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Igor R. Ivić, Christopher Curtis, and Sebastián M. Torres
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Pengfei Xia, Shirong Ye, Caijun Xu, and Weiping Jiang

Abstract

Tropospheric hydrostatic delay is one of the major sources of errors in Global Navigation Satellite System (GNSS) navigation and positioning, and an important parameter in GNSS meteorology. This work first proposes a new method of computing zenith hydrostatic delay (ZHD) based on precipitable water vapor (PWV), using radiosonde data. Next, using these calculations as a reference, the performance of three empirical ZHD models and three ZHD integral models in China is assessed using benchmark values obtained from 8 years (2010–17) of radiosonde data recorded at 75 stations across China. Finally, we provide a new revised ZHD model that can be applied to China and validate its performance using radiosonde data collected in China in 2018. The statistical results indicate that the ZHD can be estimated by this new model with an accuracy of several millimeters. Due to its performance and simplicity, this new model is shown to be the optimal ZHD model for use in China.

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Pichaya Lertvilai, Paul L.D. Roberts, and Jules S. Jaffe

Abstract

The development of a low-cost Video Velocimeter (VIV) to estimate underwater bulk flow velocity is described. The instrument utilizes a simplified particle image correlation technique to reconstruct an average flow velocity vector from video recordings of ambient particles. The VIV uses a single camera with a set of mirrors that splits the view into two stereoscopic views, allowing estimation of the flow velocity vector. The VIV was validated in a controlled flume using ambient seawater, and subsequently field tested together with an acoustic Doppler velocimeter with both mounted close to the coastal seafloor. When used in nonturbulent flow, the instrument can estimate mean flow velocity parallel to the front face of the instrument with root-mean-squared errors of the main flow within 10% of the ±20 cm s−1 measurement range when compared to an acoustic Doppler velocimeter (ADV). The predominant feature of the VIV is that it is a cost-effective method to estimate flow velocity in complex benthic habitats where velocity parallel to the sea floor is of interest.

Open access
Nawal Husnoo, Timothy Darlington, Sebastián Torres, and David Warde

Abstract

In this work, we present a new quantitative precipitation estimation (QPE) quality-control (QC) algorithm for the U.K. weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground-clutter mitigation [using Clutter Environment Analysis using Adaptive Processing (CLEAN-AP)] and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated “truth” with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data.

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Timothy J. Wagner and Ralph A. Petersen

Abstract

Routine in situ observations of the atmosphere taken in flight by commercial aircraft provide atmospheric profiles with greater temporal density and, in many parts of the country, at more locations than the operational radiosonde network. Thousands of daily temperature and wind observations are provided by largely complementary systems, the Airborne Meteorological Data Relay (AMDAR) and the Tropospheric Airborne Meteorological Data Reporting (TAMDAR). All TAMDAR aircraft also measure relative humidity while a subset of AMDAR aircraft are equipped with the Water Vapor Sensing System (WVSS) measure specific humidity. One year of AMDAR/WVSS and TAMDAR observations are evaluated against operational National Weather Service (NWS) radiosondes to characterize the performance of these systems in similar environments. For all observed variables, AMDAR reports showed both smaller average differences and less random differences with respect to radiosondes than the corresponding TAMDAR observations. Observed differences were not necessarily consistent with known radiosonde biases. Since the systems measure different humidity variables, moisture is evaluated in both specific and relative humidity using both aircraft and radiosonde temperatures to derive corresponding moisture variables. Derived moisture performance is improved when aircraft-based temperatures are corrected prior to conversion. AMDAR observations also show greater consistency between different aircraft than TAMDAR observations do. The small variability in coincident WVSS humidity observations indicates that they may prove more reliable than humidity observations from NWS radiosondes.

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Tara Howatt, Stephanie Waterman, and Tetjana Ross

Abstract

Turbulence plays a key role in many oceanic processes, but a shortage of turbulence observations impedes its exploration. Parameterizations of turbulence applied to readily available CTD data can be useful in expanding our understanding of the space–time variability of turbulence. Typically tested and applied to shipboard data, these parameterizations have not been rigorously tested on data collected by underwater gliders, which show potential to observe turbulence in conditions that ships cannot. Using data from a 10-day glider survey in a coastal shelf environment, we compare estimates of turbulent dissipation from the finescale parameterization and Thorpe scale method to those estimated from microstructure observations collected on the same glider platform. We find that the finescale parameterization captures the magnitude and statistical distribution of dissipation, but cannot resolve spatiotemporal features in this relatively shallow water depth. In contrast, the Thorpe scale method more successfully characterizes the spatiotemporal distribution of turbulence; however, the magnitude of dissipation is overestimated, largely due to limitations on the detectable density overturn size imposed by the typical glider CTD sampling frequency of 0.5 Hz and CTD noise. Despite these limitations, turbulence parameterizations provide a viable opportunity to use CTD data collected by the multitude of gliders sampling the ocean to develop greater insight into the space–time variability of ocean turbulence and the role of turbulence in oceanic processes.

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Aart Overeem, Hylke de Vries, Hassan Al Sakka, Remko Uijlenhoet, and Hidde Leijnse

Abstract

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z hk h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

Open access
Naoki Hirose, Tianran Liu, Katsumi Takayama, Katsuto Uehara, Takeshi Taneda, and Young Ho Kim

Abstract

This study clarifies the necessity of an extraordinary large coefficient of vertical viscosity for dynamical ocean modeling in a shallow and narrow strait with complex bathymetry. Sensitivity experiments and objective analyses imply that background momentum viscosity is on the order of 100 cm2 s−1, while tracer diffusivity estimates are on the order of 0.1 cm2 s−1. The physical interpretation of these estimates is also discussed in the last part of this paper. To obtain reliable solutions, this study introduces cyclic application of the dynamical response to each parameter to minimize the number of long-term sensitivity experiments. The recycling Green’s function method yields weaker bottom friction and enhanced latent heat flux simultaneously with the increased viscosity in high-resolution modeling of the Tsushima/Korea Strait.

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J.C. Hubbert, G. Meymaris, U. Romatschke, and M. Dixon

Abstract

Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars. In this two-part paper ground clutter mitigation is addressed using a time-domain regression filter. Clutter filtering is now widely accomplished with spectral processing where the times series of data corresponding to a radar resolution volume are transformed with a Discrete Fourier Transform after which the zero and near-zero velocity clutter components are eliminated by setting them to zero. Subsequently for reectivity, velocity and spectrum width estimates, interpolation techniques are used to recover some of the power loss due to the clutter filter, which has been shown to reduce bias. The spectral technique requires that the I (in-phase) and Q (quadrature) time series be windowed in order to reduce clutter power leakage away from zero and near-zero velocities. Unfortunately, window functions such as the Hamming, Hann and Blackman attenuate the time series signal by 4.01, 4.19 and 5.23 dB for 64-point times series, respectively, and thereby effectively reduce the number of independent samples available for estimating the radar parameters of any underlying weather echo. Here in Part 1 a regression filtering technique is investigated, via simulated data, which does not require the use of such window functions and thus provides for better weather signal statistics. In Part 2 (Hubbert et al. 2021) the technique is demonstrated using both S-Pol and NEXRAD data. It is shown that the regression filter rejects clutter as effectively as the spectral technique but has the distinct advantage that estimates of the radar variables are greatly improved. The technique is straightforward and can be executed in real time.

Open access
Travis Miles, Wayne Slade, and Scott Glenn

Abstract

Suspended particle size and concentration are critical parameters necessary to understand water quality, sediment dynamics, carbon flux, and ecosystem dynamics among other ocean processes. In this study we detail the integration of a Sequoia Scientific, Inc., Laser In situ Scattering and Transmissometry (LISST) sensor into a Teledyne Webb Research Slocum autonomous underwater glider. These sensors are capable of measuring particle size, concentration, and beam attenuation by particles in size ranges from 1.00 to 500 μm at a resolution of 1 Hz. The combination of these two technologies provides the unique opportunity to measure particle characteristics persistently at specific locations, or survey regional domains from a single profiling sensor. In this study we present the sensor integration framework, detail quality assurance and control (QAQC) procedures, as well as provide a case study of storm driven sediment resuspension and transport. Specifically, Rutgers glider RU28 was deployed with an integrated LISST-Glider for 18 days in September of 2017. During this time period it sampled the nearshore environment off of coastal New Jersey, capturing full water column sediment resuspension during a coastal storm event. A novel method for in situ background corrections is demonstrated and used to mitigate long-term bio-fouling of the sensor windows. Additionally, we present a method for removing Schlieren contaminated time periods utilizing coincident conductivity temperature and depth, fluorometer, and optical backscatter data. The combination of LISST sensors and autonomous platforms has the potential to revolutionize our ability to capture suspended particle characteristics throughout the world’s oceans.

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