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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 non-turbulent 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 measurement range when compared to an 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
Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

We present a novel approach for the automated quality control (QC) of precipitation for a sparse station observation network within the complex terrain of British Columbia, Canada. Our QC approach uses Convolutional Neural Networks (CNNs) to classify bad observation values, incorporating a multi-classifier ensemble to achieve better QC performance. We train CNNs using human QC’d labels from 2016 to 2017 with gridded precipitation and elevation analyses as inputs. Based on the classification evaluation metrics, our QC approach shows reliable and robust performance across different geographical environments (e.g., coastal and inland mountains), with 0.927 Area Under Curve (AUC) and type I/type II error lower than 15%. Based on the saliency-map-based interpretation studies, we explain the success of CNN-based QC by showing that it can capture the precipitation patterns around, and upstream of the station locations. This automated QC approach is an option for eliminating bad observations for various applications, including the pre-processing of training datasets for machine learning. It can be used in conjunction with human QC to improve upon what could be accomplished with either method alone.

Open access
Giuseppe Zibordi, Brent N. Holben, Marco Talone, Davide D’Alimonte, Ilya Slutsker, David M. Giles, and Mikhail G. Sorokin

Abstract

The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

Open access
Caitlin B. Whalen

Abstract

The turbulent energy dissipation rate in the ocean can be measured by using rapidly sampling microstructure shear probes, or by applying a finescale parameterization to coarser-resolution density and/or shear profiles. The two techniques require measurements that are on different spatiotemporal scales and generate dissipation rate estimates that also differ in spatiotemporal scale. Since the distribution of the measured energy dissipation rate is closer to lognormal than normal and fluctuates with the strength of the turbulence, averaging the two approaches on equivalent spatiotemporal scales is critical for accurately comparing the two methods. Here, microstructure data from the 1997 Brazil Basin Tracer Release Experiment (BBTRE) is used to demonstrate that comparing averages of the dissipation rate on different spatiotemporal scales can generate spurious discrepancies of up to a factor of order 10 in regions of strong turbulence and smaller biases of up to a factor of 2 in the presence of weaker turbulence.

Open access
Shakeel Asharaf, Duane E. Waliser, Derek J. Posselt, Christopher S. Ruf, Chidong Zhang, and Agie W. Putra

Abstract

Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.

Open access
Haruhiko Kashiwase, Kay I. Ohshima, Kazuki Nakata, and Takeshi Tamura

Abstract

Long-term quantification of sea ice production in coastal polynyas (thin sea ice areas) is an important issue to understand the global overturning circulation and its changes. The Special Sensor Microwave Imager (SSM/I), which has nearly 30 years of observation, is a powerful tool for that purpose owing to its ability to detect thin ice areas. However, previous SSM/I thin ice thickness algorithms differ between regions, probably due to the difference in dominant type of thin sea ice in each region. In this study, we developed an SSM/I thin ice thickness algorithm that accounts for three types of thin sea ice (active frazil, thin solid ice, and a mixture of two types), using the polarization and gradient ratios. The algorithm is based on comparison with the ice thickness derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 22 polynya events off the Ross Ice Shelf, off Cape Darnley, and off the Ronne Ice Shelf in the Southern Ocean. The algorithm can properly discriminate the ice type in coastal polynyas and estimate the thickness of thin sea ice (≤20 cm) with an error range of less than 6 cm. We also confirmed that the algorithm can be applied to other passive microwave radiometers with higher spatial resolution to obtain more accurate and detailed distributions of ice type and thickness. The validation of this algorithm in the Arctic Ocean suggests its applicability to the global oceans.

Open access
Norman Wildmann, Ramona Eckert, Andreas Dörnbrack, Sonja Gisinger, Markus Rapp, Klaus Ohlmann, and Annelize van Niekerk

Abstract

A Stemme S10-VT motor glider was equipped with a newly developed sensor suite consisting of a five-hole probe, an inertial navigation and global navigation satellite system, two temperature sensors, and a humidity sensor. By design, the system provides three-dimensional wind vector data that enable the analysis of atmospheric motion scales up to a temporal resolution of 10 Hz. We give a description of components and installation of the system, its calibration, and its performance. The accuracy for the measurement of the wind vector is estimated to be on the order of 0.5 m s−1. As part of the Southern Hemisphere Transport, Dynamics, and Chemistry (SouthTRAC) field campaign, 30 research flights were performed from September 2019 to January 2020. We present statistical analysis of the observations, discriminating pure motor flights from soaring flights in the lee waves of the Andes. We present histograms of flight altitude, airspeed, wind speed and direction, temperature, and relative humidity to document the atmospheric conditions. Probability density functions of vertical air velocity, turbulence kinetic energy (TKE), and dissipation rate complete the statistical analysis. Altogether, 41% of the flights are in weak, 14% in moderate, and 0.4% in strong mountain wave conditions according to thresholds for the measured vertical air velocity. As an exemplary case study, we compare measurements on 11 September 2019 to a high-resolution numerical weather prediction model. The case study provides a meaningful example of how data from soaring flights might be utilized for model validation on the mesoscale and within the troposphere.

Open access
Wen Chen, Rachel T. Pinker, Yingtao Ma, Glynn Hulley, Eva Borbas, Tanvir Islam, Kerry-A. Cawse-Nicholson, Simon Hook, Chris Hain, and Jeff Basara

ABSTRACT

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

Open access
Florian Le Guillou, Sammy Metref, Emmanuel Cosme, Clément Ubelmann, Maxime Ballarotta, Julien Le Sommer, and Jacques Verron

Abstract

During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal–spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

Open access
Michael O’Malley, Adam M. Sykulski, Romuald Laso-Jadart, and Mohammed-Amin Madoui

Abstract

We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modelling the transport of ocean-borne species such as planktonic organisms, and oating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our methodology is purely data-driven, and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time.

Open access