A Diagnostic and Modeling Study on the Onset of Summer Monsoon over South Asia

This study focuses on the onset of southwest monsoon over Kerala. India Meteorological Department (IMD) has been using a semi-objective method to define monsoon onset. The main objectives of the study are to understand the monsoon onset processes, to simulate monsoon onset in a GCM using as input the atmospheric conditions and Sea Surface Temperature, 10 days earlier to the onset, to develop a method for medium range prediction of the date of onset of southwest monsoon over Kerala and to examine the possibility of objectively defining the date of Monsoon Onset over Kerala (MOK). It gives a broad description of regional monsoon systems and monsoon onsets over Asia and Australia. Asian monsoon includes two separate subsystems, Indain monsoon and East Asian monsoon. It is seen from this study that the duration of the different phases of the onset process are dependent on the period of ISO. Based on the study of the monsoon onset process, modeling studies can be done for better understanding of the ocean-atmosphere interaction especially those associated with the warm pool in the Bay of Bengal and the Arabian Sea.

Sea Surface Temperature-Convection Relationship in Tropical Oceans with Special Emphasis to Intraseasonal Variability of Indian Summer Monsoon

The SST convection relation over tropical ocean and its impact on the South Asian monsoon is the first part of this thesis. Understanding the complicated relation between SST and convection is important for better prediction of the variability of the Indian monsoon in subseasonal, seasonal, interannual, and longer time scales. Improved global data sets from satellite scatterometer observations of SST, precipitation and refined reanalysis of global wind fields have made it possible to do a comprehensive study of the SST convection relation. Interaction of the monsoon and Indian ocean has been discussed. A coupled feedback process between SST and the Active-Break cycle of the Asian summer monsoon is a central theme of the thesis. The relation between SST and convection is very important in the field of numerical modeling of tropical rainfall. It is well known that models generally do very well simulating rainfall in areas of tropical convergence zones but are found unable to do satisfactory simulation in the monsoon areas. Thus in this study we critically examined the different mechanisms of generation of deep convection over these two distinct regions.The study reported in chapter 3 has shown that SST - convection relation over the warm pool regions of Indian and west Pacific oceans (monsoon areas) is in such a way that convection increases with SST in the SST range 26-29 C and for SST higher than 29-30 C convection decreases with increase of SST (it is called Waliser type). It is found that convection is induced in areas with SST gradients in the warm pool areas of Indian and west Pacific oceans. Once deep convection is initiated in the south of the warmest region of warm pool, the deep tropospheric heating by the latent heat released in the convective clouds produces strong low level wind fields (Low level Jet - LLJ) on the equatorward side of the warm pool and both the convection and wind are found to grow through a positive feedback process. Thus SST through its gradient acts only as an initiator of convection. The central region of the warm pool has very small SST gradients and large values of convection are associated with the cyclonic vorticity of the LLJ in the atmospheric boundary layer. The conditionally unstable atmosphere in the tropics is favorable for the production of deep convective clouds.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Tropical Mesoscale Convective Systems and Associated Energetics : Observational and Modeling Studies

The main purpose of the thesis is to improve the state of knowledge and understanding of the physical structure of the TMCS and its short range prediction. The present study principally addresses the fine structure, dynamics and microphysics of severe convective storms.The structure and dynamics of the Tropical cloud clusters over Indian region is not well understood. The observational cases discussed in the thesis are limited to the temperature and humidity observations. We propose a mesoscale observational network along with all the available Doppler radars and other conventional and non—conventional observations. Simultaneous observations with DWR, VHF and UHF radars of the same cloud system will provide new insight into the dynamics and microphysics of the clouds. More cases have to be studied in detail to obtain climatology of the storm type passing over tropical Indian region. These observational data sets provide wide variety of information to be assimilated to the mesoscale data assimilation system and can be used to force CSRM.The gravity wave generation and stratosphere troposphere exchange (STE) processes associated with convection gained a great deal of attention to modem science and meteorologist. Round the clock observations using VHF and UHF radars along with supplementary data sets like DWR, satellite, GPS/Radiosondes, meteorological rockets and aircrafl observations is needed to explore the role of convection and associated energetics in detail.