Aircraft Sampling Research Articles

Usefulness of aircraft and airport wastewater for monitoring multiple pathogens including SARS-CoV-2 variants.

As global travel resumed in coronavirus disease 2019 (COVID-19) endemicity, the potential of aircraft wastewater monitoring to provide early warning of disease trends for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and other infectious diseases, particularly at international air travel hubs, was recognized. We therefore assessed and compared the feasibility of testing wastewater from inbound aircraft and airport terminals for 18 pathogens including SARS-CoV-2 in Singapore, a popular travel hub in Asia. Wastewater samples collected from inbound medium- and long-haul flights and airport terminals were tested for SARS-CoV-2. Next Generation Sequencing was carried out on positive samples to identify SARS-CoV-2 variants. Airport and aircraft samples were further tested for 17 other pathogens through quantitative reverse transcription polymerase chain reaction. The proportion of SARS-CoV-2-positive samples and the average virus load was higher for wastewater samples from aircraft as compared with airport terminals. Cross-correlation analyses indicated that viral load trends from airport wastewater led local COVID-19 case trends by 2-5 days. A total of 10 variants (44 sub-lineages) were successfully identified from aircraft wastewater and airport terminals, and four variants of interest and one variant under monitoring were detected in aircraft and airport wastewater 18-31days prior to detection in local clinical cases. The detection of five respiratory and four enteric viruses in aircraft wastewater samples further underscores the potential to expand aircraft wastewater to monitoring pathogens beyond SARS-CoV-2. Our findings demonstrate the feasibility of aircraft wastewater testing for monitoring infectious diseases threats, potentially detecting signals before clinical cases are reported. The triangulation of similar datapoints from aircraft wastewater of international travel nodes could therefore serve as a useful early warning system for global health threats.

Measurement report: Cloud and environmental properties associated with aggregated shallow marine cumulus and cumulus congestus

Abstract. Mesoscale organization of marine convective clouds into linear or clustered states is prevalent across the tropical and subtropical oceans, and its investigation served as a guiding focus for a series of process study flights conducted as part of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) during summer 2020, 2021, and 2022. These select ACTIVATE flights involved a novel strategy for coordinating two aircraft, with respective remote sensing and in situ sampling payloads, to probe regions of organized shallow convection for several hours. The main purpose of this measurement report is to summarize the aircraft sampling approach, describe the characteristics and evolution of the cases, and provide an overview of the datasets that can serve as a starting point for more detailed modeling and analysis studies. Six flights are described, involving a total of 80 dropsonde profiles that capture the environment surrounding clustered shallow convection. The flights include detailed observations of the vertical structure of cloud systems, comprising up to 20 in situ sampling levels. Four cases involved deepening convection rooted in the marine boundary layer that developed vertically to 2–5 km with varying precipitation amounts, while two cases captured more complex and developed cumulus congestus systems extending above 5 km. In addition to the thermodynamic and dynamic characterization afforded by dropsonde and in situ measurements, the datasets include cloud and aerosol microphysics, trace gas concentrations, aerosol and droplet composition, and cloud and aerosol remote sensing from high-spectral-resolution lidar and polarimetry.

Investigation and validation of the TEG6s during rotary wing aeromedical flight.

To improve rural and austere trauma care, hospital-based testing performed at the point of injury may shorten the time lapsed from injury to intervention. This study aimed to evaluate the use of the TEG6s device (Haemonetics(R), Clinton, PA) in a rotary wing aircraft. Prior attempts suffered from limitation related to lack of vibration mitigation. This was an investigator-initiated, industry-supported study. Haemonetics provided a TEG6s analyzer. The device underwent a standard validation. It was secured in place on the aircraft using shipping foam for vibration mitigation. Donors provided two tubes of sample blood in one sitting. Paired studies were performed on the aircraft during level flight and in the hospital, using the Global Hemostasis with Lysis Cartridge(Haemonetics (R), Clinton, PA). Both normal and presumed pathologic samples were tested in separate phases. Paired t tests were performed. For normal donors, the mean R for laboratory compared with the aircraft was 6.2 minutes versus 7.2 minutes ( p = 0.025). The mean ± SD Citrated Rapid TEG Maximum Amplitude (CRT MA) was 59.3 ± 5.6 mm and 55.9 ± 7.3 mm ( p < 0.001) for laboratory and aircraft ( p < 0.001). Among normal donors, R was within normal range for 17 of 18 laboratory tests and 18 of 18 aircraft tests ( p > 0.99). During the testing of pathologic samples, the mean R time was 14.8 minutes for laboratory samples and 12.6 minutes for aircraft ( p = 0.02). Aircraft samples were classified as abnormal in 78% of samples; this was not significantly different than laboratory samples ( p = 0.5). The use of the TEG6s for inflight viscoelastic testing appears promising. While statistically significant differences are seen in some results, these values are not considered clinically significant. Classifying samples as normal or abnormal demonstrated a higher correlation. Future studies should focus on longer flight times to evaluate for LY30, takeoff, and landing effects. Overall, this study suggests that TEG6s can be used in a prehospital environment, and further study is warranted. Diagnostic Tests or Criteria; Level III.

Exhibiting samples of enemy aircraft and its equipment in Moscow (1941–1943)

The article is devoted to the exhibition of enemy aircraft samples in Moscow since 1941 (the beginning of the air raids on the capital) until 1943 (the end of the combat air raids on the capital and the creation of an Exhibition of samples of captured weapons in the Gorky Central Park). Within the framework of the study, a part of the museum and exhibition events in the USSR (almost all of them capital) of wartime was considered, at which trophies, damaged samples of enemy aircraft, their technical equipment, aviation weapons and ammunition(including high-explosive and incendiary bombs), including aviation ones, were presented. It was found that the first expositions showing samples of enemy aircraft were opened in the summer of 1941 (during the first air raids on Moscow). Over time, there have been more such artifacts that have become the object of display. These trophies performed various functions: educational (in the case of air defense of the capital), propaganda (due to the display, the idea was conveyed that such a formidable and strong enemy could be defeated). However, already during the war years, they began to be treated as objects of museum and exhibition display and artifacts of their time, which allowed them to be preserved in the future and reveal a number of topics related to the Great Patriotic War in the museum and exhibition space: from the topic of equipping enemy aircraft to the topic of fighting in the air during the Great Patriotic War. This was done by different methods. After the war, some of these trophies were melted down, but some got into museum collections.

FedDAD: Solving the Islanding Problem of SAR Image Aircraft Detection Data

In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggregation. The commonly used federal average algorithm is not effective for aircraft detection with unbalanced samples, so a federal distribution average deviation (FedDAD) algorithm, which is more suitable for aircraft detection in SAR images, was designed. Based on label distribution and client model quality, the contribution ratio of each client parameter is adaptively adjusted to optimize the global model. Client models trained through federated cooperation have an advantage in detecting aircraft with unknown scenarios or attitudes while remaining sensitive to local datasets. Based on the YOLOv5s algorithm, the feasibility of federated learning was verified on SAR image aircraft detection datasets and the portability of the FedDAD algorithm on public datasets. In tests based on the YOLOv5s algorithm, FedDAD outperformed FedAvg’s mAP0.5–0.95 on the total test set of two SAR image aircraft detection and far outperformed the local centralized training model.

Precipitation Growth Processes in the Comma-Head Region of the 7 February 2020 Northeast Snowstorm: Results from IMPACTS

Abstract On 7 February 2020, precipitation within the comma-head region of an extratropical cyclone was sampled remotely and in situ by two research aircraft, providing a vertical cross section of microphysical observations and fine-scale radar measurements. The sampled region was stratified vertically by distinct temperature layers and horizontally into a stratiform region on the west side, and a region of elevated convection on the east side. In the stratiform region, precipitation formed near cloud top as side-plane, polycrystalline, and platelike particles. These habits occurred through cloud depth, implying that the cloud-top region was the primary source of particles. Almost no supercooled water was present. The ice water content within the stratiform region showed an overall increase with depth between the aircraft flight levels, while the total number concentration slightly decreased, consistent with growth by vapor deposition and aggregation. In the convective region, new particle habits were observed within each temperature-defined layer along with detectable amounts of supercooled water, implying that ice particle formation occurred in several layers. Total number concentration decreased from cloud top to the −8°C level, consistent with particle aggregation. At temperatures &gt; −8°C, ice particle concentrations in some regions increased to &gt;100 L−1, suggesting secondary ice production occurred at lower altitudes. WSR-88D reflectivity composites during the sampling period showed a weak, loosely organized banded feature. The band, evident on earlier flight legs, was consistent with enhanced vertical motion associated with frontogenesis, and at least partial melting of ice particles near the surface. A conceptual model of precipitation growth processes within the comma head is presented. Significance Statement Snowstorms over the northeast United States have major impacts on travel, power availability, and commerce. The processes by which snow forms in winter storms over this region are complex and their snowfall totals are hard to forecast accurately because of a poor understanding of the microphysical processes within the clouds composing the storms. This paper presents a case study from the NASA IMPACTS field campaign that involved two aircraft sampling the storm simultaneously with radars, and probes that measure the microphysical properties within the storm. The paper examines how variations in stability and frontal structure influence the microphysical evolution of ice particles as they fall from cloud top to the surface within the storm.

Dependencies of Four Mechanisms of Secondary Ice Production on Cloud-Top Temperature in a Continental Convective Storm

Abstract Various mechanisms of secondary ice production (SIP) cause multiplication of numbers of ice particle, after the onset of primary ice. A measure of SIP is the ice enhancement ratio (“IE ratio”) defined here as the ratio between number concentrations of total ice (excluding homogeneously nucleated ice) and active ice-nucleating particles (INPs). A convective line observed on 11 May 2011 over the Southern Great Plains in the Mesoscale Continental Convective Cloud Experiment (MC3E) campaign was simulated with the “Aerosol–Cloud” (AC) model. AC is validated against coincident MC3E observations by aircraft, ground-based instruments, and satellite. Four SIP mechanisms are represented in AC: the Hallett–Mossop (HM) process of rime splintering, and fragmentation during ice–ice collisions, raindrop freezing, and sublimation. The vertical profile of the IE ratio, averaged over the entire simulation, is almost uniform (102 to 103) because fragmentation in ice–ice collisions dominates at long time scales, driving the ice concentration toward a theoretical maximum. The IE ratio increases with both the updraft (HM process, fragmentation during raindrop freezing, and ice–ice collisions) and downdraft speed (fragmentation during ice–ice collisions and sublimation). As reported historically in aircraft sampling, IE ratios were predicted to peak near 103 for cloud-top temperatures close to the −12°C level, mostly due to the HM process in typically young clouds with their age less than 15 min. At higher altitudes with temperatures of −20° to −30°C, the predicted IE ratios were smaller, ranging from 10 to 102, and mainly resulted from fragmentation in ice–ice collisions.

Homogeneous Freezing Events Sampled in the Tropical Tropopause Layer

AbstractHigh‐frequency (20 Hz) aircraft measurements of tropical tropopause layer (TTL) cirrus and humidity are used to examine recent homogeneous freezing ice nucleation events. Several localized regions with peak ice concentrations greater than 10 cm−3 are documented. These high ice concentrations are almost certainly generated by homogeneous freezing of aqueous aerosols. The high‐frequency data reveals considerable structure in the cloud properties and humidity at along‐track scales as short as 8.5 m. Simulations of vapor depletion by growing ice crystals are used to assess the time since nucleation in these high ice concentration patches. Comparisons between observed and simulated dependence of supersaturation on ice concentration indicate the nucleation events occurred within minutes (sometimes only a few minutes) of the aircraft sampling. The infrequency and short ages of the events suggest that the high ice concentrations do not persist for long times, but they may still be important for TTL cirrus microphysical properties by providing a source of ice crystals that can lead to extensive, persistent clouds with lower ice concentrations. We have also used the extensive TTL measurements provided by the high‐altitude aircraft campaigns to evaluate the threshold supersaturation for homogeneous freezing. Peak supersaturations observed are consistent with recent laboratory experiments indicating that ice saturation ratios of about 1.75–1.95 are required for homogeneous freezing of aqueous aerosols at temperatures of 185–200 K.

Aircraft measurements of water vapor heavy isotope ratios in the marine boundary layer and lower troposphere during ORACLES

Abstract. This paper presents aircraft in situ measurements of water concentration and heavy water isotope ratios D/H and 18O/16O during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project. The aircraft measurement system is also presented. The dataset is unique in that (1) it contains both total water and cloud condensed water isotope ratios; (2) it spans sufficient space and time to enable construction of spatially resolved climatology of isotope ratios in the lower troposphere; and (3) it is paired with a wealth of complementary measurements on atmospheric thermodynamic, chemical, aerosol, and radiative properties. Aircraft sampling took place in the southeast Atlantic marine boundary layer and lower troposphere (Equator to 22∘ S) over the months of September 2016, August 2017, and October 2018. Isotope measurements were made using cavity ring-down spectroscopic analyzers integrated into the Water Isotope System for Precipitation and Entrainment Research (WISPER). From an isotope perspective, the 300+ h of 1 Hz in situ data at levels in the atmosphere ranging from 70 m to 7 km represents a remarkably large and vertically resolved dataset. This paper provides a brief overview of the ORACLES mission and describes how water vapor heavy isotope ratios fit within the experimental design. Overviews of the sampling region and sampling strategy are presented, followed by the WISPER system setup and calibration details. The three data formats available to the users are each covered (latitude–altitude curtains, individual vertical profiles, and time series), with illustrative examples to highlight some features of the dataset and provide a plausibility check. The curtains and profiles demonstrate the dataset's potential to provide a comprehensive perspective on moisture transport and isotopic content in this region. Finally, measurement uncertainties are provided. Curtain and vertical profile data for all sampling periods can be accessed at https://doi.org/10.5281/zenodo.5748368 (see Henze et al., 2022). Time series data for the September 2016, August 2017, and October 2018 sampling periods can be accessed at https://doi.org/10.5067/Suborbital/ORACLES/P3/2016_V3, https://doi.org/10.5067/Suborbital/ORACLES/P3/2017_V3, and https://doi.org/10.5067/Suborbital/ORACLES/ P3/2018_V3, respectively (see references for ORACLES Science Team, 2020a–c, 2016 P3 data, 2017 P3 data, and 2018 P3 data).

On the Potential of Extending Aircraft Service Time Using Load Monitoring

Aircraft structures experience various kinds of loads over their entire lifetime, typically leading to fatigue, and ultimately structural failure. To avoid structural failure during operation, the maximum number of flight cycles and flight hours is regulated by laws ensuring continued airworthiness. However, because every flight impacts the aircraft differently, not all airframes are equally stressed at the time of decommissioning. This paper proposes a new retirement criterion based on the so-called fatigue damage index (FDI). The criterion takes into account that aircraft are differently operated and thus enables an individual decommissioning of aircraft without compromising its safety. Based on aircraft sample data covering 95% of the Airbus A320 fleet over two years, the enhanced decommissioning criterion is estimated to significantly extend the average aircraft service life. The impact varies within the fleet, depending on the experienced seat load factors, cruise altitudes, and taxi times considered for the individual aircraft during operation. Whereas seat load factors and flight altitudes significantly affect the defined FDI, the influence of taxi times is only minor. Based on the estimated increase in aircraft service life, the paper argues that for service life extensions, the FDI shall be considered as the limit of validity in the regulatory framework governing the decommissioning of aircraft.

Evaluating the impact of storage-and-release on aircraft-based mass-balance methodology using a regional air-quality model

Abstract. We investigate the potential for aircraft-based top-down emission rate retrieval over- and under-estimation using a regional chemical transport model, the Global Environmental Multiscale-Modeling Air-Quality and CHemistry (GEM-MACH). In our investigations we consider the application of the mass-balance approach in the Top-down Emission Rate Retrieval Algorithm (TERRA). Aircraft-based mass-balance retrieval methodologies such as TERRA require relatively constant meteorological conditions and source emission rates to reliably estimate emission rates from aircraft observations. Avoiding cases where meteorology and emission rates change significantly is one means of reducing emissions retrieval uncertainty, and quantitative metrics that may be used for retrieval accuracy estimation are therefore desirable. Using these metrics has the potential to greatly improve emission rate retrieval accuracy. Here, we investigate the impact of meteorological variability on mass-balance emission rate retrieval accuracy by using model-simulated fields as a proxy for real-world chemical and meteorological fields, in which virtual aircraft sampling of the GEM-MACH output was used for top-down mass balance estimates. We also explore the impact of upwind emissions from nearby sources on the accuracy of the retrieved emission rates. This approach allows the state of the atmosphere used for top-down estimates to be characterized in time and 3D space; the input meteorology and emissions are “known”, and thus potential means for improving emission rate retrievals and determining the factors affecting retrieval accuracy may be investigated. We found that emissions retrieval accuracy is correlated with three key quantitative criteria, evaluated a priori from forecasts and/or from observations during the sampling period: (1) changes to the atmospheric stability (described as the change in gradient Richardson number), (2) variations in the direction of transport, as a result of plume vertical motion and in the presence of vertical wind shear, and (3) the combined effect of the upwind-to-downwind concentration ratio and the upwind-to-downwind concentration standard deviations. We show here that cases where these criteria indicate high temporal variability and/or high upwind emissions can result in “storage-and-release” events within the sampled region (control volume), which decrease emission rate retrieval accuracy. Storage-and-release events may contribute the bulk of mass-balance emission rate retrieval under- and over-estimates, ranging in the tests carried out here from −25 % to 24 % of the known (input) emissions, with a median of −2 %. Our analysis also includes two cases with unsuitable meteorological conditions and/or significant upwind emissions to demonstrate conditions which may result in severe storage, which in turn cause emission rate under-estimates by the mass-balance approach. We also introduce a sampling strategy whereby the emission rate retrieval under- and over-estimates associated with storage-and-release are greatly reduced (to −14 % to +5 %, respectively, relative to the magnitude of the known emissions). We recommend repeat flights over a given facility and/or time-consecutive upwind and downwind (remote) vertical profiling of relevant fields (e.g., tracer concentrations) in order to measure and account for the factors associated with storage-and-release events, estimate the temporal trends in the evolution of the system during the flight/sampling time, and partially correct for the effects of meteorological variability and upwind emissions.

Assessment of online water-soluble brown carbon measuring systems for aircraft sampling

Abstract. Brown carbon (BrC) consists of particulate organic species that preferentially absorb light at visible and ultraviolet wavelengths. Ambient studies show that as a component of aerosol particles, BrC affects photochemical reaction rates and regional to global climate. Some organic chromophores are especially toxic, linking BrC to adverse health effects. The lack of direct measurements of BrC has limited our understanding of its prevalence, sources, evolution, and impacts. We describe the first direct, online measurements of water-soluble BrC on research aircraft by three separate instruments. Each instrument measured light absorption over a broad wavelength range using a liquid waveguide capillary cell (LWCC) and grating spectrometer, with particles collected into water by a particle-into-liquid sampler (CSU PILS-LWCC and NOAA PILS-LWCC) or a mist chamber (MC-LWCC). The instruments were deployed on the NSF C-130 aircraft during WE-CAN 2018 as well as the NASA DC-8 and the NOAA Twin Otter aircraft during FIREX-AQ 2019, where they sampled fresh and moderately aged wildfire plumes. Here, we describe the instruments, calibrations, data analysis and corrections for baseline drift and hysteresis. Detection limits (3σ) at 365 nm were 1.53 Mm−1 (MC-LWCC; 2.5 min sampling time), 0.89 Mm−1 (CSU PILS-LWCC; 30 s sampling time), and 0.03 Mm−1 (NOAA PILS-LWCC; 30 s sampling time). Measurement uncertainties were 28 % (MC-LWCC), 12 % (CSU PILS-LWCC), and 11 % (NOAA PILS-LWCC). The MC-LWCC system agreed well with offline measurements from filter samples, with a slope of 0.91 and R2=0.89. Overall, these instruments provide soluble BrC measurements with specificity and geographical coverage that is unavailable by other methods, but their sensitivity and time resolution can be challenging for aircraft studies where large and rapid changes in BrC concentrations may be encountered.

Boreal forest fire CO and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emission factors derived from tower observations in Alaska during the extreme fire season of 2015

Abstract. Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg−1 dry biomass burned. The CO / CO2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH4 / CO2 emission ratio was 0.010 ± 0.004, and the CH4 emission factor was 5.3 ± 1.8 g kg−1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH4 emission ratios varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

Study on the evaluation model of serialized civil aircraft commonality index based on fuzzy set theory

Commonality, a typical commercial feature of serialized civil aircraft study and development, refers to a series of methods of reusing and sharing assets, which were developed based on broad similarity. The common design of serialized civil aircraft is capable of maximally saving R&amp;D, production, operation, and disposal. To maximize the total benefits of manufacturers and operators, the common design of serialized civil aircrafts primarily exploits the commercial experience of serialized products in other fields (e.g., automobiles and mobile phones), whereas a scientific index system and quantitative evaluation model has not been formed. Accordingly, this study proposes a new civil aircraft commonality index evaluation model in accordance with fuzzy set theory and methods. The model follows two branches, i.e., attribute commonality and structural commonality, to develop a multi-level civil aircraft commonality index system. The proposed model can split the commonality into six commonality sub-intervals and build the corresponding standard fuzzy set with the characteristic attribute parameters of the civil aircraft as the elements. Next, based on considerable civil aircraft sample data, a fuzzy test is designed to yield the membership function of the fuzzy set. Thus, a model of evaluating civil aircraft commonality is constructed, taking the characteristic parameters of the civil aircraft to be evaluated as input, and selecting the degree of commonality of each level as output. Lastly, this study employs the evaluation model to evaluate the commonality of Boeing 757-200 with other civil aircrafts. Furthermore, the evaluated results well explain the actual situation, which verifies the effectiveness and practicability of the proposed model.

Occurrence and transport of microplastics sampled within and above the planetary boundary layer

Nowadays, there is no direct evidence about the presence of microplastics (MPs) in the atmosphere above ground level. Here, we investigated the occurrence, chemical composition, shape, and size of MPs in aircraft sampling campaigns flying within and above the planetary boundary layer (PBL). The results showed that MPs were present with concentrations ranging from 1.5 MPs m−3 above rural areas to 13.9 MPs m−3 above urban areas. MPs represented up to almost one third of the total amount of microparticles collected. Fourier Transform Infrared Spectroscopy allowed identifying seven types of MPs with the highest diversity corresponding to urban areas. Atmospheric transport and deposition simulations were performed using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Air mass trajectory analyses showed that MPs could be transported more than 1000 km before being deposited. This pioneer study is the first evidence of the microplastic presence above PBL and their potential long-range transport from their point of release even crossing distant borders.

Extreme Pyroconvective Updrafts During a Megafire

AbstractAirborne cloud radar reveals extreme wildfire updrafts (~60 m s−1) and downdrafts (~30 m s−1) rivaling those in supercell thunderstorms. These extreme vertical velocities occur through a 3‐km‐deep layer and below the base of a developing pyrocumulonimbus (pyroCb) cloud, which extends to the tropopause at 12 km. In situ aircraft sampling shows updrafts are linked to large temperature and moisture excesses but remain subsaturated at flight level (i.e., below cloud base). Parcel estimates using the in situ data help explain how these “hot‐moist” updrafts trigger the overlying pyroCb. The extreme vertical motions observed also pose a previously undocumented aviation hazard.

Development of a Method to Detect High Ice Water Content Environments Using Machine Learning

AbstractThis paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC.

Application of a PCA-ANN Based Cost Prediction Model for General Aviation Aircraft

The major objective of this paper is to build a cost prediction model for general aviation aircraft using artificial neural network (ANN) and principle component analysis (PCA) methods. A total number of 22 samples of general aviation aircraft collected from the literature are utilized to train and test the model. In the PCA, eigenvalues of PC1 and PC2 are 6.987 and 1.529, respectively, indicating that they have the strongest interpretation of the original variable information and are retained as cost influencing variables to train the ANN model. The pure multiple linear regression (MLR), stepwise regression (SR) and ANN models are built respectively for comparison. The comparative results reveal that the ANN method has better estimation effect than MLR and SR models in case of multi-collinearity of data. Combined with PCA, the ANN model is optimized, with MAPE, MAE, R and RMSE values of training and testing samples to be 0.009 and 0.015, 1.222 and 3, 0.9999 and 0.9994, 1.667 and 3.416, respectively. Finally, a more accurate and practical prediction model is developed. More importantly, this research can provide an important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategy.

Lagrangian Evolution of the Northeast Pacific Marine Boundary Layer Structure and Cloud during CSET

Abstract Flight data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r = 0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (−31% averaged over all cases), MBL depth (+560 m), estimated inversion strength (EIS; −2.2 K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc–Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics.

Контент метрологічного забезпечення в сучасній системі полігонних випробувань авіаційної техніки державної авіації України

New approaches to the organization and implementation of metrological support for tests of samples of aircraft in the system of state aviation of Ukraine are offered.