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  • View in gallery

    Schematic of SID-2 sample inlet. The overlap of the two PMT sensing volumes within the laser beam define the trigger area (the second PMT is not shown for clarity). The outer 24 HPD azimuthal segments are numbered 9–32, the six central segments are numbered 2–8, and the beam dump is labeled 1.

  • View in gallery

    Cloud particle number concentration (1-s averages) measured by the SID-2 and FFSSP in stratocumulus (flight B256, run 3 at 300 m) in (b). For clarity only a 1-min section of the run 3 data is shown in (a).

  • View in gallery

    Cloud particle size distributions measured by the SID-2 and FFSSP in stratocumulus (flight B256, run 3 at 300 m near the cloud base, and run 5 at 900 m near the cloud top).

  • View in gallery

    LWC obtained by integrating the particle size distribution from the SID-2 for stratocumulus (flight B256, run 3 at 300 m). (a) For clarity, only a 1-min section of the run 3 data is shown. (b) The SID-2 LWC is compared to that from the Nevzorov bulk water probe. (c) The SID-2 LWC is compared to the FFSSP-derived LWC.

  • View in gallery

    IWC obtained by integrating the particle size distribution from the SID-2 for cirrus (flight B257, run 6–1 at 9.75 km). (a) The time series is averaged over 5-s bins. (b) The SID-2 IWC is compared to that from the Nevzorov bulk water probe. (c) The SID-2 IWC is compared to the 2D-C–derived IWC.

  • View in gallery

    Cloud particle size distributions measured by SID-2 and the 2D-C in cirrus (flight B257, run 4 at 8.23 km near the cloud base and run 7 at 10.36 km near the cloud top).

  • View in gallery

    Aerosol particle size distributions measured by the SID-2 and PCASP in (a) desert dust (flight B301, run 3–1 at 600 m) and (b) sea salt (flight B254, run 1–1 at 30 m).

  • View in gallery

    Cloud particle interarrival times. The stratocumulus case is only for 1 min of data where the drop concentration was always high.

  • View in gallery

    Probability distribution functions of particle asphericity Af for (a) cloud particle and (c) aerosol data. Cumulative frequency distributions of Af for (b) cloud particle and (d) aerosol data. The vertical dotted lines mark Af thresholds for cloud particle shape determination.

  • View in gallery

    Frequency of occurrence as a function of the particle asphericity and radius for liquid water–only cloud [(a) stratocumulus and (b) supercooled altocumulus lenticularis], ice and mixed-phase cloud [(c) cirrus and (d) supercooled cumulus], and aerosol [(e) desert dust and (f) sea salt].

  • View in gallery

    The scattered light intensity onto each outer detector element for a sample of particles for liquid water–only cloud [(a) stratocumulus and (b) supercooled altocumulus lenticularis], ice and mixed-phase cloud [(c) cirrus and (d) supercooled cumulus], and aerosol [(e) desert dust and (f) sea salt]. The radius scale is shown inset on (d).

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The Ability of the Small Ice Detector (SID-2) to Characterize Cloud Particle and Aerosol Morphologies Obtained during Flights of the FAAM BAe-146 Research Aircraft

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  • 1 Met Office, Exeter, United Kingdom
  • | 2 STRC, University of Hertfordshire, Hatfield, United Kingdom
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Abstract

The Small Ice Detector mark 2 (SID-2), which was built by the University of Hertfordshire, has been operated by the Met Office on the Facility for Atmospheric Airborne Research (FAAM) BAe-146 aircraft during a large number of flights. The flights covered a wide range of atmospheric conditions, including stratocumulus, altocumulus lenticularis, cirrus, and mixed-phase cumulus clouds, as well as clear-sky flights over the sea and over desert surfaces. SID-2 is a laser scattering device that provides in situ data on cloud particle concentration and size. SID-2 also provides the spatial light scattering data from individual particles to give some information on the particle shape. The advantage of SID-2 is that it can characterize the cloud particle shape for particle sizes less than the resolutions of the more usual commercially available ice crystal imaging probes. The particle shape characteristics enable, for example, small just-nucleated ice particles to be discriminated from supercooled water drops. SID-2 also has an open-path inlet that reduces shattering of large cloud particles compared to other probes that use a tube inlet.

The aim of this paper is to illustrate the capability of SID-2 to count, size, and determine cloud particle and aerosol shape. This is done by comparing the response of SID-2 to water drops, ice particles, and aerosols with that from other standard aircraft-based probes.

Corresponding author address: Richard Cotton, Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, United Kingdom. Email: richard.cotton@metoffice.gov.uk

Abstract

The Small Ice Detector mark 2 (SID-2), which was built by the University of Hertfordshire, has been operated by the Met Office on the Facility for Atmospheric Airborne Research (FAAM) BAe-146 aircraft during a large number of flights. The flights covered a wide range of atmospheric conditions, including stratocumulus, altocumulus lenticularis, cirrus, and mixed-phase cumulus clouds, as well as clear-sky flights over the sea and over desert surfaces. SID-2 is a laser scattering device that provides in situ data on cloud particle concentration and size. SID-2 also provides the spatial light scattering data from individual particles to give some information on the particle shape. The advantage of SID-2 is that it can characterize the cloud particle shape for particle sizes less than the resolutions of the more usual commercially available ice crystal imaging probes. The particle shape characteristics enable, for example, small just-nucleated ice particles to be discriminated from supercooled water drops. SID-2 also has an open-path inlet that reduces shattering of large cloud particles compared to other probes that use a tube inlet.

The aim of this paper is to illustrate the capability of SID-2 to count, size, and determine cloud particle and aerosol shape. This is done by comparing the response of SID-2 to water drops, ice particles, and aerosols with that from other standard aircraft-based probes.

Corresponding author address: Richard Cotton, Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, United Kingdom. Email: richard.cotton@metoffice.gov.uk

1. Introduction

The in situ measurement of the shape of atmospheric particles, in addition to the number concentration and size, is important for the study of cloud microphysics and for the radiative properties of clouds and aerosols. The shape measurement can be used to indicate the particle’s phase: liquid drops are spherical, whereas ice crystals have a wide range of morphologies (see, e.g., Pruppacher and Klett 1997). The ability to determine the phase of every cloud particle should improve ice nucleation studies, particularly the onset of first ice formation in a supercooled liquid cloud.

There are several commercially available cloud particle and aerosol instruments usually operated on the BAe-146 aircraft that are attached to wing-mounted pylons (details of the aircraft and instruments available online at http://www.faam.ac.uk/). These either measure the intensity of scattered light or the shadow of individual particles.

The Fast Forward Scattering Spectrometer Probe (FFSSP) is a modified FSSP-100 [from Particle Measurement Systems, Inc. (PMS)]. The FFSSP is used to measure cloud particle number concentration and to size spherical particles with diameters of 2–45 μm. The particle size is derived from the maximum signal recorded by the detection diode that covers the 3°–12° forward-scattering angle. The modifications include the logging of particle arrival time with 83-ns resolution. Additionally, the dead time of 6 μs per particle for the standard FSSP has been mostly eliminated. A full list of the FSSP modifications and its response to liquid water cloud is shown in Field et al. (2003; see also Brenguier et al. 1998 for a description of a similarly modified FSSP). The FFSSP cannot, however, discriminate between water drops and ice crystals if they have equivalent optical scattering size; therefore, the FFSSP is calibrated assuming that all particles are spherical. Field et al. (2003) also present evidence that large ice particles shatter on the FFSSP’s inlet, which leads to a large overestimate of the ice particle number concentration, up to a factor of 5. The interarrival times for the fragments of the shattered ice particle when passing through the sample volume are abnormally small. A bimodality in the distribution of interarrival times can therefore be used to indicate shattering.

The PMS Passive Cavity Aerosol Spectrometer Probe (PCASP) measures number concentration and size for aerosols with diameters of 0.1–3.0 μm. The size that is determined by the PCASP assumes that the scattered light detected is from a spherical particle of refractive index 1.58. Mixed-composition, unknown refractive index, or nonspherical aerosols will not, therefore, be sized correctly. Scattered light from a laser is integrated over a 2π solid angle so that, although the PCASP is sensitive to small aerosols, there is no shape information.

Shadow detectors such as the PMS 2D-cloud (2D-C) array probe (Knollenberg 1970) can measure the cloud particle shape. Cloud particles are carried by the airflow through a laser beam, which illuminates an array of 32 photodiodes (each 25 μm wide). The time resolution is 250 ns and the measurement size range is 25–800 μm. However, the 2D-C is considered unreliable for particles less than 100 μm in diameter for the following reasons: the particle interarrival time in ice cloud shows evidence that, similar to the FFSSP, large ice particles can shatter on the housing of the 2D-C, producing many small spurious ice particles (Field et al. 2006). Theoretical calculations based on Fresnel diffraction show that a 25-μm-resolution 2D-C incorrectly sizes particles smaller than 100 μm in diameter (Korolev et al. 1998). Laboratory measurements (Strapp et al. 2001) also show significant sizing errors resulting depth of field uncertainties and undercounting of particles resulting from the finite instrument response time.

Newer shadow detectors include the Cloud Imaging Probe (CIP) from Droplet Measurement Technologies (DMT) and the 2D-stereo (2D-S) probe from Spec incorporated (Lawson et al. 2006). The photodetector widths (15 μm for CIP-15 and 10 μm for 2D-S) still lead to errors in characterizing small ice particles.

A higher-resolution instrument is the Cloud Particle Imager (CPI; Lawson et al. 2001). A 20-ns pulsed laser is used to illuminate particles, and a high-resolution 106 pixel charge-coupled device (CCD) is used to record the particle image with 256 gray levels. Each pixel is 2.3 μm, so particles from around 10 μm to 2 mm are properly imaged. The maximum frame rate for the CCD is 40 Hz, but multiple particle images can be recorded per image frame. Because of diffraction, optical aberrations, and constrained depth of field issues, the CPI can only give accurate shape information for particles above 25-μm diameter [for CPI observation of different ice crystal habits in a range of orographic wave clouds and cirrus clouds, see Baker and Lawson (2006) and Lawson et al. (2006)].

An instrument developed at the University of Hertfordshire to measure the angular distribution of scattered light so as to give information on the particle shape is the first Small Ice Detector (SID-1), which is described by Hirst et al. (2001). SID-1 has six optical detectors arranged to measure the azimuthal distribution of light scattered at a forward-scattering angle by individual cloud particles passing through a laser beam. Each detector is centered on a scattering angle of 30° with a lens half angle of 10°. SID-1 is able to count and size spherical cloud particles down to 2-μm diameter and count nonspherical particles. The upper size limit is around 70 μm because of the electronic dynamic range. Noise and differences between the optical detectors, as well as the need to use one of the six azimuthal detectors in combination with a smaller optical detector to trigger the signal readout, reduce the phase discriminating ability, especially for small particles. These issues mean that SID-1 has been used primarily to indicate the dominant liquid or ice phase over, for example, 1-s intervals. Field et al. (2004) use SID-1 in this way to determine whether each 100-m cloud segment (1 s at the aircraft true airspeed) in a cloud is ice, liquid, or mixed phase. SID-1 has also been used to characterize cloud particles in orographic wave clouds where ice was formed by heterogeneous nucleation (Field et al. 2001), and to characterize large aerosols (Haywood et al. 2003; Osborne et al. 2004) where spherical sea-salt particles were distinguished from nonspherical aerosols within a biomass-burning plume. SID-1 uses an inlet sampling tube similar to the FFSSP and therefore shattering of large ice particles on the probe housing can potentially lead to an overestimate of small ice concentrations.

The SID mark 2 (SID-2), also built by the University of Hertfordshire, is an improved version of SID-1. SID-2 was designed to discriminate the phase of each cloud particle rather than just the dominant phase in a small time interval as with SID-1. This was done by using photodetector elements with lower noise and having an increased azimuthal resolution on the scattered light detection compared to SID-1. The SID-2 electronic dynamic range is also increased, so that the upper size limit of particles before detector saturation is now around 140 μm (70 μm for SID-1). A more detailed description of SID-2 and its improvements over SID-1 is discussed in the next section. The SID-2 can discriminate between supercooled liquid drops and small ice particles, and it can estimate the size of the ice particles that are generally below the size capability of current cloud particle probes (just-nucleated ice particles may be a few microns in size).

This paper describes the response of SID-2 to various atmospheric cloud and aerosol particles and characterizes its phase discriminating capability. In section 2, the design of SID-2, the laboratory calibration, and the data processing steps are described. Section 3 compares the particle number concentration, diameter, and bulk-water contents measured by SID-2 to the FFSSP, 2D-C, and PCASP. This includes liquid water, ice, and mixed-phase clouds and in high concentrations of sea salt and desert dust aerosol. The incidence of particle shattering on the probe inlet is also determined. Section 4 considers the particle shape and uses an asphericity variable to determine cloud particle phase. The problem of multiple particles in the SID-2 sensing volume is also discussed. The performance of SID-2 is summarized in section 5.

2. SID-2 design and operation

The spatial scattering of light incident on a particle depends on various properties, including the particle size, shape, composition, surface roughness and orientation. The azimuthal and radial distribution of scattered light can therefore be used to distinguish between liquid drops that are spherical and have a smooth surface from ice crystals of similar size (Hirst and Kaye 1996; Kaye et al. 2008; Ulanowski et al. 2006).

Like its predecessor, SID-2 is designed to be mounted in a standard PMS-style canister. A schematic view of the instrument head, which protrudes from the canister, is shown in Fig. 1. SID-2 has various improvements over the first SID version, the most important are

  • the azimuthal resolution of the scattered light detection is increased,

  • the arrival time of individual particles can be recorded during data acquisition,

  • the probe geometry is designed to reduce any possible shattering of large cloud particles, and

  • the peak rather than the integrated signal from each particle is recorded.

The scattered light is detected in the instrument version described here by a hybrid photodiode (HPD) custom manufactured by B.V. Delft Electronische Producten (DEP), Roden, Netherlands. An HPD is a segmented silicon photodiode mounted in a vacuum tube with a photocathode. Photoelectrons are accelerated across a gap of several millimeters and strike a photodiode array mounted inside, generating electron–hole pairs. The DEP HPD of SID-2 contains 27 independently sensed photodiode elements, with 3 central and 24 outer ones arranged azimuthally. The outer elements cover a forward-scattering angle of 9°–20°. Particles trigger the HPD measurement when scattering is detected coincidently by two photomultiplier tubes (PMTs). The overlap of the PMT sensing volumes within the laser beam defines the area of the triggering zone. The detector electronics record the peak signal size from each of the photodiode elements rather than the integrated signal, as with SID-1. The peak signal response to the particle size can be more easily calibrated in laboratory conditions, because there is no need to match the velocities of the calibration particles to that in flight.

The 24 outer azimuthal detector elements were expected to give sufficient resolution to enable some ice crystal habit classification. Scattering patterns produced by ice analogs were obtained to determine how SID-2 might respond to different ice crystal habits. Columns and other elongated particles produce two bright scattering arms, with the relative orientation depending on the particle orientation. Smooth rosettes produce patterns that are a combination of features relating to individual arms and give multiple bright scattering arms. Rough crystals and aggregates produce patterns that are not clearly identifiable.

a. Laboratory calibrations

The overlap of the trigger PMT sensing volumes has been measured in the laboratory using an oscillating ice-analog crystal. An ice-analog rosette with a maximum dimension of 25 μm was fixed on the end of a 7-μm-diameter carbon fiber. A piezoelectric transducer caused the ice analog to oscillate into and out of the trigger sensing volume. The transducer/ice-analog assembly is moved using a micropositioner, which allows the trigger area to be mapped out (giving 0.88 mm2).

The size calibration of SID-2 was carried out in the laboratory using 5, 10, and 20 μm polystyrene latex (PSL) spheres. Scattering theory indicates that, for larger spheres, the scattering cross section scales with the projected area of the sphere. The detector response therefore depends on the square of the particle size. The function D = aS0.5, where S is the mean outer detector response, is fitted to the PSL data to give the constant a. To correct the size calibration for the different refractive index of 1.32 (water and ice average) and 1.59 (polystyrene), Lorentz–Mie theory calculations were carried out. The calculations showed that, over the forward-scattering angles 9°–20°, the PSL calibration must be divided by 1.35 to obtain the corresponding water/ice sphere diameter. For spherical particles, the corrected diameter calibration is therefore given by
i1520-0426-27-2-290-e1

The angular dependence of scattering from ice crystals is expected to be different from spheres. The phase functions of various ice crystal habits were calculated and compared with that from an ice sphere of equivalent projected area. For scattering angles corresponding to the outer azimuthal elements, the scattered flux from the sphere is larger by a factor of 3.0, with only a weak dependence on the crystal habit. These calculations were confirmed by measuring the phase function of a 72-μm ice-analog rosette and an ice sphere of equal average projected area held in a levitation trap using a laser diffractometer and CCD array. Assuming that the scattering cross section is proportional to the projected area of the particle, it is expected that the ice crystals are undersized by a factor of 30.5 = 1.7. This ignores the crystal habit details and any size dependence. Therefore, prior to particle size and concentration determination in the data processing, each particle’s phase (liquid or ice) must be classified.

Laboratory tests indicated that each photodiode element has a different background noise level (detector signal when there is no illumination) and a different gain. There is no correlation of background with gain; the background is from the dark current of the photodiode element or offsets in the detector electronics. The background noise level is temperature dependent and will therefore vary during a flight. The background noise variation must be taken into account if small particles, which give a low detector response compared to the noise, are to be accurately characterized.

Surrounding the small trigger volume is an extended sensing volume (60 times larger). The coincidence of particles in this sensing volume while another particle triggers the detector readout will add to the scattered light intensity of the triggered particle. This will lead to a nonuniform azimuthal detector element response and lead to skewing the particle size distribution toward larger particle sizes. The probability of coincidence can be calculated using Poisson statistics and depends on the particle concentration (there is a 5% probability when the concentration is 30 cm−3).

b. Data processing

The data processing involves the following steps:

  • (i) Photodetector background noise: All photodiode elements are read every second without the need for a particle trigger. These are referred to as “forced triggers.” Only out-of-cloud values are used to calculate the background noise. When in cloud, there is a high probability of a cloud particle being in the sensing volume when this forced trigger occurs. The typical variation in background noise during a flight is equivalent to a change of 1.0 μm in the diameter of a 4.0-μm particle. The ability to determine the individual detector element’s noise level is a significant improvement over SID-1, giving improved potential for single-particle shape discrimination.

  • (ii) Nonuniformity of photodetector gain: Each photodiode gain is corrected so that its mean response from a large number of particles is similar. By assuming that the nonspherical particles have random orientation through the laser beam, all particles measured during the entire flight are used. The gain ranges from 0.58 to 1.88, where 1.0 is the average response.

  • (iii) Particle asphericity: The particle asphericity Af is defined as the variation of the scattered light intensity around the 24 azimuthal detector elements, and it is given by
    i1520-0426-27-2-290-e2
    where Si is the ith detector element response (after background noise subtraction and gain correction) out of n elements and k is a scale factor (k = 100/n2n), so that Af < 100.0. The simplest particle classification for liquid drop or ice crystal tests whether Af is above some threshold value.
  • (iv) Particle radius: For spherical particles, the corrected diameter calibration is used to calculate the diameter. If the particle asphericity is high and the classification gives a nonspherical ice crystal, the calculated diameter is increased by a factor of 1.7. For the aerosol comparisons shown here, the factor 1.7 was not applied.

  • (v) Particle number concentration: For each second, the particle number concentration N is given by
    i1520-0426-27-2-290-e3
    where f is the count frequency; A is the area of the triggering zone (from the laboratory calibration); and υ is the average particle velocity, which is equated to the true airspeed in flight.
  • (vi) “Missed particles”: The recording system can, because of electronics dead time, read a maximum of 8000 particles distributed uniformly over 1 s, which corresponds to a maximum concentration of around 150 cm−3 at typical airspeed. However, the SID-2 data acquisition can keep a sum of the number of particle triggers that occur while the detector element signals are read out. These particles are added to the recorded particle data by distributing over the particle size spectrum. However, the probability of multiple particles in the sensing volume is over 50% at concentrations of 150 cm−3, so this is an upper limit for reliable sensing.

3. SID-2 comparison with the FFSSP, 2D-C, and PCASP probes

The flight data used in this study and summarized in Table 1 were taken during flights that included runs in stratocumulus, altocumulus lenticularis, cirrus, and mixed-phase cumulus clouds and during clear-sky flights over the sea and over desert surfaces. The aim was to identify a number of cloud situations that best test the SID-2 capability, including warm unglaciated liquid cloud, ice cloud with small ice crystals, mixed-phase cloud, and supercooled but unglaciated liquid cloud.

a. Liquid water cloud

One flight in extensive stratocumulus (flight B256) is selected to characterize the response of SID-2 to liquid water drops. This flight was during the Winter Experiments (WINTEX) series of Met Office winter experiments flown from Cranfield from November 2006 to January 2007 (campaign details available online at http://www.faam.ac.uk/).

The comparisons with the liquid water cloud cases provide the best test of the particle number concentration because of the overlap with the FFSSP size range (from 2 to 47 μm in diameter). Figure 2b is the scatterplot of SID-2 and FFSSP number concentrations during one straight and level run. This was at 300 m above mean sea level (AMSL) with air temperature around 9°C. The correlation coefficient is 0.929 and the best-fit gradient is 1.048: the concentration measured by SID-2 is 1.048 higher than that from the FFSSP. This level of agreement is similar in all other runs throughout this stratocumulus flight, where only liquid water was present. This confirms the estimate of the laboratory-based trigger area that defines the sample volume.

The particle size spectra for two runs, one near the base and one near the top of the stratocumulus, are shown in Fig. 3. The agreement is good, but SID-2 is slightly undersizing the liquid water drops compared with the FFSSP: the average particle size measured during the 900-m run is 23.8 μm from SID-2 and 26.5 μm from the FFSSP.

The SID-2 liquid water content (LWC) obtained from integration of the particle size spectrum is compared to the FFSSP LWC (also obtained by integration of the size spectrum) and to the Nevzorov bulk-water probe is shown in Fig. 4. The density of the water drops is assumed to be 1.0 g cm−3. In Fig. 4b, the comparison of SID-2 and the Nevzorov probe, the correlation coefficient is 0.938 and the best-fit gradient 0.640: the liquid water measured by the SID-2 is around 0.64 of that measured by the Nevzorov probe. The underestimate of the LWC by SID-2 is also evident in the comparison with the FFSSP LWC shown in Fig. 4c, where the correlation coefficient is 0.923 and the best-fit gradient is 0.775. The LWC error can be explained if SID-2 number concentration is correct, but the particle size is underestimated. The particle size spectrum for the 900-m run implies that the SID-2 is on average 0.90 that of the FFSSP. This ratio leads to an underestimate of volume and hence LWC of around 0.73, which is similar to the FFSSP LWC discrepancy.

The SID-2 sizing error might be due to a decrease in the laser power because of the laboratory calibration or a different HPD voltage supplied during the flights.

b. Ice cloud

One flight in frontal cirrus (flight B257) is selected to characterize the response of SID-2 to small ice crystals. This flight was during the Cirrus and Anvils: European Satellite and Airborne Radiation (CAESAR) experiment.

The SID-2, 2D-C, and Nevzorov ice water contents (IWC) during one straight and level run near the cirrus top are shown in Fig. 5. The SID-2 ice water content is obtained from integration of the particle size spectrum similarly to the liquid water content but assuming a density of 0.9 g cm−3 for the small ice particles. The 2D-C estimates the ice crystal mass from the mean diameter of the measured particle image using
i1520-0426-27-2-290-e4
where M is the particle mass (g), D is the mean diameter (μm), a = 7.38 × 10−11, and b = 1.9 (Brown and Francis 1995). For diameters smaller than 100 μm, ice crystals are assumed to be solid ice spheres with a density 0.9 g cm−3. This was at 9.75 km with air temperature around −57°C, and at this cold temperature no liquid water will be present. Near the cloud top, all ice particles are small enough to be measured by the SID-2 without detector saturation, and it may be expected that the incidence of any particle shattering on the probe will be low (Field et al. 2003).

The IWC shows a much larger spread than the LWC in Fig. 4. For the SID-2 and Nevzorov IWC, the correlation coefficient is 0.663 and the best-fit gradient is 3.55. This poor agreement could be due to the range of ice crystal shape (for the SID-2 IWC, all ice crystals are assumed spheres with a constant density) and problems with the Nevzorov probe. The collection efficiency of the Nevzorov probe can be less than unity, especially for small cloud particles (Korolev et al. 2003). In particular, in ice cloud, the Nevzorov probe is known to underread the ice water content, because some of the ice particles bounce out of the collection region (Korolev et al. 2008).

The particle size spectra, including the 2D-C data, for runs at two different altitudes are shown in Fig. 6. Data from the 2D-C for ice crystals smaller than 100 μm are discarded because of measurement uncertainties for small particles. The agreement in ice crystal diameter is good, and it is repeated for all other runs at different heights in the cirrus flight. The comparison of SID-2 with the 2D-C in cirrus shows the importance of measuring small ice particles. The total ice crystal number concentration using only the 2D-C will be significantly underestimated, leading to an error in effective radius (the error in ice water content is less, because most of the mass is in the larger particles).

c. Mineral dust aerosol

One flight in moderate loadings of mineral dust aerosol (flight B301) is selected. This flight was during the Geostationary Earth Radiation Budget Intercomparison of Longwave and Shortwave Radiation (GERBILS) experiment (Marsham et al. 2008). The size distribution for aerosols in the accumulation and coarse modes were measured using the PCASP and SID-2. Figure 7a shows two size spectra during a straight and level run along 18°N between 3.6° and 14°W. The airborne dust was generated by a combination of Saharan dust from the north and more localized dust uplifted in relatively moist, cool monsoon air from the south; the highest dust concentrations occurred during the latter events around mesoscale convective systems. Figure 7a shows a decrease in concentration of all particle sizes to the west. The comparison of SID-2 with the PCASP in desert dust aerosol shows a discontinuity at the overlap region. SID-2 has not been calibrated for dust aerosols, and there is no information regarding the mineral composition of the desert dust. The discontinuity and gradient change could be due to sizing error, which affects the number concentration via the particle size–dependent sample volume. The effect of particle shape on the ability of SID-1 to derive a meaningful radius was noted by Osborne et al. (2004), where it was shown that nonspherical biomass-burning aerosols tended to be undersized by a factor of 1.5–2.0. Even though the optics are different on SID-2, a similar undersizing is expected for nonspherical particles such as mineral dust; that is, the determined radius will be smaller than the true equivalent spherical radius. The size spectra for sea-salt aerosol, shown in Fig. 7b, are in better agreement.

d. Particle shattering on the probe inlet

Field et al. (2003) show observations of ice particle interarrival times measured with a FFSSP. The distribution of interarrival times was bimodal with two modes at ∼10−2 and ∼10−4 s instead of the expected exponential distribution. The smaller interarrival time interval corresponds to ∼1-cm horizontal spacings, and these are suggested to originate from shattering of ice crystals on the probe inlet tube. If shattering is responsible for the interarrival time bimodality, then the ice number concentration will be overestimated by up to a factor of 5.

Shattering on the probe housing of optical array probes such as the 2D-C is also possible, leading to incorrect particle number concentration and size spectra. Field et al. (2006) again show that the distribution of interarrival times was bimodal and indicate that the particle number concentration can be overestimated by up to a factor of 4.

The SID-2 inlet design was designed as open path to reduce possible large particle shattering. The distributions of particle interarrival times for example sections of the flight data are shown in Fig. 8. All are monomodal and indicate that no shattering is occurring, particularly for the cirrus case, where you might expect it. Figure 8 also indicates the minimum resolvable interarrival time, which is 0.04 ms.

4. SID-2 particle shape

The flight data shown later in this section include additional data from supercooled altocumulus lenticularis (flight B252) and growing cumulus (flight B246) clouds and low-level clear-sky runs over a high wind-state sea surface (flight B254). Observations of supercooled liquid water drops were obtained during flight B252 in an orographic altocumulus lenticularis cloud. This flight was during the WINTEX experiment investigating ice nucleation in such orographic clouds. During the flight, a single cloud was sampled many times. At all levels in the cloud, all other aircraft instruments indicated no ice present. The data shown are from one run at 7.62 km with air temperature around −32°C.

Mixed-phase flight conditions were encountered near the tops of growing cumulus clouds observed during the Ice and Precipitation Initiation in Cumulus (ICEPIC) experiment (flight B246). The data shown are from one run at 4.11 km with air temperature around −9°C where significant ice nucleation and multiplication are thought to have occurred (Huang et al. 2008).

The sea-salt aerosols were sampled during one low-level run at 300 m above the North Sea during an instrument test flight (B254). The marine boundary layer was clear of cloud, but there was poor visibility because of high wind speeds (25 m s−1) generating high loadings of sea-salt spray.

a. Cloud particle phase

The efficiency of using Af to discriminate particle phase is shown in Fig. 9. There is not a clear separation; however, by inspection of Fig. 9, an Af threshold of 6 best separates liquid drops from ice crystals. The fraction of cloud particles with high asphericity (Af > 6) is 0.184 for the stratocumulus data, 0.031 for the supercooled altocumulus lenticularis data, and 0.961 for the cirrus data. The relatively high fraction of droplets in the stratocumulus data with high Af is due to the high concentrations and presence of precipitation drops giving multiple particles in the SID-2 sensing volume, which leads to an asymmetric detector response. An Af threshold of 9 best separates dust from deliquesced aerosols.

The plots of frequency of occurrence as a function of the particle asphericity Af and radius (Fig. 10) show the ability of SID-2 to discriminate the phase of the cloud particles is dependent on the particle size. For the stratocumulus data (Fig. 10a), most particles have Af < 6, but there are significant numbers with larger Af . These large Af particles exhibit irregular azimuthal scattering patterns. However, in a cloud that can be assumed on physical grounds to be unglaciated, such asymmetric scattering is assumed to indicate multiple particles in the SID-2 sensing volume. For the supercooled altocumulus lenticularis data (Fig. 10b), there are negligible numbers of particles with large Af , confirming the absence of large ice observation. The lower concentrations (∼20 cm−3) and lack of large precipitation-sized drops reduces the chance of multiple particles in the SID-2 sensing volume compared to the stratocumulus case (∼40 cm−3). The asphericity tends to increase for smaller particles, because the detector noise becomes significant relative to the response to the scattered light. For the cirrus data (Fig. 10c), the cloud particles typically have larger Af . This flight data were taken near the top of thin cirrus so that there was no aggregation and the ice particles are small and pristine (most have a diameter between 10 and 20 μm). This is a more difficult test for the SID-2 phase discrimination capability than using larger, possibly aggregated particles. The ability of SID-2 to discriminate small ice particles from supercooled liquid drops is summarized by the difference between Figs. 10b,c. The ice particles typically have Af values higher than the supercooled liquid drops. Small liquid drops at the limit of the SID-2 detection threshold, however, might be misinterpreted as ice particles. Data from the supercooled altocumulus lenticularis flight show that the SID-2 detection limit for cloud particles (observed at the leading edge of the cloud) is around 3-μm diameter. The mixed-phase growing cumulus data (Fig. 10d) contain both high and low Af values, which indicate that there are significant amounts of both liquid drops and ice particles present. An asphericity threshold, which varies with particle size rather than a single Af threshold of 6, would better discriminate the liquid drops from ice crystals in this mixed-phase cloud.

b. Aerosol shape

The distinction between spherical sea-salt particles (deliquesced salt solution droplets in a humid marine boundary layer) and nonspherical mineral dust particles is clearly shown in Figs. 9 and 10. The mineral dust loadings relative to the sea-salt loadings are very low with concentrations within the SID-2 size range of 1–3 cm−3. The sea-salt data show some larger particles resulting from proximity to the aerosol source.

The dust data are biased to the smallest size bins of SID-2, smaller on average than the sea-salt data, and are at the limit of the probes resolution. Nonetheless, the differences in Af between dust and sea salt are clear.

c. Spatial scattering diagrams

Figure 11 contains polar plots related to the azimuthal variation of scattered light intensity for a sample of cloud particles and aerosols from each of the flight datasets. The plot radius for each photodetector is drawn as , where Si, the ith detector response (i = 1, 24), is proportional to the scattered light intensity. Hence, plot area is proportional to particle cross-sectional area; for spherical particles (with uniform azimuthal response), plot radius is proportional to particle radius. The scale is the same for each polar plot, with the circle representing the calculated equivalent spherical radius.

The scattering patterns for the stratocumulus data (Fig. 11a) show both large precipitation drops and small drizzle drops. The high particle concentrations lead to coincidences where there are multiple particles in the sensing volume, and the detector response is typified by the particle third from the left on the bottom row. The supercooled altocumulus lenticularis data (Fig. 11b) also show some coincidences. The cirrus data (Fig. 11c) show a wide variation of irregular scattering patterns. Surface roughness might be increasing the scattering variation from that expected from pristine ice crystals. Supercooled mixed-phase growing cumulus data (Fig. 11d) show a mixture of small cloud drops and larger ice particles. The two particles third and fourth from the top are interesting in that, over part of the azimuth, the scattering is uniform as from a spherical particle and that, over the rest, the scattering is varied. The dust data (Fig. 11e) include one large aerosol similar to the scattering from an ice particle in the mixed-phase cumulus.

5. Summary

SID-2 has been operated during a large number of flights covering a wide range of atmospheric conditions. By comparing the response of SID-2 to water drops, ice particles, and aerosols with other standard aircraft-based probes, the capability of SID-2 to count, size, and determine particle shape has been investigated, and the performance of the SID-2 is summarized in Table 2.

The significant findings are as follows:

  • The SID-2 probe is capable of counting, sizing, and determining the phase of cloud particles. The laboratory-derived sample volume (which determines the particle concentration) and size calibration (which determines the particle size and bulk-water content) have been compared with the FFSSP and 2D-C probes in stratocumulus, altocumulus lenticularis, cirrus, and mixed-phase cumulus. However, by comparing with the size spectrum from the FFSSP and bulk-water content from the FFSSP and Nevzorov probe, the stratocumulus measurements indicate that the SID-2 particle radius may be undersized by a factor of 0.90.

  • In stratocumulus where the high drop concentrations lead to multiple particles in the SID-2 sensing volume, the detector response is not uniform. This implies that SID-2 cannot discriminate the phase of all particles correctly using a simple asphericity parameter in high concentrations (above ∼20 cm−3) of cloud particles. This is important for ice nucleation studies using wave clouds where a requirement is to discriminate between supercooled drops and just-nucleated ice particles.

  • There is no indication of shattering of large ice crystals on the SID-2 probe housing.

  • SID-2 can also count and characterize large aerosols, including desert dust and sea salt. SID-2 could therefore play a crucial role in determining the shape of nonspherical aerosols. Accurate measurements of coarse mode aerosol particles on aircraft are important if we are to determine their shortwave and longwave radiative effects in conditions of sea salt and mineral dust plumes, where concentrations of large particles are significant.

In future analysis, information from the three central detector elements could be used to improve the phase discrimination and shape recovery. The detailed scattering pattern can also be used to provide information regarding the ice crystal habit. For pristine hexagonal particles, advantage can be taken of fast Fourier transforms (FFTs) of the azimuthal patterns to recover orientation-independent information on the aspect ratio and “hollowness” of prismatic crystals (Ulanowski et al. 2007; Stopford et al. 2007). A similar approach could perhaps be used to decide if particles are pristine or complex.

Acknowledgments

The BAe-146 aircrew and instrument operators are thanked. FAAM is jointly funded by the Met Office and the Natural Environment Research Council. The principal investigators of the WINTEX, GERBILS, and ICEPIC campaigns are thanked for operating SID-2.

REFERENCES

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Fig. 1.
Fig. 1.

Schematic of SID-2 sample inlet. The overlap of the two PMT sensing volumes within the laser beam define the trigger area (the second PMT is not shown for clarity). The outer 24 HPD azimuthal segments are numbered 9–32, the six central segments are numbered 2–8, and the beam dump is labeled 1.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 2.
Fig. 2.

Cloud particle number concentration (1-s averages) measured by the SID-2 and FFSSP in stratocumulus (flight B256, run 3 at 300 m) in (b). For clarity only a 1-min section of the run 3 data is shown in (a).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 3.
Fig. 3.

Cloud particle size distributions measured by the SID-2 and FFSSP in stratocumulus (flight B256, run 3 at 300 m near the cloud base, and run 5 at 900 m near the cloud top).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 4.
Fig. 4.

LWC obtained by integrating the particle size distribution from the SID-2 for stratocumulus (flight B256, run 3 at 300 m). (a) For clarity, only a 1-min section of the run 3 data is shown. (b) The SID-2 LWC is compared to that from the Nevzorov bulk water probe. (c) The SID-2 LWC is compared to the FFSSP-derived LWC.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 5.
Fig. 5.

IWC obtained by integrating the particle size distribution from the SID-2 for cirrus (flight B257, run 6–1 at 9.75 km). (a) The time series is averaged over 5-s bins. (b) The SID-2 IWC is compared to that from the Nevzorov bulk water probe. (c) The SID-2 IWC is compared to the 2D-C–derived IWC.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 6.
Fig. 6.

Cloud particle size distributions measured by SID-2 and the 2D-C in cirrus (flight B257, run 4 at 8.23 km near the cloud base and run 7 at 10.36 km near the cloud top).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 7.
Fig. 7.

Aerosol particle size distributions measured by the SID-2 and PCASP in (a) desert dust (flight B301, run 3–1 at 600 m) and (b) sea salt (flight B254, run 1–1 at 30 m).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 8.
Fig. 8.

Cloud particle interarrival times. The stratocumulus case is only for 1 min of data where the drop concentration was always high.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 9.
Fig. 9.

Probability distribution functions of particle asphericity Af for (a) cloud particle and (c) aerosol data. Cumulative frequency distributions of Af for (b) cloud particle and (d) aerosol data. The vertical dotted lines mark Af thresholds for cloud particle shape determination.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 10.
Fig. 10.

Frequency of occurrence as a function of the particle asphericity and radius for liquid water–only cloud [(a) stratocumulus and (b) supercooled altocumulus lenticularis], ice and mixed-phase cloud [(c) cirrus and (d) supercooled cumulus], and aerosol [(e) desert dust and (f) sea salt].

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Fig. 11.
Fig. 11.

The scattered light intensity onto each outer detector element for a sample of particles for liquid water–only cloud [(a) stratocumulus and (b) supercooled altocumulus lenticularis], ice and mixed-phase cloud [(c) cirrus and (d) supercooled cumulus], and aerosol [(e) desert dust and (f) sea salt]. The radius scale is shown inset on (d).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1282.1

Table 1.

Summary of flights used in this analysis.

Table 1.
Table 2.

Summary of the SID-2 performance.

Table 2.
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