The U.S. Climate Reference Network (USCRN) monitors precipitation using a well-shielded Geonor T-200B gauge. To ensure the quality and continuity of the data record, the USCRN adopted an innovative approach to monitor precipitation using redundant technology: three vibrating-wire load sensors measuring the liquid depth of a weighing-bucket gauge. In addition to detecting and flagging suboptimally operating sensors, quality assurance (QA) approaches also combine the redundant observations into a precipitation measurement. As an early adopter of this technology, USCRN has pioneered an effort to develop QA strategies for such precipitation systems.
The initial USCRN approach to calculating precipitation from redundant depth observations, pairwise calculation (pairCalc), was found to be sensitive to sensor noise and gauge evaporation. These findings led to the development of a new approach to calculating precipitation that minimized these nonprecipitation impacts using a weighted average calculation (wavgCalc). The two calculation approaches were evaluated using station data and simulated precipitation scenarios with a known signal. The new QA system had consistently lower measures of error for simulated precipitation events. Improved handling of sensor noise and gauge evaporation led to increases in network total precipitation of 1.6% on average. These results indicate the new calculation system will improve the quality of USCRN precipitation measurements, making them a more reliable reference dataset with the capacity to monitor the nation’s precipitation trends (mean and extremes). In addition, this study provides valuable insight into the development and evaluation of QA systems, particularly for networks adopting redundant approaches to monitoring precipitation.
Precipitation is a fundamental meteorological and climatological variable. Variations in precipitation patterns can disrupt agricultural productivity and in extreme cases foster drought or flood conditions. From 2011 to 2012, natural disasters related to extreme precipitation patterns (droughts and floods) cost the United States over 47 billion dollars (NOAA 2013). These extreme conditions contribute to secondary hazards that include mudslides, disease pandemics, heat waves, and forest fires, all of which further increase environmental impacts, property damage, and human casualties. Over climatological time scales, changing spatiotemporal patterns of precipitation can impact agricultural productivity (desertification/persistent floods), the availability and quality of water resources, and place further strain on already aging infrastructure (dams, levees, bridges, etc.; Kunkel et al. 2013). High-quality in situ measurements of precipitation are fundamental to ensuring the quality (through validation) of radar and satellite precipitation estimates and quantitative precipitation forecasts. In an effort to improve U.S. precipitation monitoring, NOAA’s National Climatic Data Center (NCDC) deployed the U.S. Climate Reference Network (USCRN) to support hydrological studies (flood and drought extremes) and to accurately monitor the nation’s precipitation trends over climatological time scales.
Observing ground-based precipitation accurately is a challenging task (Rasmussen et al. 2012). Few observing networks are designed adequately to monitor the variety of hydrometeor types while mitigating the adverse effects of surface winds, sensor noise, gauge evaporation, and other sources of observation biases on precipitation measurements (Goodison et al. 1998; Rasmussen et al. 2012; You et al. 2007). In addition, quality assurance (QA) methods used to ensure the validity of detected precipitation signals and to mitigate these biases are often not well documented (Fiebrich et al. 2010; Shafer et al. 2000). To address these challenges, the USCRN adopted an innovative approach to monitor precipitation redundantly from a well-shielded enclosure (Fig. 1a).
The USCRN uses the all-weather Geonor T-200B weighing precipitation gauge to monitor precipitation redundantly. The gauge is configured such that the reservoir (weighing bucket in Fig. 1b) is suspended from three independent load sensors. The load cells observe reservoir weight by magnetically plucking an internal wire and monitoring its frequency of vibration, which will vary with wire tension as described by Duchon (2008). When calibrated, the Geonor gauge can reliably detect changes in gauge depth to a resolution of 0.1 mm. The redundant sensors improve the resilience of the precipitation-observing system against single-sensor degradation and failure, as the additional sensors continue to monitor precipitation until the gauge is repaired. The redundant monitoring also ensures the continuity of the data record, which is necessary to effectively monitor climate (Diamond et al. 2013; National Research Council 1999; Trenberth et al. 2002), in addition to providing information that can be used to improve the detection of a precipitation signal from gauge noise. For instance, intercomparisons of the redundant sensor depth change can be used to determine if an increase in gauge depth is the result of random noise detected by a single sensor or precipitation equally observed by all three sensors.
The USCRN also endeavors to reduce environmental issues that can negatively impact precipitation measurements. To inhibit frozen precipitation from collecting on the interior walls and capping the gauge, a heating tape is applied to the Geonor throat for stations located in colder climates. A better-known bias often discussed in the context of precipitation measurement is wind errors, which can bias measurements as much as 50% (Sevruk et al. 2009). The USCRN lessens the impacts of surface winds by observing precipitation from a well-shielded enclosure. The majority of USCRN gauges are surrounded by a small double fence intercomparison reference shield (SDFIR) with an interior single-alter shield (Fig. 1b). In locations where siting and/or material transport become an issue (mostly in Alaska), the gauges are shielded by a double-alter shield. These shielding arrangements were found to reduce wind-related errors in sensitivity tests with the all-weather Geonor T-200B precipitation gauge (Baker et al. 2005b). Finally, each station is also equipped with a Hydrological Services tipping-bucket rain gauge model TB-3 for added redundancy during liquid precipitation; however, real-time QA processes do not currently use tipping-bucket data.
While clearly beneficial, redundant measurements of gauge depth increase the complexity of QA systems by requiring both traditional quality control (QC) checks on raw gauge depths and a computational algorithm to compute an observation quantity from the redundant measures of gauge depth. In this study, the traditional definition of a QA system is extended to include this additional calculation. As an early adopter of redundant technology, the USCRN has pioneered the development of QA systems that process redundant measurements to enhance the quality of observations (temperature and precipitation) and continuity of the data record. The current QA system features a pairwise calculation (pairCalc) of depth changes that has evolved over time as a disdrometer (used to detect atmospheric wetness) and higher time-resolution (5 min) observations were brought online during the earlier period of the network’s history, which is briefly described by Baker et al. (2005a). As operations stabilized, internal evaluation of USCRN precipitation measurements revealed pairCalc may have sensitivities to sensor noise and gauge evaporation. Comparing closely spaced members of the USCRN and the Cooperative Observer Program (COOP) network, Leeper et al. (2015) speculated that precipitation differences between the two networks were partially due to gauge attributes, such as wetting factor, sensor noise, and gauge evaporation, that at times adversely affected the performance of pairCalc. These issues in addition to COOP observer biases (Daly et al. 2007; Holder et al. 2006; Fiebrich and Crawford 2009) led to a small underreporting of USCRN precipitation compared to COOP.
In response, a new QA system has been developed based on a weighted average calculation (wavgCalc) that better utilizes the redundant information from the three load cells (wires) to mitigate the impact of both sensor noise and gauge evaporation on precipitation measurements. The purpose of this study is to outline the QA techniques USCRN has explored to process redundantly monitored gauge data and to document the performance of these methods using field data and synthetically generated precipitation events. The outcome of this comparison study not only validates the USCRN QA approach but also provides valuable insight into development and evaluation strategies for precipitation QA systems in general. This is particularly true for QA specialists of other networks considering the adoption of redundant observation systems as this approach to monitoring precipitation becomes more widespread.
2. Calculation methods for redundant systems
The two calculation methods, pairCalc and wavgCalc, were designed for the same USCRN precipitation system, which is configured to report raw gauge depths (1-min average of thirty 2-s samples) from the redundant load sensors every 5 min. The two approaches apply an identical set of QA checks on the raw gauge depth values to ensure data quality. These QA tests include a range check (each load sensor separately) that at the upper limit ensures gauge depths are within the operational capacity of the gauge (600 or 1000 mm) and that at the lower limit validates sensor health; failed sensors report negative depths (Fig. 2a). To limit false reports of precipitation that may arise from sensor noise (i.e., wind loading, electrical issues, temperature dependencies, among others; Figs. 2b and 2c), a detection threshold of 0.2 mm is applied. However, changes in gauge depth as small as 0.1 mm are discernable beyond this threshold (i.e., 0.3 mm is detectable). To handle instances of gauge maintenance, animal infestations, and/or electrical issues that result in large synchronous (among the three wires) increases in gauge depth (Fig. 2d), an upper threshold on depth change of 25 mm is enforced. For additional cases when these QA checks fail to prevent false precipitation, an independent disdrometer is also used to determine the presence of precipitation (wetness). If no wetness is observed, then any reported increases in gauge depth are not included in the precipitation calculations. As noted previously, these QA checks are applied to both computational algorithms.
While these methods have similar raw gauge data QA checks, the main distinction between them is how depth changes are computed and redundant observations are merged into a single precipitation measurement. Fundamentally, the current method relies on pairwise agreement of depth changes, using redundancy as a double or triple check on the measurement. This is the approach used by the USCRN program in calculating air temperature from redundant measurements (Palecki and Groisman 2011). However, air temperature measurements experience much less noise among the redundant sensors than that which exists with gauge measurements, which tend to have both diurnal and nonsystematic noise signals. The second approach pools available information from the redundant depth measurements to identify the most reasonable precipitation signal by giving greater weight to less noisy measurements. The following sections briefly describe the existing (pairCalc) and recently developed (wavgCalc) QA systems.
PairCalc requires gauge depths from the preceding 2 h and the current hour (a total of 3 h) to evaluate subhourly depth changes. Subhourly depth change is computed for each wire separately over the current hour. Depth changes are computed by differencing the current depth with a reference depth determined from the previous (2 h) depth measurements. Reference depths are derived in one of two ways. If precipitation was observed within the last 2 h, then the reference depth is set to the gauge depth when precipitation was last recorded; otherwise, the reference depth is an average of all wire depths over the previous 2 h. Each wire reference depth is then deducted from the current wire depth to quantify depth changes (wire deltas). Wire deltas are then compared in pairwise fashion (wire1–wire2, wire1–wire3, and wire3–wire2) as a consistency check to identify and remove poorly behaved (e.g., noisy and broken) wires. There are four possible outcomes from the pairwise comparison:
All three pairwise differences are less than 0.2 mm (all three wires pass).
A single pairwise difference is less than 0.2 mm (both wires in that pair pass).
Two pairwise differences are less than 0.2 mm (only the wire common to both pairs passes).
No pairwise difference is less than 0.2 mm (invoke the storm clause).
In case 4, intense precipitation can cause water within the gauge to occasionally slosh randomly, altering the load on each wire, resulting in failed pairwise agreement checks. In these cases, agreement is relaxed, allowing larger pairwise differences among the redundant measurements (up to 20% of the three-wire delta mean). Deltas that pass the consistency check are used to determine depth change from an arithmetic mean. For instance, if the three sensors (in order) reported a 5-min depth change of 3.2, 4.0, and 3.8 mm, all three wires would initially fail the pairwise check (none agrees to within 0.2 mm). The storm clause would relax the pairwise agreement to 0.7 mm for this subhourly period (mean delta of 3.6 mm × 20%). Using the new agreement threshold, two pairwise differences now pass this check (less than 0.7 mm) with the third wire common to both checks used to determine total precipitation (based on pairwise outcome case 3) of 3.8 mm. A flowchart describing this process has been provided in Fig. 3 with additional information for this algorithm provided in Baker et al. (2005a).
Similar to pairCalc, wavgCalc computes precipitation from the same 3-h window (previous 2 h plus current hour). However, wire deltas are computed as a change in gauge depth between successive subhourly periods in a manner similar to the way manual evaluations might be performed (current subhourly depth minus previous subhourly depth). Wire deltas are then averaged using a weighted mean. Wire weights are determined based on the average delta variance of each wire over the 3-h period. The delta variance is calculated for each of the three wires, k = 1, 2, 3 [Eq.( 1)], where X is delta, i is the ith subhourly period, is the three-wire delta mean, and n is the total number of subhourly periods:
The delta variance is a relative measure of noisiness among the wires, with wire weights assigned inversely proportional to the delta variance such that noisier wires (more variance) are weighted less. A flowchart of this algorithm is provided in Fig. 4.
Precipitation measurements from the two QA systems were evaluated against station data to compare relative differences and then by synthetically generated precipitation events to quantify QA performance against a known precipitation signal. The generated precipitation scenarios were designed to include sensor noise and gauge evaporation signals to evaluate the QA systems’ sensitivity to these processes. Initially, QA calculations of precipitation were compared using all USCRN and U.S. Regional Climate Reference Network (USRCRN) stations. A more thorough investigation based solely on a USCRN subset of 42 stations (Fig. 5) was designed to explore how environmental conditions such as temperature, wind speed, and precipitation intensity impacted total precipitation. These analyses were conducted over the period of record, where observations were taken at a 5-min frequency (2006–07 for most stations to 2012).
Evaluations of method performance were carried out with synthetic precipitation events using a precipitation generator. Precipitation scenarios of known subhourly intensity and total accumulation were used to initialize the generator (Fig. 6a). The generator produces synthetic gauge data (depths from each wire and wetness) that match the precipitation scenario and can be processed through both QC algorithms. This approach allows the two methods to be evaluated against a known precipitation signal in much the same way a “true” dataset is used. Additionally, the generator has the capacity to embed defined levels of sensor noise (Fig. 6a) and gauge evaporation (Fig. 6b) for each redundant wire separately as is observed in the field. The magnitude of noise variations is randomly generated based on user-defined range specifications using a constrained random walk that limits the number of steps that can move away from the actual precipitation value. Gauge evaporation and sensor noise are two of the most important physical processes that QA systems mitigate to reduce measurement uncertainty. Generated data are then processed through each of the two QA methods for quantitative comparisons with respect to the known artificial signal.
Given that generator-embedded signals are applied randomly for each 5-min time step, an ensemble of simulations for each artificial case was used to evaluate QA performance. In this study, synthetic cases were examined using 100-member ensembles for each gauge evaporation (0.00, 0.01, or 0.02 mm per 5 min) and wire noise (000, 111, 113, 133, and 333 in tenths of millimeters for each wire) combination. For instance, a scenario with an evaporation level of 0.02 mm and a 113 noise setting would have a randomly generated loss (from evaporation) between 0 and 0.02 mm for each wire in addition to variations in depth for wire1 and wire2 of ±0.05 mm (total range equaling 1.0) and a higher range (±0.15 mm) for wire3 due to noise. Each simulated case has a total of 1500 simulations (100 per scenario times 15 scenarios). The performance of QA calculations was evaluated using ensemble mean absolute error (MAE) as described by Legates and McCabe (1999), where Ps is the known precipitation signal, Pi is method-generated precipitation for the ith simulation, and n is the number of simulations [Eq. (2)]:
a. Station observations
1) All stations
The wavgCalc method calculated 1.6% more total precipitation than pairCalc on average over the combined networks (USCRN and USRCRN). The increase in reported precipitation was consistent across individual stations in the network (Table 2) with more than 87% of stations having an increase in accumulated precipitation of at least 0.5%. Those stations having a reduction in total precipitation by at least .5% represented less than 5% of the network. The increase in reported precipitation by wavgCalc relative to pairCalc was also consistent across annual and monthly time scales (Figs. 7a and 7b). Seasonally, QA system differences were slightly larger (>1.8%) from late winter to early spring (Fig. 7b). Seasonal trends in precipitation differences may be attributed to the performance of the auxiliary disdrometer used to detect falling precipitation by both methods. Tabler (1998) found the sensor type used by USCRN may fail to detect precipitation in colder conditions, as frozen hydrometeors can strike the sensing plate and be bounced or be blown off before detection.
2) Station subset
A more detailed analysis using a subset of 42 USCRN stations shown in Fig. 5 was performed to evaluate calculation method differences with respect to surface conditions (air temperature, wind, and intensity). Periods of continuous precipitation (referred to as precipitation events) were defined as the time interval between the first and last hour both algorithms reported zero precipitation. Precipitation events were categorized by wavgCalc total precipitation and placed into greater than, less than, or equal to pairCalc bins.
From the 42 stations, 31 825 precipitation events were identified. The wavgCalc method observed more (less) precipitation than pairCalc for 63.9% (19.1%) of events with both QA systems reporting the same precipitation (within a tenth of a millimeter) for the other 17% (Fig. 8a). Distinguishing periods of precipitation between warm (average temperature greater than or equal to 5°C), near-freezing (between 5° and −5°C), and freezing (less than or equal to −5°C) conditions revealed the two calculation methods were more dissimilar in colder conditions as noted previously (Fig. 8b). The percent of precipitation events where both algorithms had the same accumulation diminished from a high of 19.3% for warm events to a low of 7.7% of events during freezing conditions. In addition, the percent of events in which wavgCalc was “greater than” (from 62.0% to 65.7%) and “less than” (from 18.7% to 26.5%) pairCalc both increased from warm to freezing conditions. The increase of precipitation dissimilarities between the two QA systems may be linked to the ineffectiveness of some collocated disdrometers during cold, snowy conditions. Failure to detect wetness when (within the 5-min window) increases in gauge depth occur seems to result in dissimilar QA responses that are sensitive to the way depth change is evaluated.
QA differences were also examined by surface wind speed and precipitation intensity. The percentage of events, during which wavgCalc observed more precipitation, dropped from 63.7% to 42.9% as winds speeds increased from light (<2 m s−1) to strong (7 m s−1) conditions (Fig. 8c). Similar to the temperature categories, the detection of wetness may be less reliable during windier surface conditions, where hydrometeors, if light enough, can be swept past the disdrometer and drive up QA differences. Precipitation intensity revealed the two QA methods were more similar during high- than low-intensity events (Fig. 8d). In addition, the wavgCalc method tended to report more precipitation during light-precipitation-rate events when sources of measurement error (sensor noise, gauge evaporation) make up a greater percentage of total precipitation.
Case studies of individual events revealed some additional insight into QA differences. For instance, pairCalc missed a precipitation event detected by both wavgCalc and a collocated tipping bucket at Yuma, Arizona (Fig. 9a), as a result of overly stringent wire agreement checks (failed pairwise check). Conversely, poor estimates of reference depth due to sensor noise led to a presumably false precipitation event reported by pairCalc at Joplin, Missouri (Fig. 9b) that neither the wavgCalc system nor the tipping bucket observed. In other cases, suspicious disdrometer behavior was found to result in sizable differences between the two methods (Figs. 9c and 9d), as noted previously during cold conditions. In these instances, an intermittent wetness signal (frequent change in sensor resistance) was observed, which generally resulted in lower wavgCalc precipitation totals compared to pairCalc despite both using the same sensor. For wavgCalc, increases in gauge depth during periods when wetness was not observed (Figs. 9c and 9d) are excluded from reported precipitation. While this is also true for pairCalc, this method was capable of capturing these depth increases later in time because depth change is computed with respect to a moving 2-h average (reference depth). By averaging previous depth changes, pairCalc has a limited memory of earlier depth increases when evaluating the current hour. This can negatively impact subhourly precipitation rates. Figure 9e illustrates this point. Stringent pairwise checks initially caused pairCalc to miss the initiation of a precipitation event, as observed by wavgCalc. However, pairCalc was able to recapture a portion of missed precipitation 2 h later when over 23 mm of precipitation were recorded in a single 5-min period at Durham, North Carolina. During that same subhourly period, wavgCalc reported 0.5 mm, which was in better agreement with observed depth change. Not only did pairCalc underreport total precipitation but also poorly distributed reported precipitation over time, affecting precipitation rates. While the intermittent wetness signal was usually observed during colder conditions, it should be noted that not all snowy periods had an intermittent wetness signal, as shown in a January precipitation event in Fairbanks, Alaska (Fig. 9f).
b. Synthetic precipitation scenarios
Precipitation scenarios used to evaluate QA performance are listed in Table 1. In each of these events, levels of wire noise and gauge evaporation were allowed to vary between ranges observed in the field. The first case represents a heavy precipitation scenario that was 4.4 h in duration with an average and peak precipitation rate of 22.3 and 58.8 mm h−1, respectively, for a total accumulation of 98.9 mm. Second, the very light event had an average intensity of 0.19 mm h−1 over a 4.5-h period with a total accumulation of 0.89 mm. The nonprecipitation event tested the methods against several hours of zero precipitation to evaluate their tendency to report false precipitation or type II errors. The final case was a constant-precipitation event that lasted 10 h with precipitation falling at a constant rate of 0.3 mm per 5-min period, for a total of 36 mm.
1) Heavy precipitation
For the no-evaporation and no-wire-noise case, pairCalc and wavgCalc were both error free with a MAE of zero for the heavy precipitation event (Table 3). As wire noise levels increased, both algorithms displayed higher levels of error (MAE). Of the two methods, pairCalc was more sensitive to elevated noise levels with an ensemble MAE range of 0–0.24 mm. Ensemble MAEs for wavgCalc were generally less, ranging from 0 to 0.19 mm for the same set of noise levels. Ensemble MAEs for pairCalc and to a lesser extent wavgCalc were found to reduce slightly with elevated evaporation signals. These results indicate that the positive errors (false precipitation) effect of wire noise may have been countered by the negative errors (missed precipitation) resulting from gauge evaporation within some of the simulations, resulting in overall lower ensemble MAE mean. Regardless, the wavgCalc system was less sensitive to evaporation and wire noise compared to pairCalc.
2) Very light precipitation
The lighter precipitation signal in this scenario resulted in higher MAEs relative to the heavy event, particularly as a percentage of total precipitation (Table 3). However, wavgCalc ensemble errors were still consistently lower than pairCalc. For the no-evaporation and no-wire-noise case, wavgCalc had a substantially lower MAE (0.08 mm) compared to pairCalc (0.49 mm, or more than 50% of total precipitation). PairCalc ensemble errors for the levels of noise and gauge evaporation ranged from 0.23 to 0.88 mm, or from 25% to 99% of event total precipitation. The range of MAEs for wavgCalc was much lower, between 0.08 and 0.23 mm, or 9%–25% of total precipitation. Higher MAEs were expected for lower-precipitation-rate events, as errors due to gauge noise and evaporation signals make up a greater percentage of total precipitation (lower signal-to-noise ratios), as shown in Figs. 6a and 6b.
3) Nonprecipitating and constant-precipitating cases
In a similar manner, wavgCalc ensemble MAEs for both nonprecipitating and constant-precipitating scenarios were generally less than those for pairCalc (Table 3). When no gauge evaporation and no wire noise were included, both QA methods successfully reported no precipitation for the nonprecipitation case (MAEs of zero). However, this was not true, as noise levels were elevated. Ensemble MAEs for pairCalc and wavgCalc ranged between 0.00 and 0.19 mm and 0.00 and 0.03 mm, respectively. The smaller error suggests the wavgCalc system had fewer ensemble members reporting false precipitation based on noise than pairCalc. For the constant-precipitating case, the two calculation methods performed well (minimum error) when no evaporation and no wire noise were applied. However, when wire noise and gauge evaporation were included, the pairCalc MAEs ranged from 0 to 1.75 mm. Once again, wavgCalc MAEs were much lower, ranging between 0 and 0.19 mm.
5. Discussion and conclusions
Two distinct approaches to combining redundant gauge depth observations into a precipitation measurement were compared. Despite both methods using the same set of QC checks on raw data, the manner in which redundant measures were combined had important impacts on reported precipitation with more than 90% of the network having some change in precipitation of at least 0.5% or more. Synthetically generated precipitation comparisons revealed that QA methods responded differently to simulated gauge evaporation and sensor noise signals. These differences were more pronounced for the very light precipitation scenario based on MAE as a percent of total precipitation. In every simulated case, the new weighted average calculation (wavgCalc) had a lower measure of error compared to the current pairwise calculation (pairCalc) regardless of sensor noise or gauge evaporation. This was also true for the nonprecipitating scenario, which indicates wavgCalc had a lower tendency to report false precipitation (type II errors). These results also suggest that wavgCalc was less sensitive to these nonprecipitating processes. Field comparisons revealed that the lessening of these sensitivities and easing of restrictive pairwise comparisons increased total station precipitation for more than 87% of the network. On average, USCRN stations reported 1.6% more precipitation using the wavgCalc method, which is similar in magnitude to the undercatch USCRN had with respect to COOP as reported by Leeper et al. (2015). A reevaluation of Leeper et al. (2015) using wavgCalc found USCRN reporting 0.21% more precipitation than collocated COOP stations.
Subhourly precipitation rates with wavgCalc were also found to be more realistic than pairCalc. The averaging approach of reference depths in pairCalc allows missed precipitation to be recaptured in subsequent subhourly periods. While this may improve total precipitation over longer time scales (e.g., monthly and annual), recaptured precipitation was found to negatively impact subhourly precipitation rates, creating unrealistic 5-min intensity values in some events. These scenarios were not common but more pronounced for colder precipitation events when disdrometer performance may be degraded (intermittent wetness signal), as frozen hydrometeors can fall undetected (Tabler 1998). However, disdrometer performance during snowy conditions was not always degraded, so additional research is currently being conducted at the precipitation test bed in Marshall, Colorado, to further evaluate disdrometer performance and to identify sensor-related QC checks to better evaluate the quality of disdrometer measurements from the field.
One caveat of this study is the lack of a “true” precipitation dataset applied to the Geonor gauge. However, attempts were made to address this limitation by developing a precipitation generator to quantify QA performance with respect to a simulated known precipitation event. Generator simulations conducted without noise and gauge evaporation provide a true dataset equivalent from which to draw conclusions about the performance of both QA systems. With that said, further investigations evaluating both methods are ongoing, including a gauge evaporation field study conducted over the summer of 2013 and a disdrometer comparison study as noted previously.
In conclusion, two QA systems were extensively evaluated with the weighted average calculation (wavgCalc) system found to be less sensitive to wire noise and gauge evaporation, which from station comparisons generally resulted in increased precipitation and improved subhourly precipitation rates. Given the reliability of wavgCalc to detect artificial precipitation signals and the robustness of this QA system to withstand station irregularities (i.e., maintenance and broken wires), the wavgCalc system has proven to work well across the USCRN. Furthermore, by ensuring the quality of USCRN subhourly precipitation measurements, precipitation data from wavgCalc will be better suited for validation studies (model, radar, and satellite), hydrological forecasts (floods and droughts), and other high-temporal-resolution weather and climate impact studies in addition to accurately monitoring the nation’s precipitation trends over climatological time scales from both mean and extreme perspectives. This study also provides an evaluation and testing outline that other networks can use to validate QA systems for precipitation in addition to highlighting techniques USCRN has explored while developing QA approaches for redundantly monitored precipitation systems.
This work was supported by NOAA through the Cooperative Institute for Climate and Satellites–North Carolina under Cooperative Agreement NA09NES4400006. The USCRN is supported by NOAA’s Climate Program Office. We especially thank Scott Embler and Diana Kantor for their technical assistance and Michael Kruk, Scott Applequist, Russell Vose, Jay Lawrimore, Tom Peterson, and the external reviewers for their editorial suggestions. The views and opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U.S. government position, policy, or decision.
Current affiliation: Global Science and Technology, Inc., Greenbelt, Maryland.