Optical Array Probe Research Articles

The Retrieval of Drop Size Distribution Parameters Using a Dual-Polarimetric Radar

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the ‘S’-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar ‘S’-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the ‘optimized’ two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M3 and M6 along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M6 from the attenuation-corrected reflectivity and M3 from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article.

Estimating Microphysics Properties in Ice-Dominated Clouds from Airborne Ka–W-band Dual-Wavelength Ratio Reflectivity Factor in Close Proximity to In Situ Probes

Abstract Properties of frozen hydrometeors in clouds remain difficult to sense remotely. Estimates of number concentration, distribution shape, ice particle density, and ice water content are essential for connecting cloud processes to surface precipitation. Progress has been made with dual-frequency radars, but validation has been difficult because of lack of particle imaging and sizing observations collocated with the radar measurements. Here, data are used from two airborne profiling (up and down) radars, the W-band Wyoming Cloud Radar and the Ka-band Profiling Radar, allowing for Ka–W-band dual-wavelength ratio (DWR) profiles. The aircraft (the University of Wyoming King Air) also carried a suite of in situ cloud and precipitation probes. This arrangement is optimal for relating the “flight-level” DWR (an average from radar gates below and above flight level) to ice particle size distributions measured by in situ optical array probes, as well as bulk properties such as minimum snow particle density and ice water content. This comparison reveals a strong relationship between DWR and the ice particle median-volume diameter. An optimal range of DWR values ensures the highest retrieval confidence, bounded by the radars’ relative calibration and DWR saturation, found here to be about 2.5–7.5 dB. The DWR-defined size distribution shape is used with a Mie scattering model and an experimental mass–diameter relationship to test retrievals of ice particle concentration and ice water content. Comparison with flight-level cloud-probe data indicate good performance, allowing microphysical interpretations for the rest of the vertical radar transects.

Ice crystal images from optical array probes: classification with convolutional neural networks

Abstract. Although airborne optical array probes (OAPs) have existed for decades, our ability to maximize extraction of meaningful morphological information from the images produced by these probes has been limited by the lack of automatic, unbiased, and reliable classification tools. The present study describes a methodology for automatic ice crystal recognition using innovative machine learning. Convolutional neural networks (CNNs) have recently been perfected for computer vision and have been chosen as the method to achieve the best results together with the use of finely tuned dropout layers. For the purposes of this study, The CNN has been adapted for the Precipitation Imaging Probe (PIP) and the 2DS Stereo Probe (2DS), two commonly used probes that differ in pixel resolution and measurable maximum size range for hydrometeors. Six morphological crystal classes have been defined for the PIP and eight crystal classes and an artifact class for the 2DS. The PIP and 2DS classifications have five common classes. In total more than 8000 images from both instruments have been manually labeled, thus allowing for the initial training. For each probe the classification design tries to account for the three primary ice crystal growth processes: vapor deposition, riming, and aggregation. We included classes such as fragile aggregates and rimed aggregates with high intra-class shape variability that are commonly found in convective clouds. The trained network is finally tested through human random inspections of actual data to show its real performance in comparison to what humans can achieve.

Retrieval and Evaluation of Ice Water Content from the Airborne Wyoming Cloud Radar in Orographic Wintertime Clouds during SNOWIE

Abstract As part of the analysis following the Seeded and Natural Orographic Wintertime Storms (SNOWIE) project, the ice water content (IWC) in ice and mixed-phase clouds is retrieved from airborne Wyoming Cloud Radar (WCR) measurements aboard the University of Wyoming King Air (UWKA), which has a suite of integrated in situ IWC, optical array probes, and remote sensing measurements, and it provides a unique dataset for this algorithm development and evaluation. A sensitivity study with different idealized ice particle habits shows that the retrieved IWC with aggregate ice particle habit agrees the best with the in situ measurement, especially in ice or ice-dominated mixed-phase clouds with a correlation coefficient (rr) of 0.91 and a bias of close to 0. For mixed-phase clouds with ice fraction ratio less than 0.8, the variances of IWC estimates increase (rr = 0.76) and the retrieved mean IWC is larger than in situ IWC by a factor of 2. This is found to be related to the uncertainty of in situ measurements, the large cloud inhomogeneity, and the retrieval assumption uncertainty. The simulated reflectivity Ze and IWC relationships assuming three idealized ice particle habits and measured particle size distributions show that hexagonal columns with the same Ze have a lower IWC than aggregates, whose Ze–IWC relation is more consistent with the observed WCR Ze and in situ IWC relation in those clouds. The 2D stereo probe (2DS) images also indicate that ice particle habit transition occurs in orographic mixed-phase clouds; hence, the retrieved IWC assuming modified gamma particle size distribution (PSD) of aggregate particles tends to have a greater bias in this kind of clouds.

Revisiting the size of nonspherical particles recorded by optical array probes with a new method based on the convex hull

In recent years, the Cloud Imaging Probe (CIP) and Precipitation Imaging Probe (PIP) produced by Droplet Measurement Technologies (DMT) have been introduced by a number of meteorological research and operation centers in China. The supporting software provided by DMT, i.e., PADS (Particle Analysis and Display System), cannot output detailed information on each individual particle, which definitely limits the in-depth utilization of cloud and precipitation particle image data in China. In this paper, particle-by-particle information was extracted by decompressing the CIP and PIP original particle image data, based on which a new definition of the dimension for nonspherical particles is proposed by using the area of the convex hull enclosing a particle to obtain the equivalent diameter of a circle with equal area. Based on the data detected during one flight in Inner Mongolia, the particle size distribution obtained using the new particle size definition and that used by the other four existing definitions are compared. The results show that the particle number concentration calculated using different particle size definitions can vary by up to an order of magnitude. The result obtained based on the new particle size definition is closest to that calculated with the area-equivalent diameter definition.摘要 国内许多气象部门已引进美国DMT公司的云粒子图像探头 (CIP) 和降水粒子图像探头 (PIP) . 由于其配套软件不能输出逐个粒子的详细信息, 在很大程度上限制了对云降水粒子图像探测数据的深入挖掘. 通过解析CIP和PIP原始数据, 提出了一种基于包围粒子凸多边形的面积求取粒子尺度的新定义. 利用在内蒙古的一次航测数据, 对比分析了基于新定义及已有的四种粒子尺度定义求取的粒子尺度谱分布.

Automatic shape detection of ice crystals

Clouds have a crucial impact on the energy balance of the Earth-Atmosphere system. They can cool the system by partly reflecting or scattering of the incoming solar radiation (albedo effect); moreover, thermal radiation as emitted from the Earth's surface can be absorbed and partly re-emitted by clouds leading to a warming of the atmosphere (greenhouse effect). The effectiveness of both effects crucially depends on the size and the shape of a cloud's particulate constituents, i.e. liquid water droplets or solid ice crystals. For studying cloud microphysics, in situ measurements on board of aircraft are commonly used. An important class of measurement techniques comprises optical array probes (OAPs) as developed since the 1970s [13]. While water droplets can be assumed as spherical, the shape and size of ice particles are highly variable. The currently used analysis methods to determine the particles’ size from OAP detection do rarely consider shape details or fine structures of ice particles, which may lead to artificial biases in the results.In this paper, we present two new computational analysis methods, combined in an hybrid approach, for an automatic classification of ice particles and water droplets. The first method computes the principal components of a cloud particle and uses them to determine an ellipse, which can then be used to filter for spherical particles. The second method uses convolutional neural networks (CNNs) for the classification of columns and rosettes, respectively. Although we currently only classify these two particle types with CNNs, the presented method can be easily adapted for the classification of other particle types. The particularity of our method is that we use a virtual data set to pre-train the networks, which are then further trained with a smaller amount of manually classified real cloud particles in a fine tuning step. We evaluated our models on a small data set of real cloud particles and in a final field test on OAP image data that was not previously classified. The precision of this field test was better than 81% and ranged up to 98%, demonstrating that the new methods are suitable for providing profound shape classifications of cloud particle images obtained by OAP measurements. All methods we describe in this paper have been implemented in Python and C and are fully open source. Code and documentation are available on Github (https://github.com/lcsgrlch/oap).

Characterising optical array particle imaging probes: implications for small-ice-crystal observations

Abstract. The cloud particle concentration, size, and shape data from optical array probes (OAPs) are routinely used to parameterise cloud properties and constrain remote sensing retrievals. This paper characterises the optical response of OAPs using a combination of modelling, laboratory, and field experiments. Significant uncertainties are found to exist with such probes for ice crystal measurements. We describe and test two independent methods to constrain a probe's sample volume that remove the most severely mis-sized particles: (1) greyscale image analysis and (2) co-location using stereoscopic imaging. These methods are tested using field measurements from three research flights in cirrus. For these cases, the new methodologies significantly improve agreement with a holographic imaging probe compared to conventional data-processing protocols, either removing or significantly reducing the concentration of small ice crystals (< 200 µm) in certain conditions. This work suggests that the observational evidence for a ubiquitous mode of small ice particles in ice clouds is likely due to a systematic instrument bias. Size distribution parameterisations based on OAP measurements need to be revisited using these improved methodologies.

Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

Abstract. The lower-order moments of the drop size distribution (DSD) have generally been considered difficult to retrieve accurately from polarimetric radar data because these data are related to higher-order moments. For example, the 4.6th moment is associated with a specific differential phase and the 6th moment with reflectivity and ratio of high-order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution for the DSD. Many double-moment “bulk” microphysical schemes predict the total number concentration (the 0th moment of the DSD, or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted by the generalized gamma (G-G) distribution. The two reference moments are M3 and M6, which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity, and specific attenuation (from the iterative correction of measured reflectivity Zh using the total Φdp constraint, i.e., the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower-order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with about 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower-order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved from radar and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was <15 % in magnitude, with Pearson’s correlation coefficient >0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved mass-weighted mean diameter with M0 resulted in coherent “time tracks” that can potentially lead to studies of precipitation evolution that have not been possible so far.

Hurricane Dorian Outer Rain Band Observations and 1D Particle Model Simulations: A Case Study

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate, and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called McSnow) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical experiments were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

Flow-induced errors in airborne in situ measurements of aerosols and clouds

Abstract. Aerosols and clouds affect atmospheric radiative processes and climate in many complex ways and still pose the largest uncertainty in current estimates of the Earth's changing energy budget. Airborne in situ sensors such as the Cloud, Aerosol, and Precipitation Spectrometer (CAPS) or other optical spectrometers and optical array probes provide detailed information about the horizontal and vertical distribution of aerosol and cloud properties. However, flow distortions occurring at the location where these instruments are mounted on the outside of an aircraft may directly produce artifacts in detected particle number concentration and also cause droplet deformation and/or breakup during the measurement process. Several studies have investigated flow-induced errors assuming that air is incompressible. However, for fast-flying aircraft, the impact of air compressibility is no longer negligible. In this study, we combine airborne data with numerical simulations to investigate the flow around wing-mounted instruments and the induced errors for different realistic flight conditions. A correction scheme for deriving particle number concentrations from in situ aerosol and cloud probes is proposed, and a new formula is provided for deriving the droplet volume from images taken by optical array probes. Shape distortions of liquid droplets can either be caused by errors in the speed with which the images are recorded or by aerodynamic forces acting at the droplet surface caused by changes of the airflow when it approaches the instrument. These forces can lead to the dynamic breakup of droplets causing artifacts in particle number concentration and size. An estimation of the critical breakup diameter as a function of flight conditions is provided. Experimental data show that the flow speed at the instrument location is smaller than the ambient flow speed. Our simulations confirm the observed difference and reveal a size-dependent impact on particle speed and concentration. This leads, on average, to a 25 % overestimation of the number concentration of particles with diameters larger than 10 µm diameter and causes distorted images of droplets and ice crystals if the flow values recorded at the instrument are used. With the proposed corrections, errors of particle number concentration and droplet volume, as well as image distortions, are significantly reduced by up to 1 order of magnitude. Although the presented correction scheme is derived for the DLR Falcon research aircraft (Saharan Aerosol Long-range Transport and Aerosol-Cloud-Interaction Experiment (SALTRACE) campaign) and validated for the DLR Falcon (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics mission conducted in 2017 (A-LIFE) campaign) and the NASA DC-8 (Atmospheric Tomography Mission (ATom) campaigns), the general conclusions hold for any fast-flying research aircraft.

Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations

Abstract. This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and two optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties are calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, mesoscale convective systems (MCS) microphysical properties (ice water content, visible extinction, mass–size relationship coefficients, total concentrations, and second and third moments of hydrometeor size distribution) are sorted in temperature (and thus altitude) zones, and each individual campaign is subsequently analyzed with respect to median microphysical properties of the merged dataset (merging all four campaign datasets). The study demonstrates that ice water content (IWC), visible extinction, total crystal concentration, and the second and third moments of hydrometeor size distributions are similar in all four types of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first concerns the calculation of the visible extinction as a function of temperature and ice water content. The second concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.

Spatiotemporal Evolution of the Microphysical and Thermodynamic Characteristics of the 20 June 2015 PECAN MCS

Abstract This study examines microphysical and thermodynamic characteristics of the 20 June 2015 mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment, specifically within the transition zone (TZ), enhanced stratiform rain region (ESR), anvil region, melting layer (ML), and the rear inflow jet (RIJ). Analyses are developed from airborne optical array probe data and multiple-Doppler wind and reflectivity syntheses using data from the airborne NOAA Tail Doppler Radar (TDR) and ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D) radars. Seven spiral ascents/descents of the NOAA P-3 aircraft were executed within various regions of the 20 June MCS. Aggregation modified by sublimation was observed in each MCS region, regardless of whether the sampling was within the RIJ. Sustained sublimation and evaporation of precipitation in subsaturated layers led to a trend of downward moistening across the ESR spirals, with greater degrees of subsaturation maintained when in the vicinity of the descending RIJ. In all cases where melting was observed, the ML acted as a prominent thermodynamic boundary, with differing rates of change in temperature and relative humidity above and below the ML. Two spiral profiles coincident with the rear inflow notch provided unique observations within the TZ and were interpreted in the context of similar observations from the 29 June 2003 Bow Echo and Mesoscale Convective Vortex Experiment MCS. There, sublimation cooling and enhanced descent within the RIJ allowed ice particles to survive to temperatures as warm as +6.8°C before completely sublimating/evaporating.

Revisiting particle sizing using greyscale optical array probes: evaluation using laboratory experiments and synthetic data

Abstract. In situ observations from research aircraft and instrumented ground sites are important contributions to developing our collective understanding of clouds and are used to inform and validate numerical weather and climate models. Unfortunately, biases in these datasets may be present, which can limit their value. In this paper, we discuss artefacts which may bias data from a widely used family of instrumentation in the field of cloud physics, optical array probes (OAPs). Using laboratory and synthetic datasets, we demonstrate how greyscale analysis can be used to filter data, constraining the sample volume of the OAP and improving data quality, particularly at small sizes where OAP data are considered unreliable. We apply the new methodology to ambient data from two contrasting case studies: one warm cloud and one cirrus cloud. In both cases the new methodology reduces the concentration of small particles (<60 µm) by approximately an order of magnitude. This significantly improves agreement with a Mie-scattering spectrometer for the liquid case and with a holographic imaging probe for the cirrus case. Based on these results, we make specific recommendations to instrument manufacturers, instrument operators and data processors about the optimal use of greyscale OAPs. The data from monoscale OAPs are unreliable and should not be used for particle diameters below approximately 100 µm.

Study of the diffraction pattern of cloud particles and the respective responses of optical array probes

Abstract. Optical array probes (OAPs) are classical instrumental means to derive shape, size, and number concentration of cloud and precipitation particles from 2-D images. However, recorded 2-D images are subject to distortion based on the diffraction of light when particles are imaged out of the object plane of the optical device. This phenomenon highly affects retrievals of microphysical properties of cloud particles. Previous studies of this effect mainly focused on spherical droplets. In this study we propose a theoretical method to compute diffraction patterns of all kinds of cloud particle shapes in order to simulate the response recorded by an OAP. To check the validity of this method, a series of experimental measurements have been performed with a 2D-S probe mounted on a test bench. Measurements are performed using spinning glass discs with imprinted non-circular opaque particle shapes.

A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E

Abstract. Mass–dimension (m–D) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m=aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, the definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota (UND) Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S-band radar data to determine a and b using a technique that minimizes the chi-square difference between the TWC and Z derived from the OAPs and those directly measured by a TWC probe and radar. All a and b values within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant-temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events, the derived surfaces of solutions on the first 2 days where the aircraft-sampled stratiform cloud had a larger range in a and b for lower temperature environments that correspond to less variability in N(D), TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability in N(D), TWC, and Z influenced the distribution of chi-square values in the (a,b) phase space and the specified tolerance in a way that yielded 2.8 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies, rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in clouds containing ice hydrometeors.

A Versatile Method for Ice Particle Habit Classification Using Airborne Imaging Probe Data

AbstractA versatile method to automatically classify ice particle habit from various airborne optical array probes is presented. The classification is achieved using a multinomial logistic regression model. For each airborne probe, the model determines the particle habit (among six classes) based on a large set of geometrical and textural descriptors extracted from the two‐dimensional image of a particle. The technique is applied and evaluated using three probes with significantly different specifications: the high volume precipitation spectrometer, the two‐dimensional stereo probe, and the cloud particle imager. Performance and robustness of the method are assessed using standard machine learning tools on the basis of thousands of images manually labeled for each of the considered probes. The three classifiers show good performance characterized by overall accuracies and Heidke skill scores above 90%. Depending on the application and user preferences, the classification scheme can be easily adapted. For a more precise output, intraclass subclassification can be achieved in a nested fashion, illustrated here with columnar crystals and aggregates. A comparative study of the classification output obtained with the three probes is presented for two aircraft flight periods selected when the three probes were operating together. Results are globally consistent in term of proportions of habit identified (once blurry and partial images have been automatically discarded). A perfect agreement is not expected as the three considered probes are sensitive to different particle size range.

Microphysical Properties and Radar Polarimetric Features within a Warm Front

Abstract On 21 January 2009, the warm front of an extensive low pressure system affected U.K. weather. In this work, macroscopic and microphysical characteristics of this warm front are investigated using in situ (optical array probes, temperatures sensors, and radiosondes) and S-band polarimetric radar data from the Aerosol Properties, Processes and Influences on the Earth’s Climate–Clouds project. The warm front was associated with a warm conveyor belt, a zone of wind speeds of up to 26 m s−1, which played a key role in the formation of extensive mixed-phase cloud mass by ascending significant liquid water (LWC; ~0.22 g m−3) at a level ~3 km and creating an ideal environment at temperatures ~ −5°C for ice multiplication. Then, “generating cells,” which formed in the unstable and sheared layer above the warm conveyor belt, influenced the structure of the stratiform cloud layer, dividing it into two types of elongated and slanted ice fall streaks: one depicted by large ZDR values and the other by large ZH values. The different polarimetric characteristics of these ice fall streaks reveal their different microphysical properties, such as the ice habit, concentration, and size. We investigate their evolution, which was affected by the warm conveyor belt, and their impact on the surface precipitation.

Microphysical Properties of Tropical Tropopause Layer Cirrus.

Pervasive cirrus clouds in the tropical tropopause layer (TTL) play an important role in determining the composition of stratospheric air through dehydration of tropospheric air entering the stratosphere. This dehydration affects Earth's energy budget and climate, yet uncertainties remain regarding the microphysical processes that govern TTL cirrus. TTL cirrus were sampled with the NASA Global Hawk UAV for over 30 hr in the Western Pacific in 2014 during the Airborne Tropical TRopopause EXperiment. In situ measurements by a Fast Cloud Droplet Probe and Hawkeye probe (combination Fast Cloud Droplet Probe, Two-Dimensional Stereo optical array probe, and Cloud Particle Imager) provided particle concentrations and sizing between 1- and 1,280-μm diameter and high resolution images for habit identification. We present the variability in ice concentrations, size distributions, and habits as functions of temperature, altitude, and time since convective influence. Observed ice particles were predominantly small and quasi-spheroidal in shape, with the percentage of quasi-spheroids increasing with decreasing temperature. In comparison to the large fraction of the population consisting of quasi-spheroids, faceted habits (columns, plates, rosettes, and budding rosettes) constituted a smaller percentage of the overall population and exhibited the opposite correlation with temperature. The trend of higher percentages of faceted crystals occurring at warmer temperatures may be due to diffusional growth or aggregation as particles descend through cloud, and/or the more rapid diffusional growth rate at warmer temperatures. Sampling was typically well away from deep convection, however, and very few aggregates were observed, so the trend of higher percentages of faceted habits is likely attributable to diffusional growth.

Raindrop fall velocities from an optical array probe and 2-D video disdrometer

Abstract. We report on fall speed measurements of raindrops in light-to-heavy rain events from two climatically different regimes (Greeley, Colorado, and Huntsville, Alabama) using the high-resolution (50 µm) Meteorological Particle Spectrometer (MPS) and a third-generation (170 µm resolution) 2-D video disdrometer (2DVD). To mitigate wind effects, especially for the small drops, both instruments were installed within a 2∕3-scale Double Fence Intercomparison Reference (DFIR) enclosure. Two cases involved light-to-moderate wind speeds/gusts while the third case was a tornadic supercell and several squall lines that passed over the site with high wind speeds/gusts. As a proxy for turbulent intensity, maximum wind speeds from 10 m height at the instrumented site recorded every 3 s were differenced with the 5 min average wind speeds and then squared. The fall speeds vs. size from 0.1 to 2 and >0.7 mm were derived from the MPS and the 2DVD, respectively. Consistency of fall speeds from the two instruments in the overlap region (0.7–2 mm) gave confidence in the data quality and processing methodologies. Our results indicate that under low turbulence, the mean fall speeds agree well with fits to the terminal velocity measured in the laboratory by Gunn and Kinzer from 100 µm up to precipitation sizes. The histograms of fall speeds for 0.5, 0.7, 1 and 1.5 mm sizes were examined in detail under the same conditions. The histogram shapes for the 1 and 1.5 mm sizes were symmetric and in good agreement between the two instruments with no evidence of skewness or of sub- or super-terminal fall speeds. The histograms of the smaller 0.5 and 0.7 mm drops from MPS, while generally symmetric, showed that occasional occurrences of sub- and super-terminal fall speeds could not be ruled out. In the supercell case, the very strong gusts and inferred high turbulence intensity caused a significant broadening of the fall speed distributions with negative skewness (for drops of 1.3, 2 and 3 mm). The mean fall speeds were also found to decrease nearly linearly with increasing turbulent intensity attaining values about 25–30 % less than the terminal velocity of Gunn–Kinzer, i.e., sub-terminal fall speeds.

Response Time Characteristics of the Fast-2D Optical Array Probe Detector Board

AbstractTwo-dimensional optical array probes are commonly used for imaging raindrops and ice particles on research aircraft. The ability of these probes to accurately measure particle concentration and size partly depends on the response characteristics of the detection system. If the response characteristics are too slow, then small particles are less likely to be detected and the associated effective sample volume decreases. In an effort to better understand the sample volumes of optical array probes at the National Center for Atmospheric Research, the temporal response of the Fast-2D optical array probe detector board from optical input on the detector to digitization was characterized. The analysis suggests that the board electronics have a response time constant consistently near 50 ns. However, there is also a slow decay term that conforms to a decay rate. The amplitude of this slow function can impact the probe response, varying the minimum detectable pulse width between 60 and 150 ns. Also, the amplitude of the slow function is largely dictated by the illumination angle of incidence. The effects of the response time characteristics are analyzed using a simulator for a 2D cloud (2D-C) probe with 25-μm photodiode spacing. The results show the greatest sensitivity to response time characteristics when particles are smaller than 150 μm, where 10% uncertainty in the slow fraction is likely to produce sample volume uncertainties near 10%. Ignoring response time effects may bias sample volume estimates in the small size regime by as much as 25%.