Browse
Abstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, ‘bar’ effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC wasn’t applied beforehand.
Abstract
The Geostationary Lightning Mapper (GLM) has been providing unprecedented observations of total lightning since becoming operational in 2017. The potential for GLM observations to be used for forecasting and analyzing tropical cyclone (TC) structure and intensity has been complicated by inconsistencies in the GLM data from a number of artifacts. The algorithm that processes raw GLM data has improved with time; however, the need for a consistent long-term dataset has motivated the development of quality control (QC) techniques to help remove clear artifacts such as blooming events, spurious false lightning, ‘bar’ effects, and sun glint. Simple QC methods are applied that include scaled maximum energy thresholds and minima in the variance of lightning group area and group energy. QC and anomaly detection methods based on machine learning (ML) are also explored. Each QC method is successfully able to remove artifacts in the GLM observations while maintaining the fidelity of the GLM observations within TCs. As the GLM processing algorithm has improved with time, the amount of QC flagged lightning within 100 km of Atlantic TCs is reduced, from 70% during 2017, to 10% in 2018, to 2% during 2021. These QC methods are relevant to the design of ML-based forecasting techniques which could pick up on artifacts rather than the signal of interest in TCs if QC wasn’t applied beforehand.
Abstract
Out of the 45 radars composing the Terminal Doppler Weather Radar (TDWR) network, 21 are located in areas of complex terrain. Their mission to observe low-level wind shear at major airports prone to strong shear-induced accidents puts them in an ideal position to fill critical boundary layer observation gaps within the NEXRAD network in these regions. Retrievals such as Velocity Azimuth Display and Velocity Volume Processing (VVP) are used to create time-height profiles of the boundary layer from radar conical scans, but assume that a wide area around the radar is horizontally homogeneous. This assumption is rarely met in regions of complex terrain. This paper introduces a VVP retrieval with limited radius to make these profiling techniques informative for flows affected by topography. These retrievals can be applied to any operational radar to help examine critical boundary layer processes. VVP retrievals were derived from the TDWR for Salt Lake City International Airport, TSLC, during a summertime high ozone period. These observations highlighted thermally driven circulations and variations in boundary layer depth at high vertical and temporal resolution and provided insight on their influence on air quality.
Abstract
Out of the 45 radars composing the Terminal Doppler Weather Radar (TDWR) network, 21 are located in areas of complex terrain. Their mission to observe low-level wind shear at major airports prone to strong shear-induced accidents puts them in an ideal position to fill critical boundary layer observation gaps within the NEXRAD network in these regions. Retrievals such as Velocity Azimuth Display and Velocity Volume Processing (VVP) are used to create time-height profiles of the boundary layer from radar conical scans, but assume that a wide area around the radar is horizontally homogeneous. This assumption is rarely met in regions of complex terrain. This paper introduces a VVP retrieval with limited radius to make these profiling techniques informative for flows affected by topography. These retrievals can be applied to any operational radar to help examine critical boundary layer processes. VVP retrievals were derived from the TDWR for Salt Lake City International Airport, TSLC, during a summertime high ozone period. These observations highlighted thermally driven circulations and variations in boundary layer depth at high vertical and temporal resolution and provided insight on their influence on air quality.
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.
Abstract
The Ka-band Radar Interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite that launched in December 2022 is providing the first two-dimensional altimetric views of sea surface height (SSH). Measurements are made across two parallel swaths of 50-km width separated by a 20-km gap. In the data product that will be used for most oceanographic applications, SSH estimates with a footprint diameter of about 3 km are provided on a 2 km×2 km grid. Early analyses of in-flight KaRIn data conclude that the instrumental noise for this footprint diameter has a standard deviation less than σ3km = 0.40 cm for conditions of 2-m significant wave height. This is a factor of 2.3 better than the pre-launch expectation based on the science requirement specification. The SSH fields measured by KaRIn allow the first satellite estimates of essentially instantaneous surface current velocity and vorticity computed geostrophically from SSH. The effects of instrumental noise on smoothed estimates of velocity and vorticity based on early post-launch assessments are quantified here as functions of the half-power filter cutoff wavelength of the smoothing. Signal-to-noise ratios for smoothed estimates of velocity and vorticity are determined from simulated noisy KaRIn data derived from a high-resolution numerical model of the California Current System. The wavelength resolution capabilities for σ3km = 0.40 cm are found to be about 17 and 35 km for velocity and vorticity, respectively, which correspond to feature diameters of about 8.5 and 17.5 km, and are better than the pre-launch expectations by about 45% and 35%.
Abstract
The Ka-band Radar Interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite that launched in December 2022 is providing the first two-dimensional altimetric views of sea surface height (SSH). Measurements are made across two parallel swaths of 50-km width separated by a 20-km gap. In the data product that will be used for most oceanographic applications, SSH estimates with a footprint diameter of about 3 km are provided on a 2 km×2 km grid. Early analyses of in-flight KaRIn data conclude that the instrumental noise for this footprint diameter has a standard deviation less than σ3km = 0.40 cm for conditions of 2-m significant wave height. This is a factor of 2.3 better than the pre-launch expectation based on the science requirement specification. The SSH fields measured by KaRIn allow the first satellite estimates of essentially instantaneous surface current velocity and vorticity computed geostrophically from SSH. The effects of instrumental noise on smoothed estimates of velocity and vorticity based on early post-launch assessments are quantified here as functions of the half-power filter cutoff wavelength of the smoothing. Signal-to-noise ratios for smoothed estimates of velocity and vorticity are determined from simulated noisy KaRIn data derived from a high-resolution numerical model of the California Current System. The wavelength resolution capabilities for σ3km = 0.40 cm are found to be about 17 and 35 km for velocity and vorticity, respectively, which correspond to feature diameters of about 8.5 and 17.5 km, and are better than the pre-launch expectations by about 45% and 35%.
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
