Temple R. Lee

The absence of routine micrometeorological observations over complex terrain has resulted in knowledge gaps on how heat, momentum, and mass fluxes are modified by different canopies and stabilities. For weather forecast and air quality models, however, it is essential to represent nocturnal boundary layer (NBL) processes across different foliation and stability regimes. Oftentimes, the turbulence-exchange processes are overly simplified which can cause incorrectly modeled near-surface thermodynamics and kinematics. We investigated differences in turbulence characteristics within and above foliated versus nonfoliated canopies under low and high stability regimes on clear nights. We used observations from a wind lidar, which samples from 40 to 300 m above ground level, and two micrometeorological towers installed along a forested ridge in eastern Tennessee in the Southeast United States. We used the tower-derived observations to obtain below- and above-canopy horizontal wind ( U ), turbulent kinetic energy (TKE), and friction velocity ( ) and the bulk Richardson number (Ri _b ) to determine stability. Irrespective of stability or season, negative skewness in the lidar-derived vertical velocity fields was associated with the downward transport of turbulence, resulting in above-canopy TKE and increases and sporadic increases in below-canopy TKE and . Overall, the smallest TKE and corresponded with the most stable conditions. These findings illustrate how lidar measurements coupled with micrometeorological tower observations can advance knowledge of NBL processes that are critical to better represent in weather forecasting models. Significance Statement: Physical processes happening near Earth’s surface during the nighttime are poorly represented within weather forecast models, particularly areas with forests and ridges. We examined nighttime turbulence characteristics using observations from surface-based instrumentation and a new lidar capable of providing fine-scale details about lower-atmospheric structure. We found occasional large nighttime increases in above-canopy wind speeds and turbulence due to the downward transport of air from aloft. When the atmosphere remained well mixed during the nighttime in the cool season, there was also an increase in those quantities within the lower forest canopy. The findings underscore how new observing systems provide insights on meteorological processes occurring during the nighttime to better enable their representation in weather forecasting models.

There is strong evidence that evaluating different parameterization schemes over diverse land surface forcings and surface-layer (SL) conditions will enhance our understanding of the physical processes required to improve the model parameterizations. Furthermore, shortcomings for representing SL heat, moisture, momentum, and turbulence using traditional parameterizations from Monin–Obukhov similarity theory (MOST) and the bulk Richardson approach are becoming well known within the scientific community. Overcoming the parameterizations’ limitations requires evaluating the parameterizations across a range of land-cover types and meteorological conditions because the biosphere–atmosphere coupling is primarily linked to partitioning energy between sensible and latent heat fluxes. Recent studies over semiarid regions suggested that MOST better parameterized heat fluxes than the Richardson parameterizations, whereas the Richardson approach better parameterized kinematic and turbulence quantities. However, questions remain regarding whether the parameterizations’ efficacy over drylands can be explained by physical parameters, such as the observed Bowen ratio (i.e., the ratio of the surface sensible heat flux to the surface latent heat flux). Addressing these questions allows one to more confidently use the parameterizations in land surface models. In this study, we used micrometeorological observations from two semiarid grassland sites, one in southeastern Arizona and a second in northwestern Texas, for a 3-yr period (1 January 2016–31 December 2018). We found that the heat flux, moisture flux, and turbulence parameterizations’ efficacy do not vary with observed Bowen ratio. Furthermore, the MOST turbulence parameterizations sometimes performed better than the Richardson parameterizations, suggesting that caution is warranted particularly when applying the latter to semiarid regions. Significance Statement: The proper parameterization of surface-layer processes is a crucial component of weather forecasting models. To improve the current suite of surface-layer parameterizations, they need to be evaluated across a range of conditions, ranging from very moist to very dry. Although this study found that the efficacy of surface-layer parameterizations varies little across these conditions, it represents an important step toward identifying the parameterizations’ limitations in representing land–atmosphere exchanges. Understanding when these parameterizations perform well versus when their performance declines will help identify targeted areas for improvement and enhance their effectiveness in weather forecasting models.

The representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination of the air‐surface exchange processes of heat, momentum, and trace gases. Because of the relationship between wind speed and fire behaviors, the influence of the canopy on near‐surface wind speed is critical for prognostic fire spread models used in regional NWP models. In practice, the wind speed at the midflame point of fires (midflame wind speed) is used to determine the rate of fire spread. However, the wind speeds from most in situ measurements and NWP models are taken at some reference height above the canopy and fire flames. Hence, this study develops a modular and computationally‐efficient one‐dimensional model set composed of a canopy wind model and a wind adjustment factor (WAF) model for NWP applications across scales. The model set uses prescribed foliage shape functions to represent the vertical vegetation profile and its impacts on the three‐dimensional structure of horizontal wind speeds. Results from the canopy wind model well agree with ground‐based observations with average mean absolute bias, root mean square error and determination coefficients around 0.18 m s⁻¹, 0.40 m s⁻¹and 0.90, respectively. The WAF model provides midflame wind speeds by estimating the WAF based on canopy, fire and flame characteristics. Various user‐definable options provide flexibility to adapt to variations in canopy characteristics and additional complexities associated with wildfires. The model set is expected to improve NWP models by providing an improved representation of the sub‐grid wind flows at any spatial scale.

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 (), incoming shortwave radiation (SW_d) 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) , we found afternoon T biases of about 2°C (−1°C) and afternoon SW_d biases of up to 170 W m⁻² (100 W m⁻²), but negligible impacts on SM₀₅ biases. On days with small (large) SW_d, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SW_d biases of up to 190 W m⁻² (80 W m⁻²). Whereas different SM₀₅ had little impact on T and SW_d 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.

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

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 (Ri_b) 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 Ri_b parameterizations for friction velocity (), 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 Ri_b 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 Ri_b parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the Ri_b H parameterizations to drylands. These findings represent an important milestone for the applicability of Ri_b 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 (Ri_b) approach. To allow for the Ri_b 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 Ri_b 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 Ri_b relationships in weather forecasting models.

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.