Ongoing global climate change has yielded a myriad of catastrophic weather hazards, including extreme heat, drought, and severe fire weather conditions across global dryland environments. A massive wildfire ignited across the Texas Panhandle between 27 and 28 Feb 2024 (i.e., Smokehouse Creek Fire, the second largest wildfire in the US history), which consumed over 1,000,000 ha of land and resulted in an overall loss of greater than >$1 billion. Understanding aerosol mixing processes and the associated kinematics near the surface and within the nocturnal boundary layer (NBL) during such wildfire events is crucial for various applications, including predicting and monitoring environmental air quality (AQ), weather forecasting and transport and dispersion modeling. This study provides, for the first time, an empirical evidence of how a nocturnal cold front amplified the wildfire impact on AQ at a site located 250 km downwind of the second largest US wildfire, yielding hazardous concentrations of fine particulate matter (PM2.5-250 mu g m(-3)). Using a combination of lidar-derived aerosol backscatter, vertical velocity and horizontal wind profiles, 10 m-tower observations of meteorological parameters, radiosonde-derived thermodynamics, and near-surface PM2.5 measurements, our analyses revealed that narrow and intense updrafts (i.e., vertical velocity of up to 5-10 m s(-1)) along the leading edge of a nocturnal cold front triggered the entrainment of an elevated smoke plume (similar to 1500-2000 m above ground) down to the surface via broader and weaker downdrafts (-0.5 to -2.0 m s(-1)). This helped explain the transport and vertical mixing pathway of the wildfire plume near ground and aloft. Results reported enhance our understanding of NBL processes and provide critical insights for improving AQ forecasting and validating aerosol transport in dispersion models.

This study aims to (a) investigate growing-season weather conditions with the yield of soybean and corn commercially grown in alternate rotation on a farm in Champaign, Illinois, (b) identify anomalies in this relationship, and (c) evaluate changes in microclimate variables during crop growing stages and associated changes in annual yield. To achieve these aims, we evaluated 21 years (1997-2017) of annual yield data alongside hourly microclimate measurements from a 10-m flux tower at the crop site. While the annual yield for the study period varied about the mean of 350 +/- 63 g m-2 for soybean and about 1200 +/- 185 g m-2 for corn, our evaluations using linear regression best-fit equations showed generally low correlations between microclimate variables and the annual yield. The growing season daily soil water within the top 1-m soil depth showed consistent values above 300 mm that were close to the field capacity (330 mm) of the dominant field soil of silty clay loam. Fitted regression lines between yield and microclimate variables averaged over June, July and August showed modest correlations for corn yield versus growing degree days (R2 = 0.52), precipitation (R2 = 0.43) and evapotranspiration (R2 = 0.41), as well as a modest correlation between soybean yield and precipitation (R2 = 0.31). Regressions between yield and vapor pressure deficit, solar radiation, soil water content and carbon dioxide flux showed poor correlations that were less than R2 of 0.21. This study offers a useful evaluation of the yield in rainfed corn and soybean conditions and demonstrates that simple regression analysis may be insufficient in representing complex crop-microclimate interactions. However, the microclimate evaluation presented in this study could serve as a valuable contribution to the application of dynamic crop models.

In the era of rapid climate change in the 21st century, urbanized areas are projected to yield, on average, enhanced warming relative to overall warming in the rural areas while heat-related illnesses account for the most weather-related deaths per year in the United States. However, overall warming depends on cities' climate and seasons as the sustainability planning around global cities differ. The heat-related dangers can be enhanced in cities via urban heat island (UHI). Large cities remained at the forefront of UHI research, with little emphasis given to UHI in small-sized cities. To bridge this gap, we utilized a localized temperature-sensor-network, along with the existing WTM, with a key focus to investigate the UHI of a small-sized city (Lubbock, Texas), when compared to larger metropolitans in the United States and around the world focused on UHI. The Urban Heat Experiment Around Lubbock, Texas (U-HEAT) deployed a network of low-cost HOBO-temperature sensors around the city to understand the UHI intensity (UHII) and urban heat advection (UHA) using measurements of prevailing wind and upwind temperature (i. e., reference rural-background). Results revealed that traditional UHII differed from UHII using the novel U-HEAT method by 2.5 degrees C. Additionally, strong evidence of UHA under moderate windspeed (2-4 ms- 1) rather than low or high wind regimes was found. Mobile deployments showed evidence of UHA yielding greater than 4 degrees C temperature increase downwind. This work will help facilitate better forecasts of extreme heat within and around cities and provide pathways for adaptation, mitigation, and resiliency efforts for extreme UHI and UHA.

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.

Characterizing near-surface turbulent exchanges over forested, mountainous terrain is critical for a comprehensive understanding of micrometeorological and boundary-layer processes, including exchanges of mass, momentum, and energy. However, observations over mountainous terrain are relatively sparse compared with observations over flat terrain. We used turbulent fluxes obtained from three heights within and five heights above a 25-m tall mixed-deciduous canopy in eastern Tennessee in the Southeast United States. Analyses of measured vertical profiles of temperature variance (T'2), kinematic heat flux (w'T'), and normalized correlation coefficients obtained via regression analyses between the vertical wind and temperature (RwT, which is an indicator of the efficacy of turbulent heat transfer) revealed how these quantities varied between foliated and non-foliated canopies and for different wind speed, wind direction, and atmospheric stability regimes. Results indicated that T'2, w'T', and RwT peaked at the canopy top at the beginning and end of the growing season. Overall, larger values of w'T' corresponded with smaller wind speeds, whereas the relationship was less consistent between T'2 and horizontal wind. The magnitudes of T'2 and w'T' were slightly larger under northwesterly flows as compared with other wind directions. Furthermore, T'2 and w'T'were largest under the most unstable atmospheric regimes, and the connection between w'T' and static stability was strongest at the canopy top. In closing, this study is the first of its kind to investigate how vertical profiles ofT'2 and w'T' vary with season and ambient meteorological conditions. The findings motivate the need for future studies to leverage long-term micrometeorological measurements of the vertical variability in turbulence structures across different lower atmospheric stability regimes in order to improve the surface-layer parameterizations used within weather forecasting models.

Turbulence governs many atmospheric processes including mixing, transport, and energy transfer. Consequently, there is a strong need for the examination and validation of existing turbulence theories. The HOckey-Stick Transition (HOST) hypothesis was proposed to challenge traditional understanding of near-surface turbulence processes derived from Monin-Obukhov Similarity Theory (MOST). Within the MOST framework, the momentum flux entirely depends upon partial derivative U/partial derivative z (i.e., the change in mean wind speed (U) with height (z)), but this relationship is not as straightforward under HOST. Because HOST was developed using observations over relatively uniform, homogeneous terrain, questions arise regarding HOST's applicability within and above heterogeneous forest canopies where multi-level turbulence measurements are somewhat rare but are essential for developing a unified similarity scaling applicable over complex surfaces. To this end, we used one year (1 January 2016 through 31 December 2016) of turbulence measurements sampled at eight heights along a 60-m tower within and above a mixed deciduous forest at Chestnut Ridge in eastern Tennessee in the southeastern U.S. We examined the diurnal and seasonal variability of selected turbulence parameters (i.e., friction velocity (u*) and turbulence velocity scale (VTKE)) to detail the micrometeorological characteristics of the site during the study period. We then used these turbulence measurements to evaluate HOST by determining their relationship with U and to assess the dependencies of this relationship on time of day, season, wind direction, and atmospheric stability. We found that HOST is most applicable under very stable regimes, whereas the relationships between u* and U, and between VTKE and U, were more linear above the forest canopy than within the forest canopy and when the canopy was not foliated. Overall, this work builds upon previous studies that have described limitations in MOST and identifies scenarios when the HOST hypothesis may be more applicable than MOST for representing near-surface turbulence processes.

With rising global temperatures, urban environments are increasingly vulnerable to heat stress, often exacerbated by the Urban Heat Island (UHI) effect. While most UHI research has focused on large metropolitan areas around the world, relatively smaller-sized cities (with a population 100 000–300 000) remain understudied despite their growing exposure to extreme heat and meteorological significance. In particular, urban heat advection (UHA), the transport of heat by mean winds, remains a key but underexplored mechanism in most modeling frameworks. High-resolution numerical weather prediction (NWP) models are essential tools for simulating urban hydrometeorological conditions, yet most prior evaluations have focused on retrospective reanalysis products rather than forecasts. In this study, we assess the performance of a widely used operational weather forecast model, the High-Resolution Rapid Refresh (HRRR), as a representative example of current NWP systems. We investigate its ability to predict spatial and temporal patterns of urban heat and UHA within and around Lubbock, Texas, a small-sized city located in a semi-arid environment in the southwestern US. Using data collected between 1 September 2023, and 31 August 2024 from the Urban Heat Island Experiment in Lubbock, Texas (U-HEAT) network and five West Texas Mesonet stations, we compare 18 h forecasts against in situ observations. HRRR forecasts exhibit a consistent nighttime cold bias at both urban and rural sites, a daytime warm bias at rural locations, and a pervasive dry bias across all seasons. The model also systematically overestimates near-surface wind speeds, further limiting its ability to accurately predict UHA. Although HRRR captures the expected slower nocturnal cooling in urban areas, it does not well capture advective heat transport under most wind regimes. Our findings reveal both systematic biases and urban representation limitations in current high-resolution NWP forecasts. Our forecast–observation comparisons underscore the need for improved urban parameterizations and evaluation frameworks focused on forecast skill, with important implications for heat-risk warning systems and forecasting in small and mid-sized cities.

It is well known that parameterizations developed using observations from flat terrain have difficulty over complex terrain, which motivates a better understanding of turbulence exchanges occurring in these areas. In this work we addressed the question of how the vertical variability of turbulence features evolves over the lowest few hundred meters of the convective and nocturnal boundary layer above a forested ridge as a function of cloud cover and mean wind. We used one year of observations obtained from a WindCube V2.1 lidar installed in eastern Tennessee in the Southeast U.S. coupled with observations from a 60-m micrometeorological tower. The wind lidar has 20-m range gates spanning from 40 m to 300 m above ground. We used the lidar's high-frequency observations to derive turbulent kinetic energy (TKE), vertical velocity variance (62w), vertical velocity skewness (S), and kurtosis (K). We observed the largest decrease in the diurnal wind speed on clear, windy days. Under clear sky conditions, increasing TKE and 62w yielded positive S throughout the lower convective boundary layer. Under cloudy regimes, the distribution of TKE was height-independent and corresponded with smaller 62w and near-zero S. Our results provide insights into turbulence processes over forested complex terrain and support the refinement of turbulence parameterizations used in weather forecast models.

AbstractThe 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 (dT/dt), 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) dT/dt, we found afternoon T biases of about 2 degrees C (-1 degrees 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 degrees C (-2.5 degrees 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.

Multi-scale heterogeneities arising from terrain-induced and land surface variabilities present in complex ecosystems require accurate measurements and models of surface-atmospheric exchanges. The spatial and temporal variability of energy and water budgets over complex terrain can influence hydrological and biogeochemical interactions that can strongly influence local and regional biosphere-atmosphere transport. Scaling and accurate model representations of these interactions across complex terrains based on single-point or local measurements present a non-trivial scientific challenge. Observations were made of surface energy balance components at NOAA surface flux and mobile SURFRAD radiation stations separated by 5 kilometers along the East River, Colorado, watershed. We present the gap-filled diurnal and seasonal evolution of surface energy balance components as the study domain transitions from low to high snow cover. The spatial variability of the measured fluxes along the valley axis is discussed with the possible influences of local advection and thermally driven circulations. The measured fluxes are compared with a new bulk Richardson number parameterization for sensible heat fluxes over heterogeneous land surfaces, introduced in Lee et al. 2021, and are here extended for complex topography. Our findings provide credence for implementing such a parameterization for surface fluxes and turbulence statistics into land-surface models for representing surface-atmospheric exchanges, although caution should be used during the early-evening transition when there are significant and persistent countergradient fluxes.

Abstract 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.

Recent work has shown that bulk-Richardson (Ri(b)) 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 Ri(b) parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), upsilon (meridional), and w (vertical) wind components (i.e., sigma(u), sigma(upsilon), and sigma(w), respectively), which allowed us to derive Ri(b)-based parameterizations of turbulent kinetic energy (e), and standard deviations in the 30-min temperature and moisture measurements (sigma(theta) and sigma(q), 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 (m(b)) and the coefficient of correlation (r), we found that the Ri(b) relationships generally performed better than MOST at parameterizing sigma(upsilon), sigma(w), sigma(theta), and sigma(q), and the Ri(b) relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Ri(b) parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Ri(b)-based parameterizations in weather forecasting models. Significance StatementDeficiencies 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.

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical compo-nent 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 re-cently 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 Rib H parameterizations to drylands. These findings represent an important milestone for the applica-bility of Rib parameterizations, given the large fraction of Earth's surface covered by drylands.

A series of meteorological measurements with a small uncrewed aircraft system (sUAS) was collected at Oliver Springs Airport in Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary because of inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) Model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than under strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just an sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

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 evalu-ated 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 tem-perature 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 sur-face 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 radi-ation 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 m23 , 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 ac-curate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using obser-vations 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.

Abstract. 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.

Despite many observational studies on the atmospheric boundary layer (ABL) depth z(i) variability across various time scales (e.g., diurnal, seasonal, annual, and decadal), z(i) variability before, during, and after frontal passages over land, or simply z(i) variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5 years (2014-18) of daytime rawinsonde observations and surface analyses over 18 central and southeastern U.S. sites to report z(i) variability across frontal boundaries. By providing systematic observations of front-relative contrasts in z(i) (i.e., z(i) differences between warm and cold sectors, delta(zi)=z(i)(Warm)-z(i)(Cold)) and boundary layer moisture (i.e., ABL-q) regimes in summer and winter, we propose a new paradigm to study z(i) changes across cold-frontal boundaries. For most cases, we found deeper z(i) over the warm sector than the cold sector in both summer and winter, although with significant site-to-site variability in delta z(i). Additionally, our results show a positive delta q(ABL) (i.e., frontal contrasts in ABL-q) in summer and winter, supporting what is typically observed in midlatitude cyclones. We found that a front-relative delta q(ABL) of 1 g kg(-1) often yielded at least a 100-m delta z(i) across the frontal boundary in both summer and winter. This work provides a synoptic-scale basis for z(i) variability and establishes a foundation for model verification to examine the impact of airmass exchange associated with advection on z(i). This work will advance our understanding of ABL processes in synoptic environments and help unravel sources of front-relative z(i) variability.

Measurements of three flux towers operated during the land atmosphere feedback experiment (LAFE) are used to investigate relationships between surface fluxes and variables of the land–atmosphere system. We study these relations by means of two machine learning (ML) techniques: multilayer perceptrons (MLP) and extreme gradient boosting (XGB). We compare their flux derivation performance with Monin–Obukhov similarity theory (MOST) and a similarity relationship using the bulk Richardson number (BRN). The ML approaches outperform MOST and BRN. Best agreement with the observations is achieved for the friction velocity. For the sensible heat flux and even more so for the latent heat flux, MOST and BRN deviate from the observations while MLP and XGB yield more accurate predictions. Using MOST and BRN for latent heat flux, the root mean square errors (RMSE) are 107 Wm ^-2 and 121 Wm ^-2 , respectively, as well as the intercepts of the regression lines are ≈ 110 Wm ^-2 . For the ML methods, the RMSEs reduce to 31 Wm ^-2 for MLP and 33 Wm ^-2 for XGB as well as the intercepts to just 4 Wm ^-2 for MLP and -1 Wm ^-2 for XGB with slopes of the regression lines close to 1, respectively. These results indicate significant deficiencies of MOST and BRN, particularly for the derivation of the latent heat flux. In fact, in contrast to the established theories, feature importance weighting demonstrates that the ML methods base their improved derivations on net radiation, the incoming and outgoing shortwave radiations, the air temperature gradient, and the available water contents, but not on the water vapor gradient. The results imply that further studies of surface fluxes and other turbulent variables with ML techniques provide great promise for deriving advanced flux parameterizations and their implementation in land–atmosphere system models.

Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST.