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Soil bulk electrical conductivity (BEC) was evaluated alongside soil volumetric water content (VWC) and soil temperature measurements using the HydraProbe (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP) with accuracy range of BEC ≤ 0.3 S m−1, and the time domain reflectometry (TDR)‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP) suitable for BEC up to 0.6 S m−1, at 23 stations of the U.S. Climate Reference Network. Previous evaluations revealed inconsistent performance of both sensors in some clay soils using manufacturer‐recommended calibrations in converting dielectric permittivity measurements to VWC. Here, we found that hourly values of BEC reached 0.6 S m−1 in high clay content soils and exceeded 2 S m−1 in high saline soils, and these high values of BEC were associated with poor performance and failures of both HP and AP sensors. Large values of BEC occurred in predominantly saturated soils where VWC values reached about 0.5 m3 m−3 for saline soils and about 0.7 m3 m−3 for clay soils, while low magnitudes of BEC were associated with low soil water content and seldomly saturated soils. Low hourly BEC values of less than 0.1 S m−1 were observed in wide variety of soil types, where sensor performance was typically excellent. The most influential factor on BEC was high soil water content conditions. Although dielectric permittivity measurements in estimating the soil water content were sensitive to BEC as some high clay content and high salinity soils increased BEC, the impact of large BEC on dielectric permittivity measurements was smaller in the well‐drained top soil layers than in deep soil layers that remained near saturation. Soil temperature had only a small impact on BEC. With high clay content and high salinity, the specific area of clay minerals was also associated with the magnitude of BEC. Core Ideas: Estimated soil bulk electrical conductivity (BEC) and soil volumetric water content (VWC) at soil depths of 5, 10, 20, 50, and 100 cm were evaluated across 23 US Climate Reference Network stations.The estimation of BEC and VWC was based on the use of the 50 MHz HydraProbe and the Acclima 1 GHz time‐domain reflectometry (TDR)‐315L.The evaluation suggested that the clay type, specific surface area, clay content, soil salinity, and soil temperature combined to affect how soil BEC increased and decreased with soil water content. [ABSTRACT FROM AUTHOR]

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

Gauge-based precipitation measurements suffer from undercatch due to the effects of wind, with solid-precipitation measurements especially susceptible to such errors. When it is snowing and windy, unshielded precipitation gauges can catch less than half of the amount of precipitation of a gauge that is protected from the wind. For this reason, the US Climate Reference Network (USCRN) developed a large, double-layer, wooden wind shield called the Small Double Fence Intercomparison Reference (SDFIR). In past studies, the SDFIR has been demonstrated to be the most effective wind shield in use in any weather or climate network, reducing solid-precipitation undercatch to less than 10 % in wind speeds up to 8 ms-1. However, the wooden SDFIRs are subject to decay, they are difficult to replace and maintain, and they hinder access to maintaining the gauge. For these reasons, a new precipitation gauge wind shield called the Low Porosity Double Fence (LPDF) has been developed for use in the USCRN. Tested at three separate sites chosen for prevalent windy and snowy weather, the precipitation measurements recorded within the LPDF compared well to the SDFIR. After more than 2 years of measurements, the total precipitation recorded by the LPDF at each individual site differed by ±1.2 %, and the total LPDF accumulation from all sites was 0.03 % greater than the SDFIR accumulation. For the measurement of solid precipitation, the LPDF-shielded measurements were statistically indistinguishable from those in the SDFIR shield, and the time series of accumulation from precipitation gauges shielded by the SDFIR and the LPDF were almost identical. This new wind shield is much smaller and easier to install and maintain than any other reference-quality wind shield for the measurement of solid precipitation and may be of use within other meteorological, hydrological, and climate networks. It could also serve as a secondary reference precipitation measurement for precipitation intercomparisons held in remote locations where the construction of a full-sized Double Fence Intercomparison Reference (DFIR) shield is not feasible. [ABSTRACT FROM AUTHOR]

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations' applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity ( u * ), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u * and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations' performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u * ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the RibH parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth's surface covered by drylands. Significance Statement: Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST's limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models. [ABSTRACT FROM AUTHOR]

The structure and evolution of the atmospheric boundary layer (ABL) under clear‐sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground‐based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear‐sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High‐Resolution‐Rapid‐Refresh model and find large warm biases on clear‐sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well‐developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere. Plain Language Summary: We investigated how the vertical temperature structure in a high‐altitude mountain valley in Colorado's Rocky Mountains evolves over low and high surface snow cover. The vertical temperature structure in valleys determines how well air and thus pollutants in the valley can be mixed with the air above and is thus decisive for air quality and human health. During the night, air near the surface cools more than air above leading to an increase of temperature with height, a so‐called temperature inversion forms which suppresses vertical mixing. During the day, solar radiation warms the ground and vertically mixes the air in the valley. When snow cover is high, the mixing is limited to a shallow layer of a few hundred meter depth adjacent to the surface and the nocturnal inversion persists above through the daytime hours trapping air in the valley. We compared the observations to NOAA's operational forecast model and found that minimum nighttime temperatures and daytime mixing were overestimated by the model, especially over high snow cover. This might be related to the relatively coarse grid spacing or errors in the simulated snow cover. Key Points: Temperature profiles retrieved from remotely sensed infrared radiances characterize the valley boundary layer over different snow coversThe nocturnal inversion in a high‐altitude mountain valley is mixed out over low snow cover and persists when snow cover is highNOAA's operational weather prediction model struggles to correctly forecast the boundary layer especially when snow cover is high [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The U.S. Climate Reference Network (USCRN) has been engaged in ground‐based soil water and soil temperature observations since 2009. As a nationwide climate network, the network stations are distributed across vast complex terrains. Due to the expansive distribution of the network and the related variability in soil properties, obtaining site‐specific calibrations for sensors is a significant and costly endeavor. Presented here are three commercial‐grade electromagnetic sensors, with built‐in thermistors to measure both soil water and soil temperature, including the SoilVUE10 Time Domain Reflectometry (TDR) probe (hereafter called SP) (Campbell Scientific, Inc.), 50 MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP), and the TDR‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP), which were evaluated in a relatively nonconductive loam soil in Oak Ridge, TN, from 2021 to 2022. The HP manufacturer‐supplied calibration equation for loam soils was used in this study. While volumetric water content data from HP and AP were 82–99% of respective gravimetric observations at 10 cm, data from SP were only 65–81% of respective gravimetric observations in the top 20‐cm soil horizon, where soil water showed relatively large spatial variability. The poor performance of the SP is likely due to poor contact between SP sensor electrodes and soil and the presence of soil voids caused by the installation method used, which itself may have caused soil disturbance. Core Ideas: Volumetric soil water content observations were evaluated inside a custom‐built soil testbed.The SoilVUE10 TDR was compared with the 50 MHz HydraProbe and the Acclima 1 GHz Time‐Domain Reflectometry (TDR)‐315L.Soil water content measurements by the SoilVUE10 TDR were less accurate than measurements by the other probes. [ABSTRACT FROM AUTHOR]

Recent work has shown that bulk-Richardson (Rib) parameterizations for friction velocity, sensible heat flux, and latent heat flux have similar, and in some instances better, performance than long-standing parameterizations from Monin–Obukhov similarity theory (MOST). In this work, we expanded upon new Rib parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), υ (meridional), and w (vertical) wind components (i.e., σu, συ, and σw, respectively), which allowed us to derive Rib-based parameterizations of turbulent kinetic energy (e), and standard deviations in the 30-min temperature and moisture measurements (σθ and σq, respectively). We used datasets from three 10-m micrometeorological towers installed during the Land Atmosphere Feedback Experiment (LAFE) conducted in Oklahoma from 1 to 31 August 2017 and evaluated the new parameterizations by comparing them against parameterizations from MOST. We used the LAFE datasets and fully independent datasets obtained from two micrometeorological towers installed in Alabama between February 2016 and April 2017 to evaluate the performance of the parameterizations. Based on the slope of the relationship between the observed and parameterized turbulence statistics (mb) and the coefficient of correlation (r), we found that the Rib relationships generally performed better than MOST at parameterizing συ, σw, σθ, and σq, and the Rib relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Rib parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Rib-based parameterizations in weather forecasting models. Significance Statement: Deficiencies in Monin–Obukhov similarity theory (MOST) are well known, yet MOST forms the basis in weather forecasting models for describing heat, moisture, and momentum transfer between the land surface and atmosphere. We expanded upon previous work suggesting a MOST alternative called the bulk-Richardson approach. We used data collected from meteorological towers installed in Oklahoma and compared the bulk-Richardson approach with MOST. We evaluated these two approaches using data from meteorological towers installed in Oklahoma and Alabama and found that, overall, the bulk-Richardson approach performed better than MOST in determining the 30-min variability in temperature, moisture, and wind. This result provides additional motivation to use a bulk-Richardson approach in weather forecasting models because doing so will likely yield improved forecasts. [ABSTRACT FROM AUTHOR]

Small uncrewed aircraft systems (sUxS) are now being routinely used not only for sampling atmospheric boundary layer (ABL) processes and land-atmosphere interactions but also have significant potential to improve weather forecasting at National Weather Service (NWS) Weather Forecast Offices (WFOs). In the present study, we used observations obtained from a Meteomatics Meteodrone SSE sUxS flown on 31 days between 20 August and 10 December 2020 near Oliver Springs, Tennessee, located 35 km northwest of Knoxville, Tennessee. We flew the sUxS up to 700 m above ground level, starting around sunrise and continuing every half hour until 3.5-4.0 hours past sunrise under synoptically quiescent, fair weather conditions. These datasets were provided in real time to the local NWS WFO in Morristown, Tennessee and used by forecasters there to assist with short-term operational forecasting needs. The sUxS profiles also provided finescale details on the early-morning transition over complex terrain and how this evolution varied during the late summer to winter period, which can be used to support the initialization of numerical weather prediction models. [ABSTRACT FROM AUTHOR]

Drylands occupy ∼40% of the land surface and are thought to dominate global carbon (C) cycle inter‐annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2 fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site‐years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or "pivot" between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long‐term carbon‐climate feedbacks. Plain Language Summary: Drylands occupy ∼40% of the land surface and are thought to dominate the inter‐annual variability and long‐term trend of the global carbon cycle. Therefore, it is imperative that global terrestrial biosphere models (TBMs) are able to accurately predict dryland vegetation and carbon cycle processes. However, models have not been widely tested or calibrated against in situ dryland ecosystem CO2 fluxes. Here, we address this gap using a data assimilation system and daily net CO2 flux data from 12 southwest US Ameriflux sites spanning forest, shrub and grass dryland ecosystems to optimize the carbon cycle related parameters of the ORCHIDEE TBM. We find that before parameter optimization, the model drastically underestimates both the mean annual magnitude and inter‐annual variability of net CO2 flux. By testing different optimization scenarios, we showed that optimizing model parameters related to phenology dramatically improves the model's ability to capture the net CO2 flux inter‐annual variability. At the high elevation forested sites, optimizing parameters related to C allocation, respiration and biomass and soil C turnover reduces the model underestimate in simulated mean annual NEE. Our study demonstrates that all global TBMs need to be calibrated specifically for dryland ecosystems before they are used to determine dryland contributions to global carbon cycle variability and long‐term carbon‐climate feedbacks. Key Points: ORCHIDEE terrestrial biosphere model radically underestimates dryland mean annual net CO2 fluxes and their inter‐annual variability (IAV)Optimizing phenology, carbon allocation, and respiration parameters are crucial for capturing net CO2 flux mean and IAVModels need to be optimized against dryland CO2 flux data to achieve accurate predictions of dryland's role in global C cycle variability [ABSTRACT FROM AUTHOR]

Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Rib) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Rib parameterizations to stable regimes and developed parameterizations for the friction velocity (u*), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Rib parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Rib parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Rib parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange. Significance Statement: Models used to forecast the weather represent physical processes occurring in Earth's atmosphere using complex mathematical relationships. Many weather models use an approach developed in the 1950s referred to as Monin–Obukhov similarity theory, or MOST, to determine how heat, moisture, and wind change vertically above the land surface. However, MOST limitations are well known within the scientific community. We implemented a method different from MOST called the bulk Richardson approach. We found the bulk Richardson approach generally yielded predictions of how heat, moisture, and wind change with height near the ground that were just as good as, and in some instances better than, predictions of these quantities obtained using MOST. This result motivates us to consider implementing the bulk Richardson approach into weather models, as doing so is expected to lead to their improvement. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of two commercial-grade electromagnetic sensors in measuring soil water content for the U.S. Climate Reference Network (USCRN). One sensor was a 50-MHz coaxial impedance dielectric sensor (model HydraProbe, StevensWaterMonitoring Systems). The second sensorwas a 1-GHz time domain reflectometry (TDR) sensor (model TDR-315L, Acclima). There was no substantial difference between both probes in daily mean volumetric soil water content and soil temperature measurements at a 0.1-m depth in a customized uniform loamy soil testbed in Oak Ridge, TN, from 2016 to 2018, and at select USCRN stations with a variety of soils. However, the TDR-315L provided more representative soil water content measurements for stations with high-clay-content soils than the HydraProbe. These results confirm the necessity of using site-specific soil properties for soil sensors like the HydraProbe for estimating soil water content in different soil environments. They also suggest that TDR sensors may provide an opportunity to improve USCRN soil moisture measurements in higher-clay-content soils. [ABSTRACT FROM AUTHOR]

The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR's performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR's initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic. [ABSTRACT FROM AUTHOR]