Firefly optimization to determine the precipitation field on South America

Abstract

A model simulation of an intense rainfall associated with a case of South Atlantic Convergence Zone that occurred during 21-24 February 2004 using the Brazilian developments on the Regional Atmospheric Modelling System was performed. The convective parameterization scheme of Grell and De´ve´nyi was used to represent clouds of the sub-grid scale and their interaction with the large-scale environment. This method is a convective parameterization that can make use of a large variety of approaches previously introduced in earlier formulations, considering an ensemble of several hypotheses and closures. The rainfall was evaluated by six experiments, using different choices of rainfall parameterizations, providing six different simulated responses for the rainfall field. The sixth experiment ran with an average among five closures (ensemble mean). The purpose of this study was to generate a set of weights to compute a best combination of the ensemble members. This inverse problem of parameter estimation is solved as an optimization problem. The objective function was computed with the quadratic difference between five simulated precipitation fields and observation. The precipitation field estimated by the Tropical Rainfall Measuring Mission satellite was used as observed data. Weights were obtained using the firefly optimization algorithm and it was included in the cumulus parameterization code to simulate precipitation. The results indicated the better skill of the model with the new methodology compared with the old ensemble mean calculation.

A Systematic Review on Firefly Algorithm: Past, Present, and Future

“…Santos et al [105] used FA to optimize the weight for determination of precipitation area . Multi-model approach was used to determine precipitation field where weighted average of rainfall parameter were used for calculation.…”

“…he used FA to do this optimization based on local search and find it efficient. The list of various engineering application shown in table4 [83] Rigid image registration Image Processing [84] Image vector quantization Image Processing [85] Vector quantization for image compression Image Processing [86] Non-linear grayscale image enhancement Image Processing [87] Image segmentation Image Processing [88] Multilevel thresholding Image Processing [89] Multilevel image thresholding selection Image Processing [90] Cross entropy threshold Image Processing [91] Linear array antenna Antenna Designing [92] 2 ring circular array antenna Antenna Designing [93] Linear antenna designing Antenna Designing [94] Concentric ring array antenna Antenna Designing [95] Self-synchronization of robot Robotics [96] Industrial robots Robotics [97] Optimal semantic web service composition Semantic Web [98] Wireless sensor networks Wireless Network [99] Support vector machine Business Optimization [100] Financial portfolio optimization Business Optimization [101] Co-variance matrix adaptation Chemical Engineering [102] Tower structures Civil Engineering [103] Optimum design of structures Civil Engineering [104] Optimum design of trusses Civil Engineering [105] Precipitation field Meteorology Optimization [106] Economic emissions load dispatch problem Industry Optimization [107] Non-convex economic dispatch problems Industry Optimization [108] Steel slabs Casting Industry Optimization [109] Ring array antenna Industry Optimization [110] Simulated manufacturing process Industry Optimization [111] Unit commitment Industry Optimization [112] Demand response scheduling Industry Optimization [113] Train's energy saving Industry Optimization…”

An Evolutionary Discrete Firefly Algorithm with Novel Operators for Solving the Vehicle Routing Problem with Time Windows

“…Regarding the application fields in which the FA has proven to be effective, we can list the image processing [39], the antena design [40], civil engineering [41], robotics [42], semantic web [43], chemistry [44], and metereology [45].…”

Using the swarm intelligence algorithms in solution of the two-dimensional inverse Stefan problem

“…This elasticity and universality of biologically inspired algorithms justify the idea of using them in the process of minimizing the functionals resulting from investigated problem. Application of optimization algorithms inspired by the nature in solving various kinds of inverse problems can be also found in papers [55][56][57][58]. In some earlier research Authors of the present papers have used also the classical minimization algorithms.…”