Davidson, Kenneth L.

Doss-Hammel, Stephen M.

Frederickson, Paul A.

Tsintikidis, Dimitri

Calhoun, Institutional Archive of the Naval Postgraduate School

Calhoun: The NPS Institutional ArchiveFaculty and Researcher Publications Faculty and Researcher Publications2003-02Near-Surface Scintillation in the MarineAtmospheric Layer During the Red Field CampaignDoss-Hammel, Stephen M.http://hdl.handle.net/10945/40345 P6.5              NEAR-SURFACE SCINTILLATION IN THE MARINE ATMOSPHERIC LAYER  DURING THE RED FIELD CAMPAIGN   Stephen M. Doss-Hammel* and Dimitri Tsintikidis Atmospheric Propagation Branch, SPAWAR Systems Center, San Diego, CA  Paul A. Frederickson and Kenneth L. Davidson Department of Meteorology, Naval Postgraduate School, Monterey, CA                                                            * Stephen M. Doss-Hammel, Atmospheric Propa-gation Branch, SPAWAR Systems Center, Code 2858, San Diego, CA 92152; (619) 553-4578, email: hammel@spawar.navy.mil   1. INTRODUCTION  Turbulence in the marine atmospheric surface layer can induce substantial perturbations in propagating infrared (IR) or optical signals.  These perturbations result in scintillation, which is a fluc-tuation in received signal intensity.  Scintillation can be quantified by the single parameter Cn2, the refractive index structure parameter. It is neces-sary to measure Cn2 from routine measurements in order to evaluate, predict, and compare EO sys-tem performance in operational environments and optimize EO systems for expected Cn2 values from climatological measurements and models. The Rough Evaporation Duct (RED) field test, conducted at Oahu, Hawaii, during August-September 2001, presented an excellent opportu-nity to quantify scintillation and to validate scintilla-tion and transmission models.   We will comment on the progress toward the ultimate goals for this test. First, what is the influ-ence of ocean waves on the near-surface propa-gation environment?  The second goal is a deter-mination of the accuracy of current Cn2 estimation models for various ocean-atmosphere interaction scenarios including stable, near-neutral, and un-stable conditions, and possible upgrade paths for the models.    2. THE INFRARED PROPAGATION  EXPER-IMENT  The field test was based upon measurements collected by a transmissometer system. The transmissometer consists of a broad-beam source coupled to a beam-forming telescope, and a re-ceiving telescope connected to a data acquisition system. The broad-beam source comprised 18 halogen lamps modulated by a 690-Hz chopper wheel with a usable beam width exceeding 25°. The chopper signal is relayed to the receiver with a 162.1 MHz radio link. The receiver telescope has a primary mirror that is gold-plated paraboloid 20 cm in diameter and a focal length of 1.22 m (f/6).   The receiver system consisted of the tele-scope, a mid-wave infrared detector, and signal acquisition hardware.  The detector is a non-imaging device cooled to 77°K. For a given source radiance the signal-noise ratio in the field is de-termined by the detector responsivity, noise den-sity, and bandwidth.  The mid-wave detector is a 2-mm diameter circular InSb photodiode mounted below a cold optical filter with an almost square band pass between 3.5 µm and 4.1 µm. The signal from the detectors is separated from the chopped carrier waveform by means of a lock-in amplifier system.  The infrared portion of the RED experiment was conducted on a propagation path approxi-mately 10 km long connecting an IR broad-beam transmitter onboard R/V FLIP (13 m above water-line) and an IR telescope receiver at Malaekahana (3 m above ground) on the northeast coast of Oahu.  The broad-beam source accommodated the pitch, roll, yaw, and translation of FLIP. The scintillation measurements were taken in the mid-wave (3.6 µm to 4.1 µm) every 15 minutes and at a 300 Hz sampling rate. Each measurement was 110 seconds long.  Due to FLIP's tidal motion (±200 m in the NE-SW direction), hourly telescope alignments were necessary to ensure proper signal reception. The data collection proc-ess occurred almost uninterruptedly for 16 con-tinuous days.  The meteorological data were measured using the Naval Postgraduate School (NPS) `flux' buoy that was anchored approximately in the middle of the transmissometer path. The suite of meteoro-logical instruments onboard the NPS flux buoy provided near-surface meteorology: air tempera-ture, pressure, humidity (at various heights), wind speed and direction, sea surface temperature. In addition, air-sea fluxes and optical turbulence was measured. The NPS buoy meteorological data is a crucial element in the analysis of the transmission and scintillation time-series. Concurrent environ-mental measurements were obtained from a me-teorological station mounted on top of a 30-ft mast outside the receiver station.  3. DATA ANALYSIS  There are two components of the infrared sig-nal analysis. First we will utilize the abundant me-teorological and surface flux measurements made during the test. The data from the mid-path mete-orological buoy will be an essential part of this ef-fort. The second component is a signal processing effort. The signal levels recorded from the field test are unexpectedly weak and it is meaningless to perform Cn2 calculations using raw, unfiltered data. An approach to separate signal from noise without destroying the turbulent nature of infrared signal is necessary, and we will describe our use of a wavelet filtering scheme to attempt to accomplish the task.  The analysis of scintillation data for the RED test proved to be especially problematic. For transmission measurements the data acquisition mandated an averaging over a one minute inter-val, while the scintillation measurements required a 300 Hz sampling rate. Calculation of the ex-pected free-space intensity for the system im-pelled us to set the voltage scale high enough to capture events at twice the free-space value of 1.02 mV. Almost all of the recorded data were be-low 0.4 mV. This compounded the usual noise problems by introducing digitization noise as well.   3.1  Wavelet Filtering  The intensity time-series data was filtered us-ing a Daubechies wavelet filter (Daubechies 1992). Each scintillation data-set consisted of 215  samples, collected at a 300 Hz rate. In the wavelet analysis the time-series data is transformed from 215 signal amplitudes to 215 wavelet coefficients.  Figure 1: A 2-second infrared signal segment and the corresponding low-, mid-, and high-pass filtered components. Each wavelet coefficient represents the correlation between a wavelet of a particular scale at a par-ticular location in the time-series. For our data, there are 15 wavelet scales.  Our implementation of the wavelet transform generates a filter bank that trisects the data. After transforming to the wavelet scale space, two band cut-offs are selected, corresponding to a low-pass and a high-pass filter set, with a remaining middle band. The low-pass filter consisted of the six larg-est wavelet scales, and in frequency-space this roughly corresponded to all the data less than 1 Hz in frequency. The high-pass filter consists of the four smallest wavelet scales, and in frequency space these scales roughly represent the fre-quency components greater than 35 Hz.  The partition of the signal components into three bands is shown in Fig.1. The nature of the three bands can be seen in the figure with the un-filtered signal appearing in the uppermost panel. The second panel shows the low-pass band, which corresponds to the mean signal level and contains only low-frequency modulations of what is sometimes called the transmission value. The bottom panel, with the high-pass band, contains the high-frequency component.  The high-pass band contains much of the system noise that ac-cumulates in the data-analysis sequence. Finally, the mid-pass band contains the portion of the sig-nal that generates most of the variance that is generated by scintillation effects for the full time-series.   3.2  Signal-Noise Ratio  The filtering process described above has been applied to previous field datasets (Doss-Hammel, 2002, and Tsintikidis, 2002) and for those cases, the amplitude of the high-frequency was substantially smaller than the amplitude of the mid-pass band. We have calculated the signal-noise ratio SNR for these filtering operations: = 2210log10highmidSNR σσ            (1)  For most of our previous applications we have found that SNR>20. With a SNR at this level we can be assured that the mid-pass band contains almost all of the fluctuations induced by turbu-lence, and that the overall signal is not unduly con-taminated by noise. In this test, the signal quality was much poorer. As can be seen in fig. 1, we can no longer assume that the signal displayed in the mid-pass band is primarily due to turbulence-induced scintil-lation. The noise evident in the high-pass band must be assumed to contaminate the mid-pass band as well, and the noise contribution in that band is substantial. A signal with noise will gener-ate a larger variance than the same signal without noise.  An additional problem with the Cn2 calculation developed due to the geometry of the propagation path. The bearing of FLIP from the receiver site was 86°, and the sunrise azimuth varied from 80.4° to 86.8° over the fifteen-day test period. So-lar glint into the receiver field of view increased the variance for approximately 3 hours after sunrise in each of the daily Cn2 calculations.  A typical 24-hour record of SNR is shown in fig. 2. It is apparent that the SNR never exceeds 5 and that SNR<0 for the early morning solar in-terference part of the day.  To calculate the infrared turbulence we determine Cn2 using a saturation-resistant method (Hill 1978, Wang 1978):  Figure 2: The signal-noise ratio in dB for the day 246 (shown in local time). Note that the solar interference in the morning sharply increases the noise level. 33/72ln02 −= LDCC tIn σ              (2) where L=10470 m is the propagation path length from transmitter to receiver; Dt is the transmitting aperture diameter;  is the variance of the normalized log-intensity; and the factor C  is a function of the ratio between the receiver and transmitter apertures. The signal-noise ratio is a troublesome problem which we had not encoun-tered in this severity during previous field experi-ments. The low values of SNR make any variance calculation for the last 5 days of the experiment unreliable. The calculations for the other days must be considered to be contaminated with some amount of noise: the measured infrared C2ln Iσ0n2 is lar-ger than the true value. An indication of this prob-lem can be seen in the comparison of three differ-ent determinations of Cn2 in fig. 3. ttl4 tutscCWe believe that the infrared signal is inflated due to the presence of noise, and that the Cn2 determi-nation by means of direct turbulence measure-ments, or from mean measurements using mean models should be considered better estimates.   5.    REFERENCES  Daubechies, I., 1992: “Ten Lectures on Wavelets”, SIAM. Doss-Hammel, S. and C. R. Zeisse, ``Refraction and scintillation in the atmospheric surface layer'', Proc. SPIE 4821, 2002. Hill, R. J. and R. G. Ochs, 1978: “Fine calibration of large-aperture optical scintillometers and an optical estimate of inner scale of turbu-lence.” Applied Optics 17, pp. 3608-3612. Tsintikidis, D. and S. M. Doss-Hammel, ``Trans-mission and scintillation measurements for a summertime desert environment'', Proc. SPIE 4884, 2002. Wang, T.-I, G. R. Ochs, and S. F. Clifford, 1978: ``A saturation-resistant optical scintillometer  Figure 3: The refractive index structure parameter by three different methods: the top black line indi-cates the infrared signal measurement, the middle green dashed line is the direct turbulence measure-ment; the bottom blue line gives the bulk model es-timate.   We have calculated the refractive index struc-ure parameter Cn2 three different ways (fig. 3). The uppermost black line shows the infrared sig-nal measurement from the infrared transmission link, calculated with eqn. (2). The middle green dashed line is determined from the direct turbu-lence value from the sonic anemometers on the Naval Postgraduate School ‘flux’ buoy that was moored at the mid-point of the propagation path. Finally, the bottom blue line is computed from bulk Cn2 estimates, developed from the relationship between mean parameters and turbulent fluxes. This calculation is based upon the Monin-Obhukhov surface layer similarity theory.  A similar comparison for data from previous field experiments resulted in a much better com-parison between the infrared signal Cn2 and the bulk Cn2 calculations. This is another indication hat the results from the infrared signal contain a arge amount of noise.  .   CONCLUSIONS The determination of the near-surface scintilla-ion for the RED experiment was marred by an nexpectedly weak infrared signal. We are con-inuing to investigate the factors contributing to this mall signal. We have indicated an example of the omparison between the infrared determination of n2 and the determination by means of meteoro-logical measurements along the propagation path.  to measure Cn2'', J. Opt. Soc. Am. 68, pp.334-338. 

Near-Surface Scintillation in the Marine Atmospheric Layer During the Red Field Campaign

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Near-Surface Scintillation in the Marine Atmospheric Layer During the Red Field Campaign

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