POST-MONSOON SEA SURFACE TEMPERATURE AND CONVECTION ANOMALIES OVER INDIAN AND PACIFIC OCEANS

We have studied sea surface temperature (SST) anomalies over the Indian and Pacific Oceans (domain 25 °S to 25°N and 40 °E to 160 °W) during the three seasons following the Indian summer monsoon for wet monsoons and also for dry monsoons accompanied or not by El Ni˜no. A dry monsoon is followed by positive SST anomalies in the longitude belt 40 to 120 °E, negative anomalies in 120 to 160 °E and again positive anomalies east of 160 °E. In dry monsoons accompanied by El Ni˜no the anomalies have the same sign, but are much stronger. Wet monsoons have weak anomalies of opposite sign in all three of the longitude belts. Thus El Ni˜no and a dry monsoon have the same types of association with the Indian and Pacific Ocean SSTs. In the sector 40 to 120 °E SST anomalies first appear over the western part of the Indian Ocean (June to September) followed by the same sign of anomalies over its eastern part and China Sea (October to March). By March after a dry monsoon or El Ni˜no the Indian Ocean between 10 °N and 10 °S has a spatially large warm SST anomaly. Anomalies in deep convection tend to follow the SST anomalies, with warm SST anomalies producing positive convection anomalies around the seasonal location of the intertropical convergence zone

AN INVESTIGATION ON AIR-SEA INTERACTION IN THE INDIAN OCEAN DURING CONTRASTING MONSOONS

The study is undertaken with an objective to investigate the linkage between air-sea fluxes in the Indian Ocean and monsoon forcing. Since the monsoon activity is linked to fluxes, the variability of surface marine meteorological fields under the variable monsoon conditions is also studied. The very objective of the present study is to document various sea surface parameters of the Indian Ocean and to examine the anomalies found in them. Hence it is attempted to relate the anomaly to the variability of monsoon over India, highlighting the occasion of contrasting monsoon periods. The analysis of anomalies of surface meteorological fields such as SST, wind speed and direction, sea level pressure and cloud cover for contrasting monsoons are also studied. During good monsoon years, the pressure anomalies are negative indicating a fall in SLP during pre-monsoon and monsoon months. The interaction of the marine atmosphere with tropical Indian Ocean and its influence on ISMR continue to be an area of active research.

Characteristics of SST over the Arabian Sea and Bay of Bengal

Evolution of mini warm pool in the Arabian Sea just before the onset of southwest monsoon and behavior of SST in the vicinity of weather systems formed during the premonsoon, southwest monsoon and post monsoon seasons were studied using TMI SST data. The Arabian Sea mini warm pool is formed about three weeks ahead of onset of southwest monsoon. Maximum SST is found about one week ahead of monsoon onset and then the warm pool gradually dissipated. Generally, a low-pressure system is formed when the SST exceeds a certain threshold value for the formation of the system. Daily SST values are examined both in Arabian sea and Bay of Bengal to bring out the quantity of increase in SST just before the formation of the system, quantity of rapid decrease in SST during the formation of the system and the number of days required for returning to normal SST. Many cases were examined for pre-monsoon, southwest monsoon and post monsoon seasons to understand the behavior of SST pattern. It is found that the SST increases about 3° C just before the formation of the system and decreases about 4° C during the formation within 2 to 3 days and takes about 4 to 6 days to return to normal SST pattern. However, the SST pattern depends on the weather system

Anomaly Detection Using System Call Sequence Sets.

This paper discusses our research in developing a generalized and systematic method for anomaly detection. The key ideas are to represent normal program behaviour using system call frequencies and to incorporate probabilistic techniques for classification to detect anomalies and intrusions. Using experiments on the sendmail system call data, we demonstrate that concise and accurate classifiers can be constructed to detect anomalies. An overview of the approach that we have implemented is provided.