The evolution and retreat features of the summer monsoon over India

Abstract

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.