Comparison results for the CFSv2 hindcasts and statistical downscaling over the northeast of Brazil

Abstract

Abstract. An Artificial Neural Networks (ANNs) approach was used to reproduce the precipitation anomalies for the rainy seasons over the south and north parts of the Northeast of Brazil (NEB) during 1982–2009 period. The seasonal hindcasts of precipitation anomalies from Climate Forecast System v2 (CFSv2) model and the observed dominant modes of anomalous Sea Surface Temperature over the South and North Atlantic Ocean were used as explanatory variables separately. The reduction of dispersion between the explanatory and dependent variables after the fit of the networks suggest the ANN as an important complementary technique for the climate studies over the NEB. However, a large dataset are required to the models capture the non-linear process in more details. The practical implication of the results is that ANNs constructed here could be applied in further analyses, for example, to explore the ANN's ability in improving the seasonal climate forecasts considering that the numerical and statistical methods must be complementary tools.

Refinement of the daily precipitation simulated by the CMIP5 models over the north of the Northeast of Brazil

“…Also, the differences between the normalized time series of the observed daily precipitation and the CMIP5 simulations during FMA season are shown. To explain such differences is calculated the anomalies of SST and precipitation over the Atlantic Ocean using as observations the ERSST version3b (Smith et al, 2008) FIGURE 1 | Location of the study region: North of the NEB delimited by the red box between 2 and 7 • S; 40 and 45 • W. The location is similar as in Silva and Mendes (2013). Adapted from http://mapas.ibge.gov.br.…”

“…As in Silva and Mendes (2013) the Multilayer Perceptron architecture is used that is more adequate network for meteorological applications because the weather and the climate can repeat along the chronological time but never exactly in the same way (e.g., Cardoso and da Silva Dias, 2004 represent the topology of the ANN used in the present study for the present and future climate analyses, respectively. For both simulations the Multilayer Perceptron topology is interconnected in a feed-forward method.…”

“…The technique allows the establishment of statistical links between the observed large-scale circulation and the precipitation or temperature fields by applying transfer functions to the GCMs outputs. Recently, Silva and Mendes (2013) elaborated a ANN model using as predictand variables the seasonal hindcasts of precipitation anomalies from the Climate Forecast System v2 (CFSv2) model and the observed dominant modes of anomalous SST over the South and North Atlantic to reproduce the precipitation during the rainy seasons over the south and north of the NEB region. The authors obtained fairly success in using the ANN approach as a complementary tool for the climate studies.…”

Precipitation Extremes Analysis over the Brazilian Northeast via Logistic Regression

“…The increase of extreme events in a short period of time became in the society more vulnerable at weather and climate extremes variability, resulting in great socioeconomic losses [1]. These extremes are related to several environmental factors that favor the increase on their frequency and intensity: 1) ocean-atmospheric variables relationships, such as: air temperature [2], precipitation [3], wind speed [4] and sea surface temperature (SST) [5]; 2) regional micro-climate changes due to rapid urbanization of the cities without proper urban planning [6]; and 3) orographic effects [7]. These factors when combined at atmospheric circulation or meteorological systems in several spatiotemporal scales [8] can favor the extremes occurrence; the aim of this paper is to diagnose the precipitation extremes.…”

Preface 8th Alexander von Humbolt Conference of EGU Topical Conference Series "Natural Disasters, Global Change, and the Preservation of World Heritage Sites"

“…In relation to the application of non-destructive techniques to characterize the ground conditions of any site, Tokeshi et al (2013) assessed the ground compaction and general geotechnical site characterization based on the application of non-invasive Multichannel Analysis of Surface Waves (MASW) and Multichannel Simulation with One Receiver (MSOR) techniques.…”