This thesis deals with tropical intraseasonal variation (TISV) having time scales in 20-80 day range. Variations on this time scale have been observed to have profound influence on the weather and climate of the entire globe, and hence its study forms an important area of current research. A large number of studies have been carried out on this topic since the pioneering work of Madden and Julian in 1971. However, the observational studies are biased towards using the outgoing longwave radiation (OLR) as the variable of interest, and other variables, pressure in particular, have received less attention. The present thesis explores features of intraseasonal variations in sea level pressure (SLP) with the following main objectives. 1. Compare and contrast wavenumber – frequency spectra of OLR, zonal winds and SLP. 2. Quantify temporal and spatial variations of different tropical modes observed in the above variables. 3. Investigate intraseasonal variations in sea level pressure in the tropics and its meridional connections. 4. Document the movement of cloud bands during the periods of high and low TISV activity during different seasons. 5. Explore the relations between intraseasonal variations in SLP and monsoon rainfall over India. The study considered global data for a time period of 25 years from 1979 to 2003. Spectral analysis and correlations are the main tools of analysis. A combined FFT-wavelet spectral method, which uses FFT in longitude and wavelet transform in time, was developed for this purpose. This method provided an effective way of obtaining wavenumber - frequency spectra as well as in quantifying temporal variations of different modes. The transform gives spectral intensity as a function of wavenumber, frequency and time. The analysis is applied to OLR, zonal wind and SLP to understand spectral characteristics of different modes and their temporal variations. The thesis shows that the nature of spectra for OLR, SLP and wind is different although these variables are physically connected. OLR spectrum shows many of the equatorial modes observed from the previous studies for an equivalent depth of 40 m. Spectra of zonal winds at three vertical levels (850 mb, 500 mb and 200 mb) shows peaks corresponding to MJO, Kelvin modes at an equivalent depth of 75 m and Rossby Haurwitz modes. SLP spectrum is different from others. It has peaks at wavenumber zero and at MJO and Rossby Haurwitz modes. Another important new result of the thesis is the spatial and temporal behavior of SLP on intraseasonal time scales. It is shown that the the global atmosphere exhibits quasi-periodic oscillations in SLP with variations in the tropics and high latitudes strongly correlated but in opposite phases. Importantly, the strength of TISV is correlated with sea surface temperature (SST) anomalies in the equatorial Pacific Ocean. This may have some predictive value for predicting the active and weak TISV activity.

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.