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Sea Ice Motion Vector Retrievals from AMSR2 89-GHz Data: Validation Algorithm with Simultaneous Multichannel ObservationsAbstract
In retrievals of sea ice motion vectors (SIMVs) based on passive microwave observations, the use of the high-resolution 89-GHz channel of the Advanced Microwave Scanning Radiometer 2 (AMSR2) has the advantage of enhancing the theoretical precision of correlation-based motion tracking. However, its higher sensitivity to atmospheric moisture than lower-frequency channels links maximum cross-correlation peaks to outlier vectors and obscures signals of valid vectors. This study develops an algorithm to select valid vectors from candidates detected by multiple cross-correlation peaks based on validations with large-scale sea ice displacements extracted from 19- and 37-GHz data after questionable vectors are prefiltered by comparing them with reanalysis surface wind and neighboring vectors. The algorithm selects a vector corresponding to large-scale motion as the optimal vector. The retrieved results from 2013 to 2020 show that by replacing outlier vectors with valid ones detected by second or third cross-correlation peaks, validation with simultaneous observations enables retrieval of more than 60% of the Arctic motion field from 89-GHz data in winter but only 10% in summer; therefore, lower-frequency data are employed for retrievals. The uncertainty assessment using in situ data from acoustic measurements from ocean moorings shows that the algorithm provides daily SIMVs with root-mean-square errors of only 1–2 cm s–1 in idealized winter conditions with the absence of diurnal brightness temperature (Tb) changes that make tracking of the similarity of Tb fields difficult. The analysis also illustrates the applicability limit of the algorithm for summer retrievals.
Significance Statement
An algorithm was developed to validate sea ice motion vectors retrieved from AMSR2 89-GHz data by those from lower-frequency data. The validation via simultaneous observations enabled that valid vectors are sorted from invalid ones resulting from weather effects.
Abstract
In retrievals of sea ice motion vectors (SIMVs) based on passive microwave observations, the use of the high-resolution 89-GHz channel of the Advanced Microwave Scanning Radiometer 2 (AMSR2) has the advantage of enhancing the theoretical precision of correlation-based motion tracking. However, its higher sensitivity to atmospheric moisture than lower-frequency channels links maximum cross-correlation peaks to outlier vectors and obscures signals of valid vectors. This study develops an algorithm to select valid vectors from candidates detected by multiple cross-correlation peaks based on validations with large-scale sea ice displacements extracted from 19- and 37-GHz data after questionable vectors are prefiltered by comparing them with reanalysis surface wind and neighboring vectors. The algorithm selects a vector corresponding to large-scale motion as the optimal vector. The retrieved results from 2013 to 2020 show that by replacing outlier vectors with valid ones detected by second or third cross-correlation peaks, validation with simultaneous observations enables retrieval of more than 60% of the Arctic motion field from 89-GHz data in winter but only 10% in summer; therefore, lower-frequency data are employed for retrievals. The uncertainty assessment using in situ data from acoustic measurements from ocean moorings shows that the algorithm provides daily SIMVs with root-mean-square errors of only 1–2 cm s–1 in idealized winter conditions with the absence of diurnal brightness temperature (Tb) changes that make tracking of the similarity of Tb fields difficult. The analysis also illustrates the applicability limit of the algorithm for summer retrievals.
Significance Statement
An algorithm was developed to validate sea ice motion vectors retrieved from AMSR2 89-GHz data by those from lower-frequency data. The validation via simultaneous observations enabled that valid vectors are sorted from invalid ones resulting from weather effects.
A New Objective Typhoon Location Algorithm Considering a Perturbation Factor Based on FY-4A Brightness Temperature DataAbstract
In this paper, a new objective typhoon positioning algorithm was proposed. The algorithm uses L1 12-channel far-infrared data of the FY-4A geostationary meteorological satellite for objective positioning, verified against best path data provided by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). By calculating the tangential and radial perturbation values of infrared brightness temperature images, the perturbation factor can be obtained. By adopting the position of the maximum perturbation factor as the center of a circle and considering a radius of no more than 20 km, the position of the minimum perturbation factor was determined; this value represents the central position of the typhoon. Tropical cyclones in 2019 and 2020 were selected for objective positioning, and the objective positioning results were verified against the corresponding time in the best path dataset. The results included centralized verification results for 29 typhoons and optimal path data in 2019. The maximum average error reached 54.67 km, with an annual average typhoon positioning error of 16.15 km. The centralization verification results for 23 typhoons and optimal path data in 2020 indicated a minimum average error of 12.71 km, a maximum average error of 18.56 km, and an annual average typhoon positioning error of 14.82 km. The positioning results for these two years suggest that the longitude obtained with the perturbation factor positioning method is satisfactory, exhibiting an error of less than 20 km.
Significance Statement
The purpose of this study is to help researchers make scientific discoveries and help the development of typhoon center location technology in the future. This is important because accurate positioning of typhoon center can provide effective help for typhoon path prediction and typhoon intensity determination.
Abstract
In this paper, a new objective typhoon positioning algorithm was proposed. The algorithm uses L1 12-channel far-infrared data of the FY-4A geostationary meteorological satellite for objective positioning, verified against best path data provided by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). By calculating the tangential and radial perturbation values of infrared brightness temperature images, the perturbation factor can be obtained. By adopting the position of the maximum perturbation factor as the center of a circle and considering a radius of no more than 20 km, the position of the minimum perturbation factor was determined; this value represents the central position of the typhoon. Tropical cyclones in 2019 and 2020 were selected for objective positioning, and the objective positioning results were verified against the corresponding time in the best path dataset. The results included centralized verification results for 29 typhoons and optimal path data in 2019. The maximum average error reached 54.67 km, with an annual average typhoon positioning error of 16.15 km. The centralization verification results for 23 typhoons and optimal path data in 2020 indicated a minimum average error of 12.71 km, a maximum average error of 18.56 km, and an annual average typhoon positioning error of 14.82 km. The positioning results for these two years suggest that the longitude obtained with the perturbation factor positioning method is satisfactory, exhibiting an error of less than 20 km.
Significance Statement
The purpose of this study is to help researchers make scientific discoveries and help the development of typhoon center location technology in the future. This is important because accurate positioning of typhoon center can provide effective help for typhoon path prediction and typhoon intensity determination.
Abstract
This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.
Abstract
This study introduces a validation technique for quantitative comparison of algorithms that retrieve winds from passive detection of cloud- and water vapor–drift motions, also known as atmospheric motion vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh-rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds, which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages mesoscale, multilayer, multiband wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or “optical flow,” retrieval algorithms used within AMVs, 1) patch matching (PM; used within operational AMVs) and 2) an advanced variational optical flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multilayer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g., gravity waves), where the image temporal noise exceeds the natural variability, and when the height assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh-rate imagery, such as 1- versus 10-min data.
Potential and Limitations of a Commercial Broadband Echo Sounder for Remote Observations of Turbulent MixingAbstract
Stratified oceanic turbulence is strongly intermittent in time and space, and therefore generally underresolved by currently available in situ observational approaches. A promising tool to at least partly overcome this constraint are broadband acoustic observations of turbulent microstructure that have the potential to provide mixing parameters at orders of magnitude higher resolution compared to conventional approaches. Here, we discuss the applicability, limitations, and measurement uncertainties of this approach for some prototypical turbulent flows (stratified shear layers, turbulent flow across a sill), based on a comparison of broadband acoustic observations and data from a free-falling turbulence microstructure profiler. We find that broadband acoustics are able to provide a quantitative description of turbulence energy dissipation in stratified shear layers (correlation coefficient r = 0.84) if the stratification parameters required by the method are carefully preprocessed. Essential components of our suggested preprocessing algorithm are 1) a vertical low-pass filtering of temperature and salinity profiles at a scale slightly larger than the Ozmidov length scale of turbulence and 2) an automated elimination of weakly stratified layers according to a gradient threshold criterion. We also show that in weakly stratified conditions, the acoustic approach may yield acceptable results if representative averaged vertical temperature and salinity gradients rather than local gradients are used. Our findings provide a step toward routine turbulence measurements in the upper ocean from moving vessels by combining broadband acoustics with in situ CTD profiles.
Abstract
Stratified oceanic turbulence is strongly intermittent in time and space, and therefore generally underresolved by currently available in situ observational approaches. A promising tool to at least partly overcome this constraint are broadband acoustic observations of turbulent microstructure that have the potential to provide mixing parameters at orders of magnitude higher resolution compared to conventional approaches. Here, we discuss the applicability, limitations, and measurement uncertainties of this approach for some prototypical turbulent flows (stratified shear layers, turbulent flow across a sill), based on a comparison of broadband acoustic observations and data from a free-falling turbulence microstructure profiler. We find that broadband acoustics are able to provide a quantitative description of turbulence energy dissipation in stratified shear layers (correlation coefficient r = 0.84) if the stratification parameters required by the method are carefully preprocessed. Essential components of our suggested preprocessing algorithm are 1) a vertical low-pass filtering of temperature and salinity profiles at a scale slightly larger than the Ozmidov length scale of turbulence and 2) an automated elimination of weakly stratified layers according to a gradient threshold criterion. We also show that in weakly stratified conditions, the acoustic approach may yield acceptable results if representative averaged vertical temperature and salinity gradients rather than local gradients are used. Our findings provide a step toward routine turbulence measurements in the upper ocean from moving vessels by combining broadband acoustics with in situ CTD profiles.
Abstract
This article develops a novel event-triggered sliding mode control (ETSMC) approach with variable threshold to deal with trajectory tracking matters of autonomous underwater vehicles (AUVs) accompanied by actuator saturation and external disturbances, which can effectively reduce the communication burden between the controller and actuator. The proposed scheme will be very practical when some extreme situations occur. First, the closed-loop system is split into two parts: fixed terms determined by the system itself and nonlinear terms caused by uncertain factors. The nonlinear terms are estimated through adaptive technique. Then the apposite event-triggered mechanism, adaptive laws, and modeled actuator saturation characteristics are designed. The correctness of the presented scheme is illustrated via the stability analysis in the sequel, and the Zeno phenomenon is certificated to be excluded simultaneously. Finally, two different reference motion trajectories are adopted in the simulation experiments, which can indicate that the proposed ETSMC possesses performance superiority and only requires to consume a small amount of communication resources in trajectory tracking control of AUVs.
Significance Statement
Through the research of this article, we propose a novel event-triggered sliding mode control method with variable threshold applied to autonomous underwater vehicles (AUVs). When conducting ocean exploration work, we usually need the AUVs to follow particular trajectories. By using the proposed method, it can greatly reduce the loss of communication resources inside the system.
Abstract
This article develops a novel event-triggered sliding mode control (ETSMC) approach with variable threshold to deal with trajectory tracking matters of autonomous underwater vehicles (AUVs) accompanied by actuator saturation and external disturbances, which can effectively reduce the communication burden between the controller and actuator. The proposed scheme will be very practical when some extreme situations occur. First, the closed-loop system is split into two parts: fixed terms determined by the system itself and nonlinear terms caused by uncertain factors. The nonlinear terms are estimated through adaptive technique. Then the apposite event-triggered mechanism, adaptive laws, and modeled actuator saturation characteristics are designed. The correctness of the presented scheme is illustrated via the stability analysis in the sequel, and the Zeno phenomenon is certificated to be excluded simultaneously. Finally, two different reference motion trajectories are adopted in the simulation experiments, which can indicate that the proposed ETSMC possesses performance superiority and only requires to consume a small amount of communication resources in trajectory tracking control of AUVs.
Significance Statement
Through the research of this article, we propose a novel event-triggered sliding mode control method with variable threshold applied to autonomous underwater vehicles (AUVs). When conducting ocean exploration work, we usually need the AUVs to follow particular trajectories. By using the proposed method, it can greatly reduce the loss of communication resources inside the system.
Abstract
Quality control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance L
WN spectra at incremental accuracy levels identified as level 1.0, level 1.5, and level 2.0. Currently, the final QC step allowing for rising level 1.5 L
WN spectra to level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so-called
Abstract
Quality control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance L
WN spectra at incremental accuracy levels identified as level 1.0, level 1.5, and level 2.0. Currently, the final QC step allowing for rising level 1.5 L
WN spectra to level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so-called
Abstract
This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2.
Significance Statement
The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version.
Abstract
This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2.
Significance Statement
The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version.
