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Ronald D. Leeper, Bryan Petersen, Michael A. Palecki, and Howard Diamond

Abstract

Agricultural drought has traditionally been monitored using indices that are based on above-ground measures of temperature and precipitation that have lengthy historical records. However, the period-of-record length for soil moisture networks is becoming sufficient enough to standardize and evaluate soil moisture anomalies and percentiles that are spatially and temporally independent of local soil type, topography, and climatology. To explore these standardized measures in the context of drought, the U.S. Climate Reference Network hourly standardized soil moisture anomalies and percentiles were evaluated against changes in the U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore time scales (i.e., 1–6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., top and column) depths to determine those that were more closely align with evolving drought conditions. Results indicated that the upper-level depths (5, 10, and 20 cm, and top layer aggregate) and shorter averaging periods were more responsive to changes in USDM drought status. This was particularly evident during the initial and latter stages of drought when USDM status changes were thought to be more aligned with soil moisture conditions. This result indicates that standardized measures of soil moisture can be useful in drought monitoring and forecasting applications during these critical stages of drought formation and amelioration.

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Ronald D. Leeper, Jared Rennie, and Michael A. Palecki

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 establishment 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 independent 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 (+0.48°C) daily maximum and cooler (−0.36°C) 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.

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Ronald D. Leeper, Michael A. Palecki, and Egg Davis

Abstract

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.

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Brian R. Nelson, Olivier P. Prat, and Ronald D. Leeper

Abstract

Ancillary information that exists within rain gauge and radar-based datasets provides opportunities to better identify error and bias between the two observing platforms as compared to error and bias statistics without ancillary information. These variables include precipitation type identification, air temperature, and radar quality. There are two NEXRAD-based datasets used for reference: the National Centers for Environmental Prediction (NCEP) Stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded datasets. The NCEP Stage IV dataset is available at 4 km hourly and includes radar–gauge bias adjusted precipitation estimates. The NNR dataset is available at 1 km at 5-min and hourly time intervals and includes several different variables such as reflectivity, radar-only estimates, precipitation flag, radar quality indicator, and radar–gauge bias adjusted precipitation estimates. The NNR data product provides additional information to apply quality control such as identification of precipitation type, identification of storm type and ZR relation. Other measures of quality control are a part of the NNR data product development. In addition, some of the variables are available at 5-min scale. We compare the radar-based estimates with the rain gauge observations from the U.S. Climate Reference Network (USCRN). The USCRN network is available at the 5-min scale and includes observations of air temperature, wind, and soil moisture, among others. We present statistical comparisons of rain gauge observations with radar-based estimates by segmenting information based on precipitation type, air temperature, and radar quality indicator.

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Ronald D. Leeper, Jesse E. Bell, Chanté Vines, and Michael Palecki

Abstract

Accurate and timely information on soil moisture conditions is an important component to effectively prepare for the damaging aspects of hydrological extremes. The combination of sparsely dense in situ networks and shallow observation depths of remotely sensed soil moisture conditions often force local and regional decision-makers to rely on numerical methods when assessing the current soil state. In this study, soil moisture from a commonly used, high-resolution reanalysis dataset is compared to observations from the U.S. Climate Reference Network (USCRN). The purpose of this study is to evaluate how well the North American Regional Reanalysis (NARR) captured the evolution, intensity, and spatial extent of the 2012 drought using both raw volumetric values and standardized anomalies of soil moisture. Comparisons revealed that despite a dry precipitation bias of 22% nationally, NARR had predominantly wetter 5-cm volumetric soil conditions over the growing season (April–September) than observed at USCRN sites across the contiguous United States, with differences more pronounced in drier regions. These biases were partially attributed to differences between the dominant soil characteristics assigned to the modeled grid cells and localized soil characteristics at the USCRN stations. However, NARR was able to successfully capture many aspects of the 2012 drought, including the timing, intensity, and spatial extent when using standardized soil moisture anomalies. Standardizing soil moisture conditions reduced the magnitude of systematic biases between NARR and USCRN in many regions and provided a more robust basis for utilizing modeled soil conditions in assessments of hydrological extremes.

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Ronald D. Leeper, Jesse E. Bell, and Michael A. Palecki

Abstract

The interpretation of in situ or remotely sensed soil moisture data for drought monitoring is challenged by the sensitivity of these observations to local soil characteristics and seasonal precipitation patterns. These challenges can be overcome by standardizing soil moisture observations. Traditional approaches require a lengthy record (usually 30 years) that most soil monitoring networks lack. Sampling techniques that combine hourly measurements over a temporal window have been used in the literature to generate historical references (i.e., climatology) from shorter-term datasets. This sampling approach was validated on select U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) stations using a Monte Carlo analysis, which revealed that shorter-term (5+ years) hourly climatologies were similar to longer-term (10+ year) hourly means. The sampling approach was then applied to soil moisture observations from the U.S. Climate Reference Network (USCRN). The sampling method was used to generate multiple measures of soil moisture (mean and median anomalies, standardized median anomaly by interquantile range, and volumetric) that were converted to percentiles using empirical cumulative distribution functions. Overall, time series of soil moisture percentile were very similar among the differing measures; however, there were times of year at individual stations when soil moisture percentiles could have substantial deviations. The use of soil moisture percentiles and counts of threshold exceedance provided more consistent measures of hydrological conditions than observed soil moisture. These results suggest that hourly soil moisture observations can be reasonably standardized and can provide consistent measures of hydrological conditions across spatial and temporal scales.

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Philip J. Klotzbach, Eric C. J. Oliver, Ronald D. Leeper, and Carl J. Schreck III

Abstract

The winter of 2014/15 brought record snow totals to portions of southeastern New England. Additionally, over 90% of Boston Logan Airport snowfall during the winter fell during phases 7 and 8 of the Madden–Julian oscillation (MJO) index. This motivated the authors to investigate potential connections between intense southeastern New England snowstorms and the MJO in the historical record. It was found that southeastern New England snowfall, measured since the 1930s at several stations in the region, recorded higher than average winter snowfalls when enhanced MJO convection was located over the western Pacific and the Western Hemisphere (phases 7–8). Similarly, snowfall was suppressed when enhanced MJO convection was located over the Maritime Continent (phases 4–5). The MJO also modulates the frequency of nor’easters, which contribute the majority of New England’s snowfall, as measured by reanalysis-derived cyclone tracks. These tracks were more numerous during the same MJO phases that lead to enhanced snowfall, and they were less common during phases with less snowfall.

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Olivier P. Prat, Brian R. Nelson, Elsa Nickl, and Ronald D. Leeper

Abstract

Three satellite gridded daily precipitation datasets—PERSIANN-CDR, GPCP, and CMORPH—that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20 years to over 35 years. The products intercomparisons are performed at various temporal (daily to annual) resolutions and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in situ datasets from the Global Historical Climatology Network (GHCN-Daily) and the U.S. Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio, among others). All SPPs were found to underestimate extreme rainfall (i.e., above the 90th percentile) from about −20% for CMORPH to −50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned, from the long-term qualitative analysis of hydroclimatological properties to the quantification of daily extreme events, for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications.

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Ronald D. Leeper, John Kochendorfer, Timothy A. Henderson, and Michael A. Palecki

Abstract

A field experiment was performed in Oak Ridge, Tennessee, with four instrumented towers placed over grass at increasing distances (4, 30, 50, 124, and 300 m) from a built-up area. Stations were aligned in such a way to simulate the impact of small-scale encroachment on temperature observations. As expected, temperature observations were warmest for the site closest to the built environment with an average temperature difference of 0.31° and 0.24°C for aspirated and unaspirated sensors, respectively. Mean aspirated temperature differences were greater during the evening (0.47°C) than during the day (0.16°C). This was particularly true for evenings following greater daytime solar insolation (20+ MJ day−1) with surface winds from the direction of the built environment where mean differences exceeded 0.80°C. The impact of the built environment on air temperature diminished with distance with a warm bias only detectable out to tower B′ located 50 m away. The experimental findings were comparable to a known case of urban encroachment at a U.S. Climate Reference Network station in Kingston, Rhode Island. The experimental and operational results both lead to reductions in the diurnal temperature range of ~0.39°C for fan-aspirated sensors. Interestingly, the unaspirated sensor had a larger reduction in diurnal temperature range (DTR) of 0.48°C. These results suggest that small-scale urban encroachment within 50 m of a station can have important impacts on daily temperature extrema (maximum and minimum) with the magnitude of these differences dependent upon prevailing environmental conditions and sensing technology.

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Jay H. Lawrimore, David Wuertz, Anna Wilson, Scott Stevens, Matthew Menne, Bryant Korzeniewski, Michael A. Palecki, Ronald D. Leeper, and Thomas Trunk

Abstract

The National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA’s National Weather Service in 2013. Subsequently, NOAA’s National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset.

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