K. B. R. R. Hari Prasad

Formulation of stability-dependent empirical relations for turbulent intensities from surface layer turbulence measurements for dispersion parameterization in a lagrangian particle dispersion model

Season- and stability-dependent turbulence intensity (σu/u*, σv/u*, σw/u*) relationships are derived from experimental turbulence measurements following surface layer scaling and local stability at the tropical coastal site Kalpakkam, India for atmospheric dispersion parameterization. Turbulence wind components (u′, v′, w′) measured with fast response UltraSonic Anemometers during an intense observation campaign for wind field modeling called Round Robin Exercise are used to formulate the flux–profile relationships using surface layer similarity theory and Fast Fourier Transform technique. The new relationships (modified Hanna scheme) are incorporated in a Lagrangian Particle Dispersion model FLEXPART-WRF and tested by conducting simulations for a field tracer dispersion experiment at Kalpakkam. Plume dispersion analysis of a ground level hypothetical release indicated that the new turbulent intensity formulations provide slightly higher diffusivity across the plume relative to the original Hanna scheme. The new formulations for σu, σv, σw are found to give better agreement with observed turbulent intensities during both stable and unstable conditions under various seasonal meteorological conditions. The simulated concentrations using the two methods are compared with those obtained from a classical Gaussian model and the observed SF6 concentration. It has been found that the new relationships provide comparatively higher diffusion across the plume relative to the model default Hanna scheme and provide downwind concentration results in better agreement with observations.

Impact of Variational Data Assimilation for Simulating Tropical Cyclones over Bay of Bengal Using WRF-ARW

Tropical cyclones, one of the most destructive of all the natural disasters, are capable of causing loss of life and extensive damage to property. The Bay of Bengal is a potentially energetic region for the development of cyclonic storms and about 7% of the global annual tropical storms form over this region with two cyclone seasons in a year. Tropical cyclones have great socio-economic concern for the Indian subcontinent. Precise forecasting of tropical cyclone intensity and track are important for the countries bordering the Bay of Bengal, especially India, Bangladesh and Myanmar due to significant socio-economic impact. There has been remarkable improvement in forecasting of the tropical cyclones with the development of high resolution atmospheric models and the global forecasting systems such as the National Centers for Environmental Predictions (NCEP) Global Forecasting System (GFS). Assimilation of available observations has been considered to be very important for accurate description of initial conditions in numerical models (Park and Zupanski, 2003; Navon, 2009; Pu et al., 2009). In particular, assimilation methods like variational approach has the additional advantage of assimilating observations by satisfying model dynamic and thermodynamic constraints through a set of independent balance equations (in 3DVAR) (Courtier et al., 1998).

Land Surface Processes Simulation Over Thar Desert in Northwest India

Land surface processes in data scarce arid northwestern India and their influence on the regional climate including monsoon are now gaining enhanced scientific attention. In this work the seasonal variation of land surface parameters and surface-energy flux components over Lasiurus sindicus grassland system in Thar Desert, western India were simulated using the mesoscale WRF model. The data on surface fluxes from a micrometeorological station, and basic surface level weather data from the Central Arid Zone Research Institute’s experimental field station (26o59′41″N; 71o29′10″E), Jaisalmer, were used for comparison. Simulations were made for typical fair weather days in three seasons [12–14 January (peak winter); 29–31 May (peak summer), 19–21 August (monsoon)] during 2012. Sensitivity experiments conducted using a 5-layer soil thermal diffusion (5TD) scheme and a comprehensive land surface physics scheme (Noah) revealed the 5TD scheme gives large biases in surface fluxes and other land surface parameters. Simulations show large variations in surface fluxes and meteorological parameters in different seasons with high friction velocities, sensible heat fluxes, deep boundary layers in summer and monsoon season as compared to winter. The shortwave radiation is underestimated during the monsoon season, and is overestimated in winter and summer. In general, the model simulated a cold bias in soil temperature in summer and monsoon season and a warm bias in winter; the simulated surface fluxes and air temperature followed these trends. These biases could be due to a negative bias in net radiation resulting from a high bias in downward shortwave radiation in various seasons. The Noah LSM simulated various parameters more realistically in all seasons than the 5TD soil scheme due to inclusion of explicit vegetation processes in the former. The differences in the simulated fluxes with the two LSMs are small in winter and large in summer. The deep mixed layers are distributed in the northeastern parts in summer, northern areas in southwest monsoon and in southwestern parts during winter seasons and associated with the land-cover and vegetation dynamics. Our results present a baseline simulation study in this data scarce arid region.