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Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and Precipitation Comparison

RONALD D. LEEPER AND JARED RENNIE Cooperative Institute for Climate and Satellites–North Carolina, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina

MICHAEL A. PALECKI NOAA/National Climatic Data Center, Asheville, North Carolina

(Manuscript received 29 August 2014, in ﬁnal form 4 February 2015)

ABSTRACT

The U.S. Cooperative Observer Program (COOP) network was formed in the early 1890s to provide daily observations of temperature and precipitation. However, manual observations from naturally aspirated temperature sensors and unshielded precipitation gauges often led to uncertainties in atmospheric measurements. Advancements in observational technology (ventilated temperature sensors, well-shielded precipitation gauges) and measurement techniques (automation and redundant sensors), which improve observation quality, were adopted by NOAA’s National Climatic Data Center (NCDC) into the establish- ment of the U.S. Climate Reference Network (USCRN). USCRN was designed to provide high-quality and continuous observations to monitor long-term temperature and precipitation trends, and to provide an in- dependent reference to compare to other networks. The purpose of this study is to evaluate how diverse technological and operational choices between the USCRN and COOP programs impact temperature and precipitation observations. Naturally aspirated COOP sensors generally had warmer (10.488C) daily maximum and cooler (20.368C) minimum temperatures than USCRN, with considerable variability among stations. For precipitation, COOP reported slightly more precipitation overall (1.5%) with network differences varying seasonally. COOP gauges were sensitive to wind biases (no shielding), which are enhanced over winter when COOP observed (10.7%) less precipitation than USCRN. Conversely, wetting factor and gauge evaporation, which dominate in summer, were sources of bias for USCRN, leading to wetter COOP observations over warmer months. Inconsistencies in COOP observations (e.g., multiday observations, time shifts, recording errors) complicated network comparisons and led to unique bias proﬁles that evolved over time with changes in instrumentation and primary observer.

1. Introduction Fiebrich and Crawford 2009), it has since evolved into the backbone of the U.S. climatology dataset and is widely The detection and attribution of climate signals often used in national (Melillo et al. 2014; Karl et al. 2009), re- rely upon manually operated networks with lengthy re- gional (Allard et al. 2009), and local (Diem and Mote cords (Rasmussen et al. 2012). In the United States, the 2005) climate assessments. Observations of temperature Cooperative Observer Program (COOP) network has and precipitation from the COOP network are taken once collected many decades of manual observations from daily from naturally ventilated temperature shields and thousands of stations. While COOP was originally de- unshielded precipitation gauges, which can introduce sys- signed to provide daily data to agricultural communities tematic biases based on prevailing meteorological condi- (National Research Council 1998; Daly et al. 2007; tions. In addition, changes in COOP instrumentation and shielding, time-of-observation inconsistencies, and station moves affecting sensor exposure (Peterson et al. 1998; Corresponding author address: Ronald D. Leeper, CICS-NC, National Climatic Data Center, 151 Patton Avenue, Asheville, NC Pielke et al. 2007) have occurred throughout the network’s 28801. history. These and other factors can obscure climatic E-mail: [email protected] trends (Peterson and Vose 1997; Peterson et al. 1998),

DOI: 10.1175/JTECH-D-14-00172.1

Ó 2015 American Meteorological Society Unauthenticated | Downloaded 09/26/21 09:33 PM UTC 704 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 32 requiring homogenization corrections (Easterling et al. loading compared to well-ventilated (aspirated) shields 1999; Menne and Williams 2009; Menne et al. 2009) (installed at USCRN stations). A similar radiative bias that are currently available at monthly time scales. occurs during the evening hours when radiative cooling The U.S. National Research Council (NRC 1998), results in cooler nocturnal temperatures from naturally citing these and other challenges (Karl et al. 1995; ventilated sensors. The albedo of the underlying ground Goodison et al. 1998) and in alignment with more recent cover can also impart additional radiative imbalances findings (Fiebrich and Crawford 2009; Hubbard et al. during daylight hours when solar rays reflected from the 2004; Allard et al. 2009; Daly et al. 2007), identified the ground can enter temperature shielding and impact ob- need for a network that was specifically designed to servations (Hubbard et al. 2001). Reflective radiation monitor climate and support climate observation efforts biases can change seasonally with snow cover and health in the United States. Drawing upon the successful of the underlying vegetative ground cover (green-up and technological innovations that have helped fuel wide- brown-down). For precipitation, Rasmussen et al. (2012) spread adoption of automated networks for consistency and others (Devine and Mekis 2008; Goodison et al. 1998; (Fiebrich 2009; Fiebrich and Crawford 2009) and re- Groisman and Legates 1994; Nitu and Wong 2010; Sevruk dundant (three sensors) measurement techniques that et al. 2009) identified wind conditions, gauge wetting improve data quality and continuity (Karl et al. 1995), factor (buildup of precipitation on gauge orifice surfaces), NOAA’s National Climatic Data Center (NCDC) de- gauge evaporation (loss of precipitation from gauge prior ployed an automated climate observation network to measurement), and orifice blockage as some of the named the U.S. Climate Reference Network (USCRN; main factors contributing to uncertainties in precipitation Diamond et al. 2013). measurements (Sevruk et al. 2009). When surface wind Designed with the purpose of detecting U.S. climate deflects around a precipitation gauge, it can create flow trends, the USCRN program broke ground on its first patterns that inhibit precipitation from falling into the station in 2000. USCRN has deployed to date 132 stations gauge (Sieck et al. 2007). The impact of wind on pre- in stable, representative locations across the contermi- cipitation catch is more pronounced for less aerodynamic nous United States (114), Alaska (16), and Hawaii (2) hydrometeors, such as snow, with errors up to 50% of (Diamond et al. 2013). Observations of temperature and total precipitation possible (WMO 2008; Sevruk et al. precipitation are taken from precision instruments cali- 2009). The use of shields around precipitation gauges, as brated to National Institute of Standards and Technology in USCRN, disrupts such flow patterns over the gauge (NIST) traceable standards and regularly monitored and opening, allowing hydrometeors to fall into the gauge and maintained. To preserve data quality and continuity, reduce measurement errors due to winds. This impact is temperature and precipitation sensors are well shielded more discernable for frozen hydrometeors. Groisman and installed redundantly as previously noted. Station et al. (1999) in their comparison study of COOP and au- observations are transmitted hourly via satellite to NCDC, tomated Fisher and Porter gauges found that gauge ex- where data are processed through quality control (QC) posure to wind did not ‘‘noticeably affect’’ observation systems and made available over the web with a latency of error for liquid hydrometeors. one to several hours (Diamond et al. 2013). Human observers also contribute to observational un- As the longevity of the USCRN observation record certainty. Inconsistencies in time of observation, multiday increases, network design differences (instrumentation, reports, and observation methods are other sources of shielding, and observation methods) between COOP error from manually operated networks. Conversely, au- and USCRN will become increasingly relevant in future tomated networks if provided adequate power and ex- climate assessments. Network design has been found to cellent maintenance can preserve consistency by ensuring impact observations of both temperature (Guttman and measurements are taken at designated times and in the Baker 1996; Lin and Hubbard 2004) and precipitation same way (Fiebrich and Crawford 2009). Over time the (Rasmussen et al. 2012). Lin and Hubbard (2004) quality of observations taken from both automated and identified sensor sensitivity, analog signal conditioning, manual networks can degrade with sensor health (age of and data acquisition (e.g., datalogger) as sources of dif- sensor), dirt buildup, and pest infestations, which are ferences in temperature measurements between USCRN, more difficult to detect and resolve for automated net- COOP, and other networks. In addition to potential biases works given the lack of daily visits. However, USCRN associated with station exposure (Guttman and Baker redundant sensors will help mitigate some of these issues. 1996), Harrison (2011) and Nakamura and Mahrt (2005) Earlier investigations (Hubbard et al. 2004; Sun et al. also noted that during calm conditions naturally aspirated 2005) comparing USCRN with other networks [COOP, thermistors (as used in the COOP network) observe Automated Surface Observing System (ASOS)] primar- warmer daily maximum temperatures due to radiative ily focused on side-by-side measurements of temperature

Unauthenticated | Downloaded 09/26/21 09:33 PM UTC APRIL 2015 L E E P E R E T A L . 705 often excluding precipitation disparities. In addition, the and measured. After each observation, observers must side-by-side setup such as studied in Hubbard et al. (2004) empty the gauge for the next 24-h period. More in- concentrated on instrumentation biases related to sensor formation regarding COOP observations and standards shielding, which can change with prevailing meteorolog- can be found in the National Weather Service 10-1315 ical conditions. However, there are additional network (National Weather Service 2014a) manual. related biases that can arise from maintenance (e.g., USCRN stations are automated with observations of degradation of aging sensors, calibration standards), temperature and precipitation taken at a subhourly measurement practices (e.g., reporting times, changing frequency. Temperature is observed using platinum re- observers), and local site characteristics not captured in sistance thermistors (PRTs), each of which is housed those studies. The combined effects of sensor-, maintenance-, within separate fan-ventilated Met One shields (Fig. 1c) measurement-, and siting-related biases may be more with an operational range of 2608 to 858C(2768 to useful to studies using daily COOP measurements in 1858F). While observations from redundant tempera- addition to efforts to homogenize the daily record. The ture sensors are available, USCRN official temperature, purpose of this study is to compare observations of which is compared with COOP in this study, is a single temperature and precipitation from nearby members of value derived from the redundant observations that USCRN and COOP networks under normal operating passes a series of QC checks. conditions. Precipitation is observed with a Geonor T-200B weighing-bucket-type gauge located within a small dou- ble fence intercomparison reference (SDFIR) shield sur- 2. Observations rounding a single Alter shield at most stations (Fig. 1d). The COOP network currently consists of approxi- The all-weather gauge utilizes vibrating wire technology mately 8000 stations across the United States (National to detect precipitation events as small as 0.2 mm with Weather Service 2014b). Volunteers take maximum and aprecisionof0.1mm(Baker et al. 2005; Duchon 2008). minimum temperature and precipitation observations During winter, an antifreeze mixture is added to the gauge once daily at designated times, which vary from station reservoir to melt frozen hydrometeors (only the liquid to station. Maximum and minimum temperatures have equivalent is reported) and an orifice heater is activated to traditionally been observed from liquid-in-glass (LiG) prevent snow and ice from accumulating along the rim of thermometers shielded in naturally aspirated cotton- the gauge. Similar to temperature, the redundant mea- region shelters (CRS) (Fig. 1a). However, these ther- sures of gauge depth are processed through a series of mometers have gradually been phased out since 1983 QC checks before they are used to report official pre- in favor of digital thermistor systems known as the cipitation. Because it is possible for noise (small up and maximum–minimum temperature sensor (MMTS), down depth variations) to occur coincidentally among the which now make up more than 90% of COOP stations redundant sensors, an auxiliary disdrometer (the Vaisala where metadata are available. The MMTS thermometer DRD11A) is used to distinguish between dry and pre- has an operational range between 2628 and 608C(2808 cipitating subhourly periods. USCRN stations are also and 1408F) and is equipped with an indoor digital dis- equipped to monitor additional variables, including solar play. The original display had to be reset daily; however, radiation, surface wind speed, surface IR temperatures, Nimbus displays have the capacity to store up to 35 days relative humidity, and soil moisture and temperature at of previous observations, including hourly measurements 5-, 10-, 20-, 50-, and 100-cm depths. In this study, network (Sensor Instruments Co. Inc. 2000). In addition, these differences will be discriminated by surface wind speed, temperature sensors are shielded from solar insolation which is observed at a height of 1.5 m using the Met One using a multiplate shield (Fig. 1a). model 014A three-cup anemometer with mean wind Precipitation observations are taken from unshielded speed and 10-s gusts provided hourly. For additional in- 8-in. gauges that require a calibrated dipstick to measure formation on USCRN and its monitoring of surface and precipitation in an internal concentrator tube (Fig. 1b). subsurface conditions, refer to Diamond et al. (2013) and Daily accumulations can be measured to a precision of Bell et al. (2013). one-hundredth of an inch or 0.3 mm. Frozen precipitation measurements are reported as ground accumula- 3. Methodology tion and liquid equivalent, which require additional steps. If snow is anticipated, the central tube and funnel To ensure observational differences are the result of top are removed, so as to allow snow to fall naturally network discrepancies, comparisons were only evalu- into the outer diameter without interference. The snow ated for stations pairs located within 500 m. This dis- is then melted and poured into the concentrator tube tance criterion was selected based on an analysis by

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FIG. 1. Observing systems in the study: (a) COOP cotton region shelter (foreground) with LiG thermometer and two COOP beehive multiplate shielding (background) with MMTS thermistor; (b) COOP standard 8-in. precipitation gauge; (c) USCRN Met One fan-aspirated shields with platinum resistance thermometers, in triplicate; and (d) USCRN well-shielded Geonor weighing precipitation gauge (center).

Guttman and Baker (1996). Thirteen station pairs met overhanging trees, and other obstacles, which would have this criterion with collocated station pairs ranging in skewed daily comparisons (temperature and precip- distance from 42 to 400 m. However, preliminary exam- itation) given the small sample size and therefore ex- ination of station metadata showed the St. Mary, Mon- cluded from the analysis. The remaining 12 station pairs tana, pair was sited within close proximity of buildings, (see Fig. 2) were dispersed across the contiguous United

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FIG. 2. Map of USCRN and COOP collocated station pairs located within 500 m.

States for both northern and southern latitudes, providing respectively. Daily precipitation is reported as a 24-h network comparisons from mountainous (Arco, Idaho, accumulation. Start and end hours for USCRN daily and Dinosaur, Colorado) to coastal (Kingston, Rhode aggregates were deﬁned by COOP observation time as Island) and desert (Las Cruces, New Mexico) to humid noted in Table 1 with additional adjustments accounting (Holly Springs, Mississippi) locations. for daylight saving time when appropriate. For three of To compare the two networks, USCRN subhourly the stations [Holly Springs (4N), Agate (3N), Nebraska; data were aggregated into 24-h periods to match daily and Stillwater (2W), Oklahoma], COOP observation COOP measurements at the designated observation times. times changed during the study period with USCRN USCRN daily maximum and minimum temperatures were aggregation times appropriately adjusted. In addition, set to the warmest and coolest 5-min average taken every daily aggregates were only computed if no more than 10 s throughout the 24-h period. Prior to 2006, the moving one hour of data was ﬂagged or missing; otherwise, not average was not available and therefore the maximum available (N/A) would be recorded. On days with and minimum are simply the warmest and coolest 5-min missing data, USCRN and COOP observations were clock averages (1–5, 6–10, ..., 56–60 min of the hour), both assigned N/A if either network had missing daily

TABLE 1. List of USCRN and COOP collocated station pairs.

Distance Elev Temp USCRN station COOP station State separation (m) change (m) sensor Obs hour (LT) Start date Holly Springs 4 N Holly Springs 4 N MS 42.8 0.3 LiG 0630/0800 1 May 2008 Kingston 1 NW Kingston RI 52.8 0.3 LiG 1600 1 Dec 2005 Harrison 20 SSE Agate 3 E NE 94.8 280.5 Digital 1700/1600 1 Jul 2006 John Day 35 WNW Dayville 8 NW OR 99.0 24.6 LiG 1700 1 Jun 2006 Gaylord 9 SSW Gaylord 9 SSW MI 133.6 22.1 Digital 0700 1 Oct 2007 Murphy 10 W Reynolds ID 225.1 6.1 LiG 0800 1 Jun 2006 Crossville 7 NW Crossville Education TN 303.9 31.4 Digital 0700 1 Jan 2006 and Research Center Stillwater 2 W Stillwater 2 W OK 311.9 21.5 Digital 0700/0800 1 May 2006 Las Cruces 20 N Jornada Experimental NM 337.9 1.8 Digital 0800 1 Mar 2007 Range Dinosaur 2 E Dinosaur National CO 338.3 27.4 Digital 1600 1 Aug 2006 Monument Arco 17 SW Craters of the Moon ID 342.5 7.0 Digital 1700 1 Aug 2006 Muleshoe 19 S Muleshoe National TX 400.1 0.6 Digital 0800 1 May 2006 Wildlife Refuge

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FIG. 3. USCRN minus COOP average minimum (blue) and maximum (red) temperature differences for collocated station pairs. COOP stations monitoring temperature with LiG technology are denoted with asterisks. data. These efforts were taken to ensure a more straight- COOP stations observed warmer maximum tempera- forward comparison. tures on average. The direction of network differences was less consistent for minimum temperatures, where COOP stations had warmer (4 of 12) and cooler (8 of 12) 4. Results daily minima compared to USCRN on average. The variability in magnitude (for maximum and minimum) a. Temperature and direction (for minimum) of temperature differences USCRN daily maximum and minimum temperatures among station pairs may be explained by differing me- on average differed from neighboring COOP stations by teorological conditions (surface wind speed, cloudi- 20.488 and 10.368C, respectively. The more moderate ness), local siting (heat sources and sinks), and sensor USCRN daily extremes resulted in a smaller diurnal (poor calibration, health)/human (varying observation temperature range (DTR) that was on average 0.848Cless time, reporting error) error as noted in Wu et al. (2005). than COOP stations. The magnitude of temperature dif- To further explore the impacts of prevailing meteoro- ferences were on average larger for stations operating LiG logical conditions on network comparisons, temperature systems, with mean maximum (minimum) temperature differences were categorized by wind speed (Figs. 4a–d). differences of 20.668C(10.488C), compared to 20.398C While there were daily outliers greater than 6208Cfor (10.318C) differences for the MMTS system. Part of the both maximum and minimum temperatures, the range in reductioninnetworkbiaseswiththeMMTSsystemis network differences for maximum and minimum temper- likely due to the smaller-sized shielding that requires less atures visually seemed to reduce with increasing wind surface wind speed to be adequately ventilated. speeds (Figs. 4a and 4c). For maximum temperature, mean While overall mean differences were in line with side- network differences reduced with increasing wind speed as by-side comparisons of ventilated and nonventilated sensor shielding became better ventilated (Fig. 4b). How- sensors (Hubbard et al. 2004), there was considerable ever, minimum temperature differences increased slightly variability in the mean magnitude of network differ- with wind speed (Fig. 4d), which according to Fig. 4c may ences for maximum and minimum temperatures from be related to an imbalance of more large positive than large station to station (Fig. 3). The size of network differ- negative differences on windy days. ences varied regardless of sensing technology (LiG or Under calm conditions one might expect radiative digital) and station separation distance. However, there imbalances between naturally and mechanically aspi- was some consistency in the direction of network dif- rated shields or differing COOP sensing technologies ferences at least for maximum. For all station pairs, (LiG vs digital sensors) to drive network differences

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FIG. 4. USCRN minus COOP maximum (a) daily differences for COOP stations with LiG (darker hue) and digital (lighter hue) temperature instruments, and (b) mean maximum temperature difference for light 2 2 2 (#1.5 m s 1), moderate (.1.5 and ,4.6 m s 1), and strong ($4.6 m s 1) surface wind conditions as observed by USCRN. (c) Daily minimum and (d) mean minimum temperature differences for the three wind categories in (b).

(Gall et al. 1992; Quayle et al. 1991). While these biases COOP observations become more consistently cooler may account for some variability in the magnitude of than USCRN in-line with side-by-side comparisons. network differences, they do not explain the variation in However, large outliers (.208C) remained, which may the sign of minimum temperature differences (Fig. 3). have biased mean differences in a positive direction for Siting characteristics, which can change over short dis- the stronger wind categories. These results suggest that tances, have been found to inﬂuence surface tempera- some of the minimum temperature differences during tures (Guttman and Baker 1996). For instance, stations windier conditions may not be related to instrumenta- located near trees (Crossville, Tennessee, and Gaylord, tion (radiative) biases, but perhaps linked to variations Michigan) compared to those in the open can be subject in observation times or other observer errors. In these to an insulating effect as described by Groot and Carlson cases, wind speed at the time of USCRN minimum (1996) and result in warmer daily minima. Likewise, temperature may not correspond well with the wind small elevation changes between USCRN and COOP speed at time of COOP minima. In addition, multiday stations (Dinosaur) or those located near a gravel or time-shifted observations associated with periods of parking lot (Murphy, Idaho) can result in sharp thermal rapid temperature change (i.e., frontal passages) can contrasts over short distances. The impacts of siting are result in large network differences. likely more pronounced during calm conditions when Over monthly time scales, there was little seasonal localized factors have more time to affect the thermal variation in network biases for both maximum and properties of the overlying air mass. This is particularly minimum temperatures on average; however, some true during evenings when the boundary layer decouples station pairs did have monthly trends that were more and collocated station pairs are no longer sampling pronounced for maximum than minimum temperatures. a homogeneous air mass (Yao and Zhong 2009). Easterly stations (i.e., Holly Springs), for instance, had 2 At higher wind speeds ($2ms 1), any potential ef- larger maximum and minimum biases on average over fects of local land cover/terrain appeared to diminish summer than winter months (Figs. 5a and 5b). The (smaller envelope of variation) with improved mixing greater network biases over summer months were thought and radiative imbalances becoming more dominate as to be mostly attributed to increases in solar radiation

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FIG. 5. Monthly (a) maximum and (b) minimum temperature difference for station pairs (gray lines) with the mean difference for each set indicated by a bold solid line. Holly Springs (green dashed line), Dinosaur (orange dashed line), and Muleshoe (purple dashed line) were identiﬁed to reveal seasonal variations in network differences for maximum and minimum temperatures.

(radiative biases) in addition to changes in seasonal shielding ventilation, changes in ground cover albedo wind patterns, resulting in fewer well-aspirated windy (snow cover, vegetation green-up or brown-down) that days. Some stations located over the complex terrain of inﬂuence reﬂection of solar radiation from the ground the northwest (i.e., Dinosaur) had an opposite trend into senor shielding, or changes in solar zenith angles with the magnitude of network biases lessened over for higher latitude stations. warmer months; however, this was not true with all Over annual time scales, mean maximum and mini- northwestern station pairs. Still other stations (those in mum temperature biases were also fairly constant over the desert Southwest such as Muleshoe, Texas) had time (Figs. 6a and 6b) with the exception of a few sta- little or no change from month to month despite well- tions. For instance, the Arco station pair had one of known seasonal winds from the North American the largest annual changes in temperature biases be- monsoon. The exact causes of such seasonal variations tween 2007 and 2008 for both maximum and minimum or lack thereof are not completely understood given temperatures. Station metadata over this period reveal the limited number of stations considered in this study. a change in Nimbus serial numbers (instrumenta- However, some causes to seasonal variations can in- tion replacement) that coincided well with sizeable clude seasonal patterns in surface winds that impact shifts in daily temperature differences, possibly due to

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FIG. 6. Annual (a) maximum and (b) minimum temperature difference for station pairs (gray lines) with the mean difference for each set indicated by a bold solid line. Arco (dashed lines) shows a sizable shift over time in network differences. calibration difference between Nimbus units (Figs. 7a b. Precipitation and 7c). Other station pairs had apparent shifts in temperature bias that were correlated with other types Daily precipitation observations were not well corre- of COOP metadata events. In late 2009, the frequency lated between the two networks. Scatterplots of daily of large temperature differences at Dinosaur was observations (Fig. 8a) indicate the two networks were considerably reduced at about the time a new primary not temporally aligned given the clustering of points along station observer was appointed. When this primary the x and y axes. In addition, rare recording errors such observer departed in late 2011, large temperature as duplicate observations, decimal misplacement, mis- differences reemerged (Figs. 7b and 7d). Variations placement of monthly total for last day of the month, and over annual time scales identiﬁed the sensitivity of multiday sums (when COOP observations are based network differences to changes in station observer on multiple days of precipitation but entered on one cal- and impacts of sensor error, which may be difﬁcult endar day) resulted in large network contrasts. These to identify without collocated stations or redundant inconsistencies and other errors made it challenging to sensors. accurately quantify network differences at the daily scale.

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FIG. 7. Daily maximum temperature (red) and minimum temperature (blue) network differences at (a),(c) Arco and (b),(d) Dinosaur with USCRN (purple) and COOP (green) metadata records of station and/or observer changes marked with vertical bars.

To proceed, daily observations from both networks COOP observations are likely due to the lack of gauge were grouped into precipitation events (multiday accu- shielding but may also be impacted by the added com- mulation) to improve the temporal alignment. Each plexity of observations (winterizing and melting). As event begins on a day when either network observes noted by WMO (2008), wind-induced errors are more precipitation and ends on the ﬁrst day neither reports pronounced for frozen (10%–50%) than liquid (2%– precipitation. Precipitation events were discarded if ei- 10%) hydrometeors. These results may also help to ex- ther network reported missing data during an event. By plain some of the annual variations, as more of the aggregating daily data into multiday events, most timing southern stations were included in the analysis after inconsistencies (shifts of 24 h or less) were resolved. 2007. One exception to this was the Gaylord pair, where From an event perspective, precipitation observations the COOP station reported more winter precipitation. from the USCRN and COOP station pairs were better An analysis of depth change revealed USCRN algo- correlated as shown in Fig. 8b; however, shifts in ob- rithms used to calculate depth change (precipitation) servation time greater than 24 h (multiday observations) underreported precipitation at this station due to a sub- can still complicate comparison results. optimally performing disdrometer, which at times im- Overall, USCRN observed 1.5% less precipitation properly classiﬁed precipitation as sensor noise. than neighboring COOP stations. However, this was not COOP reported precipitation amounts over warmer uniform across all station pairs by year or by season months were greater than USCRN on average. These (Figs. 9a and 9b). Annual differences shifted from dryer differences may be partially related to the enhanced COOP observations (2005 and 2006) relative to USCRN spatial variability of unorganized convective activity to slightly wetter from 2007 onward. Seasonally, COOP that is more dominant over warmer months (Tokay et al. reported less (more) precipitation than USCRN stations 2014). This is particularly true for coastal areas (Kings- during winter (summer) months. The dryer wintertime ton) or in the Southeast, where daytime convection

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FIG. 8. Scatterplots of USCRN and COOP accumulated precipitation at Murphy for (a) daily and (b) event-based time periods. could trigger afternoon pop-up thundershowers. In one near-freezing (mean temperature between 08 and 58C), particular event at Gaylord, the COOP observer re- and freezing (mean temperature , 08C) conditions. For ported 20.1 mm more than the USCRN gauge located all warm and near-freezing events, USCRN observed within 133 m. Despite radar estimates that were more 4.4% and 2.9% less precipitation than nearby COOP similar to USCRN measurements, there were nearby gauges, respectively (Fig. 10a). This tendency was re- areas with intense precipitation, suggesting the COOP versed for frozen conditions, when USCRN observed observations may have been valid. 10.7% more precipitation than COOP. Shielded USCRN Wetter COOP observations over warmer months may gauges observed more precipitation for frozen hydro- also have been associated with seasonal changes in meteors in part due to shielding, as suggested by gauge biases. For instance, observation errors related to Rasmussen et al. (2012). gauge evaporation and wetting factor are more pro- Precipitation event differences were also impacted by nounced in warmer than cold conditions, while the op- wind speed and intensity. Wind speed criteria were posite is true of wind-related errors (WMO 2008), as based on Guttman and Baker (1996) and reported in noted previously. Because of design, the Geonor gauge meters per second as an average over hours USCRN 2 is more prone to wetting errors with a larger wetting observed precipitation. For light wind events (#1.5 m s 1), factor (9.0) than the standard 8-in. gauge used at COOP USCRN reported 7.5% less precipitation than COOP. As stations, which had a wetting factor of 3.2 (WMO 2008; wind speeds increased, USCRN accumulation deﬁcit was M. Hall 2014, personal communication). In addition, reduced to 2.8% less than COOP for moderate winds USCRN does not use an evaporative suppressant to with USCRN exceeding COOP by 3.4% for wind speeds 2 limit gauge evaporation during the summer, which is of 4.6 m s 1 or greater (Fig. 10b). As with temperature, also not an issue for the funnel-capped COOP gauge there was a change in sign of network differences when according to Golubev et al. (1992). The combination of the impact of gauge shielding was more inﬂuential on elevated biases for USCRN and reduction in COOP precipitation capture. wind-related errors over warmer months might help to For precipitation intensity, USCRN observed less than explain a portion of the seasonal variations in network neighboring COOP for all categories (Fig. 10c). However, differences. COOP precipitation observations were more similar to 2 To investigate these results further, precipitation events USCRN during higher rate events ($4mmh 1), with were categorized by air temperature, wind speed, and COOP having a 0.33% wet bias. This was considerably precipitation intensity (Fig. 10). Only events where both less than the 3.8% wet bias for the lightest intensity 2 networks observed precipitation (neither station reporting (#2mmh 1) category. The reduction in precipitation zero for an event) were considered, similar to Tokay et al. differences with intensity can be partially explained by (2010). USCRN hourly temperatures during periods with a reduction wind and wetting loss biases with raindrop precipitation were averaged into an event mean and used size. Intense precipitation events consist of larger hy- to group events into warm (mean temperature $ 58C), drometeors that are less aerodynamic and reduce wind

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FIG. 9. USCRN minus COOP precipitation observations for each station (gray lines) and all-station average (bold line) over (a) annual and (b) mean monthly time scales.

biases from COOP gauges (Sieck et al. 2007)andquickly and cooler daily minimum temperatures. In addition, moisten gauge oriﬁce walls, cutting down on wetting mean temperature biases were reduced with the smaller- losses from the Geonor gauge. sizedshieldoftheMMTSsensorasnotedinQuayle et al. (1991). However, comparisons from an operational setting 5. Discussion revealed that not all station pairs had similarly sized mean temperature differences. The variability in the a. Temperature magnitude of temperature bias was detected for COOP On average, temperature differences between USCRN stations operating both LiG and digital sensors, suggest- and COOP were in general agreement with side-by-side ing other factors might have inﬂuenced mean differences. comparisons conducted by Hubbard et al. (2004) of aspi- For instance, prevailing meteorological conditions can at rated and nonaspirated thermometer systems, with natu- times amplify (clear skies) and reduce (windy conditions) rally ventilated systems observing warmer daily maximum the magnitude of radiation errors of naturally aspirated

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FIG. 10. Event (USCRN minus COOP) precipitation differences grouped by prevailing meteorological conditions during events observed at the USCRN station. (a) Event mean temperature: warm ($58C), near-freezing ($08 2 and ,58C), and freezing conditions (,08C); (b) event mean surface wind speed: light (#1.5 m s 1), moderate (.1.5 2 2 2 and ,4.6 m s 1), and strong ($4.6 m s 1); and (c) event precipitation rate: low (#2.0 mm h 1), moderate (.2.0 and 2 2 ,4.0 mm h 1), and intense ($4.0 mm h 1).

systems used in the COOP network. These meteorological the impact of local siting was not as discernable, which conditions can vary from day to day and seasonally may be due to daytime boundary layers generally being (i.e., frequency of synoptic activity) in addition to geo- well mixed at time of maxima. While the effects of ground graphical variations (high latitudes, maritime, moun- cover and local siting were not fully explored here, these tainous) that were not fully explored in this study. factors when combined with prevailing meteorological Additionally, ground cover and local siting may be conditions may help to partially explain the variations other factors contributing to the variation in magnitude in magnitude (maximum and minimum) and direction and direction (for minimum) of network biases among (minimum) of mean network temperature differences station pairs. Hubbard et al. (2001) convincingly showed among station pairs in addition to seasonal variations. that the reflective properties of the underlying ground Annual evaluations of temperature differences re- cover can contribute to radiative errors during the day vealed inconsistencies in observation practice (varying by reflecting incoming solar radiation into the sensor observation time, accounting errors, and others) that housing of both CRS and mulitplate shields used in the seemed to change with station observer at Dinosaur in COOP network. The reflective properties of the un- addition to sensor-related differences at Arco. While derlying ground for northern snowy stations may change accounting errors (decimal misplacement) can result in over submonthly and seasonal scales depending on large outliers for temperature and precipitation, varying snowpack. Other stations in the network may also have observation time and multiday observations are more changes in surface albedo throughout the year with difficult to detect because the network differences are vegetative green-up and brown-down. less systematic (variable in magnitude and sign). For The effects of local siting seemed to be more pro- instance, the magnitude of network temperature biases nounced when the atmosphere was less mixed, affecting caused by inconsistent reporting time will be amplified primarily minimum temperatures of stations located in (lessened) over periods with a rapidly changing (persis- proximity of trees (Crossville and Gaylord) and gravel tent) air mass, which may also have a seasonal cycle pads in the case of Murphy. For maximum temperature, depending on location. In cases of frontal activity,

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FIG. 11. Subhourly USCRN redundant temperature observations from 13 through 15 Dec with USCRN (blue) and COOP (red) time-of-observation (X) and minimum (–) temperatures at COOP observation time (orange bar). extreme differences (.208C) in network temperature Despite important network differences for tempera- were observed. An example is shown in Fig. 11, where the ture and their possible causes as noted previously, it observer seemingly reported a multiday minimum that should be noted that differences between the two net- was 20.98C cooler than USCRN. The COOP minimum works using very different sensors with different types of temperature was likely observed after the defined COOP aspiration (natural vs mechanical) are expected. While observation time, following the passage of a cold front. such differences may affect meteorological evaluations In this study, these scenarios complicated the analysis (daily highs and lows), climate trends, presuming the of temperature and precipitation event differences. This types of errors remain consistent over time, may not is particularly true when network differences were cat- be negatively impacted. For instance, comparisons be- egorized by prevailing meteorological condition. If ob- tween USCRN and homogenized COOP data (referred servations of daily extremes are not taken from the to as the U.S. Historical Climatology Network), which stated 24-h period, then the USCRN-monitored condi- attempt to remove such inconsistences as described by tions may not reflect the actual atmospheric conditions Menne and Williams (2009), had very similar maximum when COOP measurements were taken, which may and minimum national temperatures (Fig. 12), suggest- skew the results. In rare cases, large differences due to ing these routines are effective in line with Menne et al. observer error (temperature and precipitation) were (2010). However, these homogenization methods are submitted to NCDC dataset stewards for corrective ac- currently not available at the daily scale, and this study tion and removed from this study. One possible solution identifies some of the day-to-day variations in network to observation time inconsistencies might be to allow biases that will make daily homogenization efforts observers to report observation times as was noted in challenging without another network that can provide Tokay et al. (2010), rather than presuming all observa- consistency at daily time scales, such as USCRN. tions are taken at a designated time. Such metadata b. Precipitation would greatly enhance the usability of the COOP network and likely would have improved comparisons be- Precipitation differences between the two networks tween USCRN and COOP and better explain network were primarily influenced by hydrometeor type and biases with respect to atmospheric conditions, which wind conditions. For frozen hydrometeors or strong would be more useful to daily homogenization efforts. wind conditions, USCRN well-shielded gauges reported

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FIG. 12. USCRN (blue) and USHCN version 2.5 (red) annual (a) maximum and (b) minimum temperature anomalies. more precipitation than unshielded COOP gauges. undercatch with the Geonor T-200B gauge with respect These results are in-line with other studies comparing to the pit gauge varied with total precipitation (an ap- shielded and unshielded gauges (Golubev et al. 1992). proximate to precipitation intensity) with larger biases However, the opposite was true for liquid hydrometeors for smaller rain events similar to this study. However, it or in calm conditions when COOP stations tended to should be pointed that the Canadian study did not use observe more precipitation than collocated USCRN the same algorithm to compute precipitation from the gauges. While these results are in contrast to Golubev Geonor, as USCRN technicians have developed their et al. (1992), they are similar to recent comparison own open source approach to processing Geonor gauge studies using the Geonor T-200B gauge (Devine and depths. Mekis 2008; Gordon 2003). Devine and Mekis (2008) Differences between USCRN and COOP precipita- reported that the Geonor gauge underreported a refer- tion totals over warmer periods are likely linked to ence pit gauge more so than a manually operated type B Geonor gauge design, maintenance practice, and com- gauge used in Canada. In addition, the magnitude of putational methods used to evaluate precipitation,

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TABLE 2. Daily network precipitation and differences for the Stillwater station pair for precipitation events 3249 and 3250.

Station Time (LT) and date USCRN COOP Difference (mm) Event ID Stillwater 0600 6 Aug 2005 18.8 0.0 18.8 3249 Stillwater 0600 7 Aug 2005 0.0 0.0 0.0 — Stillwater 0600 8 Aug 2005 1.5 17.3 215.8 3250 Total 20.3 17.3 3.0 which can impact measurement errors. The Geonor both networks reported precipitation were used in the T-200B gauge has an open vertical shaft instead of analysis shown in Fig. 10. An example of this is provided a funnel as used in the COOP network. The open shaft in Table 2, which shows USCRN and COOP daily ob- design, while minimizing splash-in and -out errors, in- servations for two precipitation events. Summing this creases the surface area fallen precipitation can adhere entire period, USCRN reports slightly more precipita- to, resulting in a wetting factor (9.0) that is nearly 3 times tion (3.0) than COOP. However, the COOP station did larger than the COOP gauge (3.2). However, when the not report precipitation for the earlier event (event funnel is removed for winterization, the COOP gauge 3249), instead presumably observing it all in the later will presumably have a similar wetting factor to the event (event 3250). Because this later event was the only Geonor. In addition, the absence of a funnel for the one with precipitation from both networks, it was the Geonor allows evaporation to take place over warmer only event included in the categorical analysis of months, which according to Golubev et al. (1992) is precipitation differences, which biased COOP mea- negligible for the standard 8-in. gauge used at COOP surements wetter by 15.8 mm in this case. stations. This is particularly true because no evaporative While the frequency of COOP shifts greater than 24 h suppressants are added to the Geonor gauge over the is not known, one metric that might be helpful is a count warm season. It was thought that frequent observations of precipitating days. For the station pairs considered at a subhourly temporal resolution would mitigate evap- here, USCRN had on average reported 16 more pre- oration biases, as suggested by Sevruk et al. (2009). cipitating days per station than COOP when daily totals However, this does not imply that methods used to eval- exceeded 0.3 mm (COOP detection limit). Quantita- uate depth change (precipitation) would be completely tively, the mean network difference when both USCRN insensitive to gauge evaporation. Additional analysis (not and COOP reported precipitation was much larger shown here) revealed evaporation may impact the start (3.6%) compared to overall difference (1.5%), sug- time and accuracy of USCRN event precipitation due to gesting COOP observations of precipitation tended to the use of a 2-h smoothing method. Given this potential be aggregated over multiple days. It is important to note sensitivity, USCRN computation methods used to de- that this does not apply to overall or seasonal network termine depth change (precipitation) are currently being differences when shifts in time are not as important. reevaluated with a goal of reducing evaporation and other With that said, the impact of inconsistent reporting algorithm sensitivities. times on the absolute value of network differences by The combination of both wetting factor and evapo- meteorological condition is not fully understood, but it ration create a seasonal bias cycle that is more pro- may not have impacted trends in network differences nounced over summer than winter months for the (i.e., USCRN observing more precipitation with in- Geonor gauge. In a similar manner, the winterization of creasing wind speed). the COOP gauge (greater wetting factor) and the lack of shielding also results in a seasonal bias cycle that is 6. Conclusions ampliﬁed over winter months. The out-of-phase seasonal cycle of biases for the USCRN and COOP gauges This study compared two observing networks that will may help to explain some of the seasonal differences be used in future climate and weather studies. Using between the two networks. very different approaches in sensing technologies, shield- As noted previously with temperature, evaluations of ing, operational procedures (manual vs automated), and network differences for precipitation by meteorological QC methods, the two networks provided contrasting per- conditions can be complicated by observer inconsis- spectives of daily maximum and minimum temperatures tencies. Despite grouping days with precipitation into and precipitation. Temperature comparisons between multiday events, precipitation reports shifted more than stations in local pairings displayed differences that on av- 24 h can result in wetter COOP events. This mostly af- erage were similar to side-by-side comparisons, where ra- fects categorical differences because only events where diative imbalances between shielding and aspiration types

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TABLE 3. Unique factors contributing to USCRN and COOP observational uncertainties and advantages as a result of network design.

Uncertainties Advantages COOP USCRN COOP USCRN Inconsistent Electrical Station density Consistent d Observation time (variable) d Noise d Observation time d Instrumentation (LiG or digital) d Overvoltage/brownouts d Instrumentation d Shielding (shelter or multiplate) d Shielding type d Observation methods (among d Maintenance practices observers) d Observation methods Lack of shielding for precipitation Nature degradation Historical context High temporal resolution gauge d Snowcapped sensors (subhourly) d Creature infestations Calculation algorithms Visited and Traceability (access to QC’d d Three observations used maintained daily and raw datasets) to calculate one best Data continuity and quality value for temperature (redundancy) and precipitation

resulted in warmer and cooler COOP maximum and For both temperature and precipitation, COOP ob- minimum temperatures, respectively. server inconsistencies complicated network compari- However, in contrast to side-by-side comparisons, sons. Varying COOP observation times and/or multiday mean network differences for maximum and minimum observations resulted in many of the larger network temperatures were highly variable from station to sta- differences for temperature ($208C) and precipitation tion, which was unrelated to station separation or the (.100%). These irregularities in addition to sensor er- types of sensing technology used at COOP stations (LiG rors (poor calibration) can result in unique bias proﬁles or digital). Station contrasts were partially attributed to for each station that can change over time with station local factors including siting (station exposure), ground moves, changes in the primary observer, and instrumen- cover, and geographical aspects, which were not fully tation replacement. These inconsistencies complicated explored in this study. These additional factors are network comparisons and, for analysis of precipitation by thought to accentuate or minimize anticipated radiative prevailing meteorological conditions, may have elevated imbalances between the naturally and mechanically as- network disparities. pirated systems, which may have also resulted in sea- All observing systems have observational challenges sonal trends. Additional analysis with more station pairs and advantages, as noted in Table 3 for these two net- may be useful in evaluating the relative contribution of works. The COOP network has many decades of ob- each local factor. servations from thousands of stations, but it lacks For precipitation, network differences also varied consistency in instrumentation type and observation over monthly time scales due to the seasonality of the time in addition to instrumentation biases. USCRN is respective gauge biases. For instance, the unshielded very consistent in time and by sensor type, but as a new COOP gauge expectedly had greater wind biases that network it has a much shorter station record with were more pronounced over winter (less aerodynamic sparsely located stations. While observational differ- frozen hydrometeors) windy conditions, resulting in ences between these two separate networks are to be a dry bias with respect to USCRN. For warmer low- expected, it may be possible to leverage the observa- intensity conditions when Geonor gauge biases (wetting tional advantages of both networks. The use of USCRN factor and gauge evaporation) were elevated, COOP as a reference network (consistency check) with COOP stations had a wet bias relative to USCRN. Additional may prove to be particularly useful in daily homogeni- analysis of Geonor raw depths revealed that a portion of zation efforts in addition to an improved understanding the COOP wet bias was due to gauge evaporation, which of weather and climate over time as the periods of identiﬁed a possible weakness in the current USCRN overlap between the two networks lengthen. algorithm used to determine precipitation from gauge depth data. Additional investigations into the perfor- Acknowledgments. This work was supported by mance of the USCRN precipitation algorithm and gauge NOAA through the Cooperative Institute for Climate sensitivity to evaporation are ongoing. and Satellites—North Carolina under Cooperative

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