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Airborne Radar Doppler Spectrum Width as a Scale-Dependent Turbulence MetricAbstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Abstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Validation of the CLARA-A3 Top-of-Atmosphere Radiative Fluxes Climate Data RecordAbstract
The third edition of the CM SAF Cloud, Albedo and Surface Radiation dataset from AVHRR data (CLARA-A3) contains for the first time the top-of-atmosphere products reflected solar flux (RSF) and outgoing longwave radiation (OLR), which are presented and validated using CERES, HIRS, and ERA5 reference data. The products feature an unprecedented resolution (0.25°) and time span (4 decades) and offer synergy and compatibility with other CLARA-A3 products. The RSF is relatively stable; its bias with respect to (w.r.t.) ERA5 remains mostly within ±2 W m−2. Deviations are predominantly caused by absence of either morning or afternoon satellite, mostly during the first decade. The radiative impact of the Pinatubo volcanic eruption is estimated at 3 W m−2. The OLR is stable w.r.t. ERA5 and HIRS, except during 1979–80. OLR regional uncertainty w.r.t. HIRS is quantified by the mean absolute bias (MAB) and correlates with observation density and time (satellite orbital configuration), which is optimal during 2002–16, with monthly and daily MAB of approximately 1.5 and 3.5 W m−2, respectively. Daily OLR uncertainty is higher (MAB +40%) during periods with only morning or only afternoon observations (1979–87). During the CERES era (2000–20), the OLR uncertainties w.r.t. CERES-EBAF, CERES-SYN, and HIRS are very similar. The RSF uncertainty achieves optimal results during 2002–16 with a monthly MAB w.r.t. CERES-EBAF of ∼2 W m−2 and a daily MAB w.r.t. CERES-SYN of ∼5 W m−2, and it is more sensitive to orbital configuration than is OLR. Overall, validation results are satisfactory for this first release of TOA flux products in the CLARA-A3 portfolio.
Abstract
The third edition of the CM SAF Cloud, Albedo and Surface Radiation dataset from AVHRR data (CLARA-A3) contains for the first time the top-of-atmosphere products reflected solar flux (RSF) and outgoing longwave radiation (OLR), which are presented and validated using CERES, HIRS, and ERA5 reference data. The products feature an unprecedented resolution (0.25°) and time span (4 decades) and offer synergy and compatibility with other CLARA-A3 products. The RSF is relatively stable; its bias with respect to (w.r.t.) ERA5 remains mostly within ±2 W m−2. Deviations are predominantly caused by absence of either morning or afternoon satellite, mostly during the first decade. The radiative impact of the Pinatubo volcanic eruption is estimated at 3 W m−2. The OLR is stable w.r.t. ERA5 and HIRS, except during 1979–80. OLR regional uncertainty w.r.t. HIRS is quantified by the mean absolute bias (MAB) and correlates with observation density and time (satellite orbital configuration), which is optimal during 2002–16, with monthly and daily MAB of approximately 1.5 and 3.5 W m−2, respectively. Daily OLR uncertainty is higher (MAB +40%) during periods with only morning or only afternoon observations (1979–87). During the CERES era (2000–20), the OLR uncertainties w.r.t. CERES-EBAF, CERES-SYN, and HIRS are very similar. The RSF uncertainty achieves optimal results during 2002–16 with a monthly MAB w.r.t. CERES-EBAF of ∼2 W m−2 and a daily MAB w.r.t. CERES-SYN of ∼5 W m−2, and it is more sensitive to orbital configuration than is OLR. Overall, validation results are satisfactory for this first release of TOA flux products in the CLARA-A3 portfolio.
Abstract
Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇ HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇ HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇ HN| below 8 km.
Significance Statement
These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors.
Abstract
Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇ HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇ HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇ HN| below 8 km.
Significance Statement
These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors.
Abstract
Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions.
Significance Statement
Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels.
Abstract
Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions.
Significance Statement
Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels.
Methodology, Deployment, and Performance of Pico Balloons in AntarcticaAbstract
During the 2022/23 Antarctic summer, eight pico balloon flights were deployed from Neumayer Station III (70.6666°S, 8.2667°W), yielding valuable insights into the Antarctic stratospheric wind structure. Pico balloons maintain a lower altitude compared to larger superpressure balloons, floating between 9 and 15 km MSL. The most impressive flight lasted an astounding 98 days, completing eight circumnavigations of the Southern Hemisphere. Throughout the flights, pico balloons encountered diverse air masses, displaying zonal velocities ranging from −50 to 250 km h−1 and meridional velocities between ±100 km h−1. Total wind speeds observed were extensive, spanning from 2.0 to 270 km h−1. A significant finding revealed that lower-flying pico balloons could rise due to convection underneath the flight paths, influenced by high convective available potential energy environments, resulting in changes to the balloons’ float density. Moreover, the flights demonstrated that pico balloons tended to drift farther south compared to larger stratospheric balloons, with some balloons reaching up to 8° south of the equator and 2° from the South Pole. This article explores the pressure-testing process and deployment techniques for pico balloons, showcasing their transformation from inexpensive party balloons (costing less than $20) into efficient superpressure balloons. The logistical demands for pico balloon flights were minimal, with a single person transporting all materials for the balloons (excluding lifting gas) to the Antarctic continent in carry-on luggage. The authors aim to promote the application of pico balloons to a wider scientific community by demonstrating their usefulness.
Significance Statement
Pico balloons are small party-sized balloons that are capable of floating at lower altitudes than traditional superpressure balloons. In Antarctica, where research is challenging due to harsh weather and limited resources, pico balloons present an affordable and easy-to-deploy alternative to traditional research methods. By studying the distinctive wind patterns at lower altitudes around Antarctica, pico balloons can provide valuable insights into this remote region. By demonstrating the potential use of pico balloons for scientific purposes, this study aims to offer the atmospheric community a new method of conducting research on a global scale.
Abstract
During the 2022/23 Antarctic summer, eight pico balloon flights were deployed from Neumayer Station III (70.6666°S, 8.2667°W), yielding valuable insights into the Antarctic stratospheric wind structure. Pico balloons maintain a lower altitude compared to larger superpressure balloons, floating between 9 and 15 km MSL. The most impressive flight lasted an astounding 98 days, completing eight circumnavigations of the Southern Hemisphere. Throughout the flights, pico balloons encountered diverse air masses, displaying zonal velocities ranging from −50 to 250 km h−1 and meridional velocities between ±100 km h−1. Total wind speeds observed were extensive, spanning from 2.0 to 270 km h−1. A significant finding revealed that lower-flying pico balloons could rise due to convection underneath the flight paths, influenced by high convective available potential energy environments, resulting in changes to the balloons’ float density. Moreover, the flights demonstrated that pico balloons tended to drift farther south compared to larger stratospheric balloons, with some balloons reaching up to 8° south of the equator and 2° from the South Pole. This article explores the pressure-testing process and deployment techniques for pico balloons, showcasing their transformation from inexpensive party balloons (costing less than $20) into efficient superpressure balloons. The logistical demands for pico balloon flights were minimal, with a single person transporting all materials for the balloons (excluding lifting gas) to the Antarctic continent in carry-on luggage. The authors aim to promote the application of pico balloons to a wider scientific community by demonstrating their usefulness.
Significance Statement
Pico balloons are small party-sized balloons that are capable of floating at lower altitudes than traditional superpressure balloons. In Antarctica, where research is challenging due to harsh weather and limited resources, pico balloons present an affordable and easy-to-deploy alternative to traditional research methods. By studying the distinctive wind patterns at lower altitudes around Antarctica, pico balloons can provide valuable insights into this remote region. By demonstrating the potential use of pico balloons for scientific purposes, this study aims to offer the atmospheric community a new method of conducting research on a global scale.
Estimation of Surface Current Divergence from Satellite Doppler Radar Scatterometer Measurements of Surface Ocean VelocityAbstract
The ability to estimate surface current divergence and vorticity from space is assessed from simulated satellite Doppler radar scatterometer measurements of surface velocity with an effective footprint diameter of 5 km across an 1800-km measurement swath. The focus is on non-internal-wave contributions to divergence and vorticity. This is achieved by simulating Doppler radar measurements of surface velocity from a numerical model in which internal waves are weak because of high dissipation, seasonal cycle forcing, and the lack of tidal forcing. Divergence is much more challenging to estimate than vorticity because the signals are weaker and restricted to smaller scales. With the measurement noise that was anticipated based on early engineering studies, divergence cannot be estimated with useful resolution. Recent advances in the understanding of how the noise in measurements of surface currents depends on the ambient wind speed have concluded that measurement noise will be substantially smaller in conditions of wind speed greater than 6 m s−1. A reassessment of the ability to estimate non-internal-wave contributions to surface current divergence in this study finds that useful estimates can be obtained in such wind conditions; the wavelength resolution capability for divergence estimates in the middle of the measurement swaths will be better than 100 km in 16-day averages. The improved measurement accuracy will also provide estimates of surface current vorticity with a resolution nearly a factor of 2 higher than was previously thought, resulting in wavelength resolutions of about 50, 30, and 20 km in snapshots, 4-day averages, and 16-day averages, respectively.
Significance Statement
The divergence of surface ocean velocity is of great interest to oceanographers because of its direct relation to the near-surface vertical velocity that has important implications for air–sea exchanges of CO2 and other gases, as well as the supply of nutrients from depth that are critical to biological productivity. Observational estimates of surface divergence are challenging because of the weakness of the divergence signals and the technical difficulties in acquiring two-dimensional observations of velocity with sufficient accuracy and spatial resolution to obtain accurate estimates of the divergence. The analysis presented here concludes that useful estimates of surface current divergence can be obtained from a future Doppler radar satellite mission that is in the early stages of development by NASA.
Abstract
The ability to estimate surface current divergence and vorticity from space is assessed from simulated satellite Doppler radar scatterometer measurements of surface velocity with an effective footprint diameter of 5 km across an 1800-km measurement swath. The focus is on non-internal-wave contributions to divergence and vorticity. This is achieved by simulating Doppler radar measurements of surface velocity from a numerical model in which internal waves are weak because of high dissipation, seasonal cycle forcing, and the lack of tidal forcing. Divergence is much more challenging to estimate than vorticity because the signals are weaker and restricted to smaller scales. With the measurement noise that was anticipated based on early engineering studies, divergence cannot be estimated with useful resolution. Recent advances in the understanding of how the noise in measurements of surface currents depends on the ambient wind speed have concluded that measurement noise will be substantially smaller in conditions of wind speed greater than 6 m s−1. A reassessment of the ability to estimate non-internal-wave contributions to surface current divergence in this study finds that useful estimates can be obtained in such wind conditions; the wavelength resolution capability for divergence estimates in the middle of the measurement swaths will be better than 100 km in 16-day averages. The improved measurement accuracy will also provide estimates of surface current vorticity with a resolution nearly a factor of 2 higher than was previously thought, resulting in wavelength resolutions of about 50, 30, and 20 km in snapshots, 4-day averages, and 16-day averages, respectively.
Significance Statement
The divergence of surface ocean velocity is of great interest to oceanographers because of its direct relation to the near-surface vertical velocity that has important implications for air–sea exchanges of CO2 and other gases, as well as the supply of nutrients from depth that are critical to biological productivity. Observational estimates of surface divergence are challenging because of the weakness of the divergence signals and the technical difficulties in acquiring two-dimensional observations of velocity with sufficient accuracy and spatial resolution to obtain accurate estimates of the divergence. The analysis presented here concludes that useful estimates of surface current divergence can be obtained from a future Doppler radar satellite mission that is in the early stages of development by NASA.
Abstract
Surface waves play an important role in the ocean–atmosphere coupled climate system by mediating the exchange of momentum, heat, and gas between the atmosphere and the ocean. Pseudo-Lagrangian autonomous platforms (e.g., Boeing Liquid Robotics Wave Gliders) have been used to investigate the underlying physical dynamics involved in these processes to better parameterize the air–sea exchange occurring at the scale of the surface waves. This requires accurate measurements of directional surface waves down to short scales [O(1) m], as these shorter waves support most of the stress between the atmosphere and the ocean. A challenge to overcome for pseudo-Lagrangian autonomous vehicles is that the platform’s velocity causes the observed frequency of the waves to be Doppler shifted. This leads to a modulation of the wave spectrum, particularly at high frequencies, that depends on the platform’s speed, the wave frequency, and the relative angle between the direction of wave and platform propagation. In this work, we propose a method to account for Doppler effects that considers the full directionality of the wave field. The method is validated using a unique dataset collected from a fleet of two Wave Gliders off the coast of Southern California in September 2019 operating on the perimeter of a tight square (500-m edge length) track over a 3-day deployment. This technique can be used to estimate wave spectra derived from other slow-moving surface vehicles such as Saildrones that use platform motion to characterize the surface wave field. MATLAB routines to implement this method are publicly available.
Significance Statement
The purpose of this study is to introduce a general approach that corrects observations of ocean surface waves collected on board autonomous surface vehicles (ASVs) for the effects on the wave period due to the vehicle’s forward motion. This is important because improving climate models requires accurate measurements of short-wavelength waves, which can be readily obtained from ASVs. Our method provides the tools for ASVs to better understand air–sea physics and the larger role ocean surface waves play in Earth’s climate system.
Abstract
Surface waves play an important role in the ocean–atmosphere coupled climate system by mediating the exchange of momentum, heat, and gas between the atmosphere and the ocean. Pseudo-Lagrangian autonomous platforms (e.g., Boeing Liquid Robotics Wave Gliders) have been used to investigate the underlying physical dynamics involved in these processes to better parameterize the air–sea exchange occurring at the scale of the surface waves. This requires accurate measurements of directional surface waves down to short scales [O(1) m], as these shorter waves support most of the stress between the atmosphere and the ocean. A challenge to overcome for pseudo-Lagrangian autonomous vehicles is that the platform’s velocity causes the observed frequency of the waves to be Doppler shifted. This leads to a modulation of the wave spectrum, particularly at high frequencies, that depends on the platform’s speed, the wave frequency, and the relative angle between the direction of wave and platform propagation. In this work, we propose a method to account for Doppler effects that considers the full directionality of the wave field. The method is validated using a unique dataset collected from a fleet of two Wave Gliders off the coast of Southern California in September 2019 operating on the perimeter of a tight square (500-m edge length) track over a 3-day deployment. This technique can be used to estimate wave spectra derived from other slow-moving surface vehicles such as Saildrones that use platform motion to characterize the surface wave field. MATLAB routines to implement this method are publicly available.
Significance Statement
The purpose of this study is to introduce a general approach that corrects observations of ocean surface waves collected on board autonomous surface vehicles (ASVs) for the effects on the wave period due to the vehicle’s forward motion. This is important because improving climate models requires accurate measurements of short-wavelength waves, which can be readily obtained from ASVs. Our method provides the tools for ASVs to better understand air–sea physics and the larger role ocean surface waves play in Earth’s climate system.
Abstract
The development of autonomous surface vehicles, such as the Boeing Liquid Robotics Wave Glider, has revolutionized our ability to collect surface ocean–lower atmosphere observations, a crucial step toward developing better physical understanding of upper-ocean and air–sea interaction processes. However, due to the wave-following nature of these vehicles, they experience rapid shifting, rolling, and pitching under the action of surface waves, making motion compensation of observations of ocean currents particularly challenging. We present an evaluation of the accuracy of Wave Glider–based ADCP measurements by comparing them with coincident and collocated observations collected from a bottom-mounted ADCP over the course of a week-long experiment. A novel motion compensation method, tailored to wave-following surface vehicles, is presented and compared with standard approaches. We show that the use of an additional position and attitude sensor (GPS/IMU) significantly improves the accuracy of the observed currents.
Abstract
The development of autonomous surface vehicles, such as the Boeing Liquid Robotics Wave Glider, has revolutionized our ability to collect surface ocean–lower atmosphere observations, a crucial step toward developing better physical understanding of upper-ocean and air–sea interaction processes. However, due to the wave-following nature of these vehicles, they experience rapid shifting, rolling, and pitching under the action of surface waves, making motion compensation of observations of ocean currents particularly challenging. We present an evaluation of the accuracy of Wave Glider–based ADCP measurements by comparing them with coincident and collocated observations collected from a bottom-mounted ADCP over the course of a week-long experiment. A novel motion compensation method, tailored to wave-following surface vehicles, is presented and compared with standard approaches. We show that the use of an additional position and attitude sensor (GPS/IMU) significantly improves the accuracy of the observed currents.
Abstract
While satellite altimeters have revolutionized ocean science, validation measurements in high wave environments are rare. Using geodetic Global Navigation Satellite System (GNSS) data collected from the Southern Ocean Flux Station (SOFS; −47°S, 142°E) since 2019, as part of the Southern Ocean Time Series (SOTS), we present a validation of satellite missions in this energetic region. Here we show that high rate GNSS observations at SOFS can successfully measure waves in the extreme conditions of the Southern Ocean and obtain robust measurements in all wave regimes [significant wave height (SWH) ranging from 1.5 to 12.6 m]. We find good agreement between the in situ and nadir altimetry SWH (RMSE = 0.16 m, mean bias = 0.04 m, and n = 60). Directional comparisons with the Chinese–French Ocean Satellite (CFOSAT) Surface Waves Investigation and Monitoring (SWIM) instrument also show good agreement, with dominant directions having an RMSE of 9.1° (n = 22), and correlation coefficients between the directional spectra ranging between 0.57 and 0.79. Initial sea level anomaly (SLA) estimates capture eddies propagating through the region. Comparisons show good agreement with daily gridded SLA products (RMSE = 0.03 m, and n = 205), with scope for future improvement. These results demonstrate the utility of high rate geodetic GNSS observations on moored surface platforms in highly energetic regions of the ocean. Such observations are important to maximize the geophysical interpretation from altimeter missions. In particular, the ability to provide collocated directional wave observations and SLA estimates will be useful for the validation of the recently launched Surface Water and Ocean Topography (SWOT) mission where understanding the interactions between sea state and sea surface height poses a major challenge.
Abstract
While satellite altimeters have revolutionized ocean science, validation measurements in high wave environments are rare. Using geodetic Global Navigation Satellite System (GNSS) data collected from the Southern Ocean Flux Station (SOFS; −47°S, 142°E) since 2019, as part of the Southern Ocean Time Series (SOTS), we present a validation of satellite missions in this energetic region. Here we show that high rate GNSS observations at SOFS can successfully measure waves in the extreme conditions of the Southern Ocean and obtain robust measurements in all wave regimes [significant wave height (SWH) ranging from 1.5 to 12.6 m]. We find good agreement between the in situ and nadir altimetry SWH (RMSE = 0.16 m, mean bias = 0.04 m, and n = 60). Directional comparisons with the Chinese–French Ocean Satellite (CFOSAT) Surface Waves Investigation and Monitoring (SWIM) instrument also show good agreement, with dominant directions having an RMSE of 9.1° (n = 22), and correlation coefficients between the directional spectra ranging between 0.57 and 0.79. Initial sea level anomaly (SLA) estimates capture eddies propagating through the region. Comparisons show good agreement with daily gridded SLA products (RMSE = 0.03 m, and n = 205), with scope for future improvement. These results demonstrate the utility of high rate geodetic GNSS observations on moored surface platforms in highly energetic regions of the ocean. Such observations are important to maximize the geophysical interpretation from altimeter missions. In particular, the ability to provide collocated directional wave observations and SLA estimates will be useful for the validation of the recently launched Surface Water and Ocean Topography (SWOT) mission where understanding the interactions between sea state and sea surface height poses a major challenge.
Corrections for Geostationary Cloud Liquid Water Path Using Microwave ImageryAbstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
Abstract
Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellite-16 and -17 (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
Significance Statement
Large uncertainty exists in cloud liquid water path derived from geostationary observations, which is caused by changes in the scattering geometry of sunlight throughout the day. This complicates the usefulness of geostationary satellites to analyze the time evolution of clouds using geostationary data. Therefore, microwave imagery observations of liquid water path, which do not depend on scattering geometry, are used to create a set of corrections for geostationary data that can be used in future studies to analyze the time evolution of clouds from space.
