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Water Level and Wave Height Estimates at NOAA Tide Stations from Acoustic and Microwave Sensors [Journal of Atmospheric and Oceanic Technology](Journal of Atmospheric and Oceanic Technology Via Acquire Media NewsEdge) ABSTRACT The National Oceanic and Atmospheric Administration (NOAA) is transitioning the primary water level sensor at the majority of tide stations in the National Water Level Observation Network (NWLON) from an acoustic ranging system to a microwave radar system. Field comparison of the acoustic and microwave systems finds statistically equivalent performance when temperature gradients between the acoustic sensor and water surface are small and when significant wave height is less than roughly 0.5 m. When significant wave height is greater than approximately 0.5-1 m, the acoustic system consistently reports lower water levels. An analysis of 2 months of acoustic and microwave water level data at Duck, North Carolina, finds that the majority of differences between the two sensors can be attributed to systemic errors in the acoustic system and that the microwave system captures water level variability with higher fidelity than the acoustic system. NWLON real-time data products include the water level standard deviation, a statistic that can serve as a proxy for significant wave height. This study identifies 29 coastal water level stations that are candidates for monitoring wave height based on water level standard deviation, potentially adding a significant source of data for the sparsely sampled coastal wave fields around the United States, and finds that the microwave sensor is better suited than the acoustic system for wave height estimates. (ProQuest: ... denotes formulae omitted.) 1. Introduction The National Oceanic and Atmospheric Adminis- tration (NOAA) National Ocean Service (NOS) manages the National Water Level Program (NWLP) to meet NOAA's mission requirements for coastal waterlevelinformation.TheNWLPisamajorobser- vational program within NOS and serves as a federal component of the Integrated Ocean Observing System (http://www.ioos.noaa.gov/) and the Global Sea Level Observing System (http://www.gloss-sealevel.org/). A fundamental component of the NWLP is the National Water Level Observation Network (NWLON), a net- work of more than 200 long-term, continuously oper- ating water level stations around the United States, including island possessions, territories, and the Great Lakes (http://tidesandcurrents.noaa.gov/). Since the early 1990s, the primary water level mea- surement system at most NWLON stations has been an acoustic time-of-flight range sensor encased in a protective well (Edwing 1991). From a logistical perspective, in- stallation and maintenance of the protective well requires nontrivial infrastructure and yearly servicing including dive operations, and there is potential for the well to be damaged from flotsam or vessel impacts. The emergence of microwave water level sensors with substantially re- duced installation and maintenance costs has motivated NOAA to transition from the acoustic systems to micro- wave sensors where possible (Heitsenrether 2009). NOAA field evaluations comparing the two sensors find statistically equivalent performance at stations with little or no surface wave energy and small thermal gra- dients along the sounding tube. At stations with per- sistent surface waves larger than roughly 0.5-1-m significant wave height, monthly-mean water levels consistently reveal lower levels observed by the acoustic sensor. Boon et al. (2009) also reported differences be- tween the acoustic and microwave system response with wave conditions, and Boon et al. (2012) presented evi- dence of an asymmetric water level distribution when waves are present. To assess these differences, NOAA has been collecting acoustic and microwave water level data at NWLON stations located at the U.S. Army Corps of Engineers Field Research Facility (FRF) pier at Duck, North Carolina; Scripps Institution of Ocean- ography pier in La Jolla, California; and the William O. Lockhart Municipal Pier in Lake Worth, Florida. One of the primary goals of this paper is to assess compar- ative performance of the acoustic and microwave sen- sors in response to wave and temperature forcings for NOAA water level measurement and to attribute these differences to known physical responses of the sensor systems. The other main goal is to characterize the relationship between significant wave height and NWLON water level standard deviations in order to facilitate use of the NWLON as a component of coastal wave observations. As surface wave amplitude increases, there is an increase in water level standard deviation (s) for both the acoustic and microwave systems, although the relationship has been viewed primarily as a source of error in the water level measurement (Shih and Rogers 1981; Boon et al. 2012). However, as part of the Ocean Topography Ex- periment (TOPEX)/Poseidon validation experiments (Morris et al. 1995), Parke and Gill (1995) found a direct relationship between significant wave height (H1/3) and standard deviation of the acoustic system. They concluded that standard deviation of the NOAA acoustic system is a good first-order measure of significant wave height; however, below a threshold wave height, the relationship degrades such that estimates of wave height are no longer viable. Given that the NWLON continuously monitors coastal water levels at numerous stations covering the U.S. coastline, a robust relationship between signifi- cant wave height and water level standard deviation could provide wave height estimates useful to coastal interests. Taking note of this, the Integrated Ocean Observing System (IOOS) plan for a surface wave monitoring network recognized that nondirectional wave data extracted from the NWLON can augment di- rectional wave observations will be particularly useful for understanding the transformation and dissipation of waves as they traverse shallow and complex local ba- thymetry (IOOS 2009). 2. Sensors a. Acoustic water level The acoustic ranging sensor is coupled to a sounding tube that guides an acoustic pulse to the water surface; a complete description of the system can be found in Edwing (1991). The system is self-calibrating in the sense that it monitors the effective sound speed between the transducer and an acoustic reflector at a known distance (1.219 m), thereby adjusting for temperature- induced changes in sound speed. However, this assumes that the temperature near the transducer is representa- tive of the mean temperature along the entire tube, and a potential error source arises from the strong temper- ature dependence of acoustic celerity if this assumption is violated (Porter and Shih 1996; Hunter 2003). When the sounding tube is longer than a few meters and the temperature difference between the upper section of the tube and the water surface is greater than a few degrees, water level errors of several centimeters are possible. Two temperature sensors (thermistors) are attached to the sounding tube to monitor temperatures along the tube with the upper sensor close to the acoustic trans- ducer and the second sensor located above the highest astronomical tide. These temperature data are not used in the water level estimate, but they are collected so that temperature corrections can be applied in postprocess- ing (discussed in section 5). The sounding tube is enclosed in a vented protective well, a 15.24-cm-diameter pipe extending below the wa- ter surface terminated with a brass orifice to restrict water mass transport in/out of the well. The depth of the water inlet is referred to as the submergence depth Y.When waves or other water level perturbations pass around the air-water interface, the response of the water level inside the pipe is delayed from frictional and inertial forces de- pending on the diameter of the well, the submergence depth, and the period and amplitude of the perturbations. The mismatch between the instantaneous water level in- side and outside the well results in a buoyancy-driven os- cillation of water level inside the well. Ignoring frictional effects and dependence of the protective well diameter, the natural period of oscillation is Tn 5 2p(Y /g)1/2,where g is the gravitational acceleration. For typical submergence depths of 4-6 m at coastal locations, Tn ranges from 4 to 5 s. The water inlet orifice is sized to work with the protective well to impose a mechanical low-pass filter on these pressure-induced water level variations and has a cutoff period of approximately 5 s. Significant effort was expended in the 1980s with a series of laboratory, field, and numerical experiments to design the protective well based on hydrodynamics and water level frequency response (Shih 1981; Shih and Rogers 1981). Figure 1 reproduces from Shih (1981) the dynamic water level response R inside the well to sur- face wave excitation of height H and period T with a damping factor z 5 dw / do , where dw and do are the diameters of the protective well and orifice, respectively. Examination of Fig. 1 reveals why the orifice has a di- ameter of do 5 5.08 cm. With a protective well diameter of dw 5 15.24 cm, the value of z is 3, corresponding to a critically damped response. It is important to realize that Fig. 1 represents the response to a specific set of parameters: H 5 0.3 m, orifice submergence depth Y 5 3.0 m, and water depth 5 7.6 m. Changing these param- eters alters the shape and amplitude of the response curves, and it can be expected that the single-system de- sign represented in Fig. 1 can behave quite differently under varying parameter regimes. For example, in- creasing the orifice submergence depth increases the amplitude response near resonance (T 5 Tn). In addition to the resonant oscillations, Bernoulli's principle dictates conservation of pressure and velocity at the inlet orifice. When tidal- or wave-driven currents are significant across the orifice, the pressure reduction is known to draw down the water level inside the well. Shih and Rogers (1981) quantified the water level reduction as a function of wave height and period as shown in Fig. 2.We will use this function to assess water level differences be- tween the microwave and acoustic systems in response to wave forcing. Again, it should be noted that while this curve applies to general combinations of wave height and period, protective well and orifice diameter, it is specific to an or- ifice hydraulic discharge coefficient of Cd 5 0.8, an orifice submergence depth of 3 m, and a water depth of 9.14 m, and therefore it is expected to change under different re- gimes of water depth and orifice submergence depth. b. Microwave water level The microwave sensor operates at a frequency of 26 GHz with a beamwidth of 88-108, depending on the antenna. There is no contact with the water surface so that hydraulic effects from pressure variations do not influence the measurement. The sensor is remarkably insensitive to temperature variation (0.2 mm K21, 5-mm maximum) and has accuracy of 60.03% of the measured range. However, microwave sensors have limitations, such as signal scattering/blockage from rain, ice or flotsam, sidelobe interference from pilings or other infrastructure, and a variable surface-area footprint dependent on sensor beamwidth and range from the water that introduces aspatial filter. Details of the sensor can be found in Heitsenrether and Davis (2011). It is also notable that Boon et al. (2012) estimated the sensor accuracy of water levels and found a quadratic increase of sensor error with wave height. c. Wave height and period Hourly significant wave height (Hm0) and period were obtained from a Nortek bottom-mounted acoustic waves and current (AWAC) sensor operating at 1 MHz deployed on the same depth contour as the acoustic and microwave sensors (6 m) but located approximately 500 m northward. 3. Data We present data and analysis from the NWLON sta- tion located on the U.S. Army Corps of Engineers FRF pier at Duck, a coastal location exposed to North At- lantic wave fields. Details of the NWLON station are available online at NOAA (2013b), a map and de- scription of the test equipment and configuration are given in Boon et al. (2012), and details of the pier and research facility are available online (http://www.frf. usace.army.mil/frf.shtml). Two datasets are examined in the analysis, the first covers 7-30 April 2012 and the second covers 1-29 April 2013. Data from La Jolla and Lake Worth (September-November 2013) have also been analyzed, finding results consistent with and cor- roborating the data and analysis presented here. Raw range-to-water data were sampled from both the microwave and acoustic sensors at 1 Hz. These raw 1-Hz data were used in power spectral density (PSD) estimates (Fig. 6) and to compute the NOAA water level and standard deviation every 6 min. Mean range-to-water estimates are computed from raw 1-Hz range data by application of the NOAA data quality and assurance procedure (DQAP) (NOAA 2013a). This algorithm samples 181 consecutive 1-Hz values centered on each hour in 6-min intervals (minutes 0, 6, 12, 18, 24, 30, 36, 42, 48, 54) to compute an initial mean and standard de- viation. Data points greater than three standard de- viations from the mean are discarded, and a final mean and standard deviation are computed from the remaining points. These range data are transmitted by satellite to NOAA's Center for Operational Oceanographic Prod- ucts and Services (CO-OPS), where they are converted to water level by subtracting the range estimates from the reference datum, which are then disseminated in near- real time on the Internet and stored in the NWLON ar- chives. Our analysis uses hourly values consisting of one DQAP water level on the hour as shown in Fig. 3,where the mean has been removed. These hourly data are the basis for all analysis and statistics in this paper with the exception of the PSD estimates. Figures 4 and 5 plot the difference between the hourly acoustic and microwave water levels, the temperature difference between the two thermistors, and significant wave height for April 2012 and April 2013, respectively. The water level differences are acoustic minus micro- wave, so that a positive differential implies the acoustic system reported a higher water level, a negative one that the acoustic level is lower. The temperature differences are upper thermistor minus lower thermistor, such that a negative differential represents a higher temperature along the sounding tube than at the acoustic transducer. Two inferences are apparent from examination of Figs. 4 and 5. Positive water level differences appear related to negative temperature differentials along the sounding tube, and more clearly, negative water level differences are related to significant wave height. We examine each of these observations in the following sections, but first establish some general characteristics of the sensors with spectral analysis. 4. Acoustic and microwave frequency response Examination of water level PSD estimates under different wave conditions reveals some fundamental characteristics of the two systems. We estimate PSDs on the raw 1-Hz water level with a periodogram smoothed by a modified Daneill smoother of span 600 points, resulting in a spectral amplitude 99% confidence interval of 1.1 dB. Resultant PSDs for four distinct wave regimes are shown in Fig. 6. Figures 6a-c show spectra from waves of increasing height, all with dominant wave periods in the 7-15-s range. Figure 6d plots the response to a short-period (4.1 s) wave field generated by the passage of a cold front on 27 April 2012 (discussed in section 7). Perhaps the most obvious characteristic is high- frequency attenuation of the acoustic system at periods shorter than about 5 s, owing to mechanical filtering from the water inlet orifice. Another robust feature observed across multiple datasets and environmental conditions is the enhanced response of the microwave sensor to water level variance from low to intermediate/ high wave conditions in the wind-wave frequency band (periods of 5-20 s). This can be seen by comparison of Figs. 6a and 6b.InFig. 6a the wave height is low and the microwave sensor water level variance is 5-10 dB less than that of the acoustic system. In Fig. 6b the wave height has increased by a factor of 5, and we find the microwave water level response is roughly 5 dB greater than that of the acoustic system. This ''inversion'' of water level variance translates into a superior water level sensitivity for the microwave sensor in the low to intermediate/high wave regime, and it led to identi- fication of the microwave sensor as a higher fidelity water level sensor in the presence of waves (discussed in section 7). Although the sensitivity of the microwave sensor to water level dynamics is greater than the acoustic system for low to intermediate/high wave conditions, we see in Fig. 6c that when waves are very high, the acoustic sensor reports higher variance in the 4-12-s band. NOAA is continuing to collect and analyze data in the high wave regime to ascertain whether it is a consistent characteristic between the two sensors. Resonance of the protective well is a notable feature in Fig. 6d, where we see that the 4.1-s dominant wave period is captured by the microwave sensor, while the acoustic system responds with a broad peak around a period of 5 s. Evidence of the resonance is also seen in Fig. 6a, where the acoustic spectra has a ''knee'' at a period of 5 s, while the microwave presents no such en- ergy. Examination of spectral coherence between the acoustic and microwave sensors consistently finds values of coherence squared in the range of 0.7-0.8 over the wind-wave band, with values near zero at periods of 5 s and shorter. This lack of coherence when the resonance amplitude is significant indicates that the resonance is in- troducing a distortion to the water level power spectrum- that is, the water level variance associated with the resonance is not representative of the true water level dynamics outside the well, but of undamped oscillations inside the well. These resonance features are consistent with the dynamic response of the protective well being forced by combinations of parameters (wave height and period, orifice submergence depth, water depth, orifice discharge coefficient, etc.) that deviate from the ideal design represented in Fig. 1, such that the critically damped response is not realized. At periods longer than 20 s, the two sensors respond with similar spectral shapes, but the microwave sensor consistently measures higher water level variance than the acoustic system. At these long periods, we are no longer dealing with direct-wind-generated ocean surface waves, but we are sampling infragravity waves and other nonlinear processes associated with subharmonics of wind waves, internal waves, edge waves trapped on the shelf, or other forcings (Munk 1951). It is not presently known whether this response represents an error in the microwave sensor, a higher fidelity sampling of low- frequency variability by the microwave sensor, a limita- tion imposed by the acoustic system mechanical filter, or some other effect. 5. Acoustic temperature dependence In previous work investigating the relationship be- tween temperature and accuracy of the acoustic ranging system, Porter and Shih (1996) described the system, the presently used correction algorithm, and assessed im- pacts with a case study at the La Jolla tide station. Their data reveal water level errors on the order of 5 cm arising from temperature-induced sound speed errors. Hunter (2003) conducted a comprehensive analysis of the temperature dependence, again finding the domi- nant error arising from uncertainty in sound speed. It is worth noting that the current NWLON tempera- ture correction algorithm makes a significant assumption concerning representation of the physical environment. The correction is ... (1) where ?S is the water level correction, h is the range from the acoustic transducer to the water surface, and DT is the difference between the temperature measured near the transducer and the temperature measured closer to the water surface. The factor 0.0018 is a con- stant relating the sound speed in an adiabatic ideal gas to temperature in units of degrees Celsius. This correction contains no dependence on the loca- tion of the temperature measurements. For a given range to the water, the correction from DT measured over a distance of 1 cm is the same as for DT over a dis- tance of 10 m. The assumption is that a stepwise constant temperature difference, one temperature at the sensor and another constant temperature along the sounding tube, accurately represents the effective temperature profile along the sounding tube. This first-order as- sumption may be valid in certain cases; however, in cases where the actual temperature profile is not well repre- sented by a spatially independent temperature differ- ence, the correction from Eq. (1) is known to be poor (Vogt et al. 1986). NOAA is currently exploring the use of additional thermistors and a spatially dependent al- gorithm to improve sound speed corrections. We note that for the data analyzed here, the temperature differ- ences are less than 4.28C, with mean and maximum sound speed changes of 0.6 and 2.5 m s21, respectively, resulting in temperature corrections of up to 7.2 cm. As previously noted from inspection of Figs. 4 and 5, a relation between positive water level differences of the acoustic and microwave sensors and negative tempera- ture difference of the two thermistors is evident. Even though the temperature correction of Eq. (1) is based on a simplistic physical model, it is the currently accepted algorithm and we use it to compute temperature correc- tions for the data shown in Figs. 4 and 5. These correc- tions are then compared with the observed water level differences as shown in Figs. 7 and 8, respectively. The acoustic temperature corrections are largely coherent with the positive water level differences with pronounced disagreement primarily arising when significant wave height is greater than 0.5 m. These discrepancies may reflect increased thermal mixing within the protective well driven by wind stress and pneumatic pumping from water level variance, since the protective well is vented at the top to allow ambient pressure equalization. The ex- tent to which these positive water level differences are captured by the correction of Eq. (1) can be examined by linear regression of the positive sea level differences with the negative temperature differences for data having a wave height below a certain threshold. For the April 2012 data (Figs. 4 and 7), a threshold of Hm0 , 0.5 m finds r2 5 0.49 (c 5 0.86, p , 1 3 1029), while the April 2013 datafindsr250.59(c50.82,p,13 1029),wherecisthe regression coefficient and p is the p value. Comparison of a simple temperature correction model with observational data suggests that temperature-induced errors of acoustic water level are an important source of disagreement between the acoustic and microwave sensors. 6. Mechanical filter water level drawdown To evaluate water level drawdown in the acoustic system, we apply the functional relation of Fig. 2 to both datasets (Figs. 4 and 5), with results presented in Figs. 9 and 10, respectively. One can see that the envelope of the drawdown model captures the overall negative water level differences; however, there are significant differences at short time scales (several hours), as posi- tive water level differences are observed during wave events, for example, during the period 21-25 April 2013 (Fig. 10). It is not known whether these positive water level differences represent an error of the microwave sensor when water level variability is high (Boon et al. 2012), or whether it is a response of the acoustic system protective well and orifice. Water level pile up in the protective well is a known issue, and we suspect that these short time-scale differences are driven by reso- nance of water levels from a loss of damping. To assess the drawdown model, we regress the enve- lope of negative water level differences against the predicted drawdown. The water level difference enve- lope is obtained from low-pass filtering the magnitude of the differences with an 18-h moving average filter and the result is r2 5 0.55 (c 5 0.30, p , 1 3 1029) for the 2012 data, and r2 5 0.76 (c 5 0.31, p , 1 3 1029) for the 2013 data. Not accounting for these wave-driven water level reductions in the acoustic sensors may impact long- term water level statistics. For example, based on the negative water level differences, the reduction in mean sea level between the acoustic and microwave water levels over the April 2012 and 2013 records are 1.1 and 1.0 cm, respectively. We conclude that negative water level differences between the acoustic and microwave sensors are signifi- cantly driven by water level drawdown in the protective well due to wave-forced pressure fluctuations and currents. 7. Water level standard deviations and significant wave height As wave energy increases, the standard deviation of water level estimates also increase. Parke and Gill (1995) evaluated this dependence for the acoustic sys- tem as part of the TOPEX/Poseidon validation at Platform Harvest, finding a linear increase of water levelstandarddeviationwith values in the range of 10- 20 cm for significant wave height of 1 m. This is con- sistent with our data presented in Fig. 11 exploring the relationship between standard deviation and significant wave height at Duck, with standard deviation values in the range 7-20 cm for significant wave heights of 1 m. A comparison of wave gauge hourly Hm 0 with water level standard deviations of the acoustic and microwave gauges over 24 days in April 2012 is shown in Fig. 11, suggesting a robust relationship between wave height and water level standard deviation. A direct estimation of Hm0 from s would utilize the canonical definition ... (2) However, we recognize several factors can contribute to deviations from this ideal. One is that we are relating wave gauge Hm0 estimated over a period of 1 h with a single s estimated over 181 s. Other factors include the spatial separation (500 m) between the wave gauge and water level sensors, and water level measurement system me- chanics. For example, the acoustic protective well in- troduces a nonlinear filter to the water level variance and thisresponseisknowntodependonwaveheight,period, and water depth (Shih and Rogers 1981); the microwave sensor images a variable footprint on the water surface depending on the sensor to water distance and implements some internal smoothing of the 1-Hz data. Therefore, we do not expect that NWLON water level s will explicitly satisfy Eq. (2), but we can hope for a linear scaling and seek a parameter a that best relates water level s to Hm0: ... (3) where Hm0 is the estimate of Hm0 and a is a factor that minimizes the residual ^ 5 Hm0 2 H^ m0. Fitting a linear model over the 24-day period results in am 5 6.53 and aA 5 11.08 for the microwave and acoustic sensors, re- spectively, with the resulting H^ m0 shown in Fig. 12. The mean error of these first-order estimates can be repre- sented with the RMS residual over the period and finds values of ^m 5 0.14 m and ^A 5 0.21 m for the microwave and acoustic sensors, respectively. To assess the dynamics of this linear scaling on a finer temporal scale, we regress hourly Hm0/ s over a sliding window of length 24 h with the resultant fit and correlation coefficients shown in Fig. 13. Corre- lation and fit coefficients are only shown if the p value of the fit exceeds the 99% confidence level. The dashed line quantifies an ideal model of Hm0 5 4s,andwesee that in a linear least squares sense the microwave sensor comes closer to this definition than the acous- tic sensor. Both models find a significant dependence ( p values , 0.01) during times of wave activity, and we note that in concordance with the expectation of Parke and Gill (1995) when wave activity is low (days 14, 17, 24-27), the model fails to be statistically significant, although there are exceptions (days 9 and 30). Gen- erally, the predictive skill of the acoustic system is less robust than that of the microwave system with consistently lower r2 values and fit coefficients farther away from the ideal. A reexamination of the acoustic and microwave water level s shown in Fig. 11 reveals that the microwave sensor exhibits a greater dynamic range than the acoustic system. During times of low s, the microwave response is lower in amplitude than that of the acoustic system, whereas during times of high s, the microwave response is higher. This suggests that in terms of water level var- iations, the microwave sensor has a higher sensitivity than the acoustic system, consistent with the spectral analysis presented in section 4. Another difference evidenced in Fig. 11 during day 27 is that the microwave sensor exhibits a pronounced response to a short-term wave event, while the acoustic system presents only a minor indication (Fig. 11). Ex- amination of meteorological data (NOAA 2012)re- veals that a cold front moved through the area on 27 April with a change in wind direction from 2708 to 108- 608 (offshore to onshore) with wind speeds during the period increasing from 5 to 10 m s21 (10-20 kt; 1 kt 5 0.51 m s21). These conditions are consistent with the formation of locally generated short-period wind waves. Wave gauge records over this period reveal an average wave direction of 648, a height of 0.9 m, and a period of 4.1 s. Water level PSDs encompassing this event are shown in Fig. 6d, and we observe that at pe- riods between 2 and 4 s, the acoustic system is attenu- ated from the low-pass mechanical filter by roughly 20 dB in relation to the microwave response, an am- plitude ratio of 10 to 1. The microwave response reveals a small (3 dB) but statistically significant broad peak between 3 and 5 s, corresponding to the wave gauge report of a 4.1-s period. The combination of meteoro- logical, wave gauge, and water level PSDs suggests that the wave event on 27 April was primarily locally gen- erated short-period wind waves that the acoustic sys- tem filtered out, but which drove the protective well into a resonant water level oscillation at a period of 5 s. 8. Prospective NWLON stations as wave proxies As observed by Parke and Gill (1995) and evidenced in Fig. 13, there is a threshold of wave activity below which the linear model fails. Since many NWLON stations are in protected waters with limited wave ac- tivity, not all stations exhibit significant correlation with wave heights. Further study is needed to identify these thresholds. In the interim, NOAA has adopted an heu- ristic threshold to identify stations where wave activity is persistent. The metric is a 6-yr mean of monthly averaged water level standard deviation (s)fromtheNWLON acoustic sensors, and persistent wave activity is associated with values greater than or equal to 2 cm. There are 43 NWLON stations that exceed the 6-yr averaged threshold of s $ 2 cm, and ideally we seek to regress the s of these stations against observational wave height data. Given the general lack of long-term observa- tional wave data at these stations, we turn to the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis model. We regress daily mean water level s to the ECMWF daily Hm0 over the 6-yr period from 2006 through 2011. Given the coarse model domain (0.58 latitude and longitude), the daily time scale, and the ne- glect of nearshore wave transformation, we expect corre- lations will generally be low and use a threshold of r2. 0.3 at the 99% confidence level to select prospective stations. We find that 29 of the 43 stations meet this criterion and are listed in Table 1.AmapoftheNorthAmericanand Hawaiian stations is shown in Fig. 14. We suggest that these 29 stations are good candidates for significant wave height estimates based on real-time water level s. 9. Conclusions As part of a modernization effort for NOAA's National Water Level Observation Network, acoustic ranging water level systems are being transitioned to microwave radar sensors. From a cost, maintenance, and support per- spective, the microwave sensor is more efficient than the acoustic system, since it requires no infrastructure in contact with the water, although it has limitations to be considered. When used without a protective well, flot- sam or surface ice can lead to erroneous water levels. We also find that ice accumulation in the antennas and scattering from heavy rain can degrade sensor perfor- mance. The use of a protective antenna cover (end cap) to prevent ice buildup inside the antenna does effec- tively mitigate the ice problem, but it introduces another problem, where moisture accumulation on the cover impedes the signal. The microwave beam pattern also needs to be evaluated to ensure that interference from pilings/mounting structures does not impede imaging of the water surface, and in surface wave sensing applica- tions the footprint of the beam introduces a spatial filter (Heitsenrether et al. 2008). Two benefits of the microwave sensor are that it is insensitive to temperature and it does not rely on a hydraulic pressure measurement. With regard to temperature effects, our analysis finds that 49% and 59% of positive water level differences between the acoustic and microwave sensors at Duck during April 2012 and 2013, respectively, can be attributed to the speed of sound errors in the acoustic system. We expect that an improved temperature correction algorithm would find somewhat higher proportions. When a wave- induced water level drawdown model for the acoustic protective well is applied, we find that 55% and 76% of the negative water level differences during April 2012 and 2013, respectively, can be attributed to wave- induced pressure changes. Even though differences in water level response as a function of wave height are reasonably captured by the hydrodynamic drawdown in the protective well, there are notable exceptions during high wave events when a water level pile up is observed, and at infragravity frequencies the variance of the mi- crowave system is higher than that of the acoustic sensor. Further study is needed to clarify these discrepancies. A third advantage of the microwave system is that there is no protective well and therefore no high-frequency water level resonances. We conclude that for the data analyzed here, the microwave sensor exhibits superior perfor- mance as a water level sensor when temperature gradi- ents or waves are present. NWLON data products recorded every 6 min include the standard deviation (s) of 181 water levels sampled at 1 Hz. The s statistic is known to be correlated with sig- nificant wave height, but it has been largely ignored as a wave height measure and viewed primarily as an error metric of water level estimates. To assess the link be- tween water level standard deviation and significant wave height, a linear model significant at the 99% con- fidence level finds that the microwave sensor estimates significant wave height, and therefore water level vari- ability, with higher fidelity than the acoustic system. An assessment of NWLON stations for wave sensi- tivity was performed by identifying stations with 6-yr monthly-mean water level s greater than or equal to 2 cm, and which correlated daily mean water level s to the ECMWF-Interim Re-Analysis daily Hm0 with r2 greater than 0.3. This assessment selected 29 prospective stations as viable wave height proxies with the potential to provide a valuable addition to wave height estimation within the difficult and poorly sampled coastal zones. The relationship between wave height and water level s should be calibrated at these stations through local field experiments. The availability of long-term coastal wave statistics at these stations can provide a significant con- tribution to the existing coastal wave observation net- work, and the length of the records opens the possibility of quantifying historical wave-proxy climatologies to examine storminess-related processes. 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Mooney, 1986: The Aquatrak water level sensor field test at Shady Side, Maryland. STD-N-494, Submarine Technology Dept., Johns Hopkins University Applied Physics Laboratory, 66 pp. JOSEPH PARK,ROBERT HEITSENRETHER, AND WILLIAM SWEET National Oceanic and Atmospheric Administration, Silver Spring, Maryland (Manuscript received 29 January 2014, in final form 9 May 2014) Corresponding author address: Joseph Park, NOAA/NOS/ CO-OPS, 1305 East-West Hwy., Silver Spring, MD 20910. E-mail: [email protected] (c) 2014 American Meteorological Society |