Understanding of global climate and the accurate forecasting of extreme weather in Europe rely on the validity of operational coupled atmosphere-ocean models over the north Atlantic. Robotic systems will play an increasing dual role in improving these models. Firstly climate models require improved parameterization schemes of the air-sea coupling, especially under existing data-sparse or data-disturbed conditions such as storm conditions or stratified turbulence respectively. Secondly, forecast model accuracy can be enhanced by targeted data assimilation, although this, at present, is costly.

The SRA, based at the Scottish Association for Marine Science with association with Oban Airport, is ideally placed geographically, logistically and academically to test and deploy air-sea interaction technology in the immediate and medium term.

We encourage academic and engineering collaboration from Europe and elsewhere to engage with the SRA to partner in the development of sensor and platform technology, validation of heterogenous swarms (airborne, surface and sub-surface) and trial operational studies.

How to cite: Anderson, P., Peterson, P., and Smith, L.: Introducing the Scottish Scientific Robotics Academy as a facility for testing and operating robotics for Ocean-Atmosphere Interaction studies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5957, https://doi.org/10.5194/egusphere-egu24-5957, 2024.

Unexpected encounters with aviation turbulence, a hazardous weather phenomenon affecting aircraft operations, can cause casualties and aircraft damage. The Republic Of Korea Air Force (ROKAF) currently provides turbulence forecast information for the East Asia/Korean Peninsula area based on the Korea Air Force-Weather and Research Forecasting (KAF-WRF) model-derived turbulence diagnostics. However, because the turbulence forecast information is not provided for the global area, operational weather forecasting support to overseas areas, whose importance is increasing under modern warfare, is limited. Accordingly, in this study, we developed the global integrated turbulence forecast system considering various turbulence generation mechanisms, called the KAF-Global Turbulence Forecast (KAF-GTF) system, based on the two global numerical weather prediction (NWP) models being currently used in operation by the weather group of the ROKAF. The ROKAF’s global NWP models are the Global Forecast System (GFS) and European Centre for Medium-Range Weather Forecasts (ECMWF), which have horizontal resolutions of 0.5°x0.5° and 0.25°x0.25°, respectively. The two global NWP model-based KAF-GTF systems are developed based on the methodology of version 3 of the Graphical Turbulence Guidance (GTG) system of Sharman and Pearson (2017) and consist of the following three steps: i) individual clear-air turbulence and mountain wave turbulence diagnostics representing various turbulence generation mechanisms are calculated using the global NWP model output, ii) the raw values of those turbulence diagnostics are converted into eddy dissipation rate (EDR), which represents the intensity of atmospheric turbulence, using the simple EDR conversion equation, and iii) KAF-GTF forecast is derived by combining the EDR-scaled turbulence diagnostics through ensemble averaging. The combination set of individual turbulence diagnostics and the EDR conversion equations used in the KAF-GTF system are applied as in GTG 3, considering that the combination set of turbulence diagnostics and their EDR conversion equations optimized for the global turbulence forecast were already constructed in the GTG3. In this study, the performance of two KAF-GTFs based on GFS and ECMWF are compared using turbulence cases reported from aircraft turbulence observation data for the evaluation, and the evaluation results will be represented in the conference.

Acknowledgment: This research was funded by the Republic Of Korea Air Force Weather Group Research Program (2023UMM0343), and was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00250021).

How to cite: Lee, D.-B., Kim, J.-H., Hwang, J., Song, J.-I., and Jung, H.: Development of Global Integrated Turbulence Forecast System for the Republic Of Korea Air Force, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14026, https://doi.org/10.5194/egusphere-egu24-14026, 2024.

Understanding carbon flux processes and controls is crucial to constrain greenhouse gas exchanges of different ecosystems under a changing climate. However, over heterogeneous landscapes (e.g., Arctic permafrost regions), carbon exchange fluxes (CO₂, CH₄) show significant variations even on very small spatial scales. As a result, upscaled carbon fluxes from eddy covariance towers and flux chambers hold the potential to be biased due to their limited spatial representativeness. Therefore, quantifying carbon fluxes at different scales is needed to improve understanding of spatial variability, and the interaction of processes over heterogeneous landscapes. Constraining carbon fluxes with unmanned aerial vehicles (UAVs) carrying greenhouse gas analyzers has the potential to bridge this scaling gap since UAVs allow to monitor large areas with high spatial resolution. Nevertheless, only few guidelines are available on UAV flight strategies and methods to accurately quantify the surface-atmosphere carbon exchange fluxes.

In this study, we conducted synthetic UAV flights using a Large Eddy Simulation (LES) model to evaluate various carbon flux quantification methods based on UAV observations. These methods, including e.g. mass balance and flux gradient approaches, were tested with different flight strategies to find the optimum setup that maximizes information gain for a given flight time. In addition, we conducted experiments to improve the accuracy of the UAV-based carbon flux estimation from a campaign conducted in a subarctic heterogeneous ecosystem in which the UAV platform was able to collect in-situ atmospheric CO₂ and CH₄ concentrations, and environmental parameters such as 2D wind speed, air temperature, humidity, and pressure. Replicating these UAV flight strategies within the LES model enabled us to quantify the uncertainties and provide guidelines for future UAV flight campaigns in heterogeneous landscapes.

How to cite: Bolek, A., Schlutow, M., Heimann, M., and Goeckede, M.: LES-based evaluation of UAV flight patterns to quantify local scale carbon emissions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8879, https://doi.org/10.5194/egusphere-egu24-8879, 2024.

Condensation trails, or “contrails,” are line-shaped clouds that form when airplanes fly through cold and humid parts of the atmosphere which are ice-supersaturated. Various studies have shown that long-lasting or “persistent” contrails may be responsible for more than half of aviation’s radiative forcing (RF) (Lee, et al., 2021). Efforts to mitigate persistent contrail formation include operational contrail avoidance. Current research suggests that minor (~2000 ft) deviations in altitude of flights during cruise, in conjunction with advancing technologies, have the potential to reduce contrail climate forcing by approximately 90% (Teoh, et al., 2020).

Identifying and attributing observed contrails to specific individual flights is critical to demonstrating the success of any contrail avoidance strategy, as it establishes whether a deviated flight created a contrail. Reliable attribution of contrails to individual flights is needed to provide verifiability and accountability before any large-scale implementation of contrail avoidance policies takes place. Flight attribution leverages both Earth-observation methods, such as satellite images and weather data, and flight data. However, temporal and spatial "blindspots" in satellite instruments, coupled with uncertainties in wind fields, have hindered reliable flight attribution.

In this work, we consider two approaches: a “Single-instance” probabilistic flight attribution algorithm and a “Multi-frame” probabilistic flight attribution algorithm that accounts for discrepancies in contrail observability and weather data errors. The inputs to both algorithms include contrail detections, ERA5 weather data, and FlightAware ADSB flight data. The Single-instance algorithm computes a probabilistic “match score” for flights and contrails at an individual temporal point. This probability is calculated using distance, heading, and altitude measures. The Multi-frame algorithm computes a probabilistic match score utilizing information from different wind ensembles and previous temporal points in addition to the same baseline measures.

To perform this analysis, a dataset of over a hundred manually labeled and attributed contrails was created that captured regional (across the continental United States) and diurnal variation. These were categorized depending on the perceived difficulty of the attribution (due to background cloudiness and flight track density). An initial assessment comparing the outputs of the algorithms to the manually labeled attributions shows that while both our algorithms exhibit strong performance in high contrail observability and low flight density scenes, the Single-instance algorithm demonstrates suboptimal results under conditions of interrupted contrail observability and increased flight density. The Multi-frame algorithm, however, was able to identify flight matches much more accurately in these more challenging scenes. The development of a robust flight-matching algorithm and evaluation dataset is critical to the validation of contrail avoidance efforts. Furthermore, it can provide additional insight into the relationship between meteorology, aircraft parameters, and observable contrails, supporting future efforts to reduce contrail formation through technological or operational means.

How to cite: Barbosa, M. P., Barrett, S., Eastham, S., Meijer, V., and Robion, L.: Evaluation of Robust Flight Attribution Algorithm for Contrail Avoidance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21385, https://doi.org/10.5194/egusphere-egu24-21385, 2024.

The utilization of multirotor UAS aircraft for atmospheric data collection is an expanding field, and one method employed for measuring atmospheric wind speed is the tilt angle method. This method correlates the tilt angle assumed by the multicopter during hovering to compensate for aerodynamic drag forces with the atmospheric wind.
At the Umweltphysik Group of Uni Tübingen, a cost-effective and easily replicable calibration method has been devised and tested. However, this approach overlooks the crucial vertical component of the wind, essential for calculating vertical turbulent fluxes.
To address this limitation, a follow-up study proposes an analytical approach that involves calibrating relationships between wind speed and tilt angle, motor RPM and tilt angle, as well as vertical wind speed and RPM. The necessary data for this calibration can be obtained through telemetry from an open-source flight controller's log files.
Accurate calibration of these relationships is ensured through real-world flight testing to maintain precision. Indoor tests or wind tunnel experiments might yield biased results due to interactions with walls, failing to accurately represent the aircraft's outdoor aerodynamic behavior.
Considering environmental parameters is vital, as evidenced by notable differences between calibrations conducted in winter and summer. These variations underscore the necessity of accounting for environmental influences.
Subsequently, the method will undergo further validation by flying the aircraft in close proximity to a sonic anemometer to assess its accuracy in measuring atmospheric parameters.

How to cite: Bramati, M., Schön, M., Savvakis, V., Wang, Y., Bange, J., and Platis, A.: Horizontal and vertical wind speed sampling using multirotor UAS aircraft, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8640, https://doi.org/10.5194/egusphere-egu24-8640, 2024.

The SWUF-3D fleet of unmanned aerial systems (UAS) is utilized for in situ
measurements of turbulence as a contribution to closing observational gaps in
the atmospheric boundary layer (ABL). The wind measurement algorithm used
has only been calibrated in the free field up to this point. Therefore, we present
the calibration and verification in a wind tunnel.
Calibration is performed in x- and y-coordinate directions of the UAS body
coordinate frame and in wind speeds of 2 . . . 18 m s⁻¹. We investigate the mea-
surement accuracy under different angles of sideslip (AoS) and wind speeds as
well as the portability of the calibration coefficients to other UAS of the fleet.
The wind tunnel is equipped with an active grid which is capable of generat-
ing measurement scenarios like gusts, velocity steps and statistical turbulence.
This allows systematic verification of the measurement capabilities and identifi-
cation of limitations. As a reference for the UAS measurements we use constant
temperature anemometers (CTAs).
With the derived calibration coefficients the uncertainty depends on the wind
speed magnitude and increases with higher wind speeds, resulting in an overall
root-mean-square error (RMSE) of less than 0.2 m s⁻¹. Applying the calibra-
tion coefficients from one UAS to others within the fleet results in comparable
accuracies, showing omission of wind tunnel calibration for the remaining UAS.
Furthermore, the wind measurement is susceptible to high AoS at high wind
speeds. The RMSE for measurements in different gusts is up to 0.6 m s⁻¹. In
the most extreme velocity steps (i.e. a lower speed of 5 m s⁻¹ and an amplitude
of 10 m s⁻¹) the maximum RMSE occurs and exceeds 1.3 m s⁻¹. For variances
below approx. 0.5 m⁻² s⁻² and 0.3 m⁻² s⁻², the maximum resolvable frequen-
cies of the turbulence are about 2 Hz and 1 Hz, respectively. The verification
in the wind tunnel and the determination of uncertainties helps the analyses
of atmospheric measurements in complex terrain and in wind parks where the
SWUF-3D fleet is primarily deployed.

How to cite: Kistner, J. and Wildmann, N.: Calibration and verification of high resolution wind speed measurements with quadcopter UAS in a wind tunnel with active grid, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9131, https://doi.org/10.5194/egusphere-egu24-9131, 2024.

Contrail avoidance promises to be a near-term solution for mitigating part of aviation’s climate impact [1]. Atmospheric regions that allow for contrails to form and persist have been shown to be horizontally wide but vertically thin [2], motivating the idea that small vertical deviations are sufficient for avoiding the most impactful contrails [1]. Nonetheless, the concept of contrail avoidance relies on skillful forecasts of the regions where contrails will form and persist. Recent comparisons of NWP data and humidity measurements and contrail observations show that the prediction of contrail persistence is problematic [3,4]. Since simulation studies that have previously investigated contrail avoidance have assumed the prediction of these regions to be correct [1], real-world contrail avoidance strategies may be less effective than thought previously [4]. There is thus a need to both understand and improve the performance of prediction methods that could be utilized for contrail avoidance.

Previous work has [5] has resulted in a dataset of over 3000 contrail cross-sections found in CALIOP LIDAR data, obtained by collocating contrails detected using GOES-16 imagery [6]. We have now developed an algorithm that finds the location where an aircraft’s exhaust plume intersects CALIOP data. This allows us to estimate which contrail cross-section corresponds to which flight, as well as estimate which flights did not form a persistent contrail. The resulting dataset is used for the evaluation of existing forecast methods that rely on numerical weather prediction data, as well as a nowcasting algorithm that relies on contrail detections and altitude estimates from GOES-16 data [5,6].

This new forecast evaluation dataset and method can be used to better understand the limitations of existing approaches and enable the development of improved techniques for persistent contrail prediction.

References:

[1] Teoh, R., Schumann, U., Majumdar, A., and Stettler, M. E. Mitigating the climate forcing of aircraft contrails by small-scale diversions and technology adoption. Environmental science & technology, 54(5):2941–2950, 2020.

[2] Gierens K., Spichtinger, P. and Schumann, U. Ice Supersaturation, In Atmospheric Physics. Background—Methods—Trends; Schumann, U., Ed.; Springer: Heidelberg, Germany, 2012; Chapter 9; pp. 135–150.

[3] Gierens, K.; Matthes, S.; Rohs, S. How Well Can Persistent Contrails Be Predicted? Aerospace 2020, 7, 169.

[4] Geraedts S,. Brand E., Dean T., Eastham S.D., Elkin C., Engberg Z., Hager U., Langmore I., McCloskey K., Ng J.Y., Platt J.C. A scalable system to measure contrail formation on a per-flight basis. Environmental Research Communications. 2023

[5] Meijer V.R., Eastham S.D., Barrett S.R. Contrail Height Estimation Using Geostationary Satellite Imagery. AGU23. 2023.

[6] Meijer V., Kulik L, Eastham S.D., Allroggen F., Speth R.L., Karaman S., Barrett S.R. Contrail coverage over the United States before and during the COVID-19 pandemic. Environmental Research Letters. 2022.

How to cite: Meijer, V., Eastham, S., Waitz, I., and Barrett, S.: Contrail forecast and nowcast evaluation using satellite-based LIDAR data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12255, https://doi.org/10.5194/egusphere-egu24-12255, 2024.

The Saharan desert is the main source of mineral dust in the atmosphere, and its presence in the air can have a significant impact on the solar radiation budget. In evaluating the radiative effect of airborne mineral dust, aerosol charge may be a critical factor. This space charge and its relationship to particle concentrations is not taken into account by current model simulations, and such in-situ measurements are hardly available. In this work, a novel sensor network equipped on an uncrewed aircraft system (UAS) of type MASC-3 was employed for vertical profiling of Saharan dust particle concentrations, meteorological (temperature, relative humidity and wind field) parameters and turbulent kinetic energy (TKE), during a dust event that occurred in Orounda, Cyprus in April 2022. The MASC-3 performed profiles up to 3000 m altitude, and identified the vertical extent of the dust cloud, which was located between 1900 and 2500 m. We were able to capture the evolution of the event over several days, and additional numerical simulation and remote sensing observations verify the results. During the first day of measurements, when dust load was the highest, charge data from the MASC-3 also allowed for investigation of aerosol concentrations and dust electrification. For the first time, this innovative sensor system provided in-situ UAS measurements of the aforementioned quantities and described the dust event in detail, and with high resolution. Considerations on aircraft charging, the effect of turbulent levels and local meteorology, on the space charge / aerosol data collected by the MASC-3, as well as probing Saharan dust events more generally, are elaborated for further related research.

How to cite: Savvakis, V., Schön, M., Bramati, M., Bange, J., and Platis, A.: In-situ Saharan dust observations over the Eastern Mediterranean with an uncrewed aircraft system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17567, https://doi.org/10.5194/egusphere-egu24-17567, 2024.

Aircraft icing, a hazardous phenomenon involving the accumulation of ice or supercooled water droplets on an aircraft’s wings and airframe in freezing temperatures when an airplane flies through clouds, poses substantial risks to aviation safety. From a meteorological perspective, aircraft icing is determined by the air temperature, and the number and size of water droplets. In particular high values with water contents are a crucial factor correlating with increased icing intensity. Since weather radar can detect ice and water droplets larger than 2 mm in diameter within precipitable clouds, we have developed two radar-based aircraft icing products (icing potential areas and icing intensity) to facilitate safe aviation services in real-time. 

In this study, the estimation algorithm of aircraft icing intensity using Z-LWC relationship was presented. We utilized data from a ground-based S-band radar mosaic, an icing detector, and a cloud droplet probe installed on the aircraft (KMA/NIMS atmospheric research aircraft; NARA) for 13 cases of icing. Within the icing areas determined by the icing detector, 3-dimensional gridded reflectivity (Z) and liquid water content (LWC) were matched based on time and location. A Z-LWC relationship was then derived using the paired dataset sorted by size. We calculated LWC from Z using this relationship and categorized icing intensity into Trace, Light, Moderate, Heavy, and Severe, according to FAA criteria (FAA 2001). The estimation of icing intensity was solely focused within the identified icing potential areas (Kim et al. 2023). The algorithm was validated using a “Light” intensity icing case from aircraft report (AIREP), showing good performance, but further verification is needed. 

ACKNOWLEDGEMENTS
This research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.
This work was funded by the Korea Meteorological Administration Research and Development Program "Developing Application Technology using Atmospheric Research Aircraft" under Grants (KMA2018-00222).

How to cite: Ye, B.-Y. and Kim, Y.: Estimation of Aircraft Icing Intensity Using Z-LWC Relationship from Radar and Aircraft Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7164, https://doi.org/10.5194/egusphere-egu24-7164, 2024.

The objective of this study is to characterize visibility and microphysics of cold-fog conditions during The Cold Fog Amongst Complex Terrain (CFACT) project, which was designed to investigate the life cycle of cold fog in the Heber Valley, Utah. The field campaign was conducted from 7 January to 23 February 2022 and was supported with observations and resources from the NSF Lower Atmospheric Observing Facilities (LAOF), managed by NCAR’s Earth Observing Laboratory (EOL), as well as the University of Utah and Ontario Technical University. Heber Valley is surrounded by canyons, mountains and irregular topography where the Provo River streams along the valley floor from Jordanelle Reservoir at the north to Deer Creek (DC) Reservoir at the southeastern end at 1652 m above sea level (ASL). The highest peaks surrounding the valley are at about 3500 m (ASL) to the west and southwest of the project area.

The DC supersite had extensive ice and droplet microphysical as well as precipitation measurements obtained using a ground-based Gondola (composed of a Droplet Measurement Technologies (DMT) Cloud Droplet Probe - CDP and Back-scatter Cloud Probe - BCP), a DMT Fog Monitor (FM120), a Mesaphotonics Cloud Droplet Measurement System (CDMS), a DMT Ground-based Cloud Imaging Probe (GCIP), a Vaisala Present Weather Detector (PWD52), and an OTT Parsivel. During the project, aerosol measurements were performed using a GRIMM Aerosol Spectrometer, a T.S.I. Scanning Mobility Particle Sizer (SMPS), and a DMT Cloud Condensation Nuclei (CCN) counter, as well as filter samplers. These instruments covered a size range from 8 nm up to cm size range representing aerosols, fog particles, and precipitation. Measurements from a Halo Photonics doppler wind lidar, a Vaisala CL61 ceilometer, a tethered balloon system (TBS), and a 32-m turbulence tower were used to characterize vertical profiles of fog microphysics and aerosols, as well as the dynamic and thermodynamic structure. Eleven significant weather events occurred during the 47 days of the CFACT campaign that included snowfall, freezing fog, ice fog (IF), and light ice crystal precipitation when Vis<5 km. In the presentation, IF events will be discussed with respect to ice crystal particle size spectra and habit, visibility, physical parameterizations, as well as measurement and prediction challenges faced.

How to cite: gultepe, I., Pu, Z., Pardyjak, E., Hoch, S., Perelet, A., and Agelin-Chaab, M.: Mountain Ice Fog and Visibility during CFACT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22265, https://doi.org/10.5194/egusphere-egu24-22265, 2024.

Thunderstorms can lead to heavy or extreme heavy rainfall events and impact many sectors, such as aviation, urban infrastructure, and power systems. In Aviation Meteorology, The main objective of weather radar¹ is to identify thunderstorm cells on the flight route and issue warning messages with the traces of wind motions, rainfall intensity and possible turbulence. These attributes contribute significantly to how air navigation is performed safely and efficiently against high-risk weather hazardous zones. To improve the severe thunderstorm forecast, develop an automated thunderstorm warning system application that detects the thunderstorm cells from the radar images by using image processing techniques. It recognizes the size of the thunderstorm cells and measures the gauge between the airport and each cell. Moreover, estimating the velocity of cell movement towards the airport is an added advantage. It is an added-value product of the Aviation Weather Decision Support System² (AWDSS). This application utilizes the two main Python packages OpenCV³ and Wradlib⁴ .

Keywords: Aviation Meteorology, Decision Support System, Image Processing Technique, Weather radar, OpenCV, Thunderstorm, Radar Imaging.

References

1) Theodore Fujita, T., McCarthy, J. (1990). The Application of Weather Radar to Aviation Meteorology. In: Atlas, D. (eds) Radar in Meteorology. American Meteorological Society, Boston, MA. https://doi.org/10.1007/978-1-935704-15-7_43.

2) Eilts, Michael & Shaw, Brent & Barrere, Charles & Fritchie, Robert & Carpenter, Richard & Spencer, Phillip & Li, Yanhong & Ladwig, William & Mitchell, Dewayne & Johnson, J. & Conway, J. (2015). THE AVIATION WEATHER DECISION SUPPORT SYSTEM: DATA INTEGRATION AND TECHNOLOGIES IN SUPPORT OF AVIATION OPERATIONS.

3) Open Souce Computer Vision (OpenCV),https://docs.opencv.org/4.x/.

4) wradlib: An Open Source Library for Weather Radar Data Processing, https://docs.wradlib.org/en/latest/.

How to cite: Ramadoss, M., Kumar, G., Kanaujiya, B. K., Sobhanan, A., Mishra, A. K., Thirunavukkarasu, M., and Gopal, M.: Develop an Application for Detecting Thunderstorm Cells and Tracking Their Motion Behaviour From Radar Images by Using Image Processing Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14911, https://doi.org/10.5194/egusphere-egu24-14911, 2024.