Airborne instruments to measure atmospheric aerosol particles, clouds and radiation: A cook's tour of mature and emerging technology

Baumgardner, Darrel; Brenguier, Jean‐Louis; Bucholtz, Anthony; Coe, Hugh; DeMott, Paul J.; Garrett, Timothy J.; Gayet, Jean-François; Hermann, M.; Heymsfield, Andrew J.; Korolev, Alexei; Krämer, M.; Petzold, A.; Strapp, W.; Pilewskie, P.; Taylor, Jonathan P.; Twohy, Cynthia H.; Wendisch, Manfred; Bachalo, W. D.; Chuang, P. Y.

doi:10.1016/j.atmosres.2011.06.021

Cited by 156 publications

(136 citation statements)

References 196 publications

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“…Burkard et al, 2002;Eugster et al, 2006). In-situ measurements are very challenging due to various difficulties recently discussed for aircraft devices by Lance et al (2010) and Baumgardner et al (2011) and for the FSSP in general by Baumgardner (1983) and Baumgardner et al (1992).…”

Section: Introductionmentioning

confidence: 99%

“…Burkard et al, 2002). Using light scattering interferometry, cloud droplets can also be measured in size, for example with the Phase Doppler Interferometer (PIP; 1 to 1000 µm, Baumgardner et al, 2011). However, for realistic operations a reasonable upper-bound was found to be D ≈ 100 µm (Chuang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Pinnick and Auvermann, 1979), the Cloud Droplet Probe (CDP; Model CDP-100, D = 2 to 50 µm, e.g. McFarquhar et al, 2007), the Cloud and Aerosol Spectrometer -also with Depolarization CAS-DPOL -(CAS and CAS-DPOL; D = 0.5 to 50 µm, Baumgardner et al, 2011), the Cloud Particle Spectrometer with Depolarization (CPSD; D = 0.5 to 50 µm, Baumgardner et al, 2011), the Small Ice Detectors (SID model 1 and 2; D = 2 to 140 µm, Baumgardner et al, 2011) and the Fog Monitor 100 (FM-100; D = 2 to 50 µm, e.g. Burkard et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

Spiegel¹,

Zieger²,

Bukowiecki³

et al. 2012

Atmos. Meas. Tech.

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Abstract. Droplet size spectra measurements are crucial to obtain a quantitative microphysical description of clouds and fog. However, cloud droplet size measurements are subject to various uncertainties. This work focuses on the error analysis of two key measurement uncertainties arising during cloud droplet size measurements with a conventional droplet size spectrometer (FM-100): first, we addressed the precision with which droplets can be sized with the FM-100 on the basis of the Mie theory. We deduced error assumptions and proposed a new method on how to correct measured size distributions for these errors by redistributing the measured droplet size distribution using a stochastic approach. Second, based on a literature study, we summarized corrections for particle losses during sampling with the FM-100. We applied both corrections to cloud droplet size spectra measured at the high alpine site Jungfraujoch for a temperature range from 0 • C to 11 • C. We showed that Mie scattering led to spikes in the droplet size distributions using the default sizing procedure, while the new stochastic approach reproduced the ambient size distribution adequately. A detailed analysis of the FM-100 sampling efficiency revealed that particle losses were typically below 10 % for droplet diameters up to 10 µm. For larger droplets, particle losses can increase up to 90 % for the largest droplets of 50 µm at ambient wind speeds below 4.4 m s −1 and even to >90 % for larger angles between the instrument orientation and the wind vector (sampling angle) at higher wind speeds. Comparisons of the FM-100 to other reference instruments revealed that the total liquid water content (LWC) measured by the FM-100 was more sensitive to particle losses than to re-sizing based on Mie scattering, while the total number concentration was only marginally influenced by particle losses. Consequently, for further LWC measurements with the FM-100 we strongly recommend to consider (1) the error arising due to Mie scattering, and (2) the particle losses, especially for larger droplets depending on the set-up and wind conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

Spiegel¹,

Zieger²,

Bukowiecki³

et al. 2012

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…The basic operating principle is thus: cloud and precipitation particles impact upon a substrate, leaving an imprint (or leaving the particle itself) to be replicated (in the case of the VIPS, by digital imaging) and analyzed. This type of probe is particularly useful for imaging the smallest ice crystals (Baumgardner et al, 2011;Brenguier et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Probes such as the FSSP and CDP are therefore prone to undersizing ice crystals (Baumgardner et al, 2011(Baumgardner et al, , 2017Brenguier et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC<sup>4</sup>, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Schwartz

2017

Atmos. Meas. Tech.

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Abstract. This paper addresses two straightforward questions. First, how similar are the statistics of cirrus particle size distribution (PSD) datasets collected using the TwoDimensional Stereo (2D-S) probe to cirrus PSD datasets collected using older Particle Measuring Systems (PMS) 2-D Cloud (2DC) and 2-D Precipitation (2DP) probes? Second, how similar are the datasets when shatter-correcting postprocessing is applied to the 2DC datasets? To answer these questions, a database of measured and parameterized cirrus PSDs -constructed from measurements taken during the Small Particles in Cirrus (SPARTICUS); Mid-latitude Airborne Cirrus Properties Experiment (MACPEX); and Tropical Composition, Cloud, and Climate Coupling (TC 4 ) flight campaigns -is used.Bulk cloud quantities are computed from the 2D-S database in three ways: first, directly from the 2D-S data; second, by applying the 2D-S data to ice PSD parameterizations developed using sets of cirrus measurements collected using the older PMS probes; and third, by applying the 2D-S data to a similar parameterization developed using the 2D-S data themselves. This is done so that measurements of the same cloud volumes by parameterized versions of the 2DC and 2D-S can be compared with one another. It is thereby seen -given the same cloud field and given the same assumptions concerning ice crystal cross-sectional area, density, and radar cross section -that the parameterized 2D-S and the parameterized 2DC predict similar distributions of inferred shortwave extinction coefficient, ice water content, and 94 GHz radar reflectivity. However, the parameterization of the 2DC based on uncorrected data predicts a statistically significantly higher number of total ice crystals and a larger ratio of small ice crystals to large ice crystals than does the parameterized 2D-S. The 2DC parameterization based on shatter-corrected data also predicts statistically different numbers of ice crystals than does the parameterized 2D-S, but the comparison between the two is nevertheless more favorable. It is concluded that the older datasets continue to be useful for scientific purposes, with certain caveats, and that continuing field investigations of cirrus with more modern probes is desirable.

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Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

et al. 2016

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Shortwave Infrared imaging spectroscopy enables accurate remote mapping of cloud thermodynamic phase at high spatial resolution. We describe a measurement strategy to exploit signatures of liquid and ice absorption in cloud top apparent reflectance spectra from 1.4 to 1.8 μm. This signal is generally insensitive to confounding factors such as solar angles, view angles, and surface albedo. We first evaluate the approach in simulation and then apply it to airborne data acquired in the Calwater‐2/ACAPEX campaign of Winter 2015. Here NASA's “Classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS‐C) remotely observed diverse cloud formations while the U.S. Department of Energy ARM Aerial Facility G‐1 aircraft measured cloud integral and microphysical properties in situ. The coincident measurements demonstrate good separation of the thermodynamic phases for relatively homogeneous clouds.

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Airborne instruments to measure atmospheric aerosol particles, clouds and radiation: A cook's tour of mature and emerging technology

Cited by 156 publications

References 196 publications

Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC<sup>4</sup>, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

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Airborne instruments to measure atmospheric aerosol particles, clouds and radiation: A cook's tour of mature and emerging technology

Cited by 156 publications

References 196 publications

Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100)

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC&lt;sup&gt;4&lt;/sup&gt;, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

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Product

Resources

About

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC<sup>4</sup>, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets