STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment Science and Operations Plan

Mace, Jay; Matrosov, Sergey Y.; Shupe, Matthew D.; Lawson, Paul A.; Hallar, G.; McCubbin, Ian B.; Marchand, R.; Orr, Brad W.; Coulter, R. L.; Sedlacek, A. J.; Avallone, L. M.; Long, Charles

doi:10.2172/989263

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…The results have potential implications for research and field campaigns looking to study the influence of different atmospheric regimes on orographic precipitation, such as Isotopic Fractionation in Snow (IFRACS; a 2014 campaign led by Doug Lowenthal, Gannet Haller and colleagues at the Desert Research Institute: https://www.eol.ucar.edu/field_projects/ifracs), ISPA-III [Ward and Cotton, 2011], and StormVEx [Mace et al, 2010]. As liquid processes are most responsive to CDNC, locations where precipitation primarily forms through accretion of cloud liquid and/or riming are likely to experience decreased accumulation, a shift in the location of, and a decrease in the 18 O precip .…”

Section: 1002/2015jd023763mentioning

confidence: 99%

Microphysical controls on the isotopic composition of wintertime orographic precipitation

et al. 2016

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The sensitivity of mixed-phase orographic clouds, precipitation, and their isotopic content to changes in dynamics, thermodynamics, and microphysics is explored in idealized two-dimensional flow over a mountain barrier. 18 O precip with increasing mountain height is not just a function of decreasing temperature but also reflects the changing contributions and distinct isotopic signatures of riming of cloud liquid and vapor deposition onto snow, the leading sources of precipitation in these simulations. The changes in 18 O precip with mountain height, temperature, and CDNC are governed in part by the microphysical pathways through which precipitating hydrometeors are formed and grow.

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Section: 1002/2015jd023763mentioning

confidence: 99%

Microphysical controls on the isotopic composition of wintertime orographic precipitation

et al. 2016

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“…For example, pyrheliometers typically are not equipped with ventilators and thus are more susceptible to riming. During the ARM StormVEx campaign (Mace et al, 2010) at Storm Peak Lab, the measurement system included an SPN-1 and an Eppley PSP (Fig. 7).…”

Section: Instrument Improvementsmentioning

confidence: 99%

Evaluation of Arctic broadband surface radiation measurements

et al. 2012

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Abstract. The Arctic is a challenging environment for making in-situ surface radiation measurements. A standard suite of radiation sensors is typically designed to measure incoming and outgoing shortwave (SW) and thermal infrared, or longwave (LW), radiation. Enhancements may include various sensors for measuring irradiance in narrower bandwidths. Many solar radiation/thermal infrared flux sensors utilize protective glass domes and some are mounted on complex mechanical platforms (solar trackers) that keep sensors and shading devices trained on the sun along its diurnal path. High quality measurements require striking a balance between locating stations in a pristine undisturbed setting free of artificial blockage (such as from buildings and towers) and providing accessibility to allow operators to clean and maintain the instruments. Three significant sources of erroneous data in the Arctic include solar tracker malfunctions, rime/frost/snow deposition on the protective glass domes of the radiometers and operational problems due to limited operator access in extreme weather conditions. In this study, comparisons are made between the global and component sum (direct [vertical component] + diffuse) SW measurements. The difference between these two quantities (that theoretically should be zero) is used to illustrate the magnitude and seasonality of arctic radiation flux measurement problems. The problem of rime/frost/snow deposition is investigated in more detail for one case study utilizing both SW and LW measurements. Solutions to these operational problems that utilize measurement redundancy, more sophisticated heating and ventilation strategies and a more systematic program of operational support and subsequent data quality protocols are proposed.

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“…During the winter of 2010/11, the second Atmospheric Radiation Measurement Program (Mather and Voyles 2013) Mobile Facility (AMF2) conducted its maiden deployment to the Park Range Mountains near Steamboat Springs, Colorado, as the principal component of the Storm Peak Laboratory Cloud Property Validation Experiment (StormVEx; Mace et al 2010). The purpose of StormVEx was to document the properties of orographic mixed-phase snow clouds in conjunction with the collection of correlative data at Storm Peak Laboratory (SPL) located at the summit of Mount Werner (Borys and Wetzel 1997;Hallar et al 2011a,b).…”

Section: Introductionmentioning

confidence: 99%

MCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar–Passive Microwave Cloud Property Retrieval

Posselt

Mace

2014

Journal of Applied Meteorology and Climatology

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Collocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.

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STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment Science and Operations Plan

Cited by 7 publications

References 19 publications

Microphysical controls on the isotopic composition of wintertime orographic precipitation

Microphysical controls on the isotopic composition of wintertime orographic precipitation

Evaluation of Arctic broadband surface radiation measurements

MCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar–Passive Microwave Cloud Property Retrieval

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