Airborne Wind Lidar Measurements of Vertical and Horizontal Winds for the Investigation of Orographically Induced Gravity Waves
1. Introduction
Internal waves are waves that oscillate within a stratified fluid. If the fluid is considered to be the atmosphere and the restoring force of vertical displaced air parcels is provided by buoyancy, such waves are called internal gravity waves or just gravity waves (GWs). GWs are ubiquitous in the atmosphere and their impact on the vertical transport and exchange of energy and momentum between the troposphere and the middle atmosphere is well known (Fritts and Alexander 2003). GWs are commonly excited in the troposphere by flow over orography (e.g., Smith et al. 2008; Teixeira 2014), convection (e.g., Vadas et al. 2012), or flow deformation, for instance, caused by jets and fronts (Plougonven and Zhang 2014). Although there is a general understanding of processes launching GWs, the nature of wave source spectra is more complex and less well understood. For example, steady flow over topographic features will launch GWs of zero ground phase velocity (Smith 1989). However, the spectrum generated by unsteady flow over complex topography and the associated nonlocal effects are much less well understood (Chen et al. 2007). Thus, a better characterization of GW sources is still an outstanding issue needed for a proper description of the dynamical coupling of the lower and middle atmosphere.
To study the entire life cycle of GWs starting from their generation at low altitudes over their propagation and finally dissipation, the Gravity Wave Life-Cycle (GW-LCYCLE) I field campaign was conducted from 2 to 14 December 2013 in northern Scandinavia (Wagner et al. 2017; Ehard et al. 2016). As it has been shown in the past, the region above the Scandinavian Mountains is well suited for studies of coupling between the troposphere and the middle atmosphere (Dörnbrack et al. 2001), and the region is promising because of the north–south orientation of the Scandinavian mountain ridge and the accompanying mountain wave generation induced by westerly blowing winds. Besides a variety of ground-based instruments, the German Aerospace Center [Deutsches Zentrum für Luft- und Raumfahrt (DLR)] deployed the Falcon research aircraft, equipped with a coherent Doppler wind lidar (DWL) measuring horizontal and vertical wind speeds. A detailed summary of the GW-LCYCLE I campaign, including an overview of airborne observations, numerical simulations, and a discussion of the synoptic situation during the campaign period, has recently been provided by Wagner et al. (2017) and Ehard et al. (2016). In this study, the DWL measurements performed during GW-LCYCLE I are discussed in detail and shown to be a valuable tool for GW source spectra characterization.
Lidar instruments in general allow for deriving various atmospheric parameters range resolved and thus enable investigating GW characteristics in several altitudes simultaneously. Usually, the temperature perturbations derived from ground-based Rayleigh lidar or resonance-lidar data are used for that purpose (Baumgarten 2010; Hildebrand et al. 2012). For instance, Kaifler et al. (2015) used temperature perturbations derived from Rayleigh lidar measurements (28–76 km altitude) to characterize GWs over New Zealand and showed that enhanced GW potential energy densities in the mesosphere are surprisingly associated with mountain waves excited by only low to moderate tropospheric wind speeds between 2 and 12 m s−1.
Although the aforementioned lidar technique represents a valuable tool to characterize GWs, it is mostly limited to nighttime operation and altitudes above 20 km. Thus, no information about the excitation region at lower altitudes can be derived, which is needed to distinguish different excitation sources (e.g., flow over orography, convection, flow deformation) and to study GW propagation involving processes such as secondary wave generation in the tropopause region or reflection/secondary wave generation in the lower stratosphere (Smith et al. 2008). Moreover, ground-based lidar measurements cannot be used to study the spatial evolution and distribution of GWs. An airborne DWL system, however, yields line-of-sight (LOS) wind speed measurements per 1 s, and hence with a horizontal and vertical resolution of a few hundred meters, making it a promising instrument for accurate GW characterization in the troposphere.
The usefulness of airborne vertical wind measurements was, for instance, demonstrated by Whiteway et al. (2003) and Duck and Whiteway (2005), who studied spectra of GWs, turbulence, and GW breaking at the tropopause region by means of in situ data acquired on different flight levels. Compared to such kinds of in situ measurements, horizontal and vertical winds can be measured by lidar at several altitudes simultaneously. Bluman and Hart (1988) used airborne Doppler wind measurements (from 3 km to the ground) to validate linear lee-wave model calculations, Weissmann et al. (2005a) investigated the vertical transport from the boundary layer into the free troposphere, and Kiemle et al. (2007) made use of airborne DWL data in combination with water vapor measurements of a differential absorption lidar in order to estimate the latent heat flux in the boundary layer. Recently, Chouza et al. (2016) showed that vertical wind speed can be retrieved from airborne DWL measurements with a mean systematic uncertainty of 0.05 m s−1 and that the data are valuable for characterizing island-induced GWs. They also revealed that adequate corrections of horizontal wind projections onto the LOS direction have to be done in order to retrieve reliable vertical wind speeds from airborne DWL measurement data.
In this paper, the setup, the measurement procedures, and corresponding data retrieval and correction methods of DLR’s airborne coherent DWL are discussed. Although the DWL has been in operation since 1999, no detailed description of the optical layout and the retrieval procedures has been published so far. For the first time, ECMWF horizontal wind data are used to correct LOS wind speeds in order to retrieve vertical wind with a mean bias of less than 
2. Instrument description
Over the years, DLR’s coherent DWL system has been successfully deployed in several ground-based and airborne field campaigns targeting various objectives, such as measuring aircraft wake vortices (Köpp et al. 2004), aerosol optical properties (Chouza et al. 2015), and horizontal wind speeds over the Atlantic Ocean as input data for assimilation experiments (Weissmann et al. 2005b). A general overview of DWL applications for atmospheric research and an overview of previous airborne campaigns with DLR’s coherent DWL are given by Reitebuch (2012). Recently, the system was used in the framework of three airborne field campaigns aiming to characterize the life cycle of GWs namely during the GW-LCYCLE I campaign (Wagner et al. 2017; Witschas et al. 2016), the Deep Propagating Gravity Wave Experiment (DEEPWAVE) campaign (Fritts et al. 2016; Smith et al. 2016), and the GW-LCYCLE II campaign.
To characterize orographically induced GWs—so-called mountain waves—both horizontal and vertical wind measurements with high horizontal and vertical resolution and a low statistical uncertainty are desired. All of these goals are met with the DWL. Horizontal and vertical winds are retrieved by either applying the velocity–azimuth display (VAD) technique (Browning and Wexler 1968) or steering the beam to nadir direction, leading to a horizontal resolution of about 9 or 0.2 km, respectively. The vertical resolution of 100 m for both measurement modes is determined by the laser pulse length.
A schematic block diagram of the DWL system is shown in Fig. 1. The transceiver was developed and built by CLR Photonics (today Lockheed Martin Coherent Technologies) (Henderson et al. 1991, 1993; Hannon and Henderson 1995); the double-wedge scanner system and the data acquisition unit were developed at DLR.
Simplified sketch of DLR’s coherent DWL system (not to scale) indicating the transceiver unit, including local oscillator (MO), slave oscillator (SO), acousto-optical modulator (AOM), reference pulse detector (REF), polarizing beam splitter (PBS), and detector (DET). Additionally, the acquisition chain, including signal amplifier (AMP), data acquisition unit (DAQ), housekeeping data acquisition unit (HK), global positioning system (GPS), inertial reference system (IRS) and the beam-expanding telescope and the double-wedge scanner, is shown.
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
The transceiver unit comprises a single-frequency continuous-wave master oscillator (MO) that is used as an injection seeder for the slave oscillator (SO) and additionally used as a local oscillator for the coherent heterodyne detection. The MO is a diode-pumped Tm:LuAG laser characterized by a low bandwidth providing high heterodyne efficiency. A part of the MO radiation is coupled into the SO under a small angle of about 
After the SO, the laser beam is passing a polarizing beam splitter (PBS) that is used to separate the outgoing laser pulse and the signal backscattered from the atmosphere, and to protect the sensitive detector form the emitting laser pulse. Before the laser beam is expanded to a diameter of about 10 cm by means of a telescope, its polarization is changed to circular by means of a quarter-wave plate (
Once traveling through the atmosphere, a small portion of the emitted laser pulse partly scatters on aerosols and cloud particles back to the lidar system, where it is received with the same telescope that was used for emission. The backscattered light is reflected on the PBS and directed to the optical signal detector (DET), where it is mixed with a portion of the MO laser. After preamplification directly at the detector, the analog detector signal is additionally amplified by a custom-made 500-MHz amplifier (AMP). In particular, the internal reference pulse is attenuated by 9 dB and the atmospheric signal is amplified by 24 dB such that they reach a comparable signal level before digitization. Now, the time-resolved detector signal resulting from each single-laser shot is sampled with 500 MHz and 8-bit resolution (Agilent U1064A, Acqiris DC241) before it is stored to a solid-state drive connected to a dedicated computer (ADLINK, ePCIS-6400x) (DAQ). This procedure leads to a data rate of about 15 MB s−1 and gives maximum flexibility for postprocessing.
To achieve a high timing accuracy for the data processing, all measured quantities (time-resolved laser pulse signal, scanner position, aircraft position, speed, and attitude angles) are stored with an accurate time stamp generated by a custom-made global positioning system (GPS)-controlled oscillator. In particular, a 10-MHz signal of an oven-controlled crystal oscillator is fed into a timer/counter module (National Instruments, Ni-PXI-6608). Here, the signal is divided by 100 in order to reach a 100-kHz clock signal that is synchronized by the pulse-per-second signal provided by the GPS module (Septentrio, PolaRx2), which is additionally used to measure the aircraft position and speed with a temporal resolution of 1 Hz. The latter one is important, as the aircraft speed (
In addition to the aircraft speed, the aircraft attitude has to be measured and considered for wind retrieval. For that reason, roll, pitch, and yaw angles are measured with an inertial reference system (IRS; Honeywell LASEREF YG 1779) whose data, including time stamp, are also stored on the HK computer. The velocity and the actual position of the aircraft are obtained by GPS. The accuracy of the horizontal velocity measured with the GPS receiver is specified to be 1.5 mm s−1 (1σ level). The main parameters of the DWL are summarized in Table 1.
Overview of the DWL system parameters.
3. Measurement procedure and wind retrieval
To measure vertical profiles of either the three-dimensional wind vector or the vertical wind speed, the DWL was operated in two different modes: namely, scanning mode and fixed LOS mode. While operating in scanning mode, a conical step-and-stare scan (a VAD technique) around the vertical axes with a nadir angle of 20° is performed. A total of 24 LOS wind velocities are measured per one scanner revolution and are used to retrieve the three-dimensional wind vector as described in section 3c. Considering a 1-s averaging time for each LOS measurement (24 s), 21 s for the scanner motion between each measurement position, and an aircraft speed of about 200 m s−1, the spatial resolution of horizontal wind data is about 9 km. Operating in fixed LOS mode, the laser beam is intentionally pointed to nadir direction and thus the measured LOS wind equals the vertical wind speed. Considering a 1-s averaging time, the horizontal resolution for the retrieved vertical wind profiles is about 200 m. As it is difficult to sustain an exact nadir pointing due to the permanent aircraft movement around the attitude angles (pitch, yaw, and roll), projections of the horizontal wind speed contaminate the vertical wind measurements and need to be corrected.
a. LOS wind
The basis for both the horizontal and vertical wind retrieval are LOS winds that are retrieved from the detector raw signal, which itself is stored for each single-laser pulse with a sampling rate of 500 MHz, an 8-bit resolution, and a duration of 
Schematic overview of the LOS wind processing procedure.
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
The single-shot data storage enables excluding bad pulses and correcting the laser frequency variations from pulse to pulse before accumulation (e.g., all valid laser pulses within 1 s). To do so, the power spectrum of the reference pulse signal, which is the beat signal of the local oscillator (MO) and the emitted laser pulse, is calculated and analyzed regarding its frequency. If the beat frequency differs by more than 10% of the nominal AOM frequency of 100 MHz, or if the laser pulse build-up time is larger than 
The remaining frequency shift 
To get the actual vertical wind speed, or rather the three-dimensional wind vector from respective LOS wind measurements, further processing steps are needed, as discussed below.
b. Vertical wind retrieval
Basically, the derived LOS wind speed equals the vertical wind speed in case the laser beam is pointing downward exactly in the nadir direction. Since 2014, the DWL system is equipped with an automatic flight attitude correction loop that keeps the set laser beams’ pointing direction based on the aircraft IRS data. As such a correction loop was not available during the GW-LCYCLE I campaign in 2013, slight off-nadir angles of up to 1° occur during measurement due to a change in the aircraft attitude (Witschas et al. 2016). As a consequence, the LOS wind speed additionally contains a projection of the horizontal wind speed onto the LOS direction that has to be corrected. For instance, considering a horizontal wind speed of 30 m s−1 and an off-nadir angle of 0.5° toward the wind blowing direction, the LOS-projection is 0.26 m s−1. As vertical wind speeds are expected to be small (e.g., a few meters per second during strong mountain wave events), the aforementioned effect has to be considered for a reliable vertical wind speed retrieval.
Thus, to be able to correct all LOS measurements, the horizontal wind from European Centre for Medium-Range Weather Forecasts (ECMWF; T1279 L137, cycle 40r1) operational analyses on 137 model levels with a horizontal resolution of 16 km and a temporal resolution of 6 h, interpolated to the respective flight track and time, are used to calculate the LOS projection of the horizontal wind according to Eq. (1).
The functionality of this correction procedure is demonstrated based on the lidar measurements acquired on 13 December 2013, which are later used for GW characterization (section 4). The corresponding flight track is shown in Fig. 3 (gray line). The red line indicates a flight leg performed in scanning mode; the rest of the flight was measured with a fixed LOS (nadir pointing). The dark blue line indicates a flight segment of 1300 s (22 min) before and after a turn used to demonstrate the correction procedure. Assuming constant wind conditions and a zero mean vertical wind speed on this 133-km-long flight segment (one way), the measured vertical wind speeds at all altitudes are expected to follow a Gaussian distribution with zero mean. As shown in Fig. 4, the histograms of the uncorrected LOS winds before (orange) and after the turn (black) indeed follow a Gaussian distribution with a standard deviation of 0.42 and 0.45 m s−1, respectively, where both the Gaussian distribution and the nearly equal standard deviation confirm steady atmospheric conditions during the measurement. The mean values, however, are not zero but −0.23 and 0.20 m s−1, respectively. Although the similar magnitude of the mean values again verifies stable atmospheric conditions during the measurement, the nonzero mean and the opposite sign clearly indicate that the LOS wind contains a projection of the horizontal wind and is not just containing vertical wind. The analysis of the actual laser pointing 
Flight track of the research flight performed on 13 Dec 2013 (gray line). Location of Kiruna airport (black cross) and the flight segment used to demonstrate the vertical wind speed retrieval (dark blue line) are shown. Flight segment with wind vector measurements is indicated (red line).
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
Histogram of LOS wind speeds (13 Dec 2013) acquired while flying in (top) a northwesterly direction and (bottom) a southeasterly direction. Histograms of the uncorrected winds (orange and black) and that of the corrected winds (blue and red) are indicated; inset shows the respective mean value and standard deviation (same colors).
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
Before ECMWF data are used for correction, horizontal wind speeds resulting from the lidar measurements performed in scanning mode (Fig. 3, red line) are used for model validation as indicated by Fig. 5, which shows ECMWF versus DWL data. From the scatterplot it can be seen that both datasets are in accordance. A line fit (Fig. 5, red dashed line) yields a correlation coefficient of 
ECMWF horizontal wind speeds vs lidar-measured wind speeds: 
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
After correction the LOS winds yield the histograms shown in Fig. 4, for northwesterly flight direction (blue) and for southeasterly flight direction (red). They still follow a Gaussian distribution but the mean value is close to zero now. In particular, the remaining offsets are −0.04 and 0.01 m s−1, respectively, while the standard deviation remains similar compared to the histograms, resulting from the uncorrected data. This confirms the recently published results by Chouza et al. (2016), who estimated the mean systematic uncertainty of vertical wind speeds to be smaller than 
To further estimate the statistical uncertainty of the vertical wind speed, the corresponding power spectrum (Fig. 6) is analyzed. As explicitly discussed by Frehlich (2001b) and O’Connor et al. (2010), the average of the constant high-frequency region of the power spectrum of the measured wind speed (Fig. 6, dashed vertical red line) gives an estimate of the random error produced by the average of the spectral estimates. By setting the cutoff frequency to 0.2 Hz (
Spectral power of vertical wind speed measured on 13 Dec 2013: cutoff frequency (0.2 Hz; vertical dashed red line), which is set and used for error estimation, and mean value of the noise level (horizontal dashed red line), which is considered to be the variance of the wind measurements (Frehlich 2001b). Estimated wind error is indicated in the insert. The wavelength λ (top x axis) is calculated by assuming a constant aircraft velocity of 
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
c. Horizontal wind and direction
To measure the horizontal wind speed and direction with the DWL, a conical step-and-stare scan of the laser beam around the vertical axes with an off-nadir angle of 
Schematic illustration of wind vector estimation. (top left) Maximum value 
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
First, all spectra of the 24 scan positions are shifted to be proportional to their azimuth angle and an assumed wind vector, where the north component 
By additionally analyzing the center frequency of the 
In the last step, the same procedure is repeated for a smaller wind velocity space (usually 
4. Experimental results
The usefulness of airborne coherent DWL data for GW characterization is demonstrated by means of a research flight performed during the period 0600–0935 UTC 13 December 2013 (flight track shown in Fig. 3) during favorable conditions for mountain wave generation and vertical propagation. During the flight, lower-tropospheric winds were blowing with northwesterly directions (see Fig. 10) crossing the Scandinavian mountain ridge almost perpendicularly and thus providing excellent conditions for GW excitation. Furthermore, a strong and quasi-stationary tropopause jet impacted the vertical propagation of GWs into the stratosphere (Wagner et al. 2017).
Altogether, three legs were flown at three different altitudes and with different lidar acquisition modes. In particular, vertical wind measurements were performed during the first flight leg (0648–0725 UTC, flight altitude = 5.7 km, leg length = 495.5 km, horizontal resolution = 225 m; Fig. 8) and horizontal wind measurements were performed during the second flight leg (0731–0820 UTC, flight altitude = 7.4 km, leg length = 479.6 km, horizontal resolution = 7.74 km; Fig. 10). The third leg (no measurements shown) was flown with varying altitudes in order to provide different probing heights for the in situ instruments and is not further discussed. The main details of the two flight legs discussed in the following sections are summarized in Table 2.
(top) Cross section of vertical wind derived from lidar measurements (see section 3b) during a cross-mountain leg flown 0648–0725 UTC Dec 13 (flight altitude is 5.7 km, leg length is 495.5 km, horizontal resolution is 225 m). Bar at 5.7 km altitude indicates the in situ–measured vertical wind speed. Corresponding flight track is shown in Fig. 3. (bottom) Vertical wind derived from lidar measurements at 4.9 km (black) and in situ–measured vertical wind at 5.7 km (red).
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
Overview of flight legs performed on 13 Dec 2013.
a. Vertical wind measurements
The vertical wind derived from DWL measurements (section 3b) acquired during the first flight leg is shown in Fig. 8 (top). The vertical wind measured at flight level (5.7 km) by the nose-boom-mounted five-hole probe is additionally indicated by the bar at 5.7 km altitude. In Fig. 8 (bottom), the vertical wind measured by the lidar in an altitude of 4.9 km and the in situ–measured wind speed at flight level (5.7 km) are additionally displayed for comparison.
From Fig. 8 (top) it can be seen that the lidar data are acquired with almost full vertical coverage, except for the westernmost part of the flight leg, where low-level clouds prevented measurements down to the ground (white areas). The vertical wind speed westward of the mountains is measured to be close to 0 m s−1, as is expected for an undisturbed atmosphere. Above the mountain ridge however, pronounced GW structures with vertical wind speeds up to −3 and 4 m s−1 are observed (
To study the GW characteristics in more detail, wavelet power spectra of the vertical wind speed measured at different altitudes are calculated. The wavelet analysis is performed using a Morlet wavelet with a nondimensional frequency of 
(left) Vertically averaged vertical wind derived from DWL measurements at different altitudes (3.0–3.5, 4.0–4.5, 5.0–5.5 km) and vertical wind speed from in situ measurements at flight level. Orography is indicated in the lowermost panel. (right) Corresponding wavelet power spectra, calculated by using a Morlet wavelet (Torrence and Compo 1998).
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
From both the wind measurements and the wavelet power spectra it can be seen that the GWs with the largest amplitudes are excited in the region of the highest elevation (
The wavelet power spectrum of the elevation along the flight track (Fig. 9, bottom) shows two distinct regions: a short-wave region with wavelengths between 10 and 40 km, and a long-wave region with wavelengths between 80 and 150 km. The short-wave part of the spectrum is similar to the one of the vertical wind speed but slightly shifted to the location where the orography shows a pronounced structure at the respective wavelengths. The long-wave part of the elevation spectrum (80–150 km) is not represented in the vertical wind, meaning that short-wave orography modulations have a larger impact on the vertical wind speed spectrum.
b. Horizontal wind measurements
The horizontal wind speed and direction derived from lidar measurements (section 3c) acquired during the second flight leg are shown in Fig. 10 (top left and bottom left, respectively). The data measured by the nose-boom-mounted five-hole probe at flight level (7.4 km) are additionally indicated. The wind was constantly blowing from the northwesterly direction (
(top left) Horizontal wind speed and (bottom left) wind direction derived from the lidar measurements during a cross-mountain leg flown 0731–0820 UTC 13 Dec 2013 (flight altitude = 7.4 km, leg length = 479.6 km, horizontal resolution = 7.74 km). Bars at 7.4-km altitude indicate the in situ wind data. Flight track is shown in Fig. 3 (red). (right) Corresponding 
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
To obtain information about the propagation behavior of the existing GWs, perturbations of 
Different from Rayleigh lidars, where the background wind is determined in the vertical, the background wind has to be determined along the flight track for airborne DWL measurements. To do so, an ordinary fifth-order polynomial fit is applied in the horizontal direction per each range gate altitude and used as the background horizontal wind speed for subtraction (Fig. 10, right).
It can be seen that the amplitudes of 
Additionally, the horizontal wind speed perturbations at 1.8 and 6.7 km, and the elevation along the flight track (left) and the corresponding wavelet power spectra (right) are calculated as shown in Fig. 11. It can be seen that, different from the vertical wind spectra, 
(left) Horizontal wind perturbations measured at 1.8- and 6.7-km altitude. (bottom left) Graph indicates the orography. (right) Corresponding wavelet power spectra, calculated by using a Morlet wavelet (Torrence and Compo 1998).
Citation: Journal of Atmospheric and Oceanic Technology 34, 6; 10.1175/JTECH-D-17-0021.1
With the discussion given above, it is demonstrated that airborne horizontal wind lidar measurements are a valuable tool for GW characterization, especially in the excitation region but also for investigating the propagation behavior in the entire troposphere.
5. Summary and conclusions
Airborne coherent DWL measurements acquired on 13 December 2013 in the framework of the GW-LCYCLE I campaign performed from 2 to 14 December 2013 in Kiruna, Sweden (67.8°N, 20.3°E), have been used to investigate internal gravity waves (GWs) induced by flow across the Scandinavian Mountains. The setup, the operation principle, and the corresponding data retrievals of the DLR’s DWL were discussed, showing that vertical wind speed can be derived with a bias of less than 0.05 m s−1 and a standard deviation of 0.2 m s−1 with a horizontal resolution of 200 m and a vertical resolution of 100 m by correcting horizontal wind projections by means of ECMWF wind speed data. Furthermore, the horizontal wind vector was retrieved from lidar measurements by applying a velocity–azimuth display scan and a modified spectral accumulation technique, leading to reliable wind speed data with a horizontal resolution of 9 km and a vertical resolution of 100 m.
Both vertical and horizontal wind measurements are shown to be valuable for characterizing GW properties. Because of the high horizontal resolution of the DWL measurements, GW source spectra are analyzed for wavelengths down to 400 m for vertical wind measurement, and 18 km for horizontal wind speeds. The upper wavelength limit is defined by the maximum flight leg length to be about 250 km for 500-km-long flight legs.
Wavelet power spectra of the vertical wind measured in different altitudes demonstrate that the GW spectrum is dominated by wavelengths of 10–30 km and that the GW amplitude is decreasing with increasing altitude. Compared to that, the spectrum of the horizontal wind speed perturbations in the excitation region is dominated by wavelengths of 100–125 km.
It is shown that the spectrum of the topography is also composed of two distinct spectral regions: a short-wave region between 10 and 40 km, and a long-wave region between 80 and 150 km. Thus, it was concluded that the vertical wind speed spectrum is mostly dominated by the short-wave spectrum of the topography, whereas the spectrum of horizontal wind speed perturbations is dominated by the long-wave part but additionally shows an influence on the shorter wavelengths.
In the future, it is planned to adapt the scan pattern of the lidar measuring consecutively with a certain off-nadir angle with forward/backward pointing beams with respect to the flight direction. Such a procedure may enable measuring the horizontal wind speed in the flight direction and the vertical wind speed with a high horizontal resolution of a few hundred meters and thus giving the possibility of estimating the vertical flux of the horizontal momentum, which is proportional to 
Acknowledgments
We especially thank R. Simmet and E. Nagel for the technical support, C. Lemmerz and F. Chouza for the fruitful discussions, O. Reitebuch for the extensive internal review, and C. Mallaun from the DLR flight facility for providing Falcon in situ data. Further, we thank three anonymous reviewers for providing helpful comments about the manuscript. Part of this work was supported by the project “Investigation of the Life Cycle of Gravity Waves” (GW-LCYCLE) in the framework of the research initiative the Role of the Middle Atmosphere in Climate (ROMIC) funded by the German Federal Ministry of Education and Research under Grant 01LG1206A.
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