsular rainfall, whereas the most active convective region of the NBAY is not significantly correlated with rainfall over India. It is also found that the region over northeastern parts of India and its surroundings has a negative correlation with the OLR anomalies over the NARA and is associated with an anomalous sinking (rising) motion over the northeastern parts of India during the years of increase (decrease) of convective activity over the NARA.     

Cosmological mass model in general relativity

Relativistic cosmological field equations are obtained for a non-static stationary Bertotti-Robinson-type space-time for interacting perfect fluid and electromagnetic field. The cosmological solution to the field equations are obtained and the nature of the electromagnetic field as well the perfect fluid are studied. The electromagnetic field generated here corresponds to a special generic case and the perfect fluid distribution degenerates into a barotropic perfect fluid with equation of statep+=0, >0. It is shown here that the interacting barotropic fluid can generate gravitation only when the cosmological constant being a function ofx in a dynamic field.     

Simulation of maximum surface air temperature over India using the UK Met Office Global Seasonal (GloSea) Model

Summary A comparative study was performed to evaluate the performance of the UK Met Office’s Global Seasonal (GloSea) prediction General Circulation Model (GCM) for the forecast of maximum surface air temperature (T_max) over the Indian region using the model generated hindcast of 15-members ensemble for 16 years (1987–2002). Each hindcast starts from 1^st January and extends for a period of six months in each year. The model hindcast T_max is compared with T_max obtained from verification analysis during the hot weather season on monthly and seasonal scales from March to June. The monthly and seasonal model hindcast climatology of T_max from 240 members during March to June and the corresponding observed climatology show highly significant (above 99.9% level) correlation coefficients (CC) although the hindcast T_max is over-estimated (warm bias) over most parts of the Indian region. At the station level over New Delhi, although the forecast error (forecast-observed) at the monthly scale gradually increases from March to June, the forecast error at the seasonal scale during March to May (MAM) is found to be just 1.67 °C. The GloSea model also simulates well T_max anomalies on monthly and seasonal scales during March to June with the lower Root Mean Square Error (RMSE) of bias corrected forecast (less than 1.2 °C), which is much less than the corresponding RMSE of climatology (reference) forecast. The anomaly CCs (ACCs) over the station in New Delhi are also highly significant (above 95% level) on monthly to seasonal time scales from March to June, except for April. The skill of the GloSea model for the seasonal forecast of T_max as measured from the ACC map and the bias corrected RMSE map is reasonably good during MAM and April to June (AMJ) with higher ACC (significant at 95% level) and lower RMSE (less than 1.5 °C) found over many parts of the Indian regions. Authors’ addresses: D. R. Pattanaik, H. R. Hatwar, G. Srinivasan, Y. V. Ramarao, India Meteorological Department (IMD), New Delhi, India; U. C. Mohanty, P. Sinha, Centre for Atmospheric Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India; Anca Brookshaw, UK Met Office, UK.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

List of forthcoming papers

List of forthcoming papers     

Analysis of Rainfall Over Different Homogeneous Regions of India in Relation to Variability in Westward Movement Frequency of Monsoon Depressions

Between 1941 and 2002 there has been a decreasing trend in the frequency of monsoon disturbances (MDs) during the summer monsoon season (June–September). This downwards trend is significant at the 99.9% level for the main monsoon phase (July–August) and the withdrawal phase (September); however, it is not significant during the onset phase (June). The variability in rainfall over the homogeneous regions of India on the sub-seasonal scale also shows a significant decreasing trend with respect to the amount of rainfall over Northwest India (NWI) and Central India (CEI) during all three phases of the monsoon. Meteorological observations reveal that there has been an eastward shift of the rainfall belt with time over the Indian region on the seasonal scale and that this shift is more prominent during the withdrawal phase. This decreasing trend in MDs together with its restricted westerly movement seem to be directly related to the decreasing trend in rainfall over CEI during both the main monsoon and withdrawal phases and over NWI during the withdrawal phase. The low-level circulation anomalies observed during two periods (period-I: 1951–1976; period-ii: 1977–2002) are in accordance with the changes in rainfall distribution, with comparatively more (less) rainfall falling over NWI, CEI and Southern Peninsular India (SPI) during period-I (period-ii), and are accompanied by a stronger (weaker) monsoon circulation embedded with an anomalous cyclonic (anti-cyclonic) circulation over CEI during the main monsoon and withdrawal phases. During the onset phase, completely opposite circulation anomalies are observed during both periods, and these are associated with more (less) rainfall over NWI, CEI and SPI during period-ii (period-I).     

Fluctuations of Tropical Easterly Jet during contrasting monsoons over India: A GCM study

Summary  The fluctuations of intensity of the Tropical Easterly Jet (TEJ) and its association with the Indian summer monsoon rainfall have been examined using the diagnostics from NCEP/NCAR (National Centre for Environmental Prediction/National Centre for Atmospheric Research) reanalyses project for the period 1986 to 1994. The intensity of TEJ is found to be well correlated with India summer monsoon rainfall. The TEJ is weaker/stronger during the El Ni?o/La Ni?a year of 1987/1988 and is associated with deficient (excess) summer monsoon rainfall over India. A numerical study was carried out for the same period using the Centre for Ocean-Land-Atmosphere studies General Circulation Model (COLA GCM, T30L18) with observed Sea-Surface Temperature (SST). The GCM simulates the TEJ with reasonable accuracy. The strong interannual variability of TEJ during the El Ni?o/La Ni?a years of 1987/1988 are well simulated in the GCM. Like observations, the intensity of the TEJ is positively correlated with the summer monsoon rainfall over India in the model simulation. The intensity of Tibetan anticyclone and diabatic heating over the Tibetan Plateau diminished during the El Ni?o-year of 1987. The divergence centre in the upper troposphere associated with Asian monsoon becomes weaker and shifts eastward during the weak monsoon season of 1987. However, the opposite happens for the strong monsoon season of 1988. Also the middle and upper tropospheric meridional temperature gradient between the Tibetan High and Indian Ocean region decreased (increased) during the weak(strong) monsoon season of 1987 (1988). Received May 27, 1999/Revised March 20, 2000     

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.     

Assessment of spatial and temporal dynamics of tropical forest cover: A case study in Malkangiri district of Orissa, India

Tropical forests have been recognized as having global conservation importance. However, they are being rapidly destroyed in many regions of the world. Regular monitoring of forests is necessary for an adaptive management approach and the successful implementation of ecosystem management. The present study analyses the temporal changes in forest ecosystem structure in tribal dominated Malkangiri district of Orissa, India, during 1973–2004 period based on digitized forest cover maps using geographic information system (GIS) and interpretation of satellite data. Three satellite images Landsat MSS (1973), Landsat TM (1990) and IRS P6 LISS III (2004) were used to determine changes. Six land cover types were delineated which includes dense forest, open forest, scrub land, agriculture, barren land and water body. Different forest types were also demarcated within forest class for better understanding the degradation pattern in each forest types. The results showed that there was a net decrease of 475.7 km2 forest cover (rate of deforestation = 2.34) from 1973 to 1990 and 402.3 km2 (rate of deforestation = 2.27) from 1990 to 2004. Forest cover has changed over time depending on a few factors such as large-scale deforestation, shifting cultivation, dam and road construction, unregulated management actions, and social pressure. A significant increase of 1222.8 km2 agriculture area (1973–2004) clearly indicated the conversion of forest cover to agricultural land. These alterations had resulted in significant environmental consequences, including decline in forest cover, soil erosion, and loss of biodiversity. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. Particular attention must be paid to tropical forests, which are rapidly being deforested.