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Abstract
Since the 1960’s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air-sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air-sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross-section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016-2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in km2. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data is useful for studying tropical cyclones at the air-sea interface and provide results of these behaviors closely to data derived from best-track archives.
Abstract
Since the 1960’s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air-sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air-sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross-section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016-2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in km2. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data is useful for studying tropical cyclones at the air-sea interface and provide results of these behaviors closely to data derived from best-track archives.
Abstract
Presented is the approach, implementation, and evaluation of two-way nesting in a split-implicit ocean model, the Navy Coastal Ocean Model (NCOM). Emphasis is on the strategies applied to feed back fields from the fine-mesh nest (child grid) to the coarse-mesh (parent grid). On an appropriate separation of dynamic and feedback interfaces, attention is especially needed for the feedback interface of surface elevation. One particular issue addressed is the inconsistency between the 3D baroclinic velocities and 2D barotropic transports in the feedback. The discrepancy is inherently associated with bathymetry, depth-integration, and the need to average over spatial grid points. A simple remedy is proposed and proven to be effective and necessary in realistic coastal applications. In addition to the full two-way nesting, a simplified two-way nesting approach is provided in which only the temperature and salinity are fed back from the nest, and the velocity fields are assumed to self-adjust according to the geostrophic balance. The performance of both approaches is evaluated using the idealized benchmark, propagation of a baroclinic vortex, and an application to the Mississippi River outflowin the northeast Gulf ofMexico, including a comparison with available observations. Discussions are also made on the computational efficiency of the two-way nesting and its sensitivity to the open boundary conditions in regard to noise suppression.
Abstract
Presented is the approach, implementation, and evaluation of two-way nesting in a split-implicit ocean model, the Navy Coastal Ocean Model (NCOM). Emphasis is on the strategies applied to feed back fields from the fine-mesh nest (child grid) to the coarse-mesh (parent grid). On an appropriate separation of dynamic and feedback interfaces, attention is especially needed for the feedback interface of surface elevation. One particular issue addressed is the inconsistency between the 3D baroclinic velocities and 2D barotropic transports in the feedback. The discrepancy is inherently associated with bathymetry, depth-integration, and the need to average over spatial grid points. A simple remedy is proposed and proven to be effective and necessary in realistic coastal applications. In addition to the full two-way nesting, a simplified two-way nesting approach is provided in which only the temperature and salinity are fed back from the nest, and the velocity fields are assumed to self-adjust according to the geostrophic balance. The performance of both approaches is evaluated using the idealized benchmark, propagation of a baroclinic vortex, and an application to the Mississippi River outflowin the northeast Gulf ofMexico, including a comparison with available observations. Discussions are also made on the computational efficiency of the two-way nesting and its sensitivity to the open boundary conditions in regard to noise suppression.
A Periodically Updated Adaptive Sampling Framework for Marine Mobile Observation PlatformsAbstract
While numerical models have been developed for several years, some of these have been applied to ocean-state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking a coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms.
Significance Statement
The ocean is very difficult to observe because it is so vast. How to deploy observing assets is a question worth investigating. We designed an adaptive sampling framework for mobile observing platforms to improve the prediction accuracy of numerical models. This framework uses the prediction to design the observation scheme, then the observation to update the prediction, and then the updated prediction to adjust the sampling scheme, which achieves closed-loop optimization. We first selected an optimization algorithm for this framework by comparing experiments. Based on the optimization algorithm, we simulated the closed-loop optimization process. Simulation results show that this framework can significantly increase the efficiency of long-term ocean observations with mobile devices.
Abstract
While numerical models have been developed for several years, some of these have been applied to ocean-state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking a coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms.
Significance Statement
The ocean is very difficult to observe because it is so vast. How to deploy observing assets is a question worth investigating. We designed an adaptive sampling framework for mobile observing platforms to improve the prediction accuracy of numerical models. This framework uses the prediction to design the observation scheme, then the observation to update the prediction, and then the updated prediction to adjust the sampling scheme, which achieves closed-loop optimization. We first selected an optimization algorithm for this framework by comparing experiments. Based on the optimization algorithm, we simulated the closed-loop optimization process. Simulation results show that this framework can significantly increase the efficiency of long-term ocean observations with mobile devices.
Saildrone Direct Covariance Wind Stress in Various Wind and Current Regimes of the Tropical PacificAbstract
High-frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical east Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previous work that showed the bulk input data were reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.
Significance Statement
We use data from Saildrone uncrewed oceanographic research vehicles to investigate the horizontal forces applied to the surface of the ocean by the action of the wind. We compare two methods to calculate the forces: one uses several simplifying assumptions, and the other makes fewer assumptions but is error prone if the data are incorrectly processed. The two methods agree well, suggesting that Saildrone vehicles are suitable for both methods and that the data processing methods work. Our results show that it is important to consider ocean currents, as well as winds, in order to achieve accurate magnitude and direction of the surface forces.
Abstract
High-frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical east Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previous work that showed the bulk input data were reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.
Significance Statement
We use data from Saildrone uncrewed oceanographic research vehicles to investigate the horizontal forces applied to the surface of the ocean by the action of the wind. We compare two methods to calculate the forces: one uses several simplifying assumptions, and the other makes fewer assumptions but is error prone if the data are incorrectly processed. The two methods agree well, suggesting that Saildrone vehicles are suitable for both methods and that the data processing methods work. Our results show that it is important to consider ocean currents, as well as winds, in order to achieve accurate magnitude and direction of the surface forces.
Abstract
We present nowcasts of sudden heavy rains on meso-γ-scales (2–20 km) using the high spatio-temporal resolution of a Multi-Parameter Phased-Array Weather Radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-minute lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on Long Short-Term Memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 sec. The model uses radar reflectivity at horizontal polarization (ZH) and the differential reflectivity. The input parameters are defined in a volume of 64×64×8 km3 with the lowest level at 1.9 km and a resolution of 0.4×0.4×0.25 km3. The prediction is a 10-minute sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).
Abstract
We present nowcasts of sudden heavy rains on meso-γ-scales (2–20 km) using the high spatio-temporal resolution of a Multi-Parameter Phased-Array Weather Radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-minute lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on Long Short-Term Memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 sec. The model uses radar reflectivity at horizontal polarization (ZH) and the differential reflectivity. The input parameters are defined in a volume of 64×64×8 km3 with the lowest level at 1.9 km and a resolution of 0.4×0.4×0.25 km3. The prediction is a 10-minute sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).
Abstract
We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarisation moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar; the Relative Calibration Adjustment (RCA) technique to track relative changes in the radar calibration constant; the solar calibration technique to track daily change in solar power and antenna pointing error; and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarisation moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Due to the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management) an it is operational at the Australian Bureau of Meteorology.
Abstract
We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarisation moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar; the Relative Calibration Adjustment (RCA) technique to track relative changes in the radar calibration constant; the solar calibration technique to track daily change in solar power and antenna pointing error; and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarisation moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Due to the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management) an it is operational at the Australian Bureau of Meteorology.
Abstract
This article presents a calibration transfer methodology between radars of the same and different frequency bands. This method enables the absolute calibration of a meteorological radar by transferring it from another co-located instrument with known calibration, by simultaneously measuring vertical cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can reach the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between co-located W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020-2021). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and aW-band radar with manufacturer calibration, the difference found was -16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible.
Abstract
This article presents a calibration transfer methodology between radars of the same and different frequency bands. This method enables the absolute calibration of a meteorological radar by transferring it from another co-located instrument with known calibration, by simultaneously measuring vertical cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can reach the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between co-located W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020-2021). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and aW-band radar with manufacturer calibration, the difference found was -16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible.
Abstract
Aerosol-cloud-precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlationwas observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.
Abstract
Aerosol-cloud-precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlationwas observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.
Abstract
A common dilemma for oceanographers is the choice of horizontal diffusivity. There is no single answer as we could argue that diffusion depends precisely on those processes that cannot be sampled or modeled. Here we propose the radial offset by diffusion (ROD) method as a simple model-dependent approach for estimating these coefficients, and show its application for the southwestern South Atlantic. The method compares actual displacements of field drifters with numerical trajectory predictions. The observed-predicted differences in radial positions (radial offsets), which respond to diffusive motions not captured by the numerical model, are reproduced with a one-dimensional radial-diffusive solution through a proper selection of the diffusion coefficient. The method is tested at eight depths, from the sea surface down to 2000 m, using several drifter datasets and the Parcels software applied to the GLORYS12v1 (1/12° daily) velocity outputs. In all cases the radial offsets show Gaussian distributions that are well reproduced by the radial diffusive solution. Maximum diffusivities of 4630-4980 m2 s−1 happen in the upper 200 m of the water column and minimum values of 1080-1270 m2 s−1 occur between 1400 and 2000 m. The 15-m diffusivity is fairly constant in latitude (3850 to 5270 m2 s−1), but the 1000-m diffusivity decreases from 1640-1820 m2 s−1 north of the Polar Front to 530 m2 s−1 south of the Southern Boundary. A comparison with other diffusivity studies validates the good adequacy of the ROD method for numerical and field applications.
Abstract
A common dilemma for oceanographers is the choice of horizontal diffusivity. There is no single answer as we could argue that diffusion depends precisely on those processes that cannot be sampled or modeled. Here we propose the radial offset by diffusion (ROD) method as a simple model-dependent approach for estimating these coefficients, and show its application for the southwestern South Atlantic. The method compares actual displacements of field drifters with numerical trajectory predictions. The observed-predicted differences in radial positions (radial offsets), which respond to diffusive motions not captured by the numerical model, are reproduced with a one-dimensional radial-diffusive solution through a proper selection of the diffusion coefficient. The method is tested at eight depths, from the sea surface down to 2000 m, using several drifter datasets and the Parcels software applied to the GLORYS12v1 (1/12° daily) velocity outputs. In all cases the radial offsets show Gaussian distributions that are well reproduced by the radial diffusive solution. Maximum diffusivities of 4630-4980 m2 s−1 happen in the upper 200 m of the water column and minimum values of 1080-1270 m2 s−1 occur between 1400 and 2000 m. The 15-m diffusivity is fairly constant in latitude (3850 to 5270 m2 s−1), but the 1000-m diffusivity decreases from 1640-1820 m2 s−1 north of the Polar Front to 530 m2 s−1 south of the Southern Boundary. A comparison with other diffusivity studies validates the good adequacy of the ROD method for numerical and field applications.
