Category: Responding to Natural Disasters and Environmental Changes
Project Team: U.S. Regional Energy
Team Location: NOAA National Climatic Data Center (NCDC) – Asheville, North Carolina
Dr. Carl Schreck (NOAA, Cooperative Institute for Climate and Satellites)
Dr. L. DeWayne Cecil (NOAA, Global Science & Technology, Inc.)
Demand for natural gas directly correlates to temperature anomalies and heating degree days. Once temperature models reach weeks three and four of the forecast, they become less skillful and reliable. The goal of our project was to develop a statistical model to compute the temperature anomalies in the Midwest-Eastern regional U.S. We used the Outgoing Longwave Radiation Daily Climate Data Record (OLR-Daily CDR) in our statistical model to identify patterns associated with equatorial waves and the Madden-Julian Oscillation (MJO). We collected and computed our temperature anomaly data from the Global Historical Climatology Network (GHCN) daily temperatures. The model determined which tropical and mid-latitude teleconnections contributed the most to temperature anomalies in our study area. The statistical model used a regression analysis to determine the regression coefficients and relationship between the temperature anomalies and various teleconnection indices. Through this analysis, we examined which teleconnections were considered to contribute the most to the relationship between the teleconnection indices and temperature anomalies in the Midwest-Eastern U.S. Once the highest contributing teleconnection was determined, the statistical model was rerun to further analyze other contributing teleconnections during specific phases of the highest contributing teleconnection. Based on how the coefficients change, this analysis could potentially be used as a forecasting tool for the energy market and other public entities. With this model, we have shown which teleconnections most significantly impact the temperature anomalies in the Midwest-Eastern regional U.S. This research could provide the natural gas industry with a more effective methodology for forecasting temperature anomalies.
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