Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

Abstract

Abstract. In this paper we consider an optimization problem applying the metaheuristic Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily precipitation simulations with the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) over South America during January 2006. The method is addressed as a parameter estimation problem to weight the ensemble of precipitation forecasts carried out using different options of the convective parameterization scheme. Ensemble simulations were performed using different choices of closures, representing different formulations of dynamic control (the modulation of convection by the environment) in a deep convection scheme. The optimization problem is solved as an inverse problem of parameter estimation. The application and validation of the methodology is carried out using daily precipitation fields, defined over South America and obtained by merging remote sensing estimations with rain gauge observations. The quadratic difference between the model and observed data was used as the objective function to determine the best combination of the ensemble members to reproduce the observations. To reduce the model rainfall biases, the set of weights determined by the algorithm is used to weight members of an ensemble of model simulations in order to compute a new precipitation field that represents the observed precipitation as closely as possible. The validation of the methodology is carried out using classical statistical scores. The algorithm has produced the best combination of the weights, resulting in a new precipitation field closest to the observations.

Flash signal evolution inPhotinusfireflies: Character displacement and signal exploitation in a visual communication system

“…To determine whether any given Photinus species pair was possibly sympatric, we used field observations of coexisting taxa in the same area, that is confirmed sympatry, and county records of species within the same county, combined with shared habitat types. We classified the main habitats based on descriptions from the literature and our own field work into seven main categories: (1) open fields (meadows, lawns), (2) open fields and field edge, (3) open fields and forests, (4) forest edge and forest, (5) forest (oak, palmetto, mesic), (6) marsh and bogs, and (7) desert canyon wash. We did not include potential shifts in daily or seasonal activity times into our analysis, because usually only the onset of the daily and/or seasonal (flashing) activity is well documented, but can vary from year to year and through the season, possibly due to population numbers (Wing ), sex ratios, and age of individuals (Lewis and Wang ), as well as climate conditions, including temperature (Faust and Weston ) and rainfall (dos Santos et al ). As a consequence a potential interaction between species (through at least a partial overlap of their activity in the same habitat) could not be excluded, thus we did not use these variables to subdivide habitats.…”

The Brazilian developments on the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated environmental model tuned for tropical areas

“…BRAMS/RAMS are multipurpose numerical weather prediction models designed to simulate atmospheric circulations spanning from planetary-scale waves down to large eddies of the planetary boundary layer. RAMS has progressed with its development, which includes, but is not limited to, its coupling to a biogeochemistry model (Eastman et al, 2001a, b;Lu et al, 2001), air quality applications (Lyons et al, 1995;Pielke and Uliasz, 1998), and, more recently, a climate application over South America (Beltran-Przekurat et al, 2011). On the other side, BRAMS developed its own identity and diverged from RAMS with several new features and modifications that have been included mainly to improve the numerical representation of fundamental physical processes in tropical and subtropical regions (S. R. Freitas et al, , 2009.…”

Firefly Algorithm in Telecommunications

“…It compares FA with other classical methods and demonstrates a better performance for FA, raising the consideration of putting it into practical use. In Dos Santos et al (2013), the authors use FA to weight an ensemble of rainfall forecasts from daily precipitation simulations with the developments on the Brazilian Regional Atmospheric Modeling System (BRAMS).…”