Observer Bias in Daily Precipitation Measurements at United States Cooperative Network Stations

Observer Bias in Daily Precipitation Measurements at United States Cooperative Network Stations BY CHRISTOPHER DALY, WAYNE P. G IBSON, GEORGE H. TAYLOR, MATTHEW K. DOGGETT, AND JOSEPH I. SMITH The vast majority of United States cooperative observers introduce subjective biases into their measurements of daily precipitation. he Cooperative Observer Program (COOP) was the U.S. Historical Climate Network (USHCN). The established in the 1890s to make daily meteo- USHCN provides much of the country’s official data T rological observations across the United States, on climate trends and variability over the past century primarily for agricultural purposes. The COOP (Karl et al. 1990; Easterling et al. 1999; Williams et al. network has since become the backbone of tempera- 2004). ture and precipitation data that characterize means, Precipitation data (rain and melted snow) are trends, and extremes in U.S. climate. COOP data recorded manually every day by over 12,000 COOP are routinely used in a wide variety of applications, observers across the United States. The measuring such as agricultural planning, environmental impact equipment is very simple, and has not changed statements, road and dam safety regulations, building appreciably since the network was established. codes, forensic meteorology, water supply forecasting, Precipitation data from most COOP sites are read weather forecast model initialization, climate map- from a calibrated stick placed into a narrow tube ping, flood hazard assessment, and many others. A within an 8-in.-diameter rain gauge, much like subset of COOP stations with relatively complete, the oil level is measured in an automobile (Fig. 1). long periods of record, and few station moves forms The National Weather Service COOP Observing Handbook (NOAA–NWS 1989) describes the procedure for measuring precipitation from 8-in. AFFILIATIONS: DALY, GIBSON, TAYLOR, DOGGETT, SMITH—Oregon nonrecording gauges as follows: State University, Corvallis, Oregon CORRESPONDING AUTHOR: Christopher Daly, PRISM Group, Remove the funnel and insert the measuring stick Dept. of Geosciences, Oregon State University, Corvallis, OR 97331 into the bottom of the measuring tube, leaving E-mail: [email protected] it there for two or three seconds. The water will darken the stick. Remove the stick and read the The abstract for this article can be found in this issue, following the rainfall amount from the top of the darkened part table of contents. DOI:10.1175/BAMS-88-6-899 of the stick. Example: if the stick is darkened to three marks above the 0.80 inch mark (the longer In final form 30 January 2007 ©2007 American Meteorological Society horizontal white line beneath the 0.80), the rainfall is 0.83 inch. AMERICAN METEOROLOGICAL SOCIETY JUNE 2007 | 899 The measuring stick has a large, labeled tick mark Initial mapping of some of the precipitation-related every 0.10 in., a large, unlabeled tick mark every GEM6 parameters using daily COOP data produced 0.05 in., and small, unlabeled tick mark every inter- spatial patterns that were highly discontinuous in vening 0.01 in. (Fig. 2). space, even on flat terrain away from coastlines. Observations of daily precipitation are needed When we investigated the cause of these spatial dis- to parameterize stochastic crepancies, we found that weather simulation models. precipitation data from most These models, often called of the COOP stations suffered weather generators, are in from observer bias; that is, wide use for a variety of the tendency for the observer applications. They are easy to favor or avoid some pre- to use, and have the abil- cipitation values compared ity to synthesize long, seri- to others. Biases included ally complete time series of underreporting of daily pre- weather data that mimic the cipitation amounts of less true climate of a location, than 0.05 in. (1.27 mm), and a which makes them useful strong tendency for observers in biological and hydro- to favor precipitation amounts logical modeling and cli- divisible by 5 and/or 10 when mate change investigations, expressed as inches. These among others (Richardson biases were not stationary in 1981; Johnson et al. 1996; time, and thus had significant Katz 1996). Weather gen- effects on the temporal trends erators require input param- as well as long-term means of eters, derived from station commonly used precipitation observations, which describe statistics. Stations included the statistical properties of in the USHCN dataset were the climate at a location. also affected, raising ques- Many weather generators use tions about how precipita- a two-state Markov chain of tion trends and variability first order for precipitation from this network should be occurrence, and all other interpreted. generated quantities are de- FIG. 1. Corvallis, Oregon, COOP observer The objectives of this pendent on whether a given Richard Mattix inserting the measuring paper are to make a first day is wet or dry. Therefore, stick into his rain gauge. attempt at quantifying these it is crucial that the relative biases, provide users of frequencies and sequences of wet and dry days are COOP precipitation data with some accurately portrayed in the input parameters. In basic tools and insights for identifying addition, precipitation amounts are often derived and assessing these biases, and suggest from a mixed exponential distribution that is sensi- additional investigations and actions to tive to the frequency of observations of precipitation address this issue. COOP observers in at very low amounts (i.e., less than 1 mm). the United States measure precipitation In a recent study, we used COOP precipitation in English units. Given that the observer data to extend the work of Johnson et al. (2000) bias discussed here is uniquely tied to to spatially interpolate input parameters for the this system, precipitation amounts are Generation of Climate Elements for Multiple Uses given first in inches, followed by mil- (GEM6) weather generator (USDA–ARS 1994). limeter equivalents in parenthesis. All Spatial interpolation of the input parameters other measures are given in standard would allow daily weather series to be generated at metric, or MKS, units. locations where no stations exist. Our goal was to expand the original mapping region from a portion FIG. 2. Standard measuring stick used of the Pacific Northwest to the entire conterminous to record precipitation in a COOP rain United States. gauge. 900 | JUNE 2007 TYPES OF OBSERVER BIAS AND ASSESS- divisible by five- or ten-hundredths of an inch into MENT STATISTICS. We discovered two major separate populations, and compared their means. types of observer bias in our initial investigation: If they were significantly different, a 5/10 bias was 1) so-called underreporting bias, or underreporting indicated. In order to make consistent comparisons of daily precipitation amounts of less than 0.05 in. across a spectrum of frequency bins, it was necessary (1.27 mm); and 2) so-called 5/10 bias, or overreporting to detrend the frequency histogram. We did this by of daily precipitation amounts evenly divisible by 5 fitting a gamma distribution to each station’s pre- and/or 10, such as 0.05, 0.10, 0.15, 0.20, and 0.25 in. cipitation frequency histogram (Evans et al. 2000). (1.27, 2.54, 3.81, 5.08, and 6.35 mm). These two types It was not necessary that the gamma function fit were usually related; a station with underreporting the data either precisely, or without bias; rather, the bias was likely to have a 5/10 bias as well. predictions were used only as a way to detrend the We used daily precipitation data from the National frequency distribution. Climatic Data Center’s (NCDC’s) TD3200 dataset Because of computational constraints in solving (NOAA–NCDC 2006) for this analysis. Each station the gamma distribution, predictions become un- was subjected to data completeness tests of sufficient stable as precipitation approaches zero. Therefore, rigor to ensure reasonable weather generator param- no frequency predictions were made below 0.03 in. eters, given good-quality data. To ensure the accurate (0.76 mm). (This lower bound has no effect on the calculation of wet/dry day probabilities, daily precipita- detection of underreporting bias, because frequen- tion entries that were flagged as accumulated totals for cies were detrended for the 5/10 test only.) In addi- more than one day were set to missing. For a given year tion, no frequency predictions were made above 1 in. to be complete, each of the 26 14-day periods in the year (25.40 mm), because observed frequencies at these had to have at least 12 days (85%) without missing data, precipitation amounts were typically very low. and there had to have been at least 26 (85%) complete We calculated the percent difference, or residual years within the 1971–2000 period.1 GEM6 operates (R), between expected and observed frequencies as on 14-day statistical periods, hence the use of this time block, rather than a monthly time interval. R = 100 × (P – O), (2) We devised two kinds of simple statistical tests to detect stations that exhibited one or both observa- where P is the predicted frequency (via the gamma tional biases. Our underreporting bias test consisted function) and O is the observed frequency. We tested of calculating the ratio the 5s and 10s biases separately. For the fives bias — test, the first residual mean (R1) was calculated by RL = C6–10/C1–5, (1) averaging the residuals over the so-called ones bins, which include all amounts, except those divisible by — where C6–10 is the total observation count in the 5; the second (R5) was calculated as the average of all 0.06–0.10-in. (1.52–2.54 mm) range, C1–5 is the total residuals for the so-called five bins, which include observation count in the 0.01–0.05-in.

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