Aviation Meteorology And Nowcasting: Observations and Models (AMANOM)

Topics related to In-situ observations obtained from aircraft, Uncrewed Aerial Vehicles (UAVs), balloons, and supersites, remote sensing retrievals of meteorological parameters from satellites, radars, lidars, and MicroWave Radiometers (MWRs), as well as other emerging technological platforms, and predictions of meteorological parameters from the numerical weather prediction models will be considered highly related to the goals of this session.

Convener: Ismail Gultepe | Co-conveners: Wayne Feltz, Stan Benjamin, Martin Gallagher, Chunsong Lu
| Wed, 25 May, 10:20–11:50 (CEST)
Room 0.11/12

Presentations: Wed, 25 May | Room 0.11/12

Chairperson: Georg Grell
Virtual presentation
Paul Williams and Rachel Cheyne
Clear-air turbulence (CAT) is a major hazard to flying aircraft. It is generated by vertical wind shear instabilities in the upper troposphere and lower stratosphere. The El Niño–Southern Oscillation (ENSO) is known to affect the global atmospheric circulation, including the Walker and Hadley cells and the mid-latitude jet streams. Therefore, ENSO has the potential to influence global CAT, both locally in the tropics and remotely in the extra-tropics via teleconnections. Anecdotal evidence supports such an association: there was a large increase in pilots reporting turbulence when flying over the USA during the winter of 1997–98, coinciding with one of the strongest El Niño events on record. However, the influence of ENSO on CAT has not previously been studied.
Here we use reanalysis data to investigate linkages between ENSO and vertical wind shear (and hence CAT) in northern hemisphere winter. Global maps of the anomalous vertical wind shear at 250 hPa are produced from composites of the five strongest El Niño and La Niña events since 1979. These maps indicate that the shear is significantly modified throughout the ENSO cycle across large parts of the globe, including the mid-latitudes and polar regions. The changes are quantified by regressing wind shear in selected high flight-density areas from each winter since 1979 against sea-surface temperature anomalies in the Niño 3.4 region. In the USA and Mexico, for example, we find a sensitivity of around 0.5 m s−1 (100 hPa)−1 °C−1, such that the shear increases by around 50% from 4 m s−1 (100 hPa)−1 during a strong La Niña event to 6 m s−1 (100 hPa)−1 during a strong El Niño event. Significant ENSO–shear relationships are also found in South America, the North Atlantic Ocean, East Asia, South-East Asia, Australia, and Africa.
This study provides the first evidence that ENSO has the potential to influence CAT globally. ENSO’s predictability could be exploited to produce seasonal CAT forecasts globally up to 12 months ahead, which may have practical benefits for the aviation sector, not least because turbulence increases aircraft fuel consumption.

How to cite: Williams, P. and Cheyne, R.: Does ENSO Affect Global Clear-Air Turbulence?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2404,, 2022.

Presentation form not yet defined
Quantifying the impact of climate processes on trans-Atlantic flight times in observed IAGOS data
Corwin Wright and Phoebe Noble
On-site presentation
Mohamed Foudad, Emilia Sanchez Gomez, Mélanie Rochoux, and Thomas Jaravel

Airplanes spend about 1% of cruise time in Moderate-Or-Greater (MOG) CAT (Sharman et al. 2006), which is defined as any turbulence occurring in the atmosphere away from a visible convective activity and which is particularly difficult to detect. MOG CAT events can injure passengers, cause structural damage to planes, and induce considerable economic loss. A major source of CAT is the Kelvin–Helmholtz instability (KHI), which is often induced by vertical wind shear associated with the jet stream and upper-level fronts. Recent studies have shown that under climate change, jet streams could be strengthened, and CAT frequency and intensity could significantly increase (Williams 2017). Assessing future CAT changes is a relatively new research topic and there are a lot of open questions. In particular, there is a need to understand the CAT trends in the present climate in atmospheric reanalysis and climate models and the mechanisms at play. The second step is to investigate the CAT sensitivity to global warming and the associated uncertainties.

In this study, we characterize present and future climate CAT trends in the Northern Hemisphere. For this purpose, we rely on a set of CAT indices computed with five different reanalysis datasets (among whom ERA5) and experiments performed by two CMIP6 climate models (CNRM-CM6-1 and IPSL-CM6A-LR). 

In present climate, the analysis of the CAT indices over the last four decades shows that CAT is more frequent over the North Atlantic, the Pacific Northwest, the Himalayas and the Rocky Mountains. We find that the spatial distribution of CAT over the North Atlantic is strongly related to the variability of large-scale circulation patterns. In particular, the occurrence of CAT is clearly associated with the positive phase of the North Atlantic Oscillation (NAO+) and the Atlantic Ridge weather regimes. A significant positive trend of CAT frequency is found using reanalysis in different regions of the northern hemisphere. However, the signal-to-noise ratio estimated from the climate models is still very weak in the present climate except over Northeast Asia.

We find that positive trends of CAT frequency are enhanced in response to global warming for the ssp8.5 worst-case scenario over the midlatitudes at the level 200hPa. This is coherent with previous studies. However, results also suggest that CAT future changes highly depend on altitude level and the region considered. For example, over the North Atlantic, CAT frequency significantly increases at the 200hPa (about 11 km) and 300hPa (about 9 km) levels, while it decreases at the 250hPa (about 10 km) level. This highlights the importance of study future changes in the vertical structure of the atmosphere.


Sharman R., Tebaldi C., Wiener G. et Wolff J., 2006, « An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting », Weather and Forecasting, vol. 21, no 3, p. 268‑287.

Williams Paul D., 2017, « Increased light, moderate, and severe clear-air turbulence in response to climate change », Advances in Atmospheric Sciences, vol. 34, no 5, p. 576‑586.

How to cite: Foudad, M., Sanchez Gomez, E., Rochoux, M., and Jaravel, T.: Present climate characterization and future changes in Clear-Air Turbulence (CAT) over the northern hemisphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2796,, 2022.

Virtual presentation
Vincenzo Mazzarella, Massimo Milelli, Martina Lagasio, Laura Poletti, Riccardo Biondi, Eugenio Realini, Stefano Federico, Rosa Claudio Torcasio, Markus Kerschbaum, Maria Carmen Llasat, Tomeu Rigo, Laura Esbrí, Marco-Michael Temme, Olga Gluchshenko, Annette Temme, Lennard Nöhren, and Antonio Parodi

One of the main challenges for meteorologists is to improve the prediction of events that develop on small spatial and temporal scales, having important repercussions in air traffic activities. In this regard, the H2020 SESAR Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project, aims to demonstrate that the prediction of severe weather events with high spatial and temporal resolution, can benefit the ATM and aviation safety. SINOPTICA assimilates non-conventional observations such as Global Navigation Satellite System (GNSS), weather radar, and lightning data into numerical weather prediction model with a nowcasting technique called PHAse-diffusion model for STochastic nowcasting (PHAST) allowing to predict the highly localized convective events triggering in the vicinity of airports.

As part of the project, three severe weather events were identified on the Italian territory which caused the closure of the airports, delays on arrivals and departures, and numerous diversions. The results of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) and nowcasting technique PHAST, were integrated into the Arrival Manager 4D-CARMA (4-Dimensional Cooperative Arrival Manager), an adaptive air traffic sequencing and management system for controllers, which generates and optimizes 4D trajectories to avoid areas affected by adverse phenomena and, under certain circumstances, reducing controllers’ and pilots’ workload. The results show that the nowcasting technique is able to predict the convective cells in shape, intensity and time. In addition, the assimilation of lightning and GNSS data improves the forecast accuracy of the above-mentioned events in line with expectations and ATM needs.

How to cite: Mazzarella, V., Milelli, M., Lagasio, M., Poletti, L., Biondi, R., Realini, E., Federico, S., Torcasio, R. C., Kerschbaum, M., Llasat, M. C., Rigo, T., Esbrí, L., Temme, M.-M., Gluchshenko, O., Temme, A., Nöhren, L., and Parodi, A.: Data assimilation and nowcasting of severe weather for air traffic management purposes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2823,, 2022.

Virtual presentation
Alessandra Lucia Zollo, Myriam Montesarchio, and Edoardo Bucchignani

One of the most severe weather hazards to aviation is in-flight airframe icing, i.e. the accretion of ice on airplane’s surfaces during flight. In order to increase margins of aviation safety, the early detection of regions affected by icing conditions is a challenging and desirable goal. In the framework of the H2020 EU project SENS4ICE (SENSors and certifiable hybrid architectures for safer aviation in ICing Environment), CIRA has developed a remote detection tool of icing conditions based on satellite data. Specifically, high-resolution satellite products, based on Meteosat Second Generation (MSG) data, have been considered, with spatial and temporal resolutions of about 3 km and 15 minutes respectively. The aim of this tool is to identify areas potentially affected by in-flight icing hazard, giving information about the severity of the phenomenon (light, moderate or severe) and an estimate of the altitude at which this hazard can occur. The developed algorithm also takes into account supercooled large droplets (SLD), which pose a serious threat to aviation and have been the cause of tragic accidents over the last decades. The tool relies on satellite data, to remotely infer the properties of clouds, and a set of experimental curves and envelopes that describe the interrelationship of cloud liquid water content, mean effective diameter of the cloud droplets and ambient air temperature. These curves, provided by the Federal Aviation Administration (FAA), define the atmospheric icing conditions and represent the reference legislation in this field. Furthermore, a nowcasting algorithm based on the extrapolation in time of the current icing conditions has been implemented, in order to perform a forecast over a short period ahead, responding to the great need for timely and location-specific forecasts that are relevant for aviation, e.g. for safety reasons or for planning and routing air traffic. This presentation will provide a preliminary analysis of the performance of the implemented tools, which will be evaluated in relevant icing conditions in the framework of SENS4ICE flight campaigns, planned for 2023.

Acknowledgment: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N° 824253 (SENS4ICE project).

How to cite: Zollo, A. L., Montesarchio, M., and Bucchignani, E.: In-flight icing: a remote detection tool based on satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7099,, 2022.

On-site presentation
Mark Prosser, Paul Williams, and Graeme Marlton

Although a previous study has created a global 2PVU diagnosed CAT climatology and North Atlantic trend analysis, one does not exist for a constant pressure surface on which aircraft typically cruise. Other previous work has shown a tripling in frequency of diagnosed CAT in climate model simulations with climate change. However, the question of whether an increase in global diagnosed CAT can already be seen in reanalysis since 1979, has not yet been addressed.

Here, we calculate 21 CAT diagnostics from ERA5 to produce a global 197hPa climatology of moderate/severe CAT exceedance frequencies for the period 1979-2020. Linear regressions are then performed to calculate trends in the frequency of diagnosed CAT over this period.

The results of the 197hPa climatology show three features of interest. Firstly, there appear to be roughly two distinct regimes of diagnosed CAT: a midlatitude regime and a tropical one. Secondly, diagnosed CAT appears to be more frequent over the oceans than the land and finally, mountain ranges appear to be hotspots of diagnosed CAT. The most significant result from the trend analysis is that North America and the North Atlantic have seen a substantial increase in the frequency of diagnosed CAT since 1979, far more than any other region worldwide.

The diagnosed CAT climatologies produced here are of general scientific interest to those researching turbulence theory or geophysical fluid dynamics. The result of the trend analysis will be of substantial interest and use to the aviation industry and aviation turbulence forecasters in long term planning and aviation safety.

How to cite: Prosser, M., Williams, P., and Marlton, G.: Examining Clear-Air Turbulence (CAT) climatologies and trends in ERA5 reanalysis (1979-2020), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8730,, 2022.

Virtual presentation
KyungHun Lee, ByungHyuk Kwon, SangJin Kim, Min Seong Kim, and YuJin Kim

The test operation of UHF wind profiler developed for 4 years from 2017 was carried out from March to September 2021. During the test operation, radio frequency interference (RFI) contamination was investigated in the spectrum data. We found discontinuous and overlapping RFI as well as the general form of RFI on continuous altitudes as the same magnitude. An algorithm was developed to remove them and to retrieve meteorological signal. Multi-interference refers to RFI that overlaps several times, such as discontinuous peaks by altitude or reappearing even after the first RFI is removed, unlike external RFI. After threshold filtering, a continuity check is checked on the gates that are identified as non-meteorological signals to determine the contamination by RFI. The contaminated spectrum is made noise to remove RFI and a new peak is derived. 5 points (±2 points) at the corresponding peak become noises by means of the noise-ization method. In addition, in a meteorological signal, 5 points are linearly interpolated on the gate identified as a meteorological signal after the continuity check. In order to prevent the actual meteorological signal from being removed, the continuity criterion was set to 15 gates or more for the vertical beam and 8 or more gates for the tilted beam (about 1/2, 1/4 based on the number of gates in low mode). In order to remove double and triple overlapping RFI, filtering and continuity are repeatedly tested until the peak is found below the reference point. For the newly derived peak in the iterative process, the spectral width was calculated using the single peak moment method and this is used as the threshold value.

How to cite: Lee, K., Kwon, B., Kim, S., Kim, M. S., and Kim, Y.: Quality Control of Radio Frequency Interference in UHF Wind Profiler Radar Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8969,, 2022.

On-site presentation
Raquel Evaristo, Ricardo Reinoso Rondinel, Felix Crijnen, Ju-Yu Chen, and Silke Trömel

Columns of differential reflectivity, the difference between the horizontal and vertical reflectivity,
hereafter Zdr columns, are vertical columns of enhanced Zdr that extend above the environmental 0°C
level. These are easily identified when observed by polarimetric radars. Physically, these columns consist
of rain dominated by large drops that are being lofted above the freezing level and have been recognized
as a proxy for the location of updrafts. Their potential for nowcasting severe weather has been shown in
several past studies. We have developed an algorithm that identifies and tracks Zdr columns from
volumetric radar data along with 3D wind fields from MultiDoppler analysis to spatially correlate Zdr
columns with updrafts. Since Zdr columns are a manifestation of an updraft, different Zdr columns
properties for example Zdr column maximum height, volume, and area are expected to be related to
updraft intensity levels. In turn intensification of updrafts, as indicated by changes in the Zdr columns
properties, should be translated in intensification of observed precipitation at the surface. For the
estimation of rain rates, we used a radar-based polarimetric approach, which will allow us to monitor the
temporal evolution for a number of identified convective rain cells. These cells will be identified from
summer events observed by the C-band polarimetric German network. For each cell, the properties of Zdr
columns are correlated with rain rate values. Similarly, correlations are also calculated for updraft
volumes, updraft intensity, and other updraft properties. For the nowcasting of observed rain rates, an
extrapolation algorithm based on spatial and temporal properties of rain was used. Preliminary results
have shown that higher precipitation rates are generally associated with Zdr columns, and cells without a
Zdr column produce lower precipitation rates, as expected. Zdr column height and volume show a
positive correlation with precipitation intensity at the surface. The time lag between the intensification of
the Zdr column and associated increase in precipitation at the surface varies significantly between cells,
but it is generally short compared to previous studies, varying mostly between 5 to 15 minutes. An early
identification of cells associated with ZDR columns could benefit the skill of the nowcasting of localized
rain cells, which often are smoothed during extrapolation.

How to cite: Evaristo, R., Reinoso Rondinel, R., Crijnen, F., Chen, J.-Y., and Trömel, S.: Information content of differential reflectivity columns for precipitation nowcasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11210,, 2022.

Virtual presentation
Simone Dietmüller, Sigrun Matthes, Volker Grewe, Hiroshi Yamashita, Katrin Dahlmann, and Patrick Peter

Aviation aims to reduce its climate impact by adopting climate-optimized aircraft trajectories, avoiding those regions of the atmosphere where aviation emission have a large climate impact. For this purpose, dedicated MET services have to be made available to the flight planning procedures, which need to be predicted with current numerical weather prediction models.

In order to represent spatially and temporally resolved information on the climate impact in terms of future temperature changes due to aviation emissions at a given time and location in such an advanced MET service, we propose to use algorithmic climate change functions (aCCFs) developed in earlier research projects. They include CO2 and non-CO2 effects, comprising nitrogen oxide (NOx), water vapour and contrail-cirrus. These aCCFs allow to derive such climate impact information for flight planning directly from operational meteorological weather forecast data. By combining the individual aCCFs of water vapour, NOx and contrail-cirrus, also merged non-CO2 aCCFs can be generated.

With this study we aim  to identify specific weather situations which have the potential to provide a robust climate impact reduction despite uncertainties. This work is part of the SESAR project FlyATM4E. For this purpose, a systematic analysis of the meteorological conditions and situations is required. We will present the characteristic water vapour, NOx induced and contrail-cirrus aCCFs for a set of specific weather patterns based on 2018 reanalysis data. A detailed analysis of the variation in aCCFs will be presented, including the dependency of individual and merged aCCFs to seasonal cycle, different synoptical weather situations and cruise altitude.



The current study has been supported by FlyATM4E project, which has received funding from the SESAR Joint Undertaking under grant agreement No 891317 under European Union’s Horizon 2020 research and innovation program. 

How to cite: Dietmüller, S., Matthes, S., Grewe, V., Yamashita, H., Dahlmann, K., and Peter, P.: Quantifying the spatial and temporal non-CO2 effect of aviation by using algorithmic climate change functions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12381,, 2022.

Presentation form not yet defined
Sigrun Matthes, Simone Dietmüller, Benjamin Lührs, Florian Linke, Volker Grewe, Feijia Yin, Federica Castino, Maximilian Mendiguchia Meuser, Manuel Soler, Abolfazl Simorgh, Katrin Dahlmann, Daniel Gonzales, and Hiroshi Yamashita

Aviation aims to reduce its climate impact, comprising CO2 and non-CO2 effects, by identifying climate-optimized aircraft trajectories. Such climate-optimized routes avoid regions of atmosphere where aviation emissions have a large impact on climate, e.g. by formation of contrails or strong NOx-induced ozone formation. Implementing such climate-optimized routings requires that air traffic management has spatially and temporally resolved information on these non-CO2 climate effects available during the trajectory planning process.

An overall modelling chain is required in order to expand the current flight planning procedure by considering climate impact during trajectory optimization in the overall optimization process. We explore a concept how to provide such information as an advanced MET Service: based on numerical weather prediction data and using algorithms climate change functions (aCCFs) such spatially and temporally resolved information can be provided. By integrating an uncertainty and risk analysis, we enable air traffic management (ATM) to identify climate-optimized aircraft trajectories which provide a robust and eco-efficient reduction in aviation’s climate impact. Climate optimization in this feasibility study, which is part of the SESAR ER project FlyATM4E, considers CO2 as well as non-CO2 effects, such as contrails and contrail-cirrus, water vapour, and NOx-induced effects on ozone and methane.

We will present the overall modelling concept which has been developed to explore climate-optimized aircraft trajectories considering individual weather situations in a series of one-day case studies. This concept also explores the robustness of estimated benefits in terms of mitigation of climate effects. The approach comprises a comprehensive uncertainty analysis, that provides alternative estimates as upper and lower limit estimates to reflect low level of scientific understanding or unknown efficacy of individual effects, resulting from state-of-the-art understanding from climate science. We also explore how to incorporate different physical climate metrics, as well as the usage of ensemble forecast data. We will present how these individual sources of uncertainty are statistically combined in order to provide a risk analysis together with the performance analysis of the identified alternative trajectory solutions, and hence identify robustness of mitigation gains on alternative trajectories. Finally, we will present an verification concept relying on numerical global chemistry-climate modelling with EMAC in order to explore such alternative routings during a one-year simulation.

The current study has been supported by FlyATM4E project, which has received funding from the SESAR Joint Undertaking under grant agreement No 891317 under European Union’s Horizon 2020 research and innovation program. High-performance computing simulations with the chemistry-climate model EMAC were performed at the Deutsches Klima-Rechenzentrum (DKRZ), Hamburg.

How to cite: Matthes, S., Dietmüller, S., Lührs, B., Linke, F., Grewe, V., Yin, F., Castino, F., Mendiguchia Meuser, M., Soler, M., Simorgh, A., Dahlmann, K., Gonzales, D., and Yamashita, H.: Climate optimized aircraft trajectories and risk analysis of climate impact mitigation: FlyATM4E, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12792,, 2022.