Lee Mullon (Florida) co-authored and article published in the Journal of Hydrology on March 9, 2017.
The article is entitled "Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models." It analyzes at how climate change teleconnection signals are used in predicting precipitation in the Adirondack Mountains. The research used a statistical approach to isolate unique teleconnection signals that exist in the Adirondack Mountains. Those unique signals were then utilized in a precipitation forecasting model. The study found that the seasonal time scale showed better prediction results than a monthly time scale.
Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean–atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.
For more information regarding the article, visit: Journal of Hydrology
To learn more about Lee see his profile at: https://www.linkedin.com/in/leemullon/