AUT LibraryAUT
View Item 
  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
  • View Item
  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Ensemble interpolation methods for spatio-temporal data modelling

Sallis, P; Hernandez, S
Thumbnail
View/Open
Ensemble interpolatin methods for spatial.pdf (143.8Kb)
Permanent link
http://hdl.handle.net/10292/1670
Metadata
Show full metadata
Abstract
Real time weather forecasting is a highly influential tool in decision making for agriculture. Geographic Information Systems (GIS) can be built to provide information about topographic data such as elevation and distance to oceans or water reservoirs. This data has begun to have increased availability, providing easier access for developing new applications. By using geographic information together with terrestrial measurements from weather stations, the spatial and temporal scales of the climatic variables can be analyzed by interpolation and forecasting. Most of the interpolation methods provided in common GIS tools are only related to the spatial domain, limiting its use in numerical modelling and prediction of climatic states. However, by adopting a Bayesian approach, it appears possible to estimate the dynamic behaviour of the unobserved climate pattern using a state-space representation. Using this framework, the ensemble Kalman filter or a more general sequential Monte Carlo method could be used for the estimation procedure. A wireless sensor network providing continuous data to populate such a model is described here for potential application of this approach.
Keywords
Climatemodelling; Interpolation; Ensemble methods; Kalman filters; GIS, wireless sensor networks
Date
November 1, 2010
Source
Fourth UKSim European Symposium on Computer Modeling and Simulation,(EMS)2010, pp.132-135
Item Type
Conference Contribution
Publisher
IEEE Computer Society
DOI
http://www.computer.org/portal/web/csdl/doi/10.1109/EMS.2010.32
Rights Statement
(c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library

 

 

Browse

Open ResearchTitlesAuthorsDateSchool of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, PāngarauTitlesAuthorsDate

Alternative metrics

 

Statistics

For this itemFor all Open Research

Share

 
Follow @AUT_SC

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library