
TL;DR
This paper presents a linear programming-based LAD regression model analyzing 55 years of local temperature data, successfully identifying temperature rise, seasonal effects, and the solar cycle, demonstrating accessible methods for climate data analysis.
Contribution
Introduces a LAD regression approach formulated as a linear programming problem to accurately extract temperature trends and solar cycle effects from archived temperature data.
Findings
Temperatures increased by about 2°F over 55 years.
The model correctly identified the solar cycle phase.
The linear trend and solar cycle effects were of comparable magnitude.
Abstract
Using 55 years of daily average temperatures from a local weather station, I made a least-absolute-deviations (LAD) regression model that accounts for three effects: seasonal variations, the 11-year solar cycle, and a linear trend. The model was formulated as a linear programming problem and solved using widely available optimization software. The solution indicates that temperatures have gone up by about 2 degrees Fahrenheit over the 55 years covered by the data. It also correctly identifies the known phase of the solar cycle; i.e., the date of the last solar minimum. It turns out that the maximum slope of the solar cycle sinusoid in the regression model is about the same size as the slope produced by the linear trend. The fact that the solar cycle was correctly extracted by the model is a strong indicator that effects of this size, in particular the slope of the linear trend, can be…
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Taxonomy
TopicsSolar Radiation and Photovoltaics
