# Millimeter-Wave Radar Sensor for Snow Height Measurements

Serdal Ayhan, Mario Pauli, Steffen Scherr, Benjamin Gottel, Akanksha Bhutani, Sven Thomas, Timo Jaeschke, Jean-Michel Panel, Frédéric Vivier, Laurence Eymard, et al.

► To cite this version:

Serdal Ayhan, Mario Pauli, Steffen Scherr, Benjamin Gottel, Akanksha Bhutani, et al. Millimeter-Wave Radar Sensor for Snow Height Measurements. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55 (2), pp.854 - 861. 10.1109/TGRS.2016.2616441. insu-01405807

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# Millimeter-Wave Radar Sensor for Snow Height Measurements

Serdal Ayhan, Mario Pauli, Member, IEEE, Steffen Scherr, Student Member, IEEE, Benjamin Göttel, Student Member, IEEE, Akanksha Bhutani, Student Member, IEEE, Sven Thomas, Student Member, IEEE, Timo Jaeschke, Student Member, IEEE, Jean-Michel Panel, Frédéric Vivier, Laurence Eymard, Alain Weill, Nils Pohl, Senior Member, IEEE, and Thomas Zwick, Senior Member, IEEE

**Abstract**—A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Études de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.

**Index Terms**—Frequency-modulated continuous-wave (FMCW) radar, high accuracy, millimeter wave, range detection, snow height, snow thickness.

## I. INTRODUCTION

MEASUREMENTS of atmospheric parameters in severe weather conditions are crucial tasks for meteorology and climate research. Especially in Arctic and Antarctic regions, the harsh atmospheric conditions make it difficult to use classical meteorological surface stations since these stations must work independently of atmospheric conditions and must be autonomous, which involves lightweight and low power consumption. Fundamental measurement parameters for these regions are the snow depth and the surface properties

Manuscript received June 2, 2015; revised July 6, 2016, September 11, 2016, and September 18, 2016; accepted September 26, 2016. This work was supported by the Mission pour l'Interdisciplinarité: Instrumentation aux limites from the Centre National de la Recherche Scientifique, France, within the framework of the OPTIMISM-bio Project 2014.

S. Ayhan, M. Pauli, S. Scherr, B. Göttel, A. Bhutani, and T. Zwick are with the Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany (e-mail: serdal.ayhan@kit.edu).

S. Thomas and N. Pohl are with the Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, 53343 Wachtberg, Germany.

T. Jaeschke is with Ruhr University Bochum, 44801 Bochum, Germany.

J.-M. Panel is with the Centre d'Études de la Neige, F-38400 Saint-Martin-d'Hères, France.

F. Vivier and L. Eymard are with the Laboratoire d'Océanographie et du Climat, 75252 Paris, France.

A. Weill is with the Laboratoire Atmosphères, Milieux, Observations Spatiales, 75252 Paris, France.

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2016.2616441

of the snow pack. For snow height measurements, different types of sensors like ultrasonic telemeters or laser systems are available and are already used. However, ultrasonic systems can be severely attenuated due to the influence of temperature and humidity, the signal can be defocused due to wind, and the snow height is not directly measured. Laser systems are convenient, but are generally heavy, of high energy consumption, and can be perturbed by precipitation and ambient meteorological conditions such as fog or low-level clouds. Particularly, over the snow pack, for autonomous Arctic stations [1], the use of high-frequency radar sensors could be a promising approach compared with acoustical and optical systems. Indeed, millimeter waves are nearly independent of hydrometeors such as fog, snow, and precipitation because the atmospheric attenuation is smaller than 0.3 dB, for a two-way atmospheric radar path of 10 m [2].

Millimeter-wave imagery has been already used for snow surface cover classification in [3]. In an Arctic environment, very small FMCW radars have been recently applied for topographic information of the snow surface [2] and FMCW radars are also used as altimeters [4]. Further theoretical analysis and observations of high-accurate millimeter-wave radars are presented in [5]–[9]. In contrast to the millimeter radar systems used in the past, which were rather bulky and heavy, the progressive development in radar technology along with enhanced algorithms [10], [11] and new hardware facilitates the use of such radars. As a result, low power consumption and lightweight sensors have been developed and presented by Pohl et al. [12].

In this paper, an FMCW radar system for the determination of the distance between the snow surface and a reference platform is evaluated in order to determine the snow height. The radar measurements are compared with the laser measurements and the capability of the 80-GHz FMCW radar sensor based on [12] is used to demonstrate the relevance of this technique and its accuracy for snow height measurements. After a brief description of the radar sensor, the measurement results are presented and analyzed.

## II. 80 GHz RADAR SENSOR

For the measurements, an FMCW radar system [13] based on an integrated silicon-germanium (SiGe) monolithic microwave integrated circuit (MMIC) working at a center frequency of 80 GHz presented in [12] and [14] is used. With the use of a well-established SiGe technology offered
<figure><img src="image_2.png" /><figcaption>Fig. 1. Block diagram of the radar system.</figcaption></figure>

by Infineon, a cost-effective implementation of the sensor was realized, where in high volume production unit prices below 100 € may be possible. The radar sensor offers a compact realization with a small dimension of 30 × 40 × 75 mm³ including a high-gain lens antenna and an overall weight of 186 g. A center frequency of 80 GHz in combination with a modulation bandwidth of 10 GHz has been chosen because it is a good compromise between robustness, measurement accuracy, and achievable range resolution and output power. The detailed block diagram of the radar system is given in Fig. 1. The high-frequency part is fully integrated in the SiGe transceiver chip realized in Infineon B7HF200 SiGe technology, which enables the realization of an ultracompact low-power radar system. The transceiver chip is mounted on a Rogers RO5880 substrate and is wire bonded to the peripheral components and the antenna. At the 80-GHz output of the chip, a rat race coupler is used to convert the differential signal from the MMIC into a single-ended signal. This signal is coupled into a circular waveguide feeding the dielectric lens antenna, which is described in [15]. For signal generation, an offset phase-locked loop (PLL) with an in-loop reverse frequency position downconversion mixer and a fixed reference frequency of 100 MHz is used. The concept using two PLLs and an integrated mixer for downconversion results in a linearization of the PLL loop gain, thus enabling ultrawide modulation bandwidths and allowing lower in-loop divider values for better output noise as described in [12]. The distance R to a certain target or a reflective surface can be determined according to

$$R = \frac{f_{\text{IF}} \cdot T \cdot c_0}{2 \cdot B} \quad (1)$$

where B is the modulation bandwidth, T is the ramp time of the frequency-modulated radar signal, and c₀ is the propagation velocity of the electromagnetic wave. The intermediate frequency (IF) f IF at the mixer output has to determined and is a measure for distance. In the radar used here, the IF signal is sampled by a 16-b analog-to-digital converter sampling with 1 Msamples/s and recorded by a 32-b microcontroller that passes the raw data via a USB interface to a PC where the signal processing is done. With a low overall power consumption of 3.25 W at 5 V, the USB interface is used as a power supply and also used for controlling the radar system and configuring the PLLs.

The modulation bandwidth directly determines the theoretical limit of the range resolution Δr

$$\Delta r = \frac{c_0}{2 \cdot B} \quad (2)$$

For a modulation bandwidth of B = 10 GHz, a maximum range resolution of Δr ≈ 15 mm can be achieved.

The dielectric ellipsoidal lens-based antenna with an extremely compact dimension of 35-mm diameter provides a gain of 28.7 dBi. With a maximum output power of −8 dBm, the radar system provides a total equivalent isotropically radiated power of 20.7 dBm. The main lobe has a 3 dB width of 5.2° (Θ = 0°) and 5.5° (Θ = 90°) with a sidelobe level of −14.3 dB and −17.3 dB, respectively. This narrow beam corresponds to a small footprint at the surface, which is smaller than 0.019 m² for a typical radar distance to the surface of 1.5 m. With a previous version of this radar setup, a distance measurement accuracy better than 4 µm has been obtained, using an ideal reflecting target (metal plate) in a laboratory environment [16]. The stability of the measured distance is mainly derived from the stability of the reference oscillator, in this case a Crystek CVSS-945 crystal oscillator is used. To optimize the stability, the crystal oscillator could be coupled to a GPS reference or replaced by an oven-controlled crystal oscillator. For snow surface measurements, such a high accuracy is not a priori necessary and has no physical significance, but it demonstrates the capability of the radar technique in comparison with the commonly used highly accurate laser technique.

### III. MEASUREMENT OF THE SNOW HEIGHT

The measurements for snow surface detection are carried out at the meteorological station of Centre des Études de Neige (CEN) at Col de Porte near Grenoble, France. The station is dedicated for snow measurements and allows for mounting different distance measurement systems on a platform that is moveable in height. The measurement setup with the laser telemeter and the radar is shown in Fig. 2. The radar and a laser telemeter are mounted on the platform at a specific height to measure the distance to the snow surface and the snow height variation over time. The laser is used for comparison with the radar data. Due to different mounting positions on the platform and beamwidths, the radar and the laser do not have the same reference height from the surface (see Fig. 2). Therefore, the footprints illuminated by the radar and the laser are different. The radar footprint at the surface is approximately 90 to 180 mm for distances of 1 to 2 m in contrast to the laser footprint with a diameter between 2 and 3 mm. The vertical alignment of the platform relative to the surface is controlled with an electronic inclinometer of 0.1° accuracy. The influence of the radar inclination angle, relative to the snow, is described in Section III-B. The measurement results for the snow height detection over an extended time duration are presented in Section III-C.

#### A. Measurement at Different Distances to the Snow Surface

Different distances to the surface are arbitrarily chosen to estimate the influence of distance on the radar results. The maximum adjustable distance is limited by the measurement setup. In Fig. 3, the IF signal is plotted over distance in the frequency domain. The position of the maximum amplitude in the frequency domain corresponds to the reflection detected from the snow surface. The measurements prove that even at a distance of 4 m, the signal can be clearly identified
<figure><img src="image_3.png" /><figcaption>Fig. 2. Measurement setup at the meteorologic station of CEN.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Spectrum of the measured IF signals at different distances from the snow surface.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Distance measurement results of the radar and laser.</figcaption></figure>

<table><caption>TABLE I
MEASUREMENT RESULTS OF LASER AND RADAR</caption><tr><td>laser results in m</td><td>2.584</td><td>3.086</td><td>3.337</td><td>3.631</td><td>3.874</td><td>4.094</td></tr><tr><td>radar results in m</td><td>2.612</td><td>3.111</td><td>3.406</td><td>3.666</td><td>3.905</td><td>4.128</td></tr><tr><td>|ΔR| in mm</td><td>28</td><td>25</td><td>69</td><td>35</td><td>31</td><td>34</td></tr><tr><td>|ΔR<sub>cor</sub>| in mm</td><td>6.8</td><td>11</td><td>32</td><td>2.3</td><td>6.9</td><td>4.5</td></tr></table>

<figure><img src="image_6.png" /><figcaption>Fig. 5. Measured radar height $R_{\text{radar}}$ versus measured laser height $R_{\text{laser}}$ and linear regression.</figcaption></figure>

with a signal-to-noise ratio (SNR) of approximately 20 dB, which mainly depends on the free space loss due to the radar distance. The noise floor itself remains nearly constant. Therefore, as the distance increases, the amplitude and the SNR decrease. However, a larger distance provides the benefit of a larger illumination surface by the radar. Hence, distances to the surface can be determined with sufficient accuracy and small outliers such as depressions or small embossments in the snow are averaged over the radar footprint in contrast to the point-like laser footprint. The measured distances with the radar and the corresponding laser measurements are plotted in Fig. 4 and summarized in Table I. The measurements are carried out in an interval of 2 min and the distance to the surface is varied by moving the platform along its vertical axis. In the general, there is good agreement between the 80-GHz radar and the laser. It has to be taken into account that the reference position of the radar and the laser telemeter is not identical but differs by approximately 29 mm. This means the deviation $\Delta R$ mainly corresponds to this systematic bias due to different reference heights. The systematic bias is determined by a linear regression (see Fig. 5). By considering this systematic bias, the corrected deviations $\Delta R_{\text{cor}}$ are in the range of 10 mm, as can be seen in Table I. Higher deviations,

for example, at a radar height of 3.406 m might be caused by additional disturbing reflections. For the radar measurements, no averaging and no system or temperature calibration is performed in contrast to the commercial laser system. Hence, calibration could further improve the system performance.

## B. Measurement at Different Inclination Angles of the Radar Relative to the Surface

The orientation of the radar to the surface also has an influence on the measurement results. Therefore, the dependence of radar inclination angle to the snow surface on the measurement results is investigated as shown in Fig. 6. The radar platform is tilted between $\pm 45^{\circ}$ relative to the normal incidence angle, and for each angle, a distance measurement is performed. The results of these measurements are depicted in Fig. 7. The angle $\alpha$ is measured with an electronic inclinometer relative to a horizontal surface. The distance $d_0^{\circ}$ is the measured distance at $0^{\circ}$ by the radar. The distance $d_{\alpha}$ is the measured or calculated distance with the following geometric equation for the adjusted angles:

$$ \cos \alpha = \frac{d_0^{\circ}}{d_{\alpha}}. \qquad (3) $$
<figure><img src="image_7.png" /><figcaption>Fig. 6. Schematic of the measurement setup for the analysis of inclination angles.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 7. Expected and measured distances at different inclination angles.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Distance deviation between the expected and measured distances at different inclination angles.</figcaption></figure>

In Fig. 8, the difference between the measured and the calculated distance is given. The deviations in the negative direction are about 25 cm and in the positive direction below 10 cm. The measurement results to positive angles seem to be more close to the calculated distances. Indeed, the deviations depend on the slope of the snow surface, e.g., the snow surface was not perfectly perpendicular to the radar as assumed in the calculations. Therefore, the surface slope orientation relative to the radar has to be considered. It is also shown that measurements between ±45° inclination angle of the radar are possible.

### C. Measurement of Snow Height Over an Extended Time Duration

To investigate the change of the snow height during the night, a measurement over an extended time duration of 16 h is performed in which the distance to the snow surface is monitored from 4.00 P.M. to 8.00 A.M. during the next day.

<figure><img src="image_10.png" /><figcaption>Fig. 9. Change of the snow height over a period of 16 h detected by the radar and the laser independent of the temperature.</figcaption></figure>

The initial distance to the snow surface is set to 2.6 m. In this way, the illuminated surface is approximately 0.23 m in diameter, which is sufficient to reduce the error of the local inhomogeneity of the surface, since the distance information is averaged over the illuminated surface. Simultaneously, the received signal amplitude is sufficiently high for the subsequent signal processing. The measurement results are presented in Fig. 9. In addition to the radar and laser values, where the mean distance of 2.6 m is eliminated to determine the snow height variation, the surface temperature is also presented in the same plot. A dependence of the temperature on the evaluated snow height is clearly visible. Until 00:00 A.M., the surface temperature is between ±0.5 °C, which leads to high fluctuations in the radar results compared with the laser. During this period, a transition from snow and melted water into ice was observed on the surface, which might lead to multiple reflections and to a shift in the reflection surface in the snow. This transition was observed by core bores, which were installed by CEN. Furthermore, snow shows a higher penetration depth for millimeter waves compared with ice. This could also lead to some inaccuracies in the measurement. After 00:30 A.M., the temperature decreases below 0 °C and it can be seen that the fluctuations are also decreasing with decreasing temperature. In this case, melted snow (water) at the surface is completely frozen at the beginning of the night and there is a well-defined reflection at the surface. The measurement results obtained from the laser and the radar fit well. In general, it has to be mentioned that the influence of the surface property variations (e.g., transformation of wet snow into ice snow as confirmed by local measurements) to laser or radar signals is not reported in the literature, since generally only one height measurement (Laser or acoustic telemeter) is estimated. Therefore, the measurement deviations of both systems are not directly comparable, but they show that the different properties of dry snow, wet snow, ice, and water as described by [5] and [6] are important for a millimeter-wave radar, since they influence the reflected signal and the correct snow height evaluation. The influence of these characteristics is investigated in the next section.

## IV. MEASUREMENT OF DIFFERENT SNOW TYPES AND SNOW THICKNESS

The influence of the snow surface on the reflection of the radar signal is significant. Therefore, the reflection and transmission behaviors of different snow types are investigated to obtain more information about the properties of the snow and to evaluate if the determination of different snow layers is
<figure><img src="image_11.png" /><figcaption>Fig. 10. Picture of the real and schematic measurement setup for snow layer measurements.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 11. Spectrum of the measured IF signal for one snow layer.</figcaption></figure>

possible by the use of a millimeter-wave radar system. In the first step, the thickness of different snow types is determined. For that purpose, the initial snow properties are examined (i.e., type of snow, liquid water content, dielectric constant, and snow temperature) and the different snow samples with specified thickness are prepared (see Table II). A picture of the measurement setup and the corresponding schematic are given in Fig. 10. The radar is mounted in a height of 1.412 m above a metal plate and subsequently different layers of snow are placed between the radar and the metal plate. The thickness of the snow layers is evaluated relatively to the metal plate below, as can be seen on the left of Fig. 10. To ensure constant snow properties, the radar measurements are performed in a cooling chamber at a controlled temperature of -10 °C.

1) *One Snow Layer*: In a first step, the thickness of a single snow layer is determined. For this reason, a radar measurement without snow is performed and the distance to the metal plate is evaluated. Subsequently, the different snow layers are placed on top of the metal plate and the distance to the surface reflection is evaluated by determining the IF of the first peak. In Fig. 11, the corresponding IF signals are plotted in the frequency domain. The 3-dB width of a well-defined reflection in the frequency domain is approximately 2.5 cm, where the black solid line corresponds to the metal plate reference plane. The measured absolute distances to the surface reflection are summarized in the first row of Table II. The layer thickness is

<table><caption>TABLE II
MEASUREMENT RESULTS FOR ONE SNOW LAYER</caption><thead><tr><th></th><th>metal plate</th><th>dry snow</th><th>wet snow</th><th>ice snow</th></tr></thead><tbody><tr><td>absolute distance in m</td><td>1.412</td><td>1.109</td><td>1.285</td><td>1.303</td></tr><tr><td>relative distance to metal plate in m</td><td>0.000</td><td>0.303</td><td>0.127</td><td>0.109</td></tr><tr><td>measured layer thickness in m</td><td>-</td><td>0.285</td><td>0.130</td><td>0.130</td></tr></tbody></table>

determined by the distance of the surface reflection of the layer to the metal plate and given as relative distance in the second row of Table II. To verify the radar results, the thickness of the layers is evaluated by a metering rule with an uncertainty of approximately 5 mm and also given in Table II.

It can be seen that the relative distance $\Delta R$ between the detected snow surface and the detected metal plate corresponds very well to the measured thickness of the snow layer. The measurements show that different snow types can be reliably detected by the radar. Furthermore, it can be seen that in addition to the first reflection at the surface multiple reflections occur within the different snow types. This can be seen clearly in the spectra of dry snow and ice snow with multiple reflections between 10 and 13 kHz and between 12 and 14 kHz, respectively. These reflections are located inside the snow layers and may arise by inhomogeneities due to different snow densities. It can also be seen that the attenuation of the multiple reflections is relatively low, i.e., dry snow and ice snow has only small losses. In contrast, wet snow does not show the effect of these high numbers of internal reflections. The wet snow shows, on the one hand, a more homogenous behavior, and on the other hand, the losses inside the wet snow are higher. There are two strong reflections at 12 kHz (corresponding to the metal plate below) and at 14 kHz. The reflection at 14 kHz with approximately $2 \cdot \Delta f_{IF}$ arises due to multiple-reflection-path metal plate-surface (transition snow-air)-metal plate. These multiple reflections might also lead to the inaccuracies in Fig. 9 independent of the temperature, as they will shift the maximum, e.g., the main surface reflection in the frequency domain.

2) *Two Snow Layers*: Additional measurements are performed by placing two layers of different snow types on top of each other as can be seen on the right of Fig. 10. The corresponding IF signal in the frequency domain is plotted in Fig. 12. There are significant reflections at 9.5 and 11.2 kHz, which are followed by adjacent reflections. The first maximum at 9.5 kHz corresponds to a distance of approximately 1.135 m, which is the distance to the surface of the top snow layer. Using again the metal plate as a reference this results in an overall thickness of both snow layers of approximately 0.277 m. The expected thickness of both snow layers according to Table II is 0.260 m as the sum of the thickness of wet snow (0.130 m) and the thickness of ice snow (0.130 m) plus a few millimeter thickness of the plastic box. Considering measurement inaccuracies of the radar measurement and the
<figure><img src="image_13.png" /><figcaption>Fig. 12. Spectrum of the measured IF signal for two snow layers.</figcaption></figure>

measured thickness of the snow types with a measuring tape, very good agreement is achieved. The second maximum with a frequency difference of approximately 0.6 at 10 kHz is similar to the measurement results with one single layer of ice snow and is again a reflection within the top snow layer. The third significant reflection at 11.2 kHz is the result of the boundary to the subjacent snow type. Reflections with IFs above 11.2 kHz result from multiple reflections between layer transitions and the metal plate. It can be seen that the main reflections at the surface and the boundary to the second layer are distinctive and the transitions are identifiable. The frequency difference $\Delta f_{IF}$ to the first maximum at 9.5 kHz can be used to determine the distance between the layer boundaries corresponding to the layer thickness $\Delta R_{thickness}$ of the ice snow that is set on top. In this case, the permittivity $\epsilon_r$ of the snow influences the propagation velocity of the radar signal and has to be taken into account

$$ \Delta R_{thickness} = \frac{\Delta f_{IF} \cdot T \cdot c_0}{2 \cdot B \cdot \sqrt{\epsilon_r}} \quad (4) $$

Using an $\epsilon_r$ of 3.15 for pure ice in the millimeter-wave range [17]–[19] and a ramp time T of 8 ms, the evaluated thickness is approximately 0.115 m. The deviation to the expected thickness, which was determined in the previous measurements to 0.109 m (see Table II), is very small. Additionally, the permittivity has been directly measured at CEN using a classical snow in situ measurement system at lower megahertz frequencies. However, these resulted in a permittivity value of $\epsilon_r$ = 1.85 and with that a theoretical thickness of 0.150 m. In this case, the difference of the radar frequency to the frequency used for the permittivity determination is very large, and furthermore, the imaginary part is not given by the measurement [18], both effects impeding the validation. Also in this case, the deviation is also not far from the measured thickness. In general, the permittivity is dependent on the frequency, the liquid water content, the surface, the snow density, and the air temperature and should be taken into account for very accurate measurements. However, these observations are beyond the scope of this paper, but point out the interest to derive the permittivity more precisely.

## V. CONCLUSION

The experiments conducted with an 80-GHz FMCW radar illustrate the possibility of measuring the snow height at distances up to 4 m. The overall deviation in the snow height estimation is in the millimeter range and has been found to be better than 1 cm verified by the laser measurements.

In contrast to the laser, the radar measurements are averaged over a larger spot size on the surface, making the system more robust to local unevenness of the surface. By comparing the long-term radar measurements with the laser, the radar height observations have also shown larger height variations than the laser systems. This can be explained by the fact that the radar system is not calibrated over temperature changes and by the effect of snow surface transformations from wet snow into ice snow around the freezing point. These transformations have an influence on the reflective properties of the surface and the penetration depth of the radar signal in the snow layer resulting in minor inaccuracies. Therefore, additional measurements with several snow types are presented to analyze the influence on the surface reflection. It is also shown that the radar signal is able to penetrate snow layers and is convenient to analyze physical snow properties close to the surface depending on snow properties. In [20], experimental results are presented from active sounding frequencies up to 16 GHz using differential interferometry. It is shown that the absorption for wet snow is very sensitive to the water content and that the penetration depth decreases with rising humidity content. In addition, a microwave emission model of layered snow packs [21], [22] is used to determine the penetration depth for higher frequencies. According to that model, the penetration depth at 80 GHz is about 50 cm for dry snow, while it decreases to 2 cm for the humidity content of 1%. However, due to the numerous snow parameters and the inhomogeneity of the snow packs (snow grain diameters, humidity content, snow density, and surface roughness), a snow cover model based on precisely measured snow parameters, associated with a radiative transfer model as done in [20]–[23], would be required to determine relationships between propagation loss and typical snow types at 80 GHz more accurately.

It can be concluded that the 80-GHz FMCW radar system can be used for snow height determination with a precision of a few millimeters, which was the main objective of this experimental work. The system can be employed in severe weather conditions and is immune to fog, rain, snowfall, or changing light conditions due to well-known negligible attenuation by precipitation and can thus be used in autonomous Arctic stations [24]. Although experimental conditions during these test measurements were not adapted to verify this fact, it can be stated that an intense precipitation at the beginning of the measurements did not affect the signal perturbation [2]. Furthermore, the radar system allows measurements over a wide inclination range, which is necessary if an autonomous meteorological station settled on a buoy, is sloping, e.g., due to strong wind speeds that can occur over the Arctic ice pack. For that purpose, the slope must be systematically measured by an inclinometer.

However, to go further into detail and to interpret height variations smaller than 1 cm, the radar system should be calibrated over temperature and more systematic studies of different snow surfaces have to be performed, which is not the scope of this paper.

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[14] N. Pohl, T. Klein, K. Aufinger, and H. M. Rein, "A low-power wideband transmitter front-end chip for 80 GHz FMCW radar systems with integrated 23 GHz downconverter VCO," *IEEE J. Solid-State Circuits*, vol. 47, no. 9, pp. 1974–1980, Sep. 2012.

[15] N. Pohl and M. Gerding, "A dielectric lens-based antenna concept for high-precision industrial radar measurements at 24 GHz," in *Proc. 9th Eur. Radar Conf. (EuRAD)*, Oct. 2012, pp. 405–408.

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[17] M. T. Hallikainen, F. T. Ulaby, and T. E. van Deventer, "Extinction behavior of dry snow in the 18-to 90-GHz range," *IEEE Trans. Geosci. Remote Sens.*, vol. GE-25, no. 6, pp. 737–745, Nov. 1987.

[18] C. Mätzler, Ed., *Thermal Microwave Radiation: Applications for Remote Sensing*. Stevenage, U.K.: Inside Exploration Technologies, 2006.

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[24] A. Weill et al., "First observations of energy budget and bulk fluxes at Ny Ålesund (Svalbard) during a 2010 transition period as analyzed with the BEAR station," *Int. Scholarly Res. Notices Meteorol.*, vol. 2012, p. 12, Mar. 2012.

<figure><img src="image_14.png" /></figure>

**Serdal Ayhan** was born in Nußloch, Germany, in 1981. He received the Dipl.-Ing. degree in communications and electronics and the M.Sc. degree in information technology from the Mannheim University of Applied Sciences, Mannheim, Germany, in 2009 and 2010, respectively. He is currently pursuing the Ph.D. degree in radar technology with the Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany.

<figure><img src="image_15.png" /></figure>

**Mario Pauli** (S'04–M'10) was born in Lahr, Germany, in 1975. He received the Dipl.-Ing. degree in electrical engineering and the Dr.Ing. degree from the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, in 2003 and 2011, respectively.

He was an Intern with the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, in 2002, where he was involved in time and frequency domain measurement systems for the characterization of the 60 GHz indoor radio channel.

He was a Lecturer of Radar and Smart Antennas with Carl Cranz Series for Scientific Education. From 2004 to 2011, he was a Research Assistant with the Institute of Radio Frequency Engineering and Electronics, KIT, where he has been a Research Associate since 2011. He is currently the Co-Founder and the Managing Director with PKTEC GmbH, Paderborn, Germany. His research interests include electromagnetic field theory, radar and sensor systems, antennas, millimeter-wave packaging, and microwave heating.

<figure><img src="image_16.png" /></figure>

**Steffen Scherr** (S'09) was born in Speyer, Germany, in 1985. He received the Dipl.-Ing. degree in electrical engineering from the Karlsruhe Institute of Technology, Karlsruhe, Germany, in 2010, where he is currently pursuing the Ph.D. degree in radar technology with the Institute of Radio Frequency Engineering and Electronics.

His current research interests include system design and digital signal processing for radar systems, in particular, for frequency-modulated continuous wave radar systems.

<figure><img src="image_17.png" /></figure>

**Benjamin Göttel** (S'15) received the Dipl.-Ing. and M.Sc. degrees from the Mannheim University of Applied Sciences, Mannheim, Germany, in 2010 and 2011, respectively. He is currently pursuing the Dr.-Ing. degree with the Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany.

He has experience in off-chip and on-chip antenna design at high frequencies. His current research interests include on-chip power-combining approaches at millimeter-wave frequencies and pack-

aging solutions for millimeter-wave systems.

<figure><img src="image_18.png" /></figure>

**Akanksha Bhutani** (S'15) was born in Dehradun, India, in 1985. She received the B.Tech. degree in electronics and communication engineering from the Maulana Azad National Institute of Technology, Bhopal, India, in 2007, and the M.Sc. degree in information and communication engineering from the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, in 2012.

She was with the Institute of Radio Frequency Engineering and Electronics (IHE), KIT, where she was involved in high precision frequency detection

algorithms for radar applications. Since 2012, she has been a Research Associate with IHE. Her current research interests include LTCC technology based antenna-in-package concepts.
<figure><img src="image_19.png" /></figure>

**Sven Thomas** (S'11) was born in Bochum, Germany, in 1987. He received the M.Sc. degree in electrical engineering from Ruhr University Bochum, Bochum, in 2013.

Since 2013, he has been a Research Assistant with the Fraunhofer Institute for High Frequency Physics and Radar Techniques, Wachtberg, Gemany. His current research interests include the design of integrated SiGe circuits and the development of system concepts for industrial radar based measurements systems with operating frequencies up to 300 GHz.

Mr. Thomas is a member of VDE.

<figure><img src="image_20.png" /></figure>

**Timo Jaeschke** (S'07) was born in Hattingen, Germany, in 1984. He received the Dipl.-Ing. degree in electrical engineering from Ruhr University Bochum, Bochum, Germany, in 2011.

Since 2011, he has been a Research Assistant with the Institute of Integrated Systems, Ruhr University Bochum. His current research interests include frequency synthesis, integrated ultra-wideband frequency modulated continuous wave radar systems up to 240 GHz, high-resolution radar imaging, and highest precision distance and vibration measurements for various applications.

Mr. Jaeschke is a member of VDE, ITG, EuMA, and DGON. He was a recipient of the IEEE Microwave Theory and Techniques Society Graduate Fellowship Award in 2013 and the Airbus Defense and Space ARGUS Award in 2010. He was a co-recipient of the EuMIC Prize in 2012 and the 2015 Best Demo Award of the IEEE Radio Wireless Week.

<figure><img src="image_21.png" /></figure>

**Jean-Michel Panel** received the Dipl.-Ing. degree in electronics, electrical engineering and automation from Grenoble University, Grenoble, France.

Since 1985, he has been with the Center d'Etudes de la Neige (French Snow Research Center, Saint-Martin-d'Hères, France, where he is currently the Instrumentation Team Head. His current research interests include instruments to measure snow and weather parameters, automated mountain weather stations, and cold room maintenance.

<figure><img src="image_22.png" /></figure>

**Frédéric Vivier** received the Ph.D. degree in oceanography from the University Pierre et Marie Curie, Paris, France, in 1999.

He was a Post-Doctoral Fellow with the Applied Physics Laboratory, University of Washington, Seattle, WA, USA. He was a Research Scientist with the Center National de la Recherche Scientifique, LOCEAN, Institute Pierre-Simon Laplace, Paris, France. His current research interests include polar research, high-latitude oceanography, satellite altimetry, and ocean electrodynamics.

<figure><img src="image_23.png" /></figure>

**Laurence Eymard** was born in 1954. She graduated from the École Normale Supérieure, Université Pierre et Marie Curie, Paris, France, in 1978, where she received the Ph.D. degree in physics of the atmosphere in 1985.

She is currently a Senior Scientist (Directeur de Recherche) with the Center National de la Recherche Scientifique and the Director of Observatoire des sciences de l'Univers Ecce Terra with Université Pierre et Marie Curie, Paris, France. She was involved in the field of spaceborne microwave radiometry:

PI of the ERS/ENVISAT and Jason altimeter mission and the initiator of the humidity sounder (SAPHIR) on the Megha/Tropique Indian-French mission (launched in 2011). Her current research interests include atmosphere dynamics (boundary layer), atmospheric water cycle, and ocean-atmosphere interactions: coordination and participation in several experimental studies.

<figure><img src="image_24.png" /></figure>

**Alain Weill** received the Ph.D. degree from the University of Paris VI, Paris, France, in 1971.

From 1973 to 2001, he was involved in atmospheric and ocean meteorology research and ground based remote sensing of atmosphere and ocean. He is a Senior Scientist, the Directeur Emeritus for research with Laboratoire Atmosphères Milieux Observations spatiales, CNRS, Paris, France, and a member of the Acoustical Society of America. He is currently with Arctic Research, Paris, where he is involved in the measurements

and interpretation of ice-snow-atmosphere interactions and energy budget at the ocean surface.

<figure><img src="image_25.png" /></figure>

**Nils Pohl** (GS'07–M'11–SM'14) received the Dip.-Ing. and Dr.-Ing. degrees in electrical engineering from Ruhr University Bochum, Bochum, Germany, in 2005 and 2010, respectively.

From 2006 to 2011, he was a Research Assistant with Ruhr University Bochum, where he was involved in integrated circuits for millimeter-wave (mm-wave) radar applications. In 2011, he was an Assistant Professor of Integrated Systems with Ruhr University Bochum, where he became a Full Professor of Integrated Systems in 2016. Since 2013,

he has been the Head of the Department of Millimeter-Wave Radar and High Frequency Sensors with the Fraunhofer Institute for High Frequency Physics and Radar Techniques, Wachtberg, Germany. He has authored or co-authored over 100 scientific papers and has issued several patents. His current research interests include ultrawideband mm-wave radar, design and optimization of mm-wave integrated SiGe circuits, and system concepts with frequencies up to 240 GHz and above, as well as frequency synthesis and antennas.

Prof. Pohl is a member of VDE, ITG, and URSI. He was a co-recipient of the 2009 EEEfCom Innovation Award, the 2012 EuMIC Prize, and the 2015 Best Demo Award of the IEEE Radio Wireless Week. In 2013, he was a recipient of the Karl-Arnold Award of the North Rhine-Westphalian Academy of Sciences, Humanities, and the Arts.

<figure><img src="image_26.png" /></figure>

**Thomas Zwick** (S'95–M'00–SM'06) received the Dipl.-Ing. and Dr.-Ing. degrees from the Universität Karlsruhe (TH), Karlsruhe, Germany, in 1994 and 1999, respectively.

From 1994 to 2001, he was a Research Assistant with the Institut für Höchstfrequenztechnik und Elektronik (IHE), TH. In 2001, he joined the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, as a Research Staff Member. From 2004 to 2007, he was with Siemens AG, Lindau, Germany, where he managed the RF devel-

opment team for automotive radars. In 2007, he became a Full Professor at the Karlsruhe Institute of Technology, Karlsruhe, where he is the Director with IHE. He has authored or co-authored over 300 technical papers and 20 patents. His current research interests include wave propagation, stochastic channel modeling, channel measurement techniques, material measurements, microwave techniques, millimeter-wave antenna design, wireless communica- tion, and radar system design.

Dr. Zwick and his research team received over 10 best paper awards on international conferences. He served on the Technical Program Committees (TPC) of several scientific conferences. He was the General Chair of the International Workshop on Antenna Technology at Karlsruhe in 2013 and the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility in Heidelberg in 2015. He was also the TPC Chair of the European Microwave Conference in 2013. From 2008 to 2015, he was the President of the Institute for Microwaves and Antennas. From 2013 to 2015, he was a Distinguished IEEE Microwave Lecturer, teaching “QFN based packaging concepts for millimeter-wave transceivers.”