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Jason M. Apke, Yoo-Jeong Noh, and Kristopher Bedka

Abstract

This study introduces a validation technique for quantitative comparison of algorithms which 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 meso-scale, multi-layer, multi-band 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 multi-layer-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-min versus 10-min data.

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Eri Yoshizawa, Takashi Kamoshida, and Koji Shimada

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 2013–2020 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 SIMVs with root mean square errors of only 1–2 cm/s (even at daily temporal resolutions) 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.

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Bryan Mills Karpowicz, Yanqiu Zhu, Stephen Joseph Munchak, and Will McCarty

Abstract

Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

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Tao Xie, Jiajun Chen, and Junjie Yan

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.

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Julia Muchowski, Lars Umlauf, Lars Arneborg, Peter Holtermann, Elizabeth Weidner, Christoph Humborg, and Christian Stranne

Abstract

Stratified oceanic turbulence is strongly intermittent in time and space, and therefore generally under-resolved 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 towards routine turbulence measurements in the upper ocean from moving vessels by combining broadband acoustics with in situ CTD profiles.

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Jian Xu, Xing Wang, Ping Liu, and Qiaoyu Duan

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 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 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.

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Alejandro Cáceres Euse, Anne Molcard, Natacha Bourg, Dylan Dumas, Charles-Antoine Guérin, and Giovanni Besio

Abstract

To assess the contribution of wind drag and Stokes drift on the near-surface circulation, a methodology to isolate the geostrophic surface current from High-Frequency radar data is developed. The methodology performs a joint analysis utilizing wind field and in situ surface currents along with an unsupervised neuronal network. The isolation method seems robust in the light of comparisons with satellite altimeter data, presenting a similar time variability and providing more spatial detail of the currents in the coastal region. Results show that the wind-induced current is around 2.1% the wind speed and deflected from the wind direction between [18°, 23°], whereas classical literature suggests higher values. The wave-induced currents can represent more than 13% of the ageostrophic current component as function of the wind speed, suggesting that the Stokes drift needs to be analyzed as an independent term when studying surface sea currents in the coastal zones. The methodology and results presented here could be extended worldwide, as complementary information to improve satellite-derived surface currents in the coastal regions by including the local physical processes recorded by High-Frequency radar systems. The assessment of the wave and wind-induced currents have important applications on Lagrangian transport studies.

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Li Zhao, Tao Xie, William Perrie, Ming Ma, Jingsong Yang, Chengzu Bai, and Rick Danielson

Abstract

Sea surface temperature (SST) fronts are important for fisheries and marine ecology, as well as upper ocean dynamics, weather forecasting and climate monitoring. In this paper, we propose a new approach to detect SST fronts from RADARSAT-2 ScanSAR images, based on the correlation of SAR-derived wind speeds using the gray level co-occurrence matrix (GLCM) approach. Due to the large differences between the correlation of wind speeds for SST fronts compared to other areas, SST fronts can be detected by the threshold method. To eliminate small-scale features (or noise), the 30 km scale is used as the length threshold for the detection of the SST fronts. The proposed method is effective when wind speeds are between 3 to 13 m/s. The overall accuracy of our method is about 93.6%, which is sufficient for operational applications.

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Jie Zhou, Hang Gao, Xuesong Wang, and Jianbing Li

Abstract

The hydrogen balloon is widely used for wind sensing by tracking it with optical theodolites. The traditional theodolite observation (single- and double-theodolite) methods assume that the balloon is a perfect tracer of the background wind and it rises with a constant speed during the whole observation period, but these assumptions may not hold well in complex wind circumstances. In this paper, an accurate wind field retrieval method based on multi-theodolite measurement is proposed. The extended Kalman filter algorithm is used to filter the angle data observed by the theodolites in order to accurately estimate the trajectory of the balloon, and the motion equation is used to correct the velocity difference between the background wind and the balloon. As a result, not only the horizontal velocity but also the vertical velocity can be accurately retrieved by this method. Numerical simulation and field experiments show that the multi-theodolite observation method excels the traditional single-theodolite method, and the velocity errors can be reduced by even more than 40% in comparison with the single-theodolite method for complex wind cases.

Significanace Statement

In the meteorological community, hydrogen balloon tracking is a widely used wind retrieval method, but the accuracy is limited, especially under complex wind conditions. In this paper, a new method based on tracking the hydrogen balloon with multi-theodolite is proposed, which uses the extended Kalman filter and the motion equation to get an accurate estimation of the balloon’s velocity and fix the inertia effect of balloon, respectively. Simulation and field experiment show that the new method can reduce the velocity error by more than 40% compared with the traditional method.

Open access