Title

Forecasting Precipitation From Multi-Platform Remote Sensing Systems Using Wavelet-Based Neural Network Models

Keywords

artificial neural networks; climate change; forecasting; hydrometeorology; precipitation; Remote Sensing; sea surface temperature; teleconnection patterns

Abstract

This paper explores spectral decomposition of environmental data for use in ad hoc artificial neural networks for predicting precipitation patterns by exploiting the nonlinear dynamic signals of oceanic teleconnection patterns found in the Northern Atlantic and Pacific. Using sophisticated ground and satellite remote sensing, including the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellites for sea surface temperature detection and the GOES geostationary satellite for precipitation correction of in-situ data, high predictive skill is demonstrated during the winter months within the Adirondack state Park in upstate New York, USA. Results show winter months with up to 67% of the land area accurately forecasting precipitation trends with a lead time of 3 months. © 2014 IEEE.

Publication Date

1-1-2014

Publication Title

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Number of Pages

584-589

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICNSC.2014.6819691

Socpus ID

84902377271 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84902377271

This document is currently not available here.

Share

COinS