Optimization of continuous ranked probability score using PSO

Seyedeh Atefeh Mohammadi,Morteza Rahmani,Majid Azadi

doi:10.5267/j.dsl.2015.4.001

Abstract

Weather forecast has been a major concern in various industries such as agriculture, aviation, maritime, tourism, transportation, etc. A good weather prediction may reduce natural disasters and unexpected events. This paper presents an empirical investigation to predict weather temperature using minimization of continuous ranked probability score (CRPS). The mean and standard deviation of normal density function are linear combination of the components of ensemble system. The resulted optimization model has been solved using particle swarm optimization (PSO) and the results are compared with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The preliminary results indicate that the proposed PSO provides better results in terms of CRPS deviation criteria than the alternative BFGS method.

Highlights

Weather forecast has been a major concern in various industries such as agriculture, aviation, maritime, tourism, transportation, etc
The preliminary results indicate that the proposed particle swarm optimization (PSO) provides better results in terms of continuous ranked probability score (CRPS) deviation criteria than the alternative BFGS method
This paper has presented a survey to forecast weather temperature using continuous ranked probability score (CRPS)

Summary

Introduction

Weather forecast has been a major concern in various industries such as agriculture, aviation, maritime, tourism, transportation, etc. Numerical weather prediction models involve differential equations, which describe the physical laws and dynamics of atmosphere and they mainly depend on boundary conditions. According to Eckel et al (2012) “Ambiguity is uncertainty in the prediction of forecast uncertainty, or in the forecast probability of a specific event, associated with random error in an ensemble forecast probability density function. In a deterministic forecast, only one initial condition is considered as the input to the model regardless of the inherent uncertainty of the atmosphere. In an ensemble prediction system the forecast is produced by drawing a finite sample from the probability distribution describing the uncertainty of the initial state of the atmosphere and the weather is forecasted by a probability distribution function. An attempt is made to estimate a normal density function to forecast the surface temperature over Iran through minimization of continuous ranked probability score (CRPS) using particle swarm optimization (PSO) algorithm in the training period

The proposed study

Particle Swarm Optimization

The results

Findings

Conclusion