Observer Bias in Daily Precipitation Measurements at United States Cooperative Network Stations
| Autor: | Christopher Daly, Matthew K. Doggett, Wayne Gibson, George H. Taylor, Joseph I. Smith |
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| Rok vydání: | 2007 |
| Předmět: | |
| Zdroj: | Bulletin of the American Meteorological Society. 88:899-912 |
| ISSN: | 1520-0477 0003-0007 |
| DOI: | 10.1175/bams-88-6-899 |
| Popis: | The Cooperative Observer Program (COOP), established over 100 years ago, has become the backbone of temperature and precipitation data that characterize means, trends, and extremes in U.S. climate. However, significant and widespread biases in the way COOP observers measure daily precipitation have been discovered. These include 1) underreporting of light precipitation events (daily totals of less than 0.05 in., or 1.27 mm), and 2) overreporting of daily precipitation amounts evenly divisible by five- and/or ten-hundredths of an inch, that is, 0.10, 0.25, 0.30 in., etc. (2.54, 6.35, 7.62 mm, etc.). Observer biases were found to be highly variable in space and time, which has serious implications for the spatial and temporal trends and variations of commonly used precipitation statistics. In addition, it was found that few COOP stations had sufficiently complete data to allow the calculation of stable precipitation statistics for a stochastic weather simulation model. Out of more than 12,000 COOP stations nationally, only 784 (6%) passed data completeness and observer bias screening tests for the climatological period 1971–2000. Of the 1221 COOP stations selected for the U.S. Historical Climate Network (USHCN), which provides much of the country's official data on climate trends and variability over the past century, only 221 stations (18%) passed these tests. More effective training materials and regular communication with COOP observers could reduce observer bias in the future. However, it is unlikely that observer bias can be eliminated. One solution is to automate the COOP precipitation measurement system, but this is an expensive option, and may increase other biases associated with automated precipitation measurement. Further analyses are needed to better quantify and characterize observer bias, and to develop methods for dealing with its effects. |
| Databáze: | OpenAIRE |
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