Freezing fog is a type of cold fog that forms when the air temperature (Ta) is below 0℃. Although... more Freezing fog is a type of cold fog that forms when the air temperature (Ta) is below 0℃. Although Ta is below 0℃, the water droplets can remain in a liquid state rather than freezing. Freezing-fog conditions can pose a significant hazard to aviation and marine operations because it can reduce visibility severely, and ice accumulates rapidly on the surfaces such as aircraft, ship, and roads. Observations collected during the CFACT (Cold Fog Amongst Complex Terrain) Project from 7 January – 24 February, representing cold-fog events over Heber Valley of Utah, are used in the analysis. The objectives of this study are to characterize freezing fog microstructure in detail with respect to droplet size distribution, critical diameter related to activation, and visibility. In the analysis, freezing fog (FZFG) and droplet size spectra will be examined theoretically and experimentally. The droplet activation and critical diameter forming in frozen-fog droplets will be revealed using the Köhle...
In this paper, two different computationally inexpensive methods for nowcasting/data filling spat... more In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2....
Experimental closure of the surface energy balance during convective periods is a long-standing p... more Experimental closure of the surface energy balance during convective periods is a long-standing problem. With experimental data from the Idealized horizontal Planar Array experiment for Quantifying Surface heterogeneity, the terms of the temperature-tendency equation are computed, with an emphasis on the total derivative. The experiment occurred at the Surface Layer Turbulence and Environmental Science Test facility at the U.S. Army Dugway Proving Ground during the summer of 2019. The experimental layout contained an array of 21 flux stations over a 1 km2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} grid. Sensible heat fluxes show high spatial variability, with maximum variability occurring during convective periods. Maximum variability in the vertical heat flux is 50–80 W m-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-2}$$\\end{document} (median variability of 40%), while in the horizontal flux, it is 200–500 W m-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-2}$$\\end{document} (median variability of 48% for the streamwise and 40% for the spanwise fluxes). Ensemble averages computed during convective afternoon periods show large magnitudes of horizontal advection (48 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} or 172 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) and vertical flux divergence (13 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} or 47 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}). Probability density functions of the total derivative from convective cases show mean volumetric heating rates of 43 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} (154 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) compared to 13 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} (47 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) on non-convective days. A conceptual model based on persistent mean flow structures from local-surface-temperature heterogeneities may explain the observed advection. The model describes the difference between locally-driven advection and advection driven by larger-scale forcings. Of the cases examined, 83% with streamwise and 81% with spanwise advection during unstable periods are classified as locally driven by nearby surface thermal heterogeneities.
&... more &am…
Journal of Applied Meteorology and Climatology, 2019
We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an... more We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting metho...
RationaleThe fast and accurate measurement of H and O stable isotope compositions (δ2H and δ18O v... more RationaleThe fast and accurate measurement of H and O stable isotope compositions (δ2H and δ18O values) of soil and sediment pore water remains an impediment to scaling‐up the application of these isotopes in soil and vadose hydrology. Here we describe a method and its calibration to measuring soil and sediment pore water δ2H and δ18O values using a water vapor‐permeable probe coupled to an isotope ratio infrared spectroscopy analyzer.MethodsWe compare the water vapor probe method with a vapor direct equilibration method, and vacuum extraction with liquid water analysis. At a series of four study sites in a managed desert agroecosystem in the eastern Great Basin of North America, we use the water vapor probe to measure soil depth profiles of δ2H and δ18O values.ResultsWe demonstrate the accuracy of the method to be equivalent to direct headspace equilibration and vacuum extraction techniques, with increased ease of use in its application, and with analysis throughput rates greater t...
Recently, many urban areas of the world have experienced rapid growth of population and industria... more Recently, many urban areas of the world have experienced rapid growth of population and industrial activity raising concerns of environmental deterioration. To meet challenges associated with such rapid urbanization, it has become necessary to implement wise strategies for environmental management and planning, addressing the exclusive demands of urban zones for maintaining environmental sustainability and functioning with minimum disruption. These strategies
Freezing fog is a type of cold fog that forms when the air temperature (Ta) is below 0℃. Although... more Freezing fog is a type of cold fog that forms when the air temperature (Ta) is below 0℃. Although Ta is below 0℃, the water droplets can remain in a liquid state rather than freezing. Freezing-fog conditions can pose a significant hazard to aviation and marine operations because it can reduce visibility severely, and ice accumulates rapidly on the surfaces such as aircraft, ship, and roads. Observations collected during the CFACT (Cold Fog Amongst Complex Terrain) Project from 7 January – 24 February, representing cold-fog events over Heber Valley of Utah, are used in the analysis. The objectives of this study are to characterize freezing fog microstructure in detail with respect to droplet size distribution, critical diameter related to activation, and visibility. In the analysis, freezing fog (FZFG) and droplet size spectra will be examined theoretically and experimentally. The droplet activation and critical diameter forming in frozen-fog droplets will be revealed using the Köhle...
In this paper, two different computationally inexpensive methods for nowcasting/data filling spat... more In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2....
Experimental closure of the surface energy balance during convective periods is a long-standing p... more Experimental closure of the surface energy balance during convective periods is a long-standing problem. With experimental data from the Idealized horizontal Planar Array experiment for Quantifying Surface heterogeneity, the terms of the temperature-tendency equation are computed, with an emphasis on the total derivative. The experiment occurred at the Surface Layer Turbulence and Environmental Science Test facility at the U.S. Army Dugway Proving Ground during the summer of 2019. The experimental layout contained an array of 21 flux stations over a 1 km2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} grid. Sensible heat fluxes show high spatial variability, with maximum variability occurring during convective periods. Maximum variability in the vertical heat flux is 50–80 W m-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-2}$$\\end{document} (median variability of 40%), while in the horizontal flux, it is 200–500 W m-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-2}$$\\end{document} (median variability of 48% for the streamwise and 40% for the spanwise fluxes). Ensemble averages computed during convective afternoon periods show large magnitudes of horizontal advection (48 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} or 172 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) and vertical flux divergence (13 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} or 47 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}). Probability density functions of the total derivative from convective cases show mean volumetric heating rates of 43 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} (154 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) compared to 13 W m-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-3}$$\\end{document} (47 K h-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) on non-convective days. A conceptual model based on persistent mean flow structures from local-surface-temperature heterogeneities may explain the observed advection. The model describes the difference between locally-driven advection and advection driven by larger-scale forcings. Of the cases examined, 83% with streamwise and 81% with spanwise advection during unstable periods are classified as locally driven by nearby surface thermal heterogeneities.
&... more &am…
Journal of Applied Meteorology and Climatology, 2019
We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an... more We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting metho...
RationaleThe fast and accurate measurement of H and O stable isotope compositions (δ2H and δ18O v... more RationaleThe fast and accurate measurement of H and O stable isotope compositions (δ2H and δ18O values) of soil and sediment pore water remains an impediment to scaling‐up the application of these isotopes in soil and vadose hydrology. Here we describe a method and its calibration to measuring soil and sediment pore water δ2H and δ18O values using a water vapor‐permeable probe coupled to an isotope ratio infrared spectroscopy analyzer.MethodsWe compare the water vapor probe method with a vapor direct equilibration method, and vacuum extraction with liquid water analysis. At a series of four study sites in a managed desert agroecosystem in the eastern Great Basin of North America, we use the water vapor probe to measure soil depth profiles of δ2H and δ18O values.ResultsWe demonstrate the accuracy of the method to be equivalent to direct headspace equilibration and vacuum extraction techniques, with increased ease of use in its application, and with analysis throughput rates greater t...
Recently, many urban areas of the world have experienced rapid growth of population and industria... more Recently, many urban areas of the world have experienced rapid growth of population and industrial activity raising concerns of environmental deterioration. To meet challenges associated with such rapid urbanization, it has become necessary to implement wise strategies for environmental management and planning, addressing the exclusive demands of urban zones for maintaining environmental sustainability and functioning with minimum disruption. These strategies
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