FAQs

The scattering of acoustic pulses propagating through the atmosphere is generated by turbulent density fluctuations (velocity and temperature). In case of backscattering used typically for commercial sodar systems only turbulent temperature fluctuations contribute.

Therefore, low turbulence as observed during stable stratification and/or calm winds creates a significant decrease of the received signal intensity. High environmental noise raises the noise floor and further decreases the signal quality (i.e. the signal noise ratio, “SNR”). During strong convective conditions the high turbulence may allow under perfect conditions measuring ranges up to 1000 m and more. On the top of the boundary layer rapid decrease of SNR is observed limiting the maximum range of useful sodar data. In many situations the SNR becomes insufficient already far below the top of the boundary layer.

The atmospheric attenuation of sound depends on temperature and humidity, and as a rule, dry and cold environments increase attenuation while wet and warm environments decrease attenuation.

In moderate rain conditions the noise of the rain drops hitting the loudspeakers of the acoustic antenna will raise the acoustic background noise and will reduce SNR to some extent, in heavy rain showers the sodar data could temporarily be missed completely.

In principle, a sodar system can easily measure under foggy conditions and also inside clouds, contrarily to Doppler lidar systems. However, as foggy conditions are often related to low turbulence the height availability might be reduced.

The METEK internal record of a maximum measuring height of 1600 m was achieved at a costal site in Southeast Asia. Strong winds and significant orographic structures created a strong backscattered signal while the attenuation at 35° C and 90 % humidity was low. In the Arctic on a flat, closed ice flow the maximum measuring height can be low as 50 m.

A sodar system requires a minimum of three non-coplanar beams to get three independent measures of the atmospheric wind flow (represented by a 3D vector). Unavoidably, these three beams will divert with increasing height. To compute a representative common wind vector from the three independent measurements a horizontally homogenous wind flow is assumed. This is fulfilled in most atmospheric conditions for time intervals of 10 … 30 min. However, under strong convective conditions this basic assumption is still not fulfilled. The consequence is an increase of the statistical error in the computed 3D wind vector (i.e. the derived wind vector will fluctuate in direction and length).

Besides the limitation of height availability due to the noise produced by rain or hail any kind of precipitation will also produce an extra signal contribution representing the falling speed of the precipitation particles. As the falling speed of precipitation is in good approximation the same for all antenna beams, this effect is eliminated when the 3D vector is calculated. The calculated wind profiles can be used also under such conditions.

A fixed echo is generated as a reflection (not a backscattering) of a part of the transmitted acoustic pulse at (solid) non-moving structures like buildings, towers, trees or similar. It is always an extra contribution to the received signal, therefore the backscattered intensity (reflectivity) of the sodar system will show a certain increase in a fixed height.

As the reflecting structures do not move, the extra signal will always show up at a Doppler frequency of zero. This can be used as a second identifier of a fixed influence.

During strong convective conditions a high atmospheric signal can completely mask a fixed echo contribution, but under low turbulence a fixed echo will preferably show up and can even dominate the received atmospheric signal.

Below an inversion layer a maximum of the backscattered intensity (reflectivity) is observed due to the strong turbulent fluctuations at this height. Contrarily to a fixed echo this height will not be fixed but will vary over time.

Customer often misunderstand the consequence of adjusting an exaggerated measuring height. On one hand side, the intention to get as high as possible is understandable, but such adjustment will reduce the number of pulses for a given time interval (e.g. 10 minutes). Sound speed is relatively slow and plays a role here. A reduced number of pulses will decrease the SNR (see SDR#001) also for lower measuring heights, so the signal quality and the height availability will correspondingly decline. It is a good advice to determine first the really required measuring height and to adjust the maximum measuring height accordingly.

The multi frequency mode allows a consecutive transmission of several frequencies within one acoustic pulse sequence. Because the atmospheric attenuation decreases for lower frequencies, the multi frequency pulse starts with a low frequency followed by increasing frequencies. This allows to use low frequencies for signals backscattered from upper heights while the highest frequencies are used for the lowest measuring heights. The separation of selected frequencies should be of the order of 250 Hz to avoid ambiguities in the received signal, so the number of selected frequencies should be limited to 4 to 5. Example: 1300 Hz, 1550 Hz, 1800 Hz, 2050 Hz, 2400 Hz.

For operational installations a regular maintenance on a 6- or 12-monthly interval is recommended depending on the relevance of the derived data. The core maintenance should include a visual inspection of all cables and connectors, mechanic fixtures and of the absorbing foam used inside the acoustic shield. Further each loudspeaker should be checked in view of efficiency and phase stability. Test signals with given spectral characteristics should be entered to the electronics to check the internal electronic noise and the signal analysis. A comprehensive check of the measured data in view of plausibility for a period of several weeks is mandatory to identify occasionally occurring operational constraints.

Snowflakes and raindrops can absorb partly or completely the acoustic pulse when crossing the measuring paths. Raindrops and hailstones can create artificial signal pulses on the receiving transducers. In addition, obstacles like birds can block the sensor paths. All such events are identified by the internal plausibility checks in all our uSonic sensors, and the data output is blanked. Also, a geometric misalignment of the sensor head is identified as an invalid data event, and the data output is blanked. Therefore, the derived averaged data for wind (and also for turbulence quantities) are calculated without being influenced by erroneous measurements.

Snowflakes and raindrops can absorb the acoustic pulse when crossing the measuring paths. Raindrops and hailstones can create artificial signal pulses on the receiving transducers. All such events are identified by the internal plausibility checks in all our uSonic sensors and are either blanked in the data output or rejected from further use in the turbulence extension. The amount of rejected data is reported by the value of “SDQ”. In extreme heavy rain showers the “SDQ” value can go down to 50%, the residual data can be seen as free of any interference.

The sensor head heating consists of two different heating devices, a direct heating of each transducer and a heating of the sensor head structure:

A) The transducers are heated by PTC elements which draw more power at lower PTC temperature. As a consequence, the PTC heating power is maximum immediately after the heating or the complete sensor have been switched on. With increasing time, the heating power will fall to a terminal value according to the outside temperature.
B) The sensor head structure is heated by a constant value by inside heating wires.

If operated outside, the surfaces of the transducers and of the sensor head will often show only a minimum excess temperature as compared to the ambient temperature due to the surface cooling.

All operational components of our uSonic sensors are specified for temperatures down to -30°C, test wise sensors have been operated even at temperatures below -50°C. The sensor head heating is required to prevent icing on the surfaces of the sensor head.

Whenever the sensor head heating is operated its warmed surfaces could potentially create an artificial vertical air flow when very small air velocities are present, typically <1m/s.

The mean temperature as measured by the sonic will lower by a about 1 Kelvin after the heating is switched on, and vice versa. This has an influence on the calculated turbulent heat flux of the turbulence extension, occasionally a single biased value could be observed for such event.

In order to document the operational status of the sensor head heating check the leading character of each data output line which will vary between “M:” and “H:” for standard sonics. In case of a malfunction of a sensor head heating the leading character will change to “E:”. For sonics with turbulence extension the operational status and the correct function is documented in the header line.

For uSonic sensors built in 2020 or younger the sensor head heating can be set to the parameter value “3” (“HT=3” or “HTM=3”). In this heating modes the sensor head heating is switched on if the measured outside temperature is falling below +4.5° C and is switched off if the measured outside temperature is rising above +5.5° C (as for “HT=2” or “HTM=2”, automatic mode). In addition, it is checked whether valid data for wind and temperature are derived. Only in case of missing valid data the heating will be activated as icing is assumed. For many installations “HT=3” or “HTM=3” can save a significant amount of energy.

The mean temperature is derived from the measurement of the acoustic velocity along the measuring paths (“acoustic” temperature), therefore it corresponds to the virtual temperature often used in meteorology if buoyancy effects are considered. It can be biased by +1 … +2 Kelvin in case of coverage of transducers surfaces, e.g. by dust or salt. Because of this we do not specify a certain accuracy for the temperature measurement as the installation conditions are unknown and because our uSonic sensors are seen as quasi maintenance-free.

The turbulent fluctuations of the temperature are always measured with very high accuracy (<0.01 Kelvin), therefore the measurement of the sensible heat flux is not affected.

With increasing heights of the installation site and reduction of air density the signal intensity of the transducers will decrease. Typically, installations sites up to 4000m can be operated. In case that the installation in upper heights is intended this should be reported to METEK in order to raise the internal signal intensity (which could possibly cause a certain raise of the internal noise).

All materials of the sensor head including transducers are made from seawater resistant stainless steel, so a marine environment should not cause any harm to the sensor. It is recommended to apply some adhesive grease to all connecting parts.

The most important protection is an adequate lightning rod being installed near to the sensor head, so a direct lightning will hit this rod and not the sensor head. The lightning rod must be connected to an extra high current-carrying cable isolated against the mast structures. A lightning rod can also serve as a resting place for birds instead of the sensor head.

In principle there is no need any maintenance or recalibration of the uSonic sensors as long as valid data are output. In case of easy access it is recommended to check and clean the transducers surfaces from dust, salt, bird droppings, etc..

The accuracy of sonics for the wind measurement is determined mainly by shadow effects at the transducers and flow distortion effects at the sensor head structures. All measuring paths are influenced by these effects corresponding to their position and alignment relative to the inflow angle. Beside of this, also the so called zero-calibration must be regarded which compensates a possible offset in the wind components.

For conventional uSonics, wind tunnel derived corrections schemes are applied to compensate these shadow and flow distortion effects. The typically achieved accuracy is 2 % for wind speeds >5 m/s, or 0.1 m/s for vertical inflow angles within ± 15°.

For multi path uSonics (uSonic-3 Class A MP, uSonic-3 Cage MP) the most advantageously positioned paths are automatically determined and selected. The typically achieved accuracy is 1 % for wind speeds >5 m/s, or 0.05 m/s for vertical inflow angles within ± 25°.

It should be noted that the given accuracies apply to perfectly zero-calibrated sensors.

For the turbulence extension the uSonic sensors take the valid instantaneous data and calculate online means, standard deviations and covariances of x-, y- and z- wind components and acoustic temperature. In addition, further turbulence quantities are calculated, such as turbulent fluxes of heat and momentum, Monin-Obukhov length, drag coefficient etc.. The number of used valid instantaneous data is reported in the header line of the turbulence data set.

The presented sample of a diurnal variation of the turbulent heat flux was measured by a uSonic-3 Scientific at a sample rate of 10 Hz and a height of 10 m. During the whole day clouds were absent. During nighttime the radiative cooling provides a negative heat flux of about -50 W/m², during daytime the radiative warming provides a positive heat flux of maximum values up to 700 W/m². Typically, the variation in time increases during daytime, for many applications a moving average of 30 – 60 min is recommended.

The raw spectral power is a superposition of signal due to radar echoes and of noise background. The noise background would result in a permanent non-zero drop size distribution and consequently in non-zero values of liquid water content and rain rate — even in precipitation free conditions. In order to avoid this bias, the noise background is estimated and removed. The noise estimation is based on the so called Hildebrand-Sekhon method. Assuming white noise it provides one mean value for describing the noise background, which is subtracted from the power spectrum. Ideally the noise-corrected spectral power (ncsp) would be zero, if there is no signal. Due to stochastic fluctuations of the actual spectral noise some ncsp-values are positive and some are negative. In case of a symmetric stochastic distribution the probability of both signs should be 0.5. Due to finite number effects the actual estimate of the noise background is slightly biased and in addition, the stochastic power distribution is not quite symmetric in reality. Therefore the implementation of the method for the MRR provides a slight preference of positive ncsp-values. The negative sign is now kept for the calculation of negative spectral drop numbers in order to avoid bias in integral products as radar reflectivity, liquid water content and rain rate.

Consequently one would expect occasional occurrence of negative signs also for these integral parameters. This is not the case due to the following reasons:

1. The reflectivity is given on a logarithmic scale, which can only represent positive numbers. Values with negative sign are replaced by blanks.
2. Rain rates which do not meet certain criteria (threshold of signal to noise ratio, coherence in adjacent range gates) are replaced by exact zero. (The same is true for the corresponding liquid water content).
3. Only positive velocities are calculated.

The MRR standard signal processing software is adapted to the liquid phase of precipitation. It is optimized for deriving drop size distributions and corresponding integrals as for example liquid water content, rain rate or mean Doppler velocity.

In case of snow (or graupel, hail) the standard signal processing does not provide physically meaningful results, because the relations of scattering cross section versus mass and fall velocity are very different for ice crystals than for water droplets. Moreover, certain frequency intervals of the raw spectra are discarded in order to achieve stable results (see e.g. Peters et al., 2005). Particularly in case of snow the main spectral power can be concentrated in the discarded frequency range resulting in seemingly low radar sensitivity.

Maahn and Kollias (2012) have developed a special algorithm for snow detection, which is available on the web mrr_snow. The input needed for initialization is the raw spectrum as provided by the MRR. This algorithm provides reflectivity and higher spectral moments of snow echoes with enhanced sensitivity and has been checked by the authors against simultaneous measurements with a more sensitive cloud radar.

Most variables are corrected for path integrated attenuation (PIA). If PIA exceeds 10 dB, those variables (including PIA itself) are no longer considered trustworthy and the results are replaced by blanks.

In addition to the attenuation corrected reflectivity Z also a non-corrected version z is issued, which shows relative reflectivity structures also at ranges, where the absolute reflectivity cannot be determined.

The Doppler velocity w is not biased by attenuation and is therefore issued for all heights. Note, that w is issued, even, if there is no significant signal.

Ideally there should be no signal in case of no rain and Z (represented on a logarithmic scale) should be -∞. In reality there are mean and stochastic noise contributions, which remain in case of no signal. In FAQ MRR#001 is explained how the mean noise level is estimated and subtracted. Due to stochastic fluctuations the noise corrected samples are distributed around zero. Negative values cannot be presented on the logarithmic scale and are replaced by blanks, positive values are issued.

The positive branch of the Z-distribution during precipitation-free conditions can be analyzed to determine the (height dependent) detection limit of the MRR. If we assume a Gaussian distribution of Z (in the linear domain), centered at zero, the mean value of the positive branch is equal to the standard deviation of the distribution. The most simple estimate of the detection limit is based on the mean value of (more precisely: the arithmetic mean calculated in the linear domaine of Z). A value twice (tree times) as high as the mean value indicates the presence of signal with 76% (92%) probability.

In reality the distribution is not centered at zero but at some small positive value, because the noise estimation method is slightly biased. Therefore this simple estimate is conservative. If desired, the distribution center can be determined by counting the ratio of positive and negative (blanks) values and consulting the error function. Further refinement is possible by replacing the Gaussian distribution by a Chi-Square distribution with n-1 degrees of freedom, where n is the number of averaged power spectra. (n ≈ 58 for “Processed Data”).

Definition:

w is the first moment of the noise-corrected power spectrum.

Equivalent:

w is the reflectivity weighted mean fall velocity.

Other weighted mean velocities (for example mass-weighted) can be calculated by post-processing on the basis of the drop size distributions.

It is assumed that w is always downward directed (positive sign). Upward velocities (w_up ) will be aliased to w = w_up + w_nyquist with w_nyquist = 12.08 m/s.

w is always calculated, even, if there is no significant signal present. In the latter case w has no physical meaning but it is helpful for diagnostic purposes in case of malfunction of the MRR.

The condition RR = 0.00 can be used for masking non-physical values of w at heights with PIA < 10 dB. A masking algorithm of w working at all ranges (including PIA ≥ 10 dB ) can be based on a (height dependent) threshold z_t of z , which has been defined on the basis of mean values of z (= Z ) in precipitation-free conditions. (See FAQ MRR#004 for details).

The radar reflectivity factor is defined as

with drop diameter and  drop size distribution.

If the radar wavelength  is much longer than  (= valid range of Rayleigh approximation) the volume reflectivity  (= backscatter cross section per volume) is related to  by

where  depends on the dielectric constant of water.  can be derived directly from the received echo power using the radar equation.

If  is not much smaller than  the relation between  and  does not hold, and thus cannot be inferred from the received echo power. For many purposes it is nevertheless useful to convert  using the above relation. The result is referred to as “equivalent radar reflectivity factor” .

approaches  for .

Although this condition is not fulfilled for the MRR, the radar reflectivity factor  can be determined by MRR because the drop size distribution is known.

For comparison with weather radar (with longer wave lengths than MRR wave length)  is the preferable variable.

A given reflectivity leads to an echo signal which is weaker the farther the scattering volume is. Therefore, a reflectivity-calibrated output needs a gain which increases with increasing distance of the scattering volume. Thermal and electronic noise, which is present at the input of the receiver is therefore increasingly amplified for increasing measuring ranges.

The retrieval algorithm for the Doppler velocity calculates the center of gravity in the 1/e environment of the spectral peak. Even in case of pure noise the spectrum has a peak at some random position.

Consequently, the Doppler velocity has no physical meaning, if no rain has been detected. Nevertheless, the output is not suppressed because it can contain hints on the character of potential interference sources.

For automated physical interpretation of the Doppler velocity, its values should be discarded, if no rain is detected.

The tables provide the center diameters corresponding to spectral velocities. While the spectral velocities are equidistant the diameters are not equidistant, due to the non-linear relation between fall velocity and diameter.

The height dependence of density in the (standard) atmosphere leads to a height-dependent fall velocity of drops of a given size. Therefore, the center diameter corresponding to a given spectral velocity is height dependent.

The MRR does no observe the signal of single drops but the superposition of signals from many drops in the scattering volume. Therefore, the lower size threshold, which can be observed depends on the actual number density of the corresponding size class. This is not a constant but depends on the actual rain event.

In case of MRR only drops with a fall velocity (in still air) of more than or equal to 0.75 m/s are included in the analysis. This corresponds (in still air) to a minimum diameter of 0.245 mm at ground level.