Browse
Ocean Wave Directional Distribution from GPS Buoy Observations off the West Coast of Ireland: Assessment of a Wavelet-Based MethodAbstract
Knowledge of the directional distribution of a wave field is crucial for a better understanding of complex air–sea interactions. However, the dynamic and unpredictable nature of ocean waves, combined with the limitations of existing measurement technologies and analysis techniques, makes it difficult to obtain precise directional information, leading to a poor understanding of this important quantity. This study investigates the potential use of a wavelet-based method applied to GPS buoy observations as an alternative approach to the conventional methods for estimating the directional distribution of ocean waves. The results indicate that the wavelet-based estimations are consistently good when compared to the framework of widely used parameterizations for the directional distribution. The wavelet-based method presents advantages in comparison with the conventional methods, including being purely data-driven and not requiring any assumptions about the shape of the distribution. In addition, it was found that the wave directional distribution is narrower at the spectral peak and broadens asymmetrically at higher and lower scales, particularly sharply for frequencies below the peak. The directional spreading appears to be independent of the wave age across the entire range of frequencies, implying that the angular width of the directional spectrum is primarily controlled by nonlinear wave–wave interactions rather than by wind forcing. These results support the use of the wavelet-based method as a practical alternative for the estimation of the wave directional distribution. In addition, this study highlights the need for continued innovation in the field of ocean wave measuring technologies and analysis techniques to improve our understanding of air–sea interactions.
Significance Statement
This study presents a wavelet-based technique for obtaining the directional distribution of ocean waves applied to GPS buoy. This method serves as an alternative to conventional methods and is relatively easy to implement, making it a practical option for researchers and engineers. The study was conducted in a highly energetic environment characterized by high wind speeds and large waves, providing a valuable dataset for understanding the dynamics of marine environment in extreme conditions. This research has implications for improving our understanding of directional characteristics of ocean waves, which is crucial for navigation, offshore engineering, weather forecasting, and coastal hazard mitigation. This study also highlights the challenges associated with understanding wave directionality and emphasizes a need for further observations.
Abstract
Knowledge of the directional distribution of a wave field is crucial for a better understanding of complex air–sea interactions. However, the dynamic and unpredictable nature of ocean waves, combined with the limitations of existing measurement technologies and analysis techniques, makes it difficult to obtain precise directional information, leading to a poor understanding of this important quantity. This study investigates the potential use of a wavelet-based method applied to GPS buoy observations as an alternative approach to the conventional methods for estimating the directional distribution of ocean waves. The results indicate that the wavelet-based estimations are consistently good when compared to the framework of widely used parameterizations for the directional distribution. The wavelet-based method presents advantages in comparison with the conventional methods, including being purely data-driven and not requiring any assumptions about the shape of the distribution. In addition, it was found that the wave directional distribution is narrower at the spectral peak and broadens asymmetrically at higher and lower scales, particularly sharply for frequencies below the peak. The directional spreading appears to be independent of the wave age across the entire range of frequencies, implying that the angular width of the directional spectrum is primarily controlled by nonlinear wave–wave interactions rather than by wind forcing. These results support the use of the wavelet-based method as a practical alternative for the estimation of the wave directional distribution. In addition, this study highlights the need for continued innovation in the field of ocean wave measuring technologies and analysis techniques to improve our understanding of air–sea interactions.
Significance Statement
This study presents a wavelet-based technique for obtaining the directional distribution of ocean waves applied to GPS buoy. This method serves as an alternative to conventional methods and is relatively easy to implement, making it a practical option for researchers and engineers. The study was conducted in a highly energetic environment characterized by high wind speeds and large waves, providing a valuable dataset for understanding the dynamics of marine environment in extreme conditions. This research has implications for improving our understanding of directional characteristics of ocean waves, which is crucial for navigation, offshore engineering, weather forecasting, and coastal hazard mitigation. This study also highlights the challenges associated with understanding wave directionality and emphasizes a need for further observations.
Validation of Doppler Wind Lidar Measurements with an Uncrewed Aircraft System (UAS) in the Daytime Atmospheric Boundary LayerAbstract
One of the most widely used systems for wind speed and direction observations at meteorological sites is based on Doppler wind lidar (DWL) technology. The wind vector derivation strategies of these instruments rely on the assumption of stationary and homogeneous horizontal wind, which is often not the case over heterogeneous terrain. This study focuses on the validation of two DWL systems, operated by the German Weather Service [Deutscher Wetterdienst (DWD)] and installed at the boundary layer field site Falkenberg (Lindenberg, Germany), with respect to measurements from a small, fixed-wing uncrewed aircraft system (UAS) of the type Multi-Purpose Airborne Sensor Carrier (MASC-3). A wind vector intercomparison at an altitude range from 100 to 500 m between DWL and UAS is performed, after a quality control of the aircraft’s data accuracy against a cup anemometer and wind vane mounted on a meteorological mast also operating at the location. Both DWL systems exhibit an overall root-mean-square difference in the wind vector retrieval of less than 22% for wind speed and lower than 18° for wind direction. The enhancement or deterioration of these statistics is analyzed with respect to scanning height and atmospheric stability. The limitations of this type of validation approach are highlighted and accounted for in the analysis.
Abstract
One of the most widely used systems for wind speed and direction observations at meteorological sites is based on Doppler wind lidar (DWL) technology. The wind vector derivation strategies of these instruments rely on the assumption of stationary and homogeneous horizontal wind, which is often not the case over heterogeneous terrain. This study focuses on the validation of two DWL systems, operated by the German Weather Service [Deutscher Wetterdienst (DWD)] and installed at the boundary layer field site Falkenberg (Lindenberg, Germany), with respect to measurements from a small, fixed-wing uncrewed aircraft system (UAS) of the type Multi-Purpose Airborne Sensor Carrier (MASC-3). A wind vector intercomparison at an altitude range from 100 to 500 m between DWL and UAS is performed, after a quality control of the aircraft’s data accuracy against a cup anemometer and wind vane mounted on a meteorological mast also operating at the location. Both DWL systems exhibit an overall root-mean-square difference in the wind vector retrieval of less than 22% for wind speed and lower than 18° for wind direction. The enhancement or deterioration of these statistics is analyzed with respect to scanning height and atmospheric stability. The limitations of this type of validation approach are highlighted and accounted for in the analysis.
Evaluating Existing Ocean Glider Sampling Strategies for Submesoscale DynamicsAbstract
Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesoscale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual “bow-tie” glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases is comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find that small reductions in the number of dive–climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates that the bias is not dependent on the direction of travel in our deep ocean study site.
Significance Statement
Recent observational campaigns have focused on using autonomous vehicles to better understand processes responsible for mixing in the surface region of the ocean. There exists uncertainty around how effective these missions are at returning reliable and representative information. This study seeks to quantify the performance of existing strategies in observing mixing processes, and we confirm that strategies are biased to underestimate indicators of mixing. Furthermore, compensating for the bias by increasing the number of resources or changing the manner in which resources are used has limited reward. Our findings are important for decision-making during the planning phase of an observational campaign and display that further innovations are required to account for the sampling bias.
Abstract
Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesoscale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual “bow-tie” glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases is comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find that small reductions in the number of dive–climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates that the bias is not dependent on the direction of travel in our deep ocean study site.
Significance Statement
Recent observational campaigns have focused on using autonomous vehicles to better understand processes responsible for mixing in the surface region of the ocean. There exists uncertainty around how effective these missions are at returning reliable and representative information. This study seeks to quantify the performance of existing strategies in observing mixing processes, and we confirm that strategies are biased to underestimate indicators of mixing. Furthermore, compensating for the bias by increasing the number of resources or changing the manner in which resources are used has limited reward. Our findings are important for decision-making during the planning phase of an observational campaign and display that further innovations are required to account for the sampling bias.
Continuous Peak Period Estimates from Discrete Surface-Wave SpectraAbstract
Peak periods estimated from finite-resolution frequency spectra are necessarily discrete. For wind-generated surface gravity waves, conflicting considerations of robust (quasi)-stationary statistics, and high spectral resolution, combined with the inverse relation between frequency and period, this typically implies that swell periods (above 10 s) are resolved at best at
Abstract
Peak periods estimated from finite-resolution frequency spectra are necessarily discrete. For wind-generated surface gravity waves, conflicting considerations of robust (quasi)-stationary statistics, and high spectral resolution, combined with the inverse relation between frequency and period, this typically implies that swell periods (above 10 s) are resolved at best at
Abstract
Argo floats are widely used to characterize vertical structures of ocean eddies, yet their capability to invert sea surface features of eddies, especially those overlooked by available altimeters, has not been explored. In this paper, we propose an “interior-to-surface” inversion algorithm to effectively expand the capacity of eddy detection by estimating altimeter-missed eddies’ surface attributes from their Argo-derived potential density anomaly profiles, given that the interior property and surface signature of eddies are highly correlated. An altimeter-calibrated machine learning ensemble is employed for the inversion training based on the joint altimeter–Argo eddy data and shows promising performance with mean absolute errors of 5.4 km, 0.5 cm, and 14.3 cm2 s−2 for eddy radius, amplitude, and kinetic energy, respectively. Then, the trained ensemble model is applied to independently invert the properties of eddies captured by an Argo-alone detection scheme, which yields high spatiotemporal consistency with their altimeter-captured counterparts. In particular, a portion of Argo-alone eddies is ∼25% smaller than altimeter-derived ones, indicating Argo’s unique capability of profiling weaker submesoscale eddies. Sea surface temperature and chlorophyll data are further applied to validate the reliability of eddies identified and characterized by the Argo-only algorithm. This new methodology effectively complements that of altimetry in eddy detection and can be expanded to estimate other physical/biochemical eddy variables from a variety of in situ observations.
Significance Statement
Despite thousands of eddies being routinely identified on a daily basis, it has been recognized that a substantial portion of eddies may still be missed due to inadequate sampling of altimeter constellations. Taking advantage of eddy’s correlation between surface and interior, a considerable number of eddies are discovered for the first time through an Argo-based eddy identification scheme. Here, we propose a new methodology to independently infer these recaptured eddies’ surface properties from their vertical signals through an “interior-to-surface” inversion process. The inferred eddy properties are verified by the spatiotemporal consistency with those derived from altimetry. Since Argo is capable of profiling smaller and weaker eddies, the proposed methodology significantly complements and expands that of altimetry in eddy observation.
Abstract
Argo floats are widely used to characterize vertical structures of ocean eddies, yet their capability to invert sea surface features of eddies, especially those overlooked by available altimeters, has not been explored. In this paper, we propose an “interior-to-surface” inversion algorithm to effectively expand the capacity of eddy detection by estimating altimeter-missed eddies’ surface attributes from their Argo-derived potential density anomaly profiles, given that the interior property and surface signature of eddies are highly correlated. An altimeter-calibrated machine learning ensemble is employed for the inversion training based on the joint altimeter–Argo eddy data and shows promising performance with mean absolute errors of 5.4 km, 0.5 cm, and 14.3 cm2 s−2 for eddy radius, amplitude, and kinetic energy, respectively. Then, the trained ensemble model is applied to independently invert the properties of eddies captured by an Argo-alone detection scheme, which yields high spatiotemporal consistency with their altimeter-captured counterparts. In particular, a portion of Argo-alone eddies is ∼25% smaller than altimeter-derived ones, indicating Argo’s unique capability of profiling weaker submesoscale eddies. Sea surface temperature and chlorophyll data are further applied to validate the reliability of eddies identified and characterized by the Argo-only algorithm. This new methodology effectively complements that of altimetry in eddy detection and can be expanded to estimate other physical/biochemical eddy variables from a variety of in situ observations.
Significance Statement
Despite thousands of eddies being routinely identified on a daily basis, it has been recognized that a substantial portion of eddies may still be missed due to inadequate sampling of altimeter constellations. Taking advantage of eddy’s correlation between surface and interior, a considerable number of eddies are discovered for the first time through an Argo-based eddy identification scheme. Here, we propose a new methodology to independently infer these recaptured eddies’ surface properties from their vertical signals through an “interior-to-surface” inversion process. The inferred eddy properties are verified by the spatiotemporal consistency with those derived from altimetry. Since Argo is capable of profiling smaller and weaker eddies, the proposed methodology significantly complements and expands that of altimetry in eddy observation.
OPC-Pod: A New Sensor Payload to Measure Aerosol Particles for Small Uncrewed Aircraft SystemsAbstract
Atmospheric aerosols affect human health and influence atmospheric and biological processes. Dust can be transported long distances in the atmosphere, and the mechanisms that influence dust transport are not fully understood. To improve the database for numerical models that simulate dust transport, measurements are needed that cover both the vertical distribution of the dust and its size distribution. In addition to measurements with crewed aircraft, uncrewed aircraft systems (UASs) provide a particularly suitable platform for this purpose. In this paper, we present a payload for the small fixed-wing UAS of the type Multiple-Purpose Airborne Sensor Carrier 3 (MASC-3) for aerosol particle measurements that is based on the optical particle counter (OPC) OPC-N3 (Alphasense, United Kingdom), modified by the addition of a dryer and a passive aspiration system (OPC-Pod). Based on field tests with a reference instrument in Mannheim, Germany, wind tunnel tests, and a comparison measurement with the UAS-mounted aerosol particle measurement Universal Cloud and Aerosol Sounding System (UCASS) during a dust event over Cyprus, we show that the OPC-Pod can measure particle number concentrations in the range of 0.66–31 μm as well as particle size distributions. The agreement of the OPC-Pod with UCASS is good. Both instruments resolve a vertical profile of the Saharan dust event, with a prominent dust layer between 1500 and 2800 m MSL, with particle number concentrations up to 35 cm−3 for particles between 0.66 and 31 μm.
Abstract
Atmospheric aerosols affect human health and influence atmospheric and biological processes. Dust can be transported long distances in the atmosphere, and the mechanisms that influence dust transport are not fully understood. To improve the database for numerical models that simulate dust transport, measurements are needed that cover both the vertical distribution of the dust and its size distribution. In addition to measurements with crewed aircraft, uncrewed aircraft systems (UASs) provide a particularly suitable platform for this purpose. In this paper, we present a payload for the small fixed-wing UAS of the type Multiple-Purpose Airborne Sensor Carrier 3 (MASC-3) for aerosol particle measurements that is based on the optical particle counter (OPC) OPC-N3 (Alphasense, United Kingdom), modified by the addition of a dryer and a passive aspiration system (OPC-Pod). Based on field tests with a reference instrument in Mannheim, Germany, wind tunnel tests, and a comparison measurement with the UAS-mounted aerosol particle measurement Universal Cloud and Aerosol Sounding System (UCASS) during a dust event over Cyprus, we show that the OPC-Pod can measure particle number concentrations in the range of 0.66–31 μm as well as particle size distributions. The agreement of the OPC-Pod with UCASS is good. Both instruments resolve a vertical profile of the Saharan dust event, with a prominent dust layer between 1500 and 2800 m MSL, with particle number concentrations up to 35 cm−3 for particles between 0.66 and 31 μm.
