a comparative study of sodar, lidar wind ...the region of bucharest airports were published by...

Romanian Journal of Physics 65, 810 (2020)

A COMPARATIVE STUDY OF SODAR, LIDAR WIND MEASUREMENTS AND AIRCRAFT DERIVED WIND

OBSERVATIONS

LIVIUS BUZDUGAN1,2, SABINA STEFAN1* 1 University of Bucharest, Faculty of Physics, P.O.BOX MG-11, Magurele, Bucharest, Romania

2 ROMATSA, 10 Ion Ionescu De la Brad Str., Bucharest, Romania E-mail: [email protected]

* Corresponding author: [email protected]

Received December 4, 2019

Abstract. The paper is focused on the comparison between Sodar and Lidar wind measurements and high resolution wind observations derived from Mode-S data sent by aircraft arriving at and departing from Bucharest Henri Coanda airport (LROP) to an air traffic control radar. Between 7–9 January 2018, the two prevailing wind directions – with their corresponding configurations of runways – manifested themselves at the airport as westerly winds veered to easterly, with colder air flowing in the Romanian Plain behind a cold front that swept up north. The results show that, for both prevailing wind directions, Sodar and Lidar wind profiles within the 40–600 m height domain are representative of the aerodrome and vicinity in non-convective conditions. Similarly, the “virtual” wind profiles obtained from wind data derived from Mode-S data of aircraft flying in the aerodrome area offer a possible alternate solution, where remote sensing instruments are not available. The aim of the study was to assess how the synergy of these instruments and data sources can service the detection of low level wind shear and monitoring of the wind field in the terminal area of the aerodrome.

Key words: wind profiles collocation, Romania.

1. INTRODUCTION

The potentially adverse effects on flight safety of wind shear to aviation relate to its effect on aircraft performance. Although it may be present at all levels in the atmosphere, the occurrence of wind shear in the lowest 500 m is of particular importance to aircraft landing and taking off. During the initial climb-out and approach phases, aircraft fly at low heights and near critically low airspeeds, therefore being especially exposed to the most adverse effect of wind shear: sharp variations of lift force [1]. Low-level wind shear (LLWS) at the airports or in their vicinity has been cited in a number of aircraft accidents/incidents and is considered by the aviation community be one of the major technical problems facing aviation [1].

Remote-sensing techniques such as Doppler radar, Lidar and, to some extent, Sodar, have been increasingly used, sometimes in integrated systems, at a number of airports worldwide for detecting LLWS and for providing information to pilots and air traffic controllers [1].

Among the variety of phenomena that can cause LLWS, thunderstorms and low-level jet streams (LLJ) are high ranking. Using Lidar observations, Weipert et al.

Article no. 810 L. Buzdugan, Sabina Stefan 2

(2014) [2] found that at Munich and Frankfurt airports most cases of LLWS were associated to LLJ. During the last two decades, LLJs were studied using both Lidar [3, 4] and Sodar [5, 6]. Studies related to the synoptic context of LLJ occurrence in the region of Bucharest airports were published by Balmez et al. [7, 8].

During the last decade, a new source of wind observations of high temporal and spatial resolution at heights ranging from the surface to the upper troposphere and lower stratosphere emerged: Mode-Select Enhanced Surveillance (Mode-S EHS) data, obtained from reports sent by aircraft in response to interrogations of Secondary Air Traffic Control Surveillance radars (SSR). These reports contain the aircrafts speed, direction, altitude and Mach number measured by sensors to determine the aircraft flight status [9]. Wind velocity and temperature can be derived from the reports, which can be obtained from the SSR [10]. The use of aircraft based observation data has continuously expanded during the last decades, their quality within the boundary layer being investigated in the case of Aircraft Meteorological Data Relay (AMDAR) – meteorological observations automatically made by commercial aircraft at predetermined times and relayed to the ground [10, 11, 12] and in [13, 14, 15] for Mode-S EHS.

To reduce the risk of LLWS affecting operations at LROP by increasing its probability of detection, ROMATSA (the Romanian National Air Navigation Services provider) is currently deploying a wind monitoring and wind shear detection system based on the use of a scanning Doppler Lidar system and of a Doppler Sodar system.

The aim of this paper is to assess how the synergy of these instruments and data sources can service the detection of low level wind shear and monitoring of the wind field in the terminal area of the aerodrome. The paper is structured as follows. The instruments, their locations and meteorological conditions are presented in the part 2.1 of Section 2 – Data and Methodology. In the same section are discussed, in the parts 2.2 and 2.3, data from Sodar and Lidar and how the wind observations are derived from Mode-S data, as well as the collocation algorithm used to generate comparable Lidar, Sodar and Mode-S derived wind data series. The Section 3 is dedicated to the results of the statistical analysis of the Lidar, Sodar and Mode-S derived wind data sets. The concluding remarks end the paper.

2. DATA AND METHODOLOGY

The comparative study between the Sodar and Lidar – measured wind data and Mode-S derived wind data was conducted during January 2018, as part of the initial operational assessment of the Sodar system. Thus, between 7–9 January 2018, our study captured two wind direction regimes and consequent runway operational setups. Initial westerly winds veering to easterly, starting from the eastern part of the area, as colder air flowed in the Romanian Plain behind a cold front that swept north of the area. This led to the occurrence of "type III" LLJ, according to [8] (Fig. 1).

3 A comparative study between the Sodar/Lidar measurements wind Article no. 810

Fig. 1 – (Color online) left: Windgram time-height display of wind speed and wind vectors,

1000–925 hPa layer, Bucharest, 07.01.18/06z–09.01.18/06z, generated using Copernicus Climate Change Service Information [2019]); right: Sodargram of u wind component, 40–600 m height,

07.01.18/06z–09.01.18/06z generated using METEK Graphics. The study used 3 wind data series – of Sodar and Lidar measurements and

collocated Mode-S derived wind observations, obtained by the methods described in 2.2.1 and 2.3.

2.1. INSTRUMENTS AND MEASURING SITES

The remote sensing instruments used in this study were a METEK PCS.2000-64 Doppler monostatic Sodar and a Halo Photonics Stream Line Doppler, located at 44.57N, 26.13E (elevation: 95m), 1 km east of one of the runways of LROP.

A Sodar is a ground-based remote-sensing instrument for measuring wind based on turbulence in the lower atmosphere. A monostatic Sodar emits short acoustic pulses into the atmosphere and receives backscattered sound generated by small-scale density fluctuations associated only with thermal inhomogeneity of the air [16], generated by turbulence in the presence of a temperature gradient. The change in the frequency produced by a scatter is proportional to the rate of change of the distance between receiver and scatterer and the initial frequency. This frequency change is measured and the motion of the scatter relative to the transmitter-receiver can be calculated.

The transmitted signals can be phase shifted to point the sound beam in different directions. The ranging of the measurement volume is determined from the propagation time of the acoustic wave and the estimated acoustic velocity. By measuring the Doppler shift for different beam directions, the full 3-dimensional wind at specific heights can be determined, with the assumption of a horizontally homogeneous flow in the measurement volume [17].

The Sodar was operated with a vertical resolution of 30 m, a first range gate (minimum height) of 40 m and a potential maximum range of 610 m. The zenith angle of the sound beams was set to 17° and the integration time to 10 min.

The Sodar is particularly appropriate for monitoring the development of the LLJ, due to its operability in low cloud or low visibility conditions that may accompany low-level temperature inversion and frontal related LLJ, thereby


limiting the line-of-sight Lidar measurements. A scanning Doppler Lidar was also temporarily installed at the location, for test purposes.

The Stream Line Doppler Lidar operates at a wavelength of 1.5 μm using the heterodyne technique to detect the Doppler shift of the signal backscattered by atmospheric aerosol. The pulse repetition frequency was set to 15 kHz and the pulse length to 140 ns, hence a line-of-sight effective resolution of 21 m. The Lidar was operated in a velocity azimuth display mode, with 6 azimuth steps of 60° and a zenith angle of 15°. Thus, the Lidar measurement volume is comparable to that of the Sodar measurement. Also, the Lidar integration time was set to 10 min. The “overlapping mode” was activated, i.e. consecutive height ranges were shifted only by 3 m, reducing the potential range to 900 m. At some times during the study, the detection range decreased below this value because of fog or low clouds. Liquid clouds are excellent Lidar targets, but strongly attenuate the Lidar signal, thereby usually limiting the detection range to their bases [18]. Similarly, the Sodar detection range can be reduced by increased attenuation and inhibited turbulence in thermal inversion layers and during increased background noise that can be caused by strong wind, heavy rain and hail.

The SSR used in this study is sited near LROP and operated by ROMATSA. The typical time resolution of the Mode-S reports is 5 s. For the typical aircraft descent rate in the final approach phase, this corresponds to a layer of ~20 m depth. The Mode-S data were collected from a spatial domain bounded horizontally by 44.0 N–45.0 N and 25.0 E–27.0 E and vertically by an altitude of 914 m (3000 ft).

Fig. 2 – (Color online) Height versus distance from instrument site of the Mode-S Sodar collocated observations, denoting specific flight slopes for eastbound (red) and westbound (blue) initial ascent

(top-right) and final descent flight phase (top-left) and horizontal projections of positions of the Mode-S – Sodar collocated observations, denoting specific flight trajectories

for ascending (bottom-right) and descending aircraft (bottom-left).


As shown in Fig. 2, the typical tracks of aircraft flying in the studied domain, i.e. aircraft approaching (descending to) or departing (ascending from) LROP, are approximately zonal, due to the orientation of its 2 parallel runways (84 deg – 264 degrees true [19]). For approaching and departing aircraft, westerly winds are commonly associated with westbound flights and easterly winds with eastbound flights, due to the need to fly with head wind, in order to maintain enough airspeed and lift force at low ground speeds.

2.2. MODE-S DATA

The following Mode-S data were used: a) ground speed (Vg) – the projection of the aircraft ground relative velocity onto the horizontal plane; b) true airspeed (Va), (the magnitude of the aircraft velocity relative to a reference system that moves with the wind at the aircraft position); c) true track angle (α), the angle between the projection of the ground relative velocity onto the horizontal plane and the geographic meridian; d) magnetic heading (MH), the angle between the projection of the air relative velocity onto the horizontal plane and the magnetic meridian; e) roll angle [10] was used to ensure only aircraft data with roll angles less than 3 degrees were used in the study [20].

The EHS Mode-S data were obtained from a number of 330 aircraft landing and 240 aircraft taking off at LROP airport, between Jan 7 2018, 1300 UTC – Jan 9 2018, 0600 UTC. The total number of Mode-S derived – Sodar collocated wind observations was 8837, from which 1054 in ascending flights. The total number of Mode-S derived – Lidar collocated wind observations was 5114, from which 387 in ascending flights.

2.2.1. Wind velocity calculation

The wind velocity was calculated as a difference vector of ground relative and air relative velocities according to [10].

Therefore, for normal level flight, the wind speed (V) – defined as the magnitude of the horizontal wind velocity – which is invariant to rotations of the coordinate system, is given by:

2 2 2 cos( )V Vg Va Vg Va MTA MH (1)

where MTA (magnetic track angle) = α – MD, where MD is magnetic declination.

On the same grounds, we calculated the magnetic wind direction (WDM):

WDM (deg) = (360° + 270° – (DEG (ATAN2 (v, u)))) mod 360° (2)

where u and v are the wind velocity components in the coordinate system using the magnetic north:


u = Vg · sin (MTA) – Va · sin (MH) (3)

v = Vg · cos (MTA) – Va · cos (MH) (4) ATAN2 (y, x) denotes the function that returns the angle of the vector defined

by the coordinates (x, y) with the x axis and DEG denotes the function of conversion of a radian value to hexadecimal degrees.

Conversion from the magnetic wind direction to true wind direction used the magnetic declination obtained from the international geomagnetic reference field-IGRF [21]. This is about 6 degrees E for LROP area, so that the true wind direction (TWD, degrees) was obtained by applying:

TWD = (WDM + 6) mod 360o. (5)

2.3. COLLOCATION ALGORITHM

In order to make the aircraft wind observations comparable with Sodar and Lidar wind measurements, one will need to define a collocation algorithm, requiring firstly the computation of aircraft height.

The correction of the barometric altitude from the Mode-S data to the real height h was inferred from the definition of QNH pressure [22], knowing that the aircraft altitude transmitted in Mode-S reports is referenced to the standard sea level pressure [23] in the international standard atmosphere (i.e. 1013.25 hPa) [24]:

h = int (s_lev)·33 + (qnh – 1013)·8 – ad_elev, (6)

where:

h = aircraft height, in meters s_lev = aircraft barometric altitude from Mode-S messages, in hundreds of

feet [9] ad_elev = LROP aerodrome elevation; the value of 96 m (314 ft) published

by ROMATSA (2019), was rounded to 100 m qnh = QNH atmospheric pressure extracted from LROP METAR reports, in hPa The formula (6) assumes a vertical pressure gradient of 1 hPa each 8 m, which

is acceptably accurate in the standard atmosphere, at low altitudes [24]. The aircraft height h obtained according to eq. (6) was assigned to a height

gate of Sodar measurements Hs (40 m, 70 m, 100 m ... 610 m), using:

Hs = INT (40 + 30 ROUND ((h – 40) / 30), (7)

where INT denotes the integer part function and ROUND denotes the function of rounding to the nearest integer.


Similarly, aircraft heights were assigned to a height gate of Lidar measurements Hl (12 m, 64 m, 116 m ... 584 m), using:

Hl = INT (12 + 52 ROUND ((h – 1) / 52) (8)

Consequently, the height difference between collocated Mode-S and Sodar

observations is less than 15 m, the height difference between collocated Mode-S and Lidar observations less than 26 m, but this will result in an increased number of discarded Mode-S observations, if no instrumental wind data is available for the specific gate height corresponding to a Mode-S observation height. We noted the algorithm used in [15] selected the Sodar observation with the closest height gate to h, thereby allowing more than one Sodar gate height to be used.

Also, the time of the Mode-S derived wind data was rounded to the nearest 10 min. Sodar and Lidar measurement time, etc. in order to be assigned to the time of a instrument observation. In this way, some of the Mode-S derived wind data were assigned to instrumental wind observation times and gate heights i.e. collocated. It follows the time difference between collocated Sodar/Lidar and Mode-S derived observations is less than 5 min., as in [15].

Thus, series of collocated Sodar, Lidar and Mode-S wind observations were constructed. One should keep in mind this is not a triple collocation [15], as the times and positions of collocated Mode-S – Sodar observations may well differ from the times and positions of collocated Mode-S – Lidar observations, e.g. due to different conditions for measured value availability for the two instrument types.

No collocation condition was imposed to the horizontal separation of the instrument site and Mode-S observations.

Using the aircraft geographic coordinates from the Mode-S data retrievals, the horizontal distance between the Sodar and the aircraft and the Mode-S wind data determination point was calculated. As shown in the next section, the geometry of the flight trajectories in the airport area (see Fig. 2) and the Sodar maximum detection height limit the horizontal distance between the Sodar site and any Mode-S EHS observation to ~16 km, compared to 5 km – used as collocation limiting distance in [15].

No smoothing, by time or height averaging, was performed on the wind data, either instrumentally measured or Mode-S derived, as opposed to [15], where a combination of linear regression and a running average over a time window was used to smooth the time-series of aircraft reported true airspeed and Mach number.

In comparing Mode-S derived wind with Sodar and Lidar measured wind, one should take into account the averaging interval. The Sodar and Lidar wind measurements are averaged over 10 minutes, while the Mode-S derived winds are rather instantaneous. Therefore, the natural dispersion of such values around the average was considered. Apart from the mean difference (bias), the standard deviation of the wind speed and wind direction difference between the Mode-S derived and instrument measured wind, were calculated for the whole dataset and


for defined samples of ascending/descending and eastbound/westbound flying aircraft: a) for each 10 min. instrument measurement interval for a height gate hi; b) for each height gate over all measurement times and c) over the entire data sample.

3. RESULTS

In this study, wind data derived from aircraft responding to EHS Mode-S interrogations of a SSR sited near LROP were compared with Sodar and Lidar wind measurements during two moderate easterly and westerly wind spells between Jan 7–9th 2018.

The representativity of Sodar and Lidar wind measurements was investigated through the dependence of the wind speed difference of collocated Mode-S – Sodar or Lidar observations with the aircraft distance from the instrument site. No statistically significant dependence on the distance to the instrument site appeared, either for ascending or descending aircraft, in both aircraft flight directions (Fig. 3, Fig. 4). This is in spite of the higher horizontal distance threshold for collocated observations than the one used in [15], but it is noteworthy that the current comparison was made in wintertime conditions, thereby reducing the spatial variability of wind associated to convective gusts to a minimum.

Fig. 3 – Sodar-Mode-S derived wind speed versus distance from sodar site, westbound (left)

and eastbound (right) descending flights.

Fig. 4– Lidar-Mode S derived wind speed versus distance from Sodar site, westbound (left)

and eastbound (righ) descending flights.


The biases and standard deviations of wind speed and wind direction and the vector differences between EHS Mode S-derived winds and Sodar or Lidar measured winds are presented in Table 1.

Table 1

Mode-S derived wind versus Lidar and Sodar measured wind data statistics summary

Instrument compared

against

Flight direction

Speed bias

[m s–1]

σ speed difference

[m s–1]

Direction bias

[deg]

σ direction difference

[deg]

Module vector

difference [m s–1]

Lidar all flights 1.0 1.9 11 36 0.6 Lidar eastbound 0.9 1.9 13 33 1.8 Lidar westbound 1.2 1.8 9 39 1.9 Sodar all flights 0.6 1.7 23 39 0.8 Sodar eastbound 0.6 1.7 26 39 1.8 Sodar westbound 0.8 1.7 14 36 2.1

The Mode-S derived wind speeds show a slight positive speed bias versus

both the Sodar and Lidar wind measurements (Fig. 5, Fig. 6, Table 1). The higher bias of wind speed derived from EHS Mode-S data from ascending aircraft can be attributed to the overestimation of calculated wind speed, due to higher pitch angles [10]. This will make object of a future paper.

Fig. 5 – (Color online) Profiles of average sodar (top) and lidar (bottom) (red, dashed) and Mode-S

derived (blue, solid) wind speed and their difference (brown, dotted), descending (left) and ascending (right), westbound flights.


Fig. 6 – (Color online) Profiles of average Sodar (top) and Lidar (bottom) measured (red, dashed) and Mode-S based (blue, solid) wind speed and their difference (brown, dotted), descending (left)

and ascending (right), eastbound flights. The Sodar and Mode-S derived wind directions show a good agreement (Fig. 7),

again with no significant dependence on the distance to the Sodar. We note that, for westbound ascending aircraft, the cluster of mainly Sodar NE directions was captured when the westerly winds veered to easterly, progressively from east to west of the area. The Sodar first detected this direction change due to its easterly position-whereas, the operational direction of runway use remaining westbound, some aircraft climbed westward in winds from N–NW, at 6–10 km distance from the Sodar.

As for the collocated Mode-S observations per Sodar and Lidar height gate and per measurement time (10 minutes), one can estimate their maximum number starting from the maximum number of aircraft approaching to land during the 10 min. duration of a Sodar measurement. This is limited by the minimum separation time of 2 minutes imposed on approaching aircraft [25] to 5 aircraft descending during a 10 min. Sodar measurement time through the 40–600 m height domain. Considering a typical value of ground speed of 70 ms–1 and a descent glide slope of ~3 degrees, according to the published instrument approach charts [19] (see Fig. 2), the average vertical component of descending aircraft ground velocity can be estimated to 3.5 ms–1.

Given the 4 s Mode-S interrogation period of the SSR, an estimate of 2 data points per every 30 m Sodar height gate per aircraft is obtained, leading to an estimated upper limit of 10 collocated observations per Sodar/Lidar height gate at a particular observation time. Due to lower values of the descending vertical velocity


component of aircraft at low heights, this number was observed to exceed 10 in the lower height gates (the maximum value was 22, at 70 m height).

Fig. 7 – (Color online) sodar (blue filled diamonds) and Mode-S derived (red empty squares) wind

directions for ascending (top) and descending flights (bottom) of collocated Mode-S – Sodar observations versus their distance from the Sodar site, westbound (left) and eastbound (right) aircraft.

For ascending aircraft, the higher speed values and steeper ascent slopes at lower

levels mean a high rate of ascent, fewer Mode-S observations being assigned to any particular Sodar height gate. Fig. 8 shows the number of collocated observations becoming significant only above 200 m. The combined availability profiles of Mode-S observations and Sodar and Lidar wind data (Fig. 9) determines a maximum of data counts near 400 m height.

De Haan noted that the wind observations derived from two successive Mode-S EHS reports, from a single aircraft, can exhibit large fluctuations. These fluctuations occur due to the precision of the reported true airspeed of c. 1 ms–1, upon which the observation errors between two successive reports for the horizontal wind were estimated in the range 0.5 ms–1 to 1.5 ms–1 [13]. This was confirmed in our study by the standard deviations of the Mode-S derived wind speed within a Sodar or Lidar observation time shown in Fig. 10: 0.5–1 ms–1 for descending flights and 0.5–1.5 ms–1 for ascending flights.

The standard deviations of the wind speed differences (Table 1) are in agreement with the results of the comparisons between Lidar and EHS Mode-S derived wind performed at Frankfurt and Munich airports [26] and (albeit slightly larger, probably due to the contribution of ascending aircraft) with the results in


[15], estimating the Mode-S EHS wind observation error of wind speed to be less than 1.4 ± 0.1 ms–1 near the surface.

Fig. 8 – (Color online) Data points number of Mode-S – Lidar (top) and Sodar (bottom) collocated observations vs. height, eastbound (blue, solid) and westbound (red, dotted) descending (left) and

ascending aircraft (right).

Fig. 9 – (Color online) Variation with height of Sodar (red, dotted) and Lidar (blue, solid) measured

wind speed availability (0–600 m); the relatively lower availability of Lidar data was caused by the temporary occurrence of low cloud and fog between 08.01.18 12z and 09.01.18 06z,

reducing the lidar range below 300 m. The positive bias of Mode-S derived wind direction independently of the

flight direction and of the compared instrument is consistent with a directionally dependent systematic error. Errors propagating from the magnetic heading errors only a wind vector components transversal to the aircraft direction of travel [14], thereby inducing such a type of error. The fact that the bias values of Mode-S derived


wind directions are higher in the case of Sodar collocated observations than in the case of Lidar collocated observations (Table 1) may indicate a combined Sodar orientation – Mode-S error propagation that will be further investigated.

Fig. 10 – (Color online) Height profile of average standard deviation of Mode-S derived wind speed

Sodar-(top) and lidar (bottom) collocated observations (within a Sodar/Lidar observation time) for westbound (red, dotted) and eastbound (blue, solid) descending ( left)

and ascending (right) flights.

4. CONCLUDING REMARKS

The paper proves that Sodar and Lidar wind profiles within the 40–600 m height domain are representative of LROP aerodrome and its vicinity – up to a 16 km radius, in wintertime, non-convective conditions, with the exception of the situations when a surface of wind discontinuity crosses the area.

The wind calculations based on Mode-S data from aircraft in the initial climb phase of the flight tend to overestimate the wind speed, possibly due to higher pitch angles and vertical component of aircraft speed, as indicated in Painting [10].

Sodar versus Mode-S wind speed bias values were found in agreement with the determined the uncertainty of the Sodar measured wind speed (0.5 ms–1, according to METEK Gmbh manual [17], while the speed standard deviations and vector differences for LROP, are in agreement with the uncertainty level of aircraft derived wind data, as determined in Painting [10] and also in de Haan (2011) [13].


The horizontal Mode-S derived wind direction bias is consistent with a systematic error of wind-components transversal to the aircraft’s direction of travel, as found by S. de Haan (2011) [13]. This systematic error will be further analyzed.

Acknowledgements. The authors grateffuly acknowledge ROMATSA for the provision of the

data used in the scientific paper.

REFERENCES

1. International Civil Aviation Organization (ICAO), Manual on Low-Level Wind Shear, doc. 9817 edn.1, 2005.

2. A. Weipert, S. Kauczok, R. Hannesen, T. Ernsdorf, B Stiller, Wind shear detection using radar and lidar at Frankfurt and Munich airports, 8th European Conference on Radar in Meteorology and Hydrology, Garmisch-Partenkirchen, Germany, 2014.

3. R. M. Banta, Y. L. Pichugina, N. D. Kelley, B. Jonkman, and W. A. Brewer, Doppler lidar measurements of the Great Plains low-level jet: Applications to wind energy. In IOP Conference Series: Earth and Environmental Science, vol. 1, no. 1, p. 012020. IOP Publishing, 2008.

4. Y. Wang, C. L. Klipp, D. M. Garvey, D. A. Ligon, C. C. Williamson, S.S. Chang, R.K. Newsom, R. Calhoun, Nocturnal low-level-jet-dominated atmospheric boundary layer observed by a Doppler lidar over Oklahoma city during JU2003, J. Appl. Meteorol. Climatol. 46:2098–2109 (2007).

5. M. A. Kallistratova, R. D. Kouznetsov, D. D. Kuznetsov, I. N. Kuznetsova, M. Nakhaev & G. Chirokova, Summertime low-level jet characteristics measured by sodars over rural and urban areas, Meteorologische Zeitschrift 18(3), 289–295 (2009).

6. M. A. Kallistratova, R. D. Kouznetsov, Low-level jets in the Moscow region in summer and winter observed with a sodar network, Boundary-layer meteorology 143(1), 159–175 (2012).

7. M. Balmez, F. Georgescu, The low-level jet for Bucharest’s airports – a study of its characteristics in winter season between 1959 and 1982, Romanian Reports in Physics 67(2), 638–652 (2015).

8. M. Balmez, S. Stefan, On the formation mechanism of low-level jet over Bucharest’s airports, Romanian Journal of Physics 59(7–8), 792–807 (2014).

9. International Civil Aviation Organization (ICAO), Technical Provisions for Mode S Services and Extended Squitter, Technical Report, doc 9871 edn.2, 2017.

10. J. D. Painting, WMO AMDAR reference manual, WMO-958, WMO, Geneva, Switzerland (2003). 11. C. Drüe, W. Frey, A. Hoff & T. Hauf. Aircraft type‐specific errors in AMDAR weather reports from

commercial aircraft, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography 134(630), 229–239 (2008).

12. C. Drüe, T. Hauf, A. Hoff, Comparison of boundary-layer profiles and layer detection by AMDAR and WTR/RASS at Frankfurt airport, Boundary-layer meteorology 135(3), 407–432 (2010).

13. S. de Haan, S, High-resolution wind and temperature observations from aircraft tracked by Mode-S air traffic control radar, J. Geophys. Res. 116 (2011).

14. S. de Haan, An improved correction method for high quality wind and temperature observations derived from Mode-S EHS, KNMI (2013).

15. S. de Haan, Estimates of Mode-S EHS aircraft-derived wind observation errors using triple collocation. Atmospheric Measurement Techniques 9(8), 4141–4150 (2016).

16. C.G. Little, Acoustic methods for the remote probing of the lower atmosphere, Proc. IEEE 57, 571–578 (1969).

17. METEK Gmbh, PCS.2000-24/64/MF/LP Sodar User Manual (2017). 18. M. Tuononen, E.J. O’Connor, V. A Sinclair, & V. Vakkari, Low-level jets over Utö, Finland,

based on Doppler lidar observations, Journal of Applied Meteorology and Climatology 56(9), 2577–2594 (2017).


19. ROMATSA, Aeronautical Information Publication, www.aisro.ro (2019). 20. B. Strajnar (2012), Validation of Mode-S Meteorological Routine Air Report aircraft observations,

J. Geophys. Res. 117, D23110, doi: 10.1029. 21. E. Thébault, C. C. Finlay, C. D. Beggan, P. Alken, J. Aubert, O. Barrois & E. Canet. International

geomagnetic reference field: the 12th generation. Earth, Planets and Space 67(1), 79 (2015). 22. World Meterological Organization (WMO), Guide to Meteorological Instruments and Methods of

Observation, WMO-No.8, Geneva, Switzerland, 2014. 23. International Civil Aviation Organization (ICAO), Annex 10 to the Convention on International

Civil Aviation, Aeronaut. Telecommun. Ser., vol. III, Montreal, Quebec, Canada, 1995. 24. International Civil Aviation Organization (ICAO), Manual of the ICAO Standard Atmosphere:

extended to 80 kilometres (262 500 feet). Third edition, Doc. 7488, Montreal, Quebec, Canada, 1993.

25. International Civil Aviation Organization (ICAO), Procedures for Air Navigation Services – Air Traffic Management, doc. 4444 edn.16, 2016.

26. T. Ernsdorf, B.R. Beckmann, Testing the performance of radar and lidar vertical wind shear detection at Frankfurt and Munich airports, 17th Conference on Aviation, Range and Aerospace Meteorology (ARAM), 2015.

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