Abstract. Precipitation and winds impart spatially and temporally variable thermodynamic signatures on surfaces. Here, we describe two new hotplate sensors, the DTI-pm and DTI-de, that sample energy flows using a discretized thermodynamic imaging (DTI) principle. Unlike prior single-element hotplate devices, DTI measurements are obtained at sufficiently high spatial resolution to separate component thermodynamic signatures. The DTI-pm is a sensor array of individually controlled micro-hotplates, each using pulse-width modulation to maintain a constant elevated temperature of each sensor element, measuring the power required to do so. The DTI-de uses an infrared camera that passively monitors radiative temperatures on a heated metal plate by exploiting the differential emissivity between metal and water; heat transfer physics is used to infer the power of thermodynamic cooling. Both techniques are shown here to be capable of measuring with exceptionally high accuracy and precision a wide range of precipitation characteristics, including mass, size, and the density of individual multi-phase hydrometeors, as well as continuous bulk precipitation rates and snow density. The DTI-de achieves a spatial resolution of 0.2 mm with a mass sensitivity of 1 µg, while the DTI-pm provides a spatial resolution of 1 mm and a mass sensitivity of 0.5 µg. Preliminary results suggest the potential for the DTI-pm and DTI-de to concurrently measure wind speed and direction.

Xanthomonas hortorum pv. carotae (Xhc) is a plant-pathogenic bacterium that causes bacterial blight of carrot. It impacts international trade because there is little to no tolerance for the pathogen in carrot seed. Because the biennial crop has overlapping growing seasons and Xhc has been detected in the air in areas of carrot seed production, an improved understanding of the dispersion pathways is needed. Experiments (Airborne Xanthomonas Experiments-Madras [AXE-M]) conducted in central Oregon were designed to characterize the airborne transport and deposition of particles dispersing Xhc during harvest events. Debris samples were collected with a novel passive sampling device, the Cascade Settling Trap (CST), that sorted particles into size classes of interest as the particles were deposited out of the air column. CSTs were used during one harvest event in 2021 and three in 2022. Negative binomial regression analysis conducted on data collected in 2022 indicated that particle size and the distance from which particles were sampled can be predictive of the amount of Xhc detected. Burkard samplers were used in 2021 and 2022 to quantify airborne Xhc during the growing season and specific events of interest. Meteorological data, in conjunction with the use of optical particle counters, allowed for the estimation of real-time airborne particle concentrations and their potential for transport. By developing a more detailed understanding of the aerobiology of Xhc, better risk assessment tools and pathogen management strategies can be employed to assess the potential for these particles to disperse Xhc across varying scales.

The objective of this study is to characterize visibility and microphysics of cold-fog conditions during The Cold Fog Amongst Complex Terrain (CFACT) project, which was designed to investigate the life cycle of cold fog in the Heber Valley, Utah. The field campaign was conducted from 7 January to 23 February 2022 and was supported with observations and resources from the NSF Lower Atmospheric Observing Facilities (LAOF), managed by NCAR’s Earth Observing Laboratory (EOL), as well as the University of Utah and Ontario Technical University. Heber Valley is surrounded by canyons, mountains and irregular topography where the Provo River streams along the valley floor from Jordanelle Reservoir at the north to Deer Creek (DC) Reservoir at the southeastern end at 1652 m above sea level (ASL). The highest peaks surrounding the valley are at about 3500 m (ASL) to the west and southwest of the project area.The DC supersite had extensive ice and droplet microphysical as well as precipitation measurements obtained using a ground-based Gondola (composed of a Droplet Measurement Technologies (DMT) Cloud Droplet Probe - CDP and Back-scatter Cloud Probe - BCP), a DMT Fog Monitor (FM120), a Mesaphotonics Cloud Droplet Measurement System (CDMS), a DMT Ground-based Cloud Imaging Probe (GCIP), a Vaisala Present Weather Detector (PWD52), and an OTT Parsivel. During the project, aerosol measurements were performed using a GRIMM Aerosol Spectrometer, a T.S.I. Scanning Mobility Particle Sizer (SMPS), and a DMT Cloud Condensation Nuclei (CCN) counter, as well as filter samplers. These instruments covered a size range from 8 nm up to cm size range representing aerosols, fog particles, and precipitation. Measurements from a Halo Photonics doppler wind lidar, a Vaisala CL61 ceilometer, a tethered balloon system (TBS), and a 32-m turbulence tower were used to characterize vertical profiles of fog microphysics and aerosols, as well as the dynamic and thermodynamic structure. Eleven significant weather events occurred during the 47 days of the CFACT campaign that included snowfall, freezing fog, ice fog (IF), and light ice crystal precipitation when Vis

Marine fog remains a challenging process for environmental prediction; in coastal areas, these challenges are complicated by the heterogeneity of the land-sea boundary. Here, the findings from an investigation into the boundary-layer development downstream of a remote, low-relief island will be presented. This work arose from an intense observing period (IOP) during the Fog And Turbulence Interactions in the Marine Atmosphere (FATIMA) 2022 field campaign near Sable Island (SI) in the North Atlantic. The premise was to examine a unique fog dissipation mechanism known as a fog shadow, hypothesized to be caused by fog-laden boundary interaction with the island's surface. Using coordinated measurements from the island observing station and a heavily instrumented research vessel, we have conducted an extensive analysis into the spatio-temporal evolution of the atmospheric boundary-layer state downstream of SI. A low-level jet during the IOP prevented the development of an extensive fog layer in the region, and hence localized dissipation in the lee of the island. In terms of boundary-layer development, the measurements captured the rough-smooth internal boundary-layer formation, with some secondary impacts of the temperature adjustment from land to water surfaces. Analysis of the fetch-limited wave growth followed the expected self-similarity, but at a much higher intensity than predicted for the stability regime; this was linked to spatial inhomogeneity in wind acceleration and stress downstream, which may have increased the lateral extent of island impacts on the boundary layer.

Solid structures (buildings and topography) act as obstacles and significantly influence the wind flow. Because of their importance, faithfully representing the geometry of structures in numerical predictions is critical to modeling accurate wind fields. A higher-order geometry representation (the cut-cell method) is incorporated in the mass-consistent wind model, Quick Environmental System (QES)-Winds. To represent the differences between a stair-step and the cut-cell method, an urban case study (the Oklahoma City JU2003 experiments) and a complex terrain case (from the MATERHORN campaign) are modeled in QES-Winds. Comparison between the simulation results with the stair-step and cut-cell methods and the measured data for sensors close to walls and buildings showed that the sensitivity of the cut-cell method to changes in resolution is less than the stair-step method. Another way to improve the effects of solid geometries on the flow is to correct the velocity gradient near the surface. QES-Winds solves a conservation of mass equation and not a conservation of momentum equation. This means that QES-Winds overestimates velocity gradients near the surface which leads to higher rates of scalar transport. The near-surface parameterization is designed to correct the tangential near-surface velocity component using the logarithmic assumption. Results, including the near-wall parameterization, are evaluated with data from the Granite Mountain case (the MATERHORN campaign), which indicates that the parameterization slightly improves the performance of the model for cells near the surface. The new geometry representation and near-wall parameterization added to a mass-consistent platform, enhances the model's ability to simulate the effects of solid geometries on wind fields.

New particle formation (NPF) is a complex atmospheric phenomenon defined by the gas‐to‐particle conversion that leads to the sudden burst and growth in aerosol particles. Although chemical mechanisms for aerosol nucleation and growth are well established, the role of physical processes, such as turbulent mixing, within the atmospheric boundary layer (ABL) is beginning to emerge with recent studies. This study, based on the observations from the 2022 CFACT (Cold Fog Amongst Complex Terrain) field study in the Heber Valley of northern Utah, demonstrates an interconnection between turbulence and the occurrence of NPF. Using a spatially distributed boundary layer instrumentation, a novel feature of CFACT, three case studies depict unique boundary layer conditions that modulate the development of NPF characterized by sustained turbulence and weak intermittent turbulence. Quantitative analysis using in situ measurements and derived variables demonstrate that periods of weak intermittent turbulence hinder particle growth, whereas sustained turbulence helps modulate NPF. These findings provide new insights into the physical drivers of NPF, underscoring the role of turbulence in impacting particle formation with the ABL.

Fog constitutes a thick, opaque blanket of air hugging Earth's surface, laden with small water droplets or ice crystals. Fog disrupts transportation, poses security threats, disorients human perception, and impacts communications and ecosystems. Collusion of atmospheric, terrestrial, and hydrologic processes produces fog droplets that pullulate over hygroscopic aerosols that act as condensation nuclei. Marine fog is particularly complex, since underlying dynamic, thermodynamic, and (bio)physicochemical processes span fifteen decades of spatial scales, from megameter-sized synoptic weather systems to nanometer-scale bioaerosols. This paper overviews the first international field campaign [Fog and Turbulence Interactions in the Marine Atmosphere-Grand Banks campaign (Fatima-GB)] of the project dubbed Fatima conducted during 1-31 July 2022 in the Grand Banks region of the North Atlantic. Therein, weather systems and commingling cold and warm oceanic waters provide entr & eacute;e for fog genesis. Measurement platforms included an islet southwest of Nova Scotia (Sable Island), a research vessel (Atlantic Condor), an offshore oil platform, and autonomous surface vehicles. The instrument array comprised of extant remote and in situ sensors augmented by novel sensing systems prototyped and deployed in marine fog to penetrate the smallest scales of turbulence, examine aerosols, and quantify radiation budget. The comprehensive dataset so gathered, together with satellite and reanalysis products, mesoscale model, and large-eddy simulations, demonstrated that the long-held hypotheses of marine fog formation by warm air advection over colder water and in areas of enhanced (shelf) turbulence need to be revisited. The study also elicited new phenomena, for example, the fog shadow (clearings of fog downstream of islands).

The modeled settling speed of frozen hydrometeors has important implications for the prediction of weather and climate. However, it is usually assumed, erroneously, that they fall in still air. Here, we present novel field measurements of individual snowflake microphysical properties and their settling velocities in atmospheric surface-layer turbulence. Individual snowflake motions are tracked in a laser light sheet using particle streak velocimetry (PSV). A hotplate device, the Differential Emissivity Imaging Disdrometer (DEID), is used to obtain precise estimates of snowflake mass, density, and size. Relative to calculated terminal velocities in still air, we present enhancements and reductions of snowflake settling speeds in turbulent air for a broad range of Reynolds and Stokes numbers. Functional forms describing actual snowflake fall speeds are presented and explored. In particular, a promising non-dimensional functional form for the ratio of actual particle fall speed to terminal velocity is presented in terms of turbulence intensity and a new variable called the shape density index or SDI, which is related to an individual hydrometeor's microphysical structure.

Accurate measurement of ambient air temperature is critical to numerous applications. This task is complicated by solar radiation which can heat the air temperature sensor body, causing it to record temperatures in excess of the true air temperature. The standard way to protect against this interference is to house the sensors in passive radiation shields which block the majority of solar radiation. However, solar radiation still heats the shield body which can then heat the sensor, causing measurement errors under low wind conditions. An alternative method is to aspirate the sensor using a fan to force air movement over the sensor body. Aspirated temperature sensing units are commercially available, but they can be expensive and consume a significant amount of power. Here, we present the design and initial rigorous testing of a low-cost, low-power aspirated temperature sensing unit designed to integrate with any low-cost distributed sensor platform. The design uses a freely available, custom-designed 3D-printed housing that enables rapid assembly. The new, open-source unit is shown to perform as well as passive shields and commercial aspirated shields of significantly higher cost. This success shows the potential of leveraging 3D printing technologies for other housing units. Further testing is needed to better quantify the long-term robustness of the shield.

Radiative surface heating over slopes induces buoyantly-driven anabatic winds that can transport air pollutants, heat, and moisture, affecting air quality and mountain weather via convection and evapotranspiration. This study uses multi-level turbulence observations over a steep Alpine slope in Val Ferret, Switzerland, to characterize daytime anabatic winds’ mean and turbulence structures and their evolution. It further investigates multiscale interactions between mean flow and turbulence across valley and local slope scales. The observed anabatic flow structure exhibits significant momentum and heat flux divergence associated with the jet-shaped velocity profiles, challenging the constant-flux layer assumption in Monin–Obukhov similarity theory. Sign reversals in surface-normal momentum and terrain-following heat fluxes provide a basis for estimating the anabatic jet height ( h_jet) , with higher h_jet linked to stronger valley flows during the early afternoon. Budget analyses indicate that slope-parallel heat fluxes suppress turbulence kinetic energy (TKE) below the sign reversal height, while enhancing TKE above it. The Fourier cospectra for momentum fluxes exhibit multiscale transport near the estimated h_jet , with large and small eddies demonstrating opposite signs, suggesting counter-gradient flux transport. These findings can help to improve surface-layer turbulence parameterizations and turbulent flux estimates derived from meteorological observations, e.g., using distributed sensor networks in watersheds.

The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RHw) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH4NO3). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations.

Numerical model predictions of precipitation rates rely heavily on representations of how fast hydrometeors fall, assuming settling is determined only by the opposing force balance of gravity and drag. Here, we use a novel suite of ground‐based winter measurements to show large departures of the mean snowflake settling speed from the terminal fall speed of a particle falling broadside. Where is lower than the air root‐mean‐square turbulent velocity fluctuation , settling is sub‐terminal by up to a factor of five, and if it is higher, then settling is super‐terminal by up to a factor of three. Mean winds and aerodynamic lift appear to play an unexpectedly important role, by tilting snowflake orientations edge‐on while slowing their mean rate of descent. New parameterizations are provided for relating winds and small‐scale turbulence to hydrometeor orientations, drift angles, and precipitation rate reductions and enhancements.

Cold fog refers to a type of fog that forms when the temperature is below 0 degrees C. It can be composed of liquid, ice, and mixed-phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small-scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near-surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above-freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m2s-2), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high-resolution (400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold-fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold-fog events. At the same time, they are also critical fields for the NWP model validation. This study finds that fog formation is extremely sensitive to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated clear skies and a stable boundary layer; (2) dry, near-surface inversion paired with calm winds; (3) warm (above-freezing) air and soil temperatures during the daytime, accompanied by a period of elevated turbulence kinetic energy (above 0.5 m2s-2), followed by calm conditions throughout the fog duration; and (4) supersaturation with respect to ice. image

Saline pans are environments with ephemeral to persistent evaporite crusts, surface and groundwater brine, little to no vegetation, and low topographic gradients. These characteristics make them sensitive to diverse hydrological processes. This research provides guidance on assessing and interpreting fluctuations in saline pan groundwater levels. Observations from the center of the Bonneville Salt Flats, Utah, USA, focused on meteorological and groundwater level fluctuations and were used to quantify evaporation and identify natural environmental controls on saline pan groundwater level variation. Primary water fluxes consist of precipitation and evaporation. Eddy-covariance evaporation measurements, spanning over 1.5 years and capturing diverse surface conditions, were collected. An artificial neural network, trained on meteorological measurements and eddy-covariance-measured evaporation, estimated evaporation over a 6-year period. The saline pan has two states: (1) dry, when water availability rather than evaporative potential limits evaporation, and (2) wet, when evaporative potential limits evaporation. In dry conditions, characterized by evaporation rates of 0.1 mm/day, groundwater levels with daily average depths ≥5 cm below the surface, demonstrated daily variations >6 cm during summer and seasonal fluctuations >50 cm in response to temperature changes. Groundwater levels did not respond to temperature changes when there was surface water. Groundwater levels rose to the surface under wet conditions. Over multiple years, the system is in balance, with evaporation equaling precipitation.

In this work, we explore the intricacies of the potential-temperature variance budget in coastal fog. We propose an improvement to the theoretical framework of the budget, whereby we include the heat exchange due to water-phase changes. We then show this framework's consistency with a real-world case study from the Coastal Fog (C-FOG) Research Program. Results show that the presence of intermittent energy bursts is driven by the sudden turbulent injection of heat into the environment caused by the condensation of water vapour, and the improved theoretical framework proves satisfactory in detailing the observed process. The heat excess is transported vertically, creating a two-term balance of high-order moments. A bulk parametrization of this balance is also proposed to provide a simplified representation of the phase-change process and suggest that it could be used for operational purposes. Finally, the length-scales of the processes are evaluated from the parametrizations. The analysis indicates that the scales of the phase change of water vapour are consistent with the buoyancy production and Taylor scales. We propose an improvement to the theoretical framework of the potential-temperature variance theta ' 2$$ {\theta}<^>{\prime 2} $$ budget under fog conditions, whereby we include the heat exchange due to water-phase changes, which is responsible for sudden, localized turbulent injections of heat into the environment (see figure). The heat excess is transported vertically, creating a two-term balance of high-order moments that we parametrize using the aerodynamic formulations of the sensible and latent heat fluxes. Finally, the length-scales of the processes are evaluated, indicating that the phase change of water vapour is consistent with the buoyancy-production and Taylor scales. image

It is a challenge to obtain accurate measurements of the microphysical properties of delicate, structurally complex, frozen, and semi-frozen hydrometeors. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), has been shown through laboratory and field experiments to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density (which can be interpreted as the snow-to-liquid ratio or SLR). The method exploits the rate of heat transfer during the melting of a hydrometeor on a heated metal plate, which is a function of the temperature difference between the hotplate surface and the top of the hydrometeor. The product of the melting speed and melting time yields an effective particle thickness normal to the hotplate surface, which can then be used in combination with the particle mass and area on the plate to determine a particle density. Uncertainties in estimates of particle density are approximately 4 % based on calibrations with laboratory-produced particles made from water and frozen solutions of salt and water and field comparisons with both high-resolution imagery of falling snow and traditional snowpack density measurements obtained at 12 h intervals. For 17 storms, individual particle densities vary from 19 to 495 kg m−3, and storm mean snow densities vary from 40 to 100 kg m−3. We observe probability distribution functions for hydrometeor density that are nearly Gaussian with kurtosis of ≈ 3 and skewness of ≈ 0.01.

We present a case study of a coastal-fog stratus-cloud-lowering event on September 13-14, 2018, during the C-FOG field campaign conducted along the east coast of Newfoundland, Canada. The goal of this work is to understand the mechanisms governing the life cycle of a 4-hr-long coastal-fog event that resulted from the complex interplay of dynamic, thermodynamic, and microphysical processes. In addition to standard meteorological measurements, turbulence, irradiance, droplet-size spectra, tethered-balloon wind and thermodynamic profiles, visibility, precipitation, and spatial heterogeneity of microphysics measurements are presented to discriminate and interpret the fog formation, development, and dissipation. After sunset, strong radiative cloud-top cooling induced top-down convection length scales that can be characterised with the Thorpe scale. Top-down mixing and turbulence kinetic energy generated due to buoyant/shear mixing are characterised using the flux and bulk Richardson number near the surface. Use of these parameters is unique in the analysis of fog events and helped describe mixing processes. Downward mixing led to fog droplet formation that precipitated from the cloud base, which in turn cooled the sub-cloud layers via droplet evaporation and moistened the air beneath the cloud. Once fog formed, it was affected by dry-air entrainment from its top. As a result, the fog thinned, creating patchy fog that was characterised by remarkable oscillations in visibility near the surface. Dissipation of the fog was driven by strong turbulence above the fog layer and horizontal thermal advection demonstrated using the temperature tendency equation. This work provides novel measurements and analysis techniques that have previously not been used to understand the mechanisms governing stratus-lowering events. These observations and analyses help highlight processes and explain mechanisms related to the fog life cycle that are inherently challenging to predict in mesoscale models. Turbulent mixing processes are shown to be essential for the formation and dissipation of fog and the life cycle of stratus clouds. This case study helps explain the complex nature of patchy fog that creates the vertical and horizontal heterogeneity of microphysical data that plays an essential role in predicting visibility that oscillates near the coastal surface. This study observed the entire cloud subsidence rather than the expansion of a cloud base to the surface, as often reported in the literature.image

Transitional changes in the atmospheric boundary layer (ABL) are known to facilitate the onset of terrestrial fog, which is defined as a condition with near-surface visibility <1 km due to airborne water droplets. In particular, the evening transition from a daytime convective ABL to a night-time stable ABL provides favorable conditions for fog. This article describes a local fog event observed during the evening transition at a Canadian islet in the north Atlantic known as Sable Island during the "Fog and Turbulence Interactions in the Marine Atmosphere (Fatima)" field campaign. The comprehensive set of data collected using a myriad of instruments covering a wide range of scales allowed identification of a novel mechanism underlying this fog event. Therein an ocean-land discontinuity created a flow regime consisting of several stacked boundary layers, interplay of which produced a thin low-level cloud that then diffused downward to the surface, causing visibility reduction. This mechanism offers useful insights on the role of boundary layers, stratification, and turbulence in fog genesis over oceanic islands.

To determine near-surface winds within and above vegetation canopies for operational environmental applications, a wind model must run at high-resolution (O(1–10 m)), in a few minutes, using limited input information, and requiring minimal computing resources (e.g., personal computers). Current research models simulate large domains at coarse resolution or small domains at fine scale, but canopy simulations can take days. Fast-modeling approaches are used to solve large complex wind fields, but they oversimplify the roughness elements’ distribution impact on momentum exchanges. To overcome these deficits, the fast-running wind model QUIC-URB (Quick Urban and Industrial Complex) was augmented with a high-resolution canopy wind solver. The wind model includes a non-local factor that describes how momentum propagates through the canopy and how sub-canopy jets appear under certain conditions. QUIC-URB was also coupled with the mesoscale WRF (Weather Research and Forecasting) model to downscale wind fields from a few kilometers to a meter. The new QUIC Canopy Model resolves 3-D wind fields over hundreds of millions of cells in less than 30 s per time step on a personal computer. It was compared to two canopy models for real quasi-homogeneous and heterogeneous canopies. An error analysis shows that the model was relatively accurate with a normalized root-mean-square error of about 0.2 m s−1 in the quasi-homogeneous canopy, and a mean absolute error of 0.3 m s−1. The new model is suitable for coupling with pollution dispersion, wildfire spread, and numerical weather prediction models over weakly complex terrain, defined here as a mildly undulating environment with gradual changes in elevation and a heterogeneous distribution of plants.