The Use of Unconfounded Climatic Data Improves Atmospheric Prediction
Robert C. Soltysik, M.S., and Paul R. Yarnold, Ph.D.
Optimal Data Analysis, LLC
This report improves measurement properties of data and analytic methods widely used in meteorological modeling and forecasting. Paradoxical confounding is defined and demonstrated using global temperature land-ocean index data. It is shown that failure to address paradoxical confounding results in suboptimal atmospheric circulation pattern models, and correcting prior measurement and analytic deficiencies results in more accurate prediction of temperature and precipitation anomalies, and export of Arctic sea ice.