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Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The city of Rio de Janeiro is often affected by meteorological systems that cause intense rainfalls. These events bring about economic and social disruptions of significant magnitude. This paper analyses the meteorological event occurred between the 05th and 06th of April 2010 in the city of Rio de Janeiro, using numerical modelling by MM5 model (PSU/NCAR Mesoscale Model). From 20h of the 5th until 8h of the 6th there was on average 178 mm of rainfall in the this city, almost twice the expected for April, bringing about floods, landslides, and many deaths. In this study, eight parameterization groups were used, with different configurations of cloud microphysics, cumulus and atmospheric boundary layer. Two simulation rounds were performed, starting at different times and the simulation results were compared with the observed precipitation data, obtained from pluviometric stations from the "Alerta-Rio" system. Thus, it was possible to analyse the effectiveness of the MM5 model for prognosticate extreme rainfall events as some of the model configurations have captured the position and/or intensity of the cores with intense rainfall with a three-hour time gap in some cases. Therefore, this prognostic model had proven that it can be a useful tool to forecast and reduce the consequences of extreme rainfall events in Rio de Janeiro city. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electric energy planning of the National Interconnected System - SIN has a close correlation between present stocks of water in the reservoirs of hydroelectric plants and future inflows. For time periods above one month, inflows scenarios are generated without incorporating any kind of climate information. The inclusion of such information can improve the representation of future hydrological conditions. The objective of this study is to evaluate the gain that can be achieved with the use of precipitation information on generating reservoir inflows scenarios. We generated reservoir inflows scenarios to the Grande River basin using univariate autoregressive modeling - AR, which considers only past inflows, and multivariate modeling - ARx, which also considers precipitation information. Statistical indices were calculated to evaluate the performance of the scenarios generated using the two types of autoregressive models by comparing the predicted inflow with the observed inflow. The results highlighted that both the univariate and multivariate modeling represented well the seasonal behavior of the inflows, however the univariate methodology presented difficulties in capturing the corresponding natural variability. Statistical indices showed the best performance of the ARx model, indicating that the inclusion of the precipitation information proved to be an important addition to generate reservoir inflows scenarios. [ABSTRACT FROM AUTHOR]