Influence of the Azores high on sea level pressure and wind, and on precipitation, in the Eastern Tropical Pacific Ocean

ENGLISH: Intensification of the Azores high pressure cell in mid-year, with concomitant air flow from the Caribbean into the Pacific, is shown to be responsible for a secondary minimum of precipitation observed along the tropical Pacific coast of the Americas, and to have a measurable effect on wind and precipitation several hundred kilometers offshore. SPANISH: La intensificación de la célula de alta presión de las Azores a mediados del año, y la corriente de aire concomitante que entra al Pacífico procedente del Caribe, se demuestra que es la causante de un mínimo secundario de precipitación observado a lo largo de la costa tropical de las Américas en el Pacífico y que tiene un efecto mensurable sobre el viento y la precipitación varios cientos de kilómetros mar afuera. (PDF contains 23 pages.)

Dendroclimatic reconstructions of temperature, precipitation, and pressure patterns over the western and central USA, 1602-1961 [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Verified reconstructions of seasonal temperature, precipitation and sea-level pressure over North America and the North Pacific have been derived from 65 arid-site tree-ring chronologies in the North American West. Significant reconstructions were obtained for temperature for wide areas in the West and mid-continent. Precipitation reconstructions were significant only in the West, and pressure was reconstructed over wide areas of the North Pacific Ocean and the North American continent.

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.