Probabilistic estimation of solar radiation and wind power by data assimilation

2015 Fiscal Year Final Research Report

• Project/Area Number: 25280010
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Partial Multi-year Fund
• Section: 一般
• Research Field: Statistical science
• Research Institution: The Institute of Statistical Mathematics
• Principal Investigator: Ueno Genta 統計数理研究所, モデリング研究系, 准教授 (40370093)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: データ同化

Outline of Final Research Achievements

I have developed a Bayesian technique for estimating the parameters in the observation-noise covariance matrix for ensemble data assimilation. I designed a posterior distribution by using the ensemble-approximated likelihood and a Wishart prior distribution and presented an iterative algorithm for parameter estimation. The temporal smoothness of the covariance matrix can be controlled by an adequate choice of two parameters of the prior distribution, the prior covariance matrix and the number of degrees of freedom. The number of degrees of freedom can be estimated by maximizing the marginal likelihood. The present formalism can handle cases in which the number of data points or data positions varies with time. I verified that the proposed algorithm works well and that only a limited number of iterations are necessary.

Free Research Field

データ同化