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Guillaume Dodet, Saleh Abdalla, Matias Alday, Mickaël Accensi, Jean Bidlot, and Fabrice Ardhuin

Abstract

Ocean wave measurements are of major importance for a number of applications including climate studies, ship routing, marine engineering, safety at sea, and coastal risk management. Depending on the scales and regions of interest, a variety of data sources may be considered (e.g., in situ data, Voluntary Observing Ship observations, altimeter records, numerical wave models), each one with its own characteristics in terms of sampling frequency, spatial coverage, accuracy, and cost. To combine multiple source of wave information (e.g., for data assimilation scheme in numerical weather prediction models), the error characteristics of each measurement system need to be defined. In this study, we use the triple collocation technique to estimate the random error variance of significant wave heights from a comprehensive collection of collocated in situ, altimeter, and model data. The in situ dataset is a selection of 122 platforms provided by the Copernicus Marine Service In Situ Thematic Center. The altimeter dataset is the ESA Sea State CCI version1 L2P product. The model dataset is the WW3-LOPS hindcast forced with bias-corrected ERA5 winds and an adjusted T475 parameterization of wave generation and dissipation. Compared to previous similar analyses, the extensive (∼250 000 entries) triple collocation dataset generated for this study provides some new insights on the error variability associated to differences in in situ platforms, satellite missions, sea state conditions, and seasonal variability.

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Boyan Hu, Jinfeng Ding, Gang Liu, and Jianping Tang

Abstract

This study analyzes the spatial and temporal distribution characteristics of the in situ aircraft observations in the middle and higher troposphere in 2019. These aircraft observations are mainly distributed in China, and relatively evenly recorded between 0000 and 1500 UTC in time and 6 and 10 km in height. Based on the 3395 stronger clear-air turbulence (CAT) events and 4038 weaker CAT events selected from the observations in the study region (15°–55°N, 70°–140°E), the performances of 24 CAT diagnostics calculated from the ERA5 data are evaluated. Results show that the diagnostics connected with vertical wind shear (i.e., version 1 of the North Carolina State University index, negative Richardson number, variant 3 and variant 1 of Ellrod’s turbulence index) have the best performances. However, the performances vary greatly from season to season, and overall performances are the best in winter and worst in summer. The annual and seasonal best thresholds for these diagnostics are also listed in this study.

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Beth Reid and Tom Swanson

Abstract

Loon LLC collected 794 000 h of corona current observations between 15 and ∼20 km above sea level with time resolution between 1 and 30 min. We are publicly releasing this dataset to enable the research community’s understanding of electrical activity in the stratosphere. We validate the reliability of these measurements by aligning our flight data with both nearby Geostationary Lightning Mapper (GLM) events and the Convective Diagnostic Oceanic (CDO) indicator. Corona current observations that exceeded the sensor maximum of 10 μA were associated with high GLM optical flux accumulations along the flight trajectory. Using the CDO indicator as a persistence forecast for future electrical activity was effective at predicting corona current events, and so we highly recommend this data source for real-time stratospheric navigation for vehicles sensitive to the harsh electrical environment of the stratosphere.

Significance Statement

Loon LLC operated a fleet of balloons in the stratosphere, between 15 and 20 km above sea level. The balloons were instrumented with a sensor that measured the current flowing through a wire dangling from the flight vehicle. The observed currents were caused by the motion of nearby charged particles that are often associated with thunderstorms and lightning activity. In this paper we show that Loon’s sensor registered current at the same time lightning was recorded near the balloon by other instruments like the Geostationary Lightning Mapper satellite. This is the first dataset of its kind and size, reaching 794 000 flight hours. We are publicly releasing these data in hopes of aiding scientific discovery by researchers and to help future stratospheric vehicle operators better understand and plan for the electrical environment.

Open access
Terhi Mäkinen, Jenna Ritvanen, Seppo Pulkkinen, Nadja Weisshaupt, and Jarmo Koistinen

Abstract

ABSTRACT: The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and non-meteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, non-meteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 non-meteorological classes, belonging to five top level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic Naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of non-meteorological targets also enables a large array of emerging applications, utilizing non-precipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and on the specificity of the customers’ requirements.

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Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

This study develops a new thin cirrus detection algorithm applicable to over-land scenes. The methodology builds from a previously developed over-water algorithm (McHardy et al. 2021), which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378 μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in over-land scenes. Clear sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of < 0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ~1 cm ensures that most low/mid-level clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a pre-determined altitude H removes significant land-surface and low/mid-level cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes non-cirrus pixels such that the remaining sample is comprised of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

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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.

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Dudley B. Chelton, Roger M. Samelson, and J. Thomas Farrar

Abstract

The Ka-band Radar Interferometer on the Surface Water and Ocean Topography (SWOT) satellite will revolutionize satellite altimetry by measuring sea surface height (SSH) with unprecedented accuracy and resolution across two 50-km swaths separated by a 20-km gap. The original plan to provide an SSH product with a footprint diameter of 1 km has changed to providing two SSH data products with footprint diameters of 0.5 km and 2 km. The swathaveraged standard deviations and wavenumber spectra of the uncorrelated measurement errors for these footprints are derived from the SWOT science requirements that are expressed in terms of the wavenumber spectrum of SSH after smoothing with a filter cutoff wavelength of 15 km. The availability of 2-dimensional fields of SSH within the measurement swaths will provide the first spaceborne estimates of instantaneous surface velocity and vorticity through the geostrophic equations. The swath-averaged standard deviations of the noise in estimates of velocity and vorticity derived by propagation of the uncorrelated SSH measurement noise through the finite difference approximations of the derivatives are shown to be too large for the SWOT data products to be used directly in most applications, even with the footprint diameter of 2 km. It is shown from wavenumber spectra and maps constructed from simulated SWOT data that additional smoothing will be required for most applications of SWOT estimates of velocity and vorticity. Equations are presented for the swath-averaged standard deviations and wavenumber spectra of residual noise in SSH and geostrophically computed velocity and vorticity after isotropic 2-dimensional smoothing for any user-defined filter cutoff wavelength of the smoothing.

Open access
Meng-Yuan Chen, Ching-Lun Su, Yuan-Han Chang, and Yen-Hsyang Chu

Abstract

In this study, a data processing based on the empirical mode decomposition (EMD) of Hilbert-Huang Transform (HHT) is developed at Chung-Li VHF radar to identify and remove the aircraft clutter for improving the atmospheric wind measurement. The EMD decomposes the echo signals into the so-called intrinsic mode functions (IMFs) in the time domain, and then the aircraft clutter that is represented by a number of specific IMFs can be identified in the radar returns and separated from the clear air echoes that are observed concurrently by the VHF radar. The identified clutter is validated by using the aircraft information collected by the Automatic Dependent Surveillance-Broadcast (ADS-B) receiver. It shows that the proposed algorithm can detect the aircraft echoes that are mixed with the clear air echoes. After implementing the algorithm on the experimental data, the atmospheric horizontal wind velocities are estimated after the aircraft clutter is removed. In order to evaluate the degree of the improvement of the horizontal wind measurement, a comparison in the horizontal wind velocities between Chung-Li VHF radar and a co-located UHF wind profiler radar is made. The results show that the use of EMD and the proposed data processing can effectively reduce the uncertainty and substantially improve the precision and reliability of the horizontal wind measurement.

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Rolf G. Lueck

Abstract

This manuscript provides (i) the statistical uncertainty of a shear spectrum and (ii) a new universal shear spectrum, and (iii) shows how these are combined to quantify the quality of a shear spectrum. The data from four co-located shear probes, described in Part 1 (Lueck 2022) are used to estimate the spectra of shear, Ψ(k), for wavenumbers k ≥ 2 cpm, from data lengths of 1.0 to 50.5 m, using Fourier transform (FT) segments of 0.5 m length. The differences of the logarithm of pairs of simultaneous shear spectra are stationary, distributed normally, independent of the rate of dissipation, and only weakly dependent on wavenumber. The variance of the logarithm of an individual spectrum, σ 2 lnΨ, equals one-half of the variance of these differences and is σ 2 lnΨ = 1.25N −7/9 ƒ, where is the number of FT segments used to estimate the spectrum. σlnΨ provides the statistical basis for constructing the confidence interval of the logarithm of spectrum, and thus, the spectrum itself.

A universal spectrum of turbulence shear is derived from the nondimensionalization of 14600 spectra estimated from 5 m segments of data. This spectrum differs from the Nasmyth spectrum (Oakey 1982) and from the spectrum of Panchev and Kesich (1969) by 8% near its peak, and is approximated to within 1% by a new analytic equation.

The difference between the logarithms of a measured and a universal spectrum, together with the confidence interval of a spectrum, provides the statistical basis for quantifying the quality of a measured shear (and velocity) spectrum, and the quality of a dissipation estimate that is derived from the spectrum.

Open access
Helga S. Huntley, Maristella Berta, Giovanni Esposito, Annalisa Griffa, Baptiste Mourre, and Luca Centurioni

Abstract

Horizontal velocity gradients of a flow field and the related kinematic properties (KPs) of divergence, vorticity, and strain rate can be estimated from dense drifter deployments. E.g., the spatio-temporal average divergence (and other KPs) over a triangular area defined by three drifters and over a given time interval can be computed from the initial and final areas of said triangle. Unfortunately, this computation can be subject to large errors, especially when the triangle shape is far from equilateral. Therefore, samples with small aspect ratios are generally discarded. Here we derive the thresholds on two shape metrics that optimize the balance between retention of good and removal of bad divergence estimates. The primary tool is a high-resolution regional ocean model simulation, where a baseline for the average divergence can be established, so that actual errors are available. A value of 0.2 for the scaled aspect ratio Λ and a value of 0.86π for the largest interior angle θ are found to be equally effective thresholds, especially at scales of 5 km and below. While discarding samples with low Λ or high θ values necessarily biases the distribution of divergence estimates slightly toward positive values, this bias is small compared to (and in the opposite direction of) the Lagrangian sampling bias due to drifters preferably sampling convergence regions. Errors due to position uncertainty are suppressed by the shape-based subsampling. The subsampling also improves the identification of the areas of extreme divergence or convergence. An application to an observational dataset demonstrates that these model-derived thresholds can be effectively used on actual drifter data.

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