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Joey J. Voermans, Alexander V. Babanin, Cagil Kirezci, Jonas T. Carvalho, Marcelo F. Santini, Bruna F. Pavani, and Luciano P. Pezzi

Abstract

Quality control measures for ocean waves observations are necessary to give confidence of their accuracy. It is common practice to detect anomalies or outliers in surface displacement observations by applying a standard deviation threshold. Besides being a purely statistical method, this quality control procedure is likely to flag extreme wave events erroneously, thereby impacting higher-order descriptions of the wave field. In this paper we extend the use of the statistical phase-space threshold, an established outlier detection method in the field of turbulence, to detect anomalies in a wave record. We show that a wave record in phase space (here defined as a diagram of displacement against acceleration) can be enclosed by a predictable ellipse where the major and minor axes are defined by the spectral properties of the wave field. By using the parameterized ellipse in phase space as a threshold to identify wave anomalies, this is a semiphysical filtering method. Wave buoy data obtained from a mooring deployed near King George Island, Antarctica [as part of the Antarctic Modeling Observation System (ATMOS)], and laser altimeter data obtained at the Northwest Shelf of Australia were used to demonstrate the functioning of the filtering methodology in identifying wave anomalies. Synthetic data obtained using a high-order spectral model are used to identify how extreme waves are positioned in phase space.

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Yuichiro Takeshita, Brent D. Jones, Kenneth S. Johnson, Francisco P. Chavez, Daniel L. Rudnick, Marguerite Blum, Kyle Conner, Scott Jensen, Jacqueline S. Long, Thom Maughan, Keaton L. Mertz, Jeffrey T. Sherman, and Joseph K. Warren

Abstract

The California Current System is thought to be particularly vulnerable to ocean acidification, yet pH remains chronically undersampled along this coast, limiting our ability to assess the impacts of ocean acidification. To address this observational gap, we integrated the Deep-Sea-DuraFET, a solid-state pH sensor, onto a Spray underwater glider. Over the course of a year starting in April 2019, we conducted seven missions in central California that spanned 161 glider days and >1600 dives to a maximum depth of 1000 m. The sensor accuracy was estimated to be ± 0.01 based on comparisons to discrete samples taken alongside the glider (n = 105), and the precision was ±0.0016. CO2 partial pressure, dissolved inorganic carbon, and aragonite saturation state could be estimated from the pH data with uncertainty better than ± 2.5%, ± 8 μmol kg−1, and ± 2%, respectively. The sensor was stable to ±0.01 for the first 9 months but exhibited a drift of 0.015 during the last mission. The drift was correctable using a piecewise linear regression based on a reference pH field at 450 m estimated from published global empirical algorithms. These algorithms require accurate O2 as inputs; thus, protocols for a simple predeployment air calibration that achieved accuracy of better than 1% were implemented. The glider observations revealed upwelling of undersaturated waters with respect to aragonite to within 5 m below the surface near Monterey Bay. These observations highlight the importance of persistent observations through autonomous platforms in highly dynamic coastal environments.

Open access
Kenneth R. Knapp, Alisa H. Young, Hilawe Semunegus, Anand K. Inamdar, and William Hankins

Abstract

The International Satellite Cloud Climatology Project (ISCCP) began collecting data in the 1980s to help understand the distribution of clouds. Since then, it has provided important information on clouds in time and space and their radiative characteristics. However, it was apparent from some long-term time series of the data that there are some latent artifacts related to the changing satellite coverage over the more than 30 years of the record. Changes in satellite coverage effectively create secular changes in the time series of view zenith angle (VZA) for a given location. There is an inconsistency in the current ISCCP cloud detection algorithm related to VZA: two satellites viewing the same location from different VZAs can produce vastly different estimates of cloud amount. Research is presented that shows that a simple change to the cloud detection algorithm can vastly increase the consistency. This is accomplished by making the cloud–no cloud threshold VZA dependent. The resulting cloud amounts are more consistent between different satellites and the distributions are shown to be more spatially homogenous. Likewise, the more consistent spatial data lead to more consistent temporal statistics.

Open access
Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

Abstract

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

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Hongbo Liu, Janina V. Büscher, Kevin Köser, Jens Greinert, Hong Song, Ying Chen, and Timm Schoening

Abstract

The cold-water coral Lophelia pertusa builds up bioherms that sustain high biodiversity in the deep ocean worldwide. Photographic monitoring of the polyp activity represents a helpful tool to characterize the health status of the corals and to assess anthropogenic impacts on the microhabitat. Discriminating active polyps from skeletons of white Lophelia pertusa is usually time consuming and error prone due to their similarity in color in common red–green–blue (RGB) camera footage. Acquisition of finer-resolved spectral information might increase the contrast between the segments of polyps and skeletons, and therefore could support automated classification and accurate activity estimation of polyps. For recording the needed footage, underwater multispectral imaging systems can be used, but they are often expensive and bulky. Here we present results of a new, lightweight, compact, and low-cost deep-sea tunable LED-based underwater multispectral imaging system (TuLUMIS) with eight spectral channels. A branch of healthy white Lophelia pertusa was observed under controlled conditions in a laboratory tank. Spectral reflectance signatures were extracted from pixels of polyps and skeletons of the observed coral. Results showed that the polyps can be better distinguished from the skeleton by analysis of the eight-dimensional spectral reflectance signatures compared to three-channel RGB data. During a 72-h monitoring of the coral with a half-hour temporal resolution in the laboratory, the polyp activity was estimated based on the results of the multispectral pixel classification using a support vector machine (SVM) approach. The computational estimated polyp activity was consistent with that of the manual annotation, which yielded a correlation coefficient of 0.957.

Open access
Matthew B. Wilson and Matthew S. Van Den Broeke

Abstract

Supercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward-flank regions, including an arc-shaped region of high differential reflectivity (Z DR) along the inflow edge of the forward flank known as the Z DR arc and a clear horizontal separation between this area of high Z DR values and an area of enhanced K DP values deeper into the storm core. Recent work has indicated that Z DR arc and K DPZ DR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although Z DR arc and K DPZ DR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually generated time series of Z DR arc and K DPZ DR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang

Abstract

Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

Open access
Michiko Otsuka, Hiromu Seko, Masahiro Hayashi, and Ko Koizumi

Abstract

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of datasets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to midlevels. Next, pseudorelative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model–based Variational Data Assimilation System. Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

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Shihe Ren, Xueming Zhu, Marie Drevillon, Hui Wang, Yunfei Zhang, Ziqing Zu, and Ang Li

Abstract

A frontal detection algorithm is developed with the capability of detecting significant frontal segments of sea surface temperature (SST) in the high-resolution South China Sea Operational Forecasting System (SCSOFS). To effectively obtain frontal information, a gradient-based Canny edge detection algorithm is improved with postprocessing designed for high-resolution numerical models, aiming at extracting primary ocean fronts while ensuring the balance of frontal continuity and positioning accuracy. Metrics of frontal probability and strength are used to measure the robustness of the results in terms of mean state and seasonal variability of frontal activities in the South China Sea (SCS). Most fronts are found in the nearshore and form a strip shape extending from the Taiwan Strait to the coast of Vietnam. The SCSOFS is found to reproduce strong seasonal signals dominating the variability of the frontal strength and occurrence probability in the SCS. We implement the algorithm on the daily averaged SST derived from two other SST analyses for intercomparison in the SCS.

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Mohammad Kazemi, Babak Khorsandi, and Laurent Mydlarski

Abstract

The relatively large sampling volume of acoustic Doppler velocimeters (ADVs) is expected to influence their measurement of turbulence. To study this effect, a series of experiments using different sampling volume sizes was conducted in an axisymmetric turbulent jet. The results show that the mean velocities are not significantly affected by the size of the sampling volume. On the other hand, reducing the sampling volume size results in an increase in the variances of the u and υ velocities, while its effect on the variance of the w velocity is negligible. Application of a noise-reduction method to the data renders the velocity variances nearly independent of sampling volume size, suggesting that the difference was mainly due to Doppler noise. The principal conclusion of this work is, therefore, that—as long as the characteristic length of sampling volume is much smaller than the integral length scale of flow—increasing the sampling volume size (i.e., increasing spatial averaging over highly correlated scatterers) can reduce Doppler noise and result in more accurate measurements of the velocity variances. Application of noise-reduction methods to the data is found to be especially important when the sampling volume size is reduced to capture smaller scales, or for near-boundary measurements. Furthermore, noise due to mean velocity shear, even at the largest velocity gradient along the jet radial profile, is found to be negligible in the present work.

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