An observational study of precipitation types in the Alaskan Arctic

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

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.

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. 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.

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

Satellite passive microwave (PMW) sensors rely on the scattering signature from ice and snow particles at high frequencies (>89 GHz). The local atmospheric conditions dictate the ice particle sizes, shapes, and distribution that ultimately add uncertainty to snowfall retrieval algorithms. The Goddard Profiling Algorithm (GPROF), a widely used precipitation retrieval algorithm, uses the Multi-Radar Multi-Sensor (MRMS) system snowfall product as the reference snowfall in its a priori databases. The MRMS snowfall is estimated using a static reflectivity–snowfall (Z–S) relationship that does not account for local meteorological conditions. First, snowfall rates estimated from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are compared using three types of GPROF algorithms: one Bayesian (GPROF V7) and two neural network (GPROF-NN 1D and GPROF-NN 3D). GPROF-NN 3D performed the best of the three models across snowfall detection and quantitative metrics with Heidke skill scores (HSS) as high as 60% and a probability of detection of up to 70%. It is also shown that artificial biases can be introduced when evaluating model performance using a snowfall threshold to determine snowing versus nonsnowing pixels. Second, investigation into the MRMS Z–S is performed by incorporating in situ snowfall data from the U.S. Climate Reference Network (USCRN). A large range of snowfall rates was found for a given reflectivity value, and temperature is able to constrain the corresponding Z–S relationships. This dynamic temperature-dependent Z–S is found, when applied to the MRMS reflectivity, to slightly improve annual biases between MRMS and USCRN as well as GPROF snowfall detection metrics.

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.

Ancillary information that exists within rain gauge and radar-based data sets 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 data sets used for reference; the National Centers for Environmental Prediction (NCEP) stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded data sets. The NCEP stage IV data set is available at 4km hourly and includes radar-gauge bias adjusted precipitation estimates. The NNR data set is available at 1km at 5-minute 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 Z-R 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-minute 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-minute 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.

Future measurements for climate monitoring

Due to the limitation of remote sensing observation, the research on the three-dimensional precipitation in Northwest China is insufficient. However, the launch of GPM provides convenience for the study of precipitation structures and types in Northwest China. In this work, the spatio-temporal distributions of convective and stratiform precipitation and corresponding thermal structures are analyzed during summer of 2014-2019 in Northwest China based on GPM observations, EAR5 reanalysis and IGRA2 datasets. The result shows that the stratiform precipitation is dominant in Northwest China and four representative sub-regions are divided to further discuss (Tianshan Mountain, Tarim Basin, Qilian Mountain and eastern part of Northwest China). The storm tops of convective precipitation are generally 2-3 km higher than those of stratiform precipitation, and the storm top reaches the maximum in the Tianshan Mountain (16 km) and the lowest in the Tarim Basin (10 km). Moreover, the maximum rain rate of convective precipitation below 4 km occurs in the Tianshan Mountain, while maximum rain rate of stratiform precipitation occurs in the eastern part of Northwest China. The maximum latent heating of both precipitation types occurs at 4-6 km. The peak frequency of convective precipitation mainly appears in the afternoon, whereas the diurnal variation of stratiform precipitation displays a bimodal peak (in the early morning and evening). Furthermore, the intensities of both precipitation types vary with the total column water vapor and follow an approximate quadratic function relationship. The precipitation conversion of convective precipitation and CAPE are obviously larger than those of stratiform precipitation. There is convergence in the lower troposphere and divergence in the upper troposphere, which is favorable to occurrence of precipitation (except for Tarim Basin). Additionally, the positive temperature and humidification are significant in the lower-middle troposphere during process of both precipitation types. This study aims to reveal the features of convective and stratiform precipitation from the perspective of GPM remote sensing observation and provide reference for numerical simulation in arid-semiarid regions.

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.

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.