Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear canonical correlation analysis (NLCCA) method is used to study the relationship between the tropical Pacific sea level pressure (SLP) and sea surface temperature (SST) fields...

High-resolution underway shipboard atmospheric and oceanic observations collected in Drake Passage from 2000 to 2009 are used to examine the spatial scales of turbulent heat fluxes and flux-related state variables. The magnitude of the seasonal cycle of sea surface temperature (SST) south of the Polar Front is found to be twice that north of the front, but the seasonal cycles of the turbulent h...

Nowadays, in consideration of environmental issues and limitation of fossil fuels, there is a particular consideration of renewable energy including Ocean Energy, that can extracted going through various methods such as Wave Energy, Tidal Energy, Salinity Gradient, OTEC: Ocean Thermal Energy Conversion.Herein this research, operation of OTEC Method in Southern Caspian Sea has been discussed. Fo...

Satellite techniques for measurement of sea surface temperature (SST) are reviewed briefly, and a discussion of satellite SST applications and recent research in oceanography is provided. These applications include the areas of climate, mesoscale oceanography, and fisheries. Examples given focus mainly on the Pacific and California Current regions. Satellite SST data are currently used operatio...

Title of Document: INTERANNUAL VARIABILITY OF SEA SURFACE TEMPERATURE IN THE EASTERN TROPICAL PACIFIC OCEAN AND CENTRAL AMERICAN RAINFALL Kristopher B. Karnauskas, Ph.D., 2007 Directed By: Professor Antonio J. Busalacchi Department of Atmospheric and Oceanic Science Sea surface temperature (SST) in the east Pacific warm pool (EPWP) plays a potentially important role in Central American rainfall...

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors...

Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of...

The dominant pattern of tropical and subtropical North Atlantic sea surface temperature (SST) anomalies simulated in the GFDL coupled ocean-atmosphere model is identified and compared to observations. The spatial pattern and temporal variability of that pattern resemble observational results. On interannual time scales it is shown that anomalous surface heat fluxes, consistent with variations i...

Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of...

[1] The evolution of oceanographic conditions in the upwelling region off northern Chile (18 –24 S) between 1996 and 1998 (including the 1997–1998 El Niño) is presented using hydrographic measurements acquired on quarterly cruises of the Chilean Fisheries Institute, with sea surface temperature (SST), sea level, and wind speeds from Arica (18.5 S), Iquique (20.5 S), and Antofagasta (23.5 S) and...