U.S. Climate Reference Network after One Decade of Operations: Status and Assessment

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.

The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.

The radiation shield bias of the maximum and minimum temperature system (MMTS) relative to the US Climate Reference Network (CRN) was investigated when the ground surface is snow covered. The goal of this study is to seek a debiasing model to remove temperature biases caused by the snow-covered surface between the MMTS and the US CRN. The side-by-side comparison of air temperature measurements was observed from four combinations of temperature sensor and temperature radiation shield in both MMTS and US CRN systems: (a) a standard MMTS system; (b) an MMTS sensor housed in the CRN shield; (c) a standard US CRN system; and (d) a CRN temperature sensor housed in the MMTS shield. The results indicate that the MMTS shield bias can be seriously elevated by the snow surface and the daytime MMTS shield bias can additively increase by about 1 °C when the surface is snow covered compared with a non-snow-covered surface. A non-linear regression model for the daytime MMTS shield bias was developed from the statistical analysis. During night-time, both the cooling bias and the warming bias of the MMTS shield existed with approximately equal frequencies of occurrence. However, the debiased night-time data based on the linear model developed in this study was less significant due to relatively smaller biases during night-time. The debiasing model could be used for the integration of the historical temperature data in the MMTS era and the current US CRN temperature data and it also could be useful for achieving a future homogeneous climate time series. This article is a US Government work and is in the public domain in the USA. Published in 2005 by John Wiley & Sons, Ltd.

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

The U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (+52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation.

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.

The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land–atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models. Significance Statement Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.

Core Ideas This study quantifies the variability of soils at US Climate Reference Network sites. Soil properties were determined from the analysis of soil core samples. Soil properties displayed large variability with depth and location within and among sites. Variability of soil properties helped determine and interpret soil moisture variability. The objective of this study was to provide direct measurements of soil properties for 70 of the 114 US Climate Reference Network (USCRN) sites across the continental United States. Soil properties determined from the analysis of soil core samples include the particle size distribution (PSD, consisting of sand, silt, and clay contents), soil texture classifications, bulk density (BD), and the soil moisture content at water potentials of 33 kPa (field capacity, FC) and 1500 kPa (wilting point, WP). Sand, silt, and clay contents of the 70 sites indicated about 10 soil texture classifications as follows: three sites with loamy sand, 15 with sandy loam, two with clay, 11 with silt loam, five with clay loam, 10 with loam, seven with sand, eight with silty clay loam, four with sandy clay, and three with silty clay. The comparison of soil properties among soil depths and pits indicated considerable variability, with the silt, clay, and sand contents varying more with soil depth than with location at individual sites. The silt content tended to decrease with soil depth, clay tended to increase, and sand tended to vary randomly with depth. Regression lines fitted to values of FC and WP between the pits indicated a slope > 0.8, R2 > 0.88, and RMSE ranging from 2.7 to 4%. Compared with FC and WP, BD was less consistent among the pits, with slope = 0.6, R2 = 0.4, and RMSE of about 0.2 g cm−3.

Core Ideas Hydraprobe measurements can be quantified. USCRN gauges distributed nationally are analyzed. Random error magnitudes are a function of how recently precipitation has occurred. Soil moisture estimates are crucial for hydrologic modeling and agricultural decision‐support efforts. These measurements are also pivotal for long‐term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been the past decade during which ground‐based soil moisture sensory resources have become sufficient to tackle these important challenges. Despite this progress, random and systematic errors remain in ground‐based soil moisture observations. Such errors must be quantified (and/or adequately minimized) before such observations can be used with full confidence. In response, this paper calibrates and analyzes US Climate Reference Network (USCRN) profile estimates at each of three sensors collocated at each USCRN location. With each USCRN location consisting of three independent, Hydraprobe measurements, triple collocation analysis of these sensory triads reveals the random error associated with this particular sensing technology in each individual location. This allows quantification of the accuracy of these individual profiles, the random errors associated with these measurements in different geographic locations, and offers the potential for more adept quality control procedures in near real time. Averaged over USCRN gauge locations nationally, this random error is determined to be approximately 0.012 m 3 /m 3 .

Future measurements for climate monitoring

Surface incident solar radiation (Rs) drives weather and climate changes. Observations of Rs have been widely used as reference data to evaluate climate model simulations and satellite retrievals. However, few have studied uncertainties of Rs observations, especially long term. This paper compares Rs from 1995 to 2011 at collocated sites collected by the Surface Radiation Budget Network (SURFRAD), the U.S. Climate Reference Network (USCRN) and the AmeriFlux network. SURFRAD stations have measured separately the diffuse and direct components of Rs as well as Rs by a pyranometer, while Rs was measured by a pyranometer or a net radiometer at the USCRN and AmeriFlux sites. Rs can be calculated by summing the diffuse and direction radiation measurements. Rs measured by the summation technique was compared those measured by a pyranometer or a net radiometer at collocated sites. Agreement among these four independent Rs measurements is good with correlation coefficients higher than 0.98 and an average error (one standard deviation) of about 4% at both hourly and monthly time scales. Rs has a large spatial variability at the hourly time scale, even exceeding 100 W m−2 in ∼6 km. This spatial variability is substantially reduced at the monthly time scale. The two independent measurement systems at the SURFRAD sites agree rather well in annual variability of Rs with an average relative standard deviation error of 34%. The errors are 71% and 85% for the USCRN and AmeriFlux sites. Evidently, caution should be taken when using the Rs data collected at the USCRN and AmeriFlux sites to study annual variability of Rs.

Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U.S. Climate Reference Network (USCRN)

Temperature normals have been estimated for stations of the newly developed US Climate Reference Network (USCRN) by using USCRN temperatures and temperature anomalies interpolated from neighboring stations of the National Weather Service Cooperative Station Network (COOP). To seek the best normal estimation approach, several variations on estimation techniques were considered: the sensitivity of error of estimated normals to COOP data quality; the number of neighboring COOP station used; a spatial interpolation scheme; and the number of years of data used in normal estimation. The best estimation method we found is the one in which temperature anomalies are spatially interpolated from COOP stations within approximately 117 km of the target station using a weighting scheme involving the inverse of square difference in temperature (between the neighboring and target station). Using this approach, normals of USCRN stations were generated. Spatial and temporal characteristics of errors are presented, and the applicability of estimated normals in climate monitoring is discussed. Copyright © 2005 Royal Meteorological Society

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in‐situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006–2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA‐Interim) involved in NA‐CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA‐Interim, MERRA2 and NARR) and a few in situ‐based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ‐based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year‐round, while RCMs are colder (warmer) in winter (summer) with biases ranging from −0.5 (0.9)°C for RCMs at 0.44° to −0.2 (1.4)°C for CRCM5‐UQAM‐11. In situ‐based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis‐driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.