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Coltin Grasmick, Bart Geerts, Jeffrey R. French, Samuel Haimov, and Robert M. Rauber

Abstract

Properties of frozen hydrometeors in clouds remain difficult to sense remotely. Estimates of number concentration, distribution shape, ice particle density, and ice water content are essential for connecting cloud processes to surface precipitation. Progress has been made with dual-frequency radars, but validation has been difficult because of lack of particle imaging and sizing observations collocated with the radar measurements.

Here, data are used from two airborne profiling (up & down) radars, the W-band Wyoming Cloud Radar and the Ka-band Profiling Radar, allowing for Ka-W-band Dual-Wavelength Ratio (DWR) profiles. The aircraft (the University of Wyoming King Air) also carried a suite of in situ cloud and precipitation probes. This arrangement is optimal for relating the “flight-level” DWR (an average from radar gates below and above flight level) to ice particle size distributions measured by in situ optical array probes, as well as bulk properties such as minimum snow particle density and ice water content. This comparison reveals a strong relationship between DWR and the ice particle median-volume diameter. An optimal range of DWR values ensures the highest retrieval confidence, bounded by the radars’ relative calibration and DWR saturation, found here to be about 2.5 to 7.5 dB. The DWR-defined size distribution shape is used with a Mie scattering model and an experimental mass-diameter relationship to test retrievals of ice particle concentration and ice water content. Comparison with flight-level cloudprobe data indicate good performance, allowing microphysical interpretations for the rest of the vertical radar transects.

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A. Addison Alford, Michael I. Biggerstaff, Conrad L. Ziegler, David P. Jorgensen, and Gordon D. Carrie

Abstract

Mobile weather radars at high frequencies (C, X, K and W-band) often collect data using staggered Pulse Repetition Time (PRT) or dual Pulse Repetition Frequency (PRF) modes to extend the effective Nyquist velocity and mitigate velocity aliasing while maintaining a useful maximum unambiguous range. These processing modes produce widely dispersed “processor” dealiasing errors in radial velocity estimates. The errors can also occur in clusters in high shear areas. Removing these errors prior to quantitative analysis requires tedious manual editing and often produces “holes” or regions of missing data in high signal-to-noise areas. Here, data from three mobile weather radars were used to show that the staggered PRT errors are related to a summation of the two Nyquist velocities associated with each of the PRTs. Using observations taken during a mature mesoscale convective system, a landfalling tropical cyclone, and a tornadic supercell storm, an algorithm to automatically identify and correct staggered PRT processor errors has been developed and tested. The algorithm creates a smooth profile of Doppler velocities using a Savitzky-Golay filter independently in radius and azimuth and then combined. Errors are easily identified by comparing the velocity at each range gate to its smoothed counterpart and corrected based on specific error characteristics. The method improves past dual PRF correction methods that were less successful at correcting “grouped” errors. Given the success of the technique across low, moderate, and high radial shear regimes, the new method should improve research radar analyses by affording the ability to retain as much data as possible rather than manually or objectively removing erroneous velocities.

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Silvia Innocenti, Pascal Matte, Vincent Fortin, and Natacha Bernier

Abstract

Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses Ordinary Least Squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation.

This study introduces two residual resamplings (moving-block and semi-parametric bootstraps) for estimating the variability of tidal regression parameters and shows they are powerful methods to assess the effects of regression errors with non-trivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an Iteratively Reweighted Least Squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance-covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications.

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Ryan C. Scott, Fred G. Rose, Paul W. Stackhouse Jr., Norman G. Loeb, Seiji Kato, David R. Doelling, David A. Rutan, Patrick C. Taylor, and William L. Smith Jr.

Abstract

Satellite observations from Clouds and the Earth’s Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time-space variations in Earth’s top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu-Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ~30-40% (~20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <Ÿ1% (2-3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ~3.5% (~9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes.

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Baixin Li, Huan Tang, Dongfang Ma, and Jianmin Lin

Abstract

Mesoscale eddies are a mechanism for ocean energy transfer, and identifying them on a global scale provides a means of exploring ocean mass and energy exchange between ocean basins. There are many widely used model-driven methods for detecting mesoscale eddies; however, these methods are not fully robust or generalizable. This study applies a data-driven method and proposes a mesoscale detection network based on the extraction of eddy-related spatiotemporal information from multisource remote sensing data. Focusing on the northwest Pacific, the study first analyzes mesoscale eddy characteristics using a combination of gridded data for the absolute dynamic topography (ADT), sea surface temperature (SST), and absolute geostrophic velocity (UVG). Then, a deep learning network with a dual-attention mechanism and a convolutional long short-term memory module is proposed, which can deeply exploit spatiotemporal feature relevance while encoding and decoding information in the gridded data. Based on the analysis of mesoscale eddy characteristics, ADT and UVG gridded data are selected to be the inputs for the detection network. The experiments show that the accuracy of the proposed network reaches 93.38%, and the weighted mean dice coefficient reaches 0.8918, which is a better score than those achieved by some of the detection networks proposed in previous studies, including U-Net, SymmetricNet, and ResU-Net. Moreover, compared with the model-driven approach used to generate the ground-truth dataset, the network method proposed here demonstrates better performance in detecting mesoscale eddies at smaller scales, partially addressing the problem of ghost eddies.

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Han Liu, Zezong Chen, Chen Zhao, and Sitao Wu

Abstract

Wavenumber–frequency spectra obtained with coherent microwave radar at upwind-grazing angle consist of energy along the ocean wave dispersion relation and additional features that lie above this relation labeled as “high-order harmonic” and below this relation known as “group line.” Due to these nonlinear features, low-frequency components appear in the radar-estimated wave spectrum and the energy and peak frequency of the dominant wave spectrum decrease, which are responsible for the overestimation of radar-measured wave period. According to the component distribution in the wavenumber–frequency spectrum, a mean wave period inversion method based on a dispersion relation filter for coherent S-band radar is proposed. The method filters out the “group line” and preserves the high-order harmonic to compensate for the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber–frequency spectrum. Then the radar-measured velocity sequence is selected to obtain the velocity spectrum via a one-dimension Fourier transform. The wave height spectrum is estimated from the one-dimensional velocity spectrum by the direct transform relationship between the one-dimensional velocity spectrum and the wave height spectrum. Later, mean wave periods can be derived by the first moment of the wave height spectrum. A 13-day dataset collected with a shore-based coherent S-band radar deployed at Zhelang, China, is reanalyzed and used to retrieve mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted. The results indicate that the proposed method improves the wave period measurement for coherent S-band radar.

Significance Statement

This work provides a mean wave period inversion method for coherent S-band radar. The mean wave period is always overestimated due to the “group line” in the wavenumber–frequency spectrum and the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. Therefore, dealing with these estimation errors is important.

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Adi Kurniawan, Paul H. Taylor, Jana Orszaghova, Hugh Wolgamot, and Jeff Hansen

Abstract

An apparent giant wave event having a maximum trough-to-crest height of 21 m and a maximum zero-upcrossing period of 27 s was recorded by a wave buoy at a nearshore location off the southwestern coast of Australia. It appears as a group of waves that are significantly larger both in height and in period than the waves preceding and following them. This paper reports a multifaceted analysis into the plausibility of the event. We first examine the statistics of the event in relation to the rest of the record, where we look at quantities such as maximum-to-significant wave height ratios, ordered crest–trough statistics, and average wave profiles. We then investigate the kinematics of the buoy, where we look at the relationship between the horizontal and vertical displacements of the buoy, and also attempt to numerically reconstruct the giant event using Boussinesq and nonlinear shallow water equations. Additional analyses are performed on other sea states where at least one of the buoy’s accelerometers reached its maximum limit. Our analysis reveals incompatibilities of the event with known behavior of real waves, leading us to conclude that it was not a real wave event. Wave events similar to the one reported in our study have been reported elsewhere and have sometimes been accepted as real occurrences. Our methods of forensically analyzing the giant wave event should be potentially useful for identifying false rogue wave events in these cases.

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Rachel W. Obbard, Alice C. Bradley, and Ignatius Rigor

Abstract

This paper describes a remotely monitored buoy that, when deployed in open water prior to freeze up, permits scientists to monitor not only temperature with depth, and hence freeze up and sea ice thickness, but also the progression of sea ice development—e.g., the extent of cover at a given depth as it grows (solid fraction), the brine volume of the ice, and the salinity of the water just below, which is driven by brine expulsion. Microstructure and In situ Salinity and Temperature (MIST) buoys use sensor “ladders” that, in our prototypes, extend to 88 cm below the surface. We collected hourly measurements of surface air temperature and water temperature and electrical impedance every 3 cm to track the seasonal progression of sea ice growth in Elson Lagoon (Utqiaġvik, Alaska) over the 2017/18 ice growth season. The MIST buoy has the potential to collect detailed sea ice microstructural information over time and help scientists monitor all parts of the growth/melt cycle, including not only the freezing process but the effects of meteorological changes, changing snow cover, the interaction of meltwater, and drainage.

Significance Statement

There is a need to better understand how an increasing influx of freshwater, one part of a changing Arctic climate, will affect the development of sea ice. Current instruments can provide information on the growth rate, extent, and thickness of sea ice, but not direct observations of the structure of the ice during freeze up, something that is tied to salinity and local air and water temperature. A first deployment in Elson Lagoon in Utqiaġvik, Alaska, showed promising results; we observed fluctuations in ice temperatures in response to brief warmings in air temperature that resulted in changes in the conductivity, liquid fraction, and brine volume fraction within the ice.

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Paul Chamberlain, Bruce Cornuelle, Lynne D. Talley, Kevin Speer, Cathrine Hancock, and Stephen Riser

Abstract

Acoustically-tracked subsurface floats provide insights into ocean complexity and were first deployed over 60 years ago. A standard tracking method uses a Least-Squares algorithm to estimate float trajectories based on acoustic ranging from moored sound sources. However, infrequent or imperfect data challenge such estimates, and Least-Squares algorithms are vulnerable to non-Gaussian errors. Acoustic tracking is currently the only feasible strategy for recovering float positions in the sea ice region, a focus of this study. Acoustic records recovered from under-ice floats frequently lack continuous sound source coverage. This is because environmental factors such as surface sound channels and rough sea ice attenuate acoustic signals, while operational considerations make polar sound sources expensive and difficult to deploy. Here we present a Kalman Smoother approach that, by including some estimates of float behavior, extends tracking to situations with more challenging data sets. The Kalman Smoother constructs dynamically constrained, error-minimized float tracks and variance ellipses using all possible position data. This algorithm outperforms the Least-Squares approach and a Kalman Filter in numerical experiments. The Kalman Smoother is applied to previously-tracked floats from the southeast Pacific (DIMES experiment), and the results are compared with existing trajectories constructed using the Least- Squares algorithm. The Kalman Smoother is also used to reconstruct the trajectories of a set of previously untracked, acoustically-enabled Argo floats in the Weddell Sea.

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Raphael Dussin

Abstract

Anovel method to adjust the precipitation produced by atmospheric reanalyses using observational constraints to force ocean models is described. The method allows the preservation of the qualities of the high resolution and high frequency output from the reanalyses while eliminating their bias and spurious trends. The method is shown to be robust to degradation both in space and time of the observation dataset. This method is applied to the ERAinterim precipitation dataset using the Global Precipitation Climatology Project (GPCP) v2.3 as the observational reference in order to create a debiased dataset that can be used to force ocean models. The produced debiased dataset is then compared to ERAinterim and GPCP in a suite of forced ice-ocean numerical experiments using the GFDL OM4 model. Ocean states obtained with the new precipitation dataset are consistent with results from GPCP-forced experiments with respect to global metrics but produces the extra Sea Surface Salinity variability at the timescales unresolved by the observation-based dataset. Discrepancies between modeled and observed freshwater fluxes are discussed as well as the strategies to mitigate them and their impacts.

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