Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Characterizing Terminal Airspace Operational States and Detecting Airspace-Level Anomalies

Global modernization efforts focus on increasing aviation system capacity and efficiency, while maintaining high levels of safety. To accomplish these objectives, new analysis methods are required that consider Air Traffic Management (ATM) system operations at both the flight level and the airspace level. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. Specifically, anomaly detection has been identified as being paramount. However, previous analyses of airspace-level operational states have not considered the observation of transitional (transitioning between two distinct airspace-level operational patterns) or anomalous operational states. Therefore, a method is proposed in which the time-series trajectory data of all aircraft operating within a terminal airspace during a specified time period is aggregated to generate a representation of the airspace-level operational states such that a recursive DBSCAN procedure to characterize airspace-level operational states as either nominal, transitional, or anomalous as well as to identify the distinct nominal operational patterns. This method is demonstrated on one year of ADS-B trajectory data for aircraft arriving at San Francisco International Airport (KSFO). Overall, visual inspection of results indicate the method’s promise in assisting ATM system operators, decision-makers, and planners in designing the implementation of new operational concepts.

Can we assess the value meteorological ensembles add to dispersion modelling using hypothetical releases?

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.

The Unconventional Eyewall Replacement Cycle of Hurricane Ophelia (2005)

Hurricane Ophelia (2005) underwent an unconventional eyewall replacement cycle (ERC) as it was a Category 1 storm located over cold sea surface temperatures near 23°C. The ERC was analyzed using airborne radar, flight-level, and dropsonde data collected during the Hurricane Rainband and Intensity Change Experiment (RAINEX) intensive observation period on 11 September 2005. Results showed that the spin-up of the secondary tangential wind maximum during the ERC can be attributed to the efficient convergence of absolute angular momentum by the mid-level inflow of Ophelia’s dominantly stratiform rainbands. This secondary tangential wind maximum strongly contributed to the azimuthal mean tangential wind field, which is conducive for increased low-level supergradient winds and corresponding outflow. The low-level supergradient forcing enhanced convergence to form a secondary eyewall. Ophelia provides a unique example of an ERC occurring in a weaker storm with predominantly stratiform rainbands, suggesting an important role of stratiform precipitation processes in the development of secondary eyewalls.

Investigation on flight level contamination using volcanic SO2 plume and cloud top height satellite products

<p>Volcanic emission is a major risk for air traffic. Flying through a volcanic cloud can have a strong impact on engines (damage caused by ash and/or sulphur dioxide – SO<sub>2</sub>) and persons. The knowledge of the height of the volcanic plume is indeed essential for pilots, airlines and passengers.</p><p>In this presentation, we study recent volcanic emissions to illustrate the difficulty for obtaining information about the height of the SO<sub>2</sub> plume in a form relevant to aviation. Our study uses satellite data products. We consider SO<sub>2</sub> layer height from TROPOMI (UV-vis hyperspectral sensor on board S5P, a polar orbiting platform), as shown by SACS (Support to Aviation Control Service), combined with cloud top observations (from the same sensors or from geostationary broadband imagers) to determine the minimum SO<sub>2</sub>-cloud height. This is a validation which is of interest to aviation.</p><p>The flight level, not the km, is the measure, the unit for expressing height during cruise flight used on board by the pilots to ensure safe vertical separation between aircraft, despite natural local variations in atmospheric air pressure and temperature. Thus, it is critical to provide the corresponding SO<sub>2</sub> contamination expressed as flight levels. Our study will focus on this conversion that is one item currently being developed in the frame of ALARM H2020 project (https://alarm-project.eu) and SACS early warning system (https://sacs.aeronomie.be) in the creation of NetCDF alert products.</p>

The sensitivity of cloud radiative forcing with respect to the macrophysical properties and organization of the trade-wind cloud field during EUREC4A

<p>The clouds in the Atlantic trade-wind region are known to have an important role in the global climate system, but the interactions between the microphysical, macrophysical and radiative properties of these clouds are complex. This work seeks to understand how the macrophysical properties and organization of the cloud field impact the large-scale cloud radiative forcing in order to provide the necessary information for the evaluation of the representation of these clouds in models. During the 2020 EUREC<sup>4</sup>A campaign, the German HALO aircraft was equipped for the first time with two instruments - the BACARDI instrument, a broadband radiometer that encompasses a set of pyrgeometers and pyranometers to measure the upward and downward solar and terrestrial radiation at flight level, and the VELOX Thermal IR imager. Simultaneously, one-minute resolution observations of the flight domain were obtained by the GOES-E satellite, thus providing information about the properties of the clouds on a spatial scale compatible with the large footprint of the BACARDI instrument. Using the products of these three instruments, we observe how the changing cloud field (e.g. cloud fraction, mean liquid water path (LWP), cloud top height, degree of clustering) in the EUREC<sup>4</sup>A domain impacts the radiation measured at flight level. We see that although cloud fraction plays a significant role as expected, it is not sufficient to parameterize the cloud radiative effects. Furthermore, the results indicate that the general organization of the cloud field as well as other properties describing the cloud population are necessary, but their relative importance varies between different cloud scenes.</p>

Prevalence and trigger factors of functional gastrointestinal disorders among male civil pilots in China

Functional gastrointestinal disorders (FGIDs) are common among the aircrew due to their arduous working environment. This study investigated the prevalence of FGIDs in Chinese male pilots and assessed the effects of trigger factors on the FGIDs. A cross-sectional study including 212 male pilots was performed in a Chinese large civil airline company. FGIDs were diagnosed according to the Rome IV diagnostic criteria. The psychological performance, dietary pattern, sleep situation, and physical activity of the respondents were assessed. Logistic regression analysis and structural equation modeling were used to explore the association between these trigger factors and FGIDs. FGIDs were observed in 83 (39.22%) respondents, of which 31 (37.35%) had overlap syndromes. Age, flight level, flight time, high-salt food pattern, anxiety, and sleep performance were found to be associated with FGIDs (all P < 0.05). Stepwise logistic regression analysis revealed that the flight level (OR 0.59, 95% CI 0.31–0.080), high-salt food pattern (OR 2.31, 95% CI 1.28–4.16), and sleep performance (OR 2.39, 95% CI 1.11–5.14) were the influencing factors associated with FGIDs. Structural equation modeling confirmed the correlations between FGIDs and the occupational, dietary, and psychological factors with a reasonable fit. The preventive strategies were necessitated according to occupational and psychological characteristics.