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We propose here a novel method of analysing turbulent momentum flux signals. The data for the analysis come from a nearly neutral atmospheric boundary layer and are taken at a height of 4m above ground corresponding to 1.1 x 10(5) wall units, within the log layer for the mean velocity. The method of analysis involves examining the instantaneous flux profiles that exceed a given threshold, for which an optimum value is found to be 1 s.d. of the flux signal. It is found feasible to identify normalized flux variation signatures separately for positive and negative 'flux events'-the sign being determined by that of the flux itself. Using these signatures, the flux signal is transformed to one of events characterized by the time of occurrence, duration and intensity. It is also found that both the average duration and the average time-interval between successive events are of order 1s, about four orders of magnitude higher than a wall unit in time. This episodic description of the turbulence flux in the time domain enables us to identify separately productive, counter-productive and idle periods (accounting, respectively, for 36, 15 and 49% of the time), taking as criterion the generation of the momentum flux. A 'burstiness' index of 0.72 is found for the data. Comparison with laboratory data indicates higher (/lower) ejection (/sweep) quadrant occupancy but lower (/higher) contributions to flux from the ejection (/sweep) quadrant at the high Reynolds numbers of the atmospheric boundary layer. Possible connections with the concept of active and passive motion in a turbulent boundary layer are briefly discussed.

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

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