Search

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.

Abstract 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.

Abstract 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.

The variability of trace gases such as carbon monoxide (CO) at surface monitoring stations is affected by meteorological forcings that are particularly complicated over mountainous terrain. A detailed understanding of the impact of meteorological forcings on trace gas variability is challenging, but is vital to distinguish trace gas measurements affected by local pollutant sources from measurements representative of background mixing ratios. In the present study, we investigate the meteorological and CO characteristics at Pinnacles (38.61 N, 78.35 W, 1017 m above mean sea level), a mountaintop monitoring site in northwestern Virginia, USA, in the Appalachian Mountains, from 2009 to 2012, and focus on understanding the dominant meteorological forcings affecting the CO variability on diurnal timescales. The annual mean diurnal CO cycle shows a minimum in the morning between 0700 and 0900 LST and a maximum in the late afternoon between 1600 and 2000 LST, with a mean (median) daily CO amplitude of 39.2±23.7 ppb (33.2 ppb). CO amplitudes show large day-to-day variability. The largest CO amplitudes, in which CO mixing ratios can change >100 ppb in

The authors wish to make the following correction to this paper [...]

The daytime planetary boundary layer (PBL) depth is an essential parameter in, for example, dispersion models and numerical weather prediction. We applied a recently developed technique to estimate afternoon PBL depths from 516,887 daily rawinsonde measurements obtained at 0000 UTC between 1 Jan 1991 and 31 Dec 2015 from 67 US sites. We discuss salient features in the PBL depth variability and examine the climatology of afternoon PBL depths as a function of geographical region and Köppen-Geiger climate regime. Results indicated a strong east to west PBL depth gradient, with higher PBL depths over the semiarid regions of the western US in spring and summer. Many sites located near coastlines exhibited no seasonal cycle due to marine boundary layer influences, and an inverse relationship was found between distance from the coast and observed afternoon PBL depth. Differentiating by climate type, BWk (arid, desert, and cold) has the largest median winter (1075 m) and summer (2500 m) PBL depths. Median PBL depths for Cfa (temperate, no dry season, and hot summers), Dfa (cold, no dry season, and warm summer), and Dfb (cold, no dry season, and hot summers) climate types are 750 m and 1000 m during winter and summer, respectively.

Rotary-wing small unmanned aircraft systems (sUAS) are increasingly being used for sampling thermodynamic and chemical properties of the Earth’s atmospheric boundary layer (ABL) because of their ability to measure at high spatial and temporal resolutions. Therefore, they have the potential to be used for long-term quasi-continuous monitoring of the ABL, which is critical for improving ABL parameterizations and improving numerical weather prediction (NWP) models through data assimilation. Before rotary-wing aircraft can be used for these purposes, however, their performance and the sensors used therein must be adequately characterized. In the present study, we describe recent calibration and validation procedures for thermodynamic sensors used on two rotary-wing aircraft: A DJI S-1000 and MD4-1000. These evaluations indicated a high level of confidence in the on-board measurements. We then used these measurements to characterize the spatiotemporal variability of near-surface (up to 300-m AGL) temperature and moisture fields as a component of two recent field campaigns: The Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast U.S. (VORTEX-SE) in Alabama, and the Land Atmosphere Feedback Experiment (LAFE) in northern Oklahoma.

Downscaling future temperature projections to mountainous regions is vital for many applications, including ecological and water resource management. In this study, we demonstrate a method to downscale maximum temperatures to subkilometer resolutions using the Parameter-elevation Regression on Independent Slopes Model (PRISM). We evaluate the downscaling method with observations from a network of temperature sensors deployed along western and eastern slopes of Virginia’s Shenandoah National Park in the southern Appalachian Mountains. We find that the method overestimates mean July maximum temperatures by about 2°C (4°C) along the western (eastern) slopes. Based on this knowledge, we introduce corrections to generate maps of current and future maximum temperatures in the Shenandoah National Park.

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 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. Plain Language Summary: Compared to bare ground, wind speeds are reduced by the presence of vegetation at the surface by the leaves and woody parts of plants, especially within forest canopies. The altered wind speeds affect the atmosphere's stability and transport processes, as well as the spread of wildfires. Several fire prediction models are connected with weather models; however, most weather models do not consider such sub‐canopy effects on reducing wind speeds as the vertical structure of vegetation are not included in the models. Thus, this study presents a canopy model that can simulate the effects of vegetation, primarily forest canopies, on horizontal wind speeds used in weather models. The canopy model also provides the estimation of the wind speeds at the height of the midpoint of flame above ground level (midflame wind speed), which can better describe fire behaviors for general use in fire prediction models. The model results agree well with both above and below canopy top wind speed observations, and the midflame wind speeds are reliable when compared to previous studies. The canopy model is expected to improve weather modeling by providing a better representation of near‐surface wind flow, particularly for areas covered by dense forest canopies. Key Points: This study presents and evaluates a canopy wind model that simulates horizontal wind speeds above and within vegetation canopiesModel results agree well with observations and should provide a better representation of wind flow in numerical weather prediction modelsThe wind adjustment factor model provides midflame wind speed estimation, which can be used for the estimation of the rate of fire spread [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]