15th Conference on Applied Climatology
13th Symposium on Meteorological Observations and Instrumentation


Comparison of Co-Located Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina

Christopher Thomas Holder, State Climate Office of North Carolina and North Carolina State Univ., Raleigh, NC; and R. P. Boyles, A. Syed, D. Niyogi, K. Wireman, and S. Raman

The cooperative observer network (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A statistical comparison between these two observing methods is performed in North Carolina, where thirteen of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (96% correlation for minimum temperature, 91% for maximum temperature). Rainfall values recorded by the two different systems correlate poorly (47%), but the correlations are improved significantly (to 87%) when corrections are made for the differences in observation times between the COOP and automated stations. Rainfall correlations especially improve with rainfall amounts less than 50 mm per day. Temperature and rainfall correlations are best (nearly 100% for maximum and minimum temperatures, 88% for rainfall) when monthly averages are used. Biases consistently indicate that COOP instruments record warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Standard deviations improve significantly with the various corrections.

This study shows that COOP and automated data (such as from NC ECONET) can, with certain corrections, be used in conjunction for various climate analysis applications. This allows a greater spatial density of data and a larger density of atmospheric and soil parameters, thus improving the accuracy of the data that is relayed to the public and used in climate tend analyses.

Joint Poster Session 1, General Poster Session I (Joint with Applied Climatology, SMOI, and AASC)
Monday, 20 June 2005, 5:30 PM-7:30 PM

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