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I. A. Houghton, P. B. Smit, D. Clark, C. Dunning, A. Fisher, N. J. Nidzieko, P. Chamberlain, and T. T. Janssen

Abstract

A distributed sensor network of over 100 free-drifting, real-time marine weather sensors was deployed in the Pacific Ocean beginning in early 2019. The Spotter buoys used in the network represent a next-generation ocean weather sensor designed to measure surface waves, wind, currents, and sea surface temperature. Large distributed sensor networks like these provide much needed long-dwell sensing capabilities in open-ocean regions. Despite the demand for better weather forecasts and climate data in the oceans, direct in situ measurements of marine surface weather (waves, winds, currents) remain exceedingly sparse in the open oceans. Because of the large expanse of Earth’s oceans, distributed paradigms are necessary to create sufficient data density at global scale, similar to advances in sensing on land and in space. Here we discuss initial findings from this long-dwell open-ocean distributed sensor network. Through triple-collocation analysis, we determine errors in collocated satellite-derived observations and model estimates. The correlation analysis shows that the Spotter network provides wave height data with lower errors than both satellites and models. The wave spectrum was also further used to infer wind speed. Buoy drift dynamics are similar to established drogued drifters, particularly when accounting for windage. We find a windage correction factor for the Spotter buoy of approximately 1%, which is in agreement with theoretical estimates. Altogether, we present a completely new open-ocean weather dataset and characterize the data quality against other observations and models to demonstrate the broad value for ocean monitoring and forecasting that can be achieved using large-scale distributed sensor networks in the oceans.

Open access
Amy K. Huff, Shobha Kondragunta, Hai Zhang, Istvan Laszlo, Mi Zhou, Vanessa Caicedo, Ruben Delgado, and Robert Levy

Abstract

Aerosol optical depth (AOD) retrieved from the GOES-16 Advanced Baseline Imager (ABI) was used to track a smoke plume from a prescribed fire in northeastern Virginia on 8 March 2020. Weather and atmospheric conditions created a favorable environment to transport the plume through the Washington, D.C., and Baltimore, Maryland, metro areas in the afternoon and concentrate smoke near the surface, degrading air quality for several hours. ABI AOD with 5-min temporal resolution and 2-km spatial resolution definitively identified the timing and geographic extent of the plume during daylight hours. Comparison to AERONET AOD indicates that ABI AOD captured the relative change in AOD due to passage of the smoke, with a mean absolute error of 0.047. Ground-based measurements of fine particulate matter (PM2.5) confirm deteriorations in air quality coincident with the progression of the smoke. Ceilometer aerosol backscatter profiles verify plume transport timing and indicate that smoke aerosols were well mixed in a shallow boundary layer. This event illustrates the advantages of using multiple datasets to analyze the impacts of aerosols on ambient air quality. Given the quickly evolving nature of the event over several hours, ABI AOD provided information for the public and decision-makers that was not available from any other source, including polar-orbiting satellite sensors. This study suggests that PM2.5 concentrations estimated from ABI AOD can be used to fill in the gaps in nationwide regulatory PM2.5 monitor networks and may be a valuable addition to EPA’s PM2.5 NowCast of current air quality conditions.

Restricted access
Ganesh Gopalakrishnan, Bruce D. Cornuelle, Matthew R. Mazloff, Peter F. Worcester, and Matthew A. Dzieciuch

Abstract

A strongly nonlinear eddy field is present in and around the Subtropical Countercurrent in the Northern Philippine Sea (NPS). A regional implementation of the Massachusetts Institute of Technology general circulation model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system is found to be able to produce a series of two-month-long dynamically-consistent optimized state estimates between April 2010 and April 2011 for the eddy-rich NPS region. The assimilation provides a stringent dynamical test of the model, showing that a free run of the model forced using adjusted controls remains consistent with the observations for two months. The 4DVAR iterative optimization reduced the total cost function for the observations and controls by 40–50% from the reference solution, initialized using the Hybrid Coordinate Ocean Model 1/12° global daily analysis, achieving residuals approximately equal to the assumed uncertainties for the assimilated observations. The state estimates are assessed by comparing with assimilated and withheld observations and also by comparing one-month model forecasts with future data. The state estimates and forecasts were more skillful than model persistence and the reference solutions. Finally, the continuous state estimates were used to detect and track the eddies, analyze their structure, and quantify their vertically-integrated meridional heat and salt transports.

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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
Rupayan Saha, Firat Y. Testik, and Murat C. Testik

Abstract

This study investigates the OTT Pluvio2 weighing precipitation gauge’s random and systematic error components as well as stabilization of the measurements on time-varying rainfall intensities (RI) under laboratory conditions. A highly precise programmable peristaltic pump that provided both constant and time-varying RI was utilized in the experiments. Abrupt, gradual step, and cyclic step changes in the RI values were evaluated. RI readings were taken in real time (RT) at different time resolutions (6–60 s) for the RI range of 6–70 mm h−1. Our analysis indicates that the lower threshold for the OTT Pluvio2’s real-time RI measurements should be redefined as 7 mm h−1 at a 1-min resolution. Tolerance intervals containing 95% of the repeated measurements with a probability of 0.95 are given. It is shown that the measurement variances are unequal over the range of RI and the measurement repeatability is not uniform. A statistically significant negative bias was observed for the RI values of 7 and 8 mm h−1, while there was not a statistically significant linearity problem. Through the use of statistical control limits, it is shown that means of the RI measurements stabilized on the actual RI value. A detailed investigation on RT bucket weight measurements revealed a time delay in bucket weight measurements, which causes notable errors in reported RI measurements under dynamic rainfall conditions. To demonstrate the potentiality of large errors in Pluvio2’s real-time RI measurements, a set of equations was developed that faithfully reproduced the Pluvio2’s internal (hidden) algorithm, and results from dynamic laboratory and in situ rainfall scenarios were simulated. The results of this investigation show the necessity of modifying the present Pluvio2 RI algorithm to enhance its performance and show the possibility of postprocessing the existing Pluvio2 RI datasets for improved measurement accuracies.

Restricted access
Sean Celona, Sophia T. Merrifield, Tony de Paolo, Nate Kaslan, Tom Cook, Eric J. Terrill, and John A. Colosi

Abstract

A method based on machine learning and image processing techniques has been developed to track the surface expression of internal waves in near–real time. X-band radar scans are first preprocessed and averaged to suppress surface wave clutter and enhance the signal-to-noise ratio of persistent backscatter features driven by gradients in surface currents. A machine learning algorithm utilizing a support vector machine (SVM) model is then used to classify whether or not the image contains an internal solitary wave (ISW) or internal tide bore (bore). The use of machine learning is found to allow rapid assessment of the large dataset, and provides insight on characterizing optimal environmental conditions to allow for radar illumination and detection of ISWs and bores. Radon transforms and local maxima detections are used to locate these features within images that are determined to contain an ISW or bore. The resulting time series of locations is used to create a map of propagation speed and direction that captures the spatiotemporal variability of the ISW or bore in the coastal environment. This technique is applied to 2 months of data collected near Point Sal, California, and captures ISW and bore propagation speed and direction information that currently cannot be measured with instruments such as moorings and synthetic aperture radar (SAR).

Restricted 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
Laurence C. Breaker and Dustin Carroll

Abstract

The purpose of this study is to extract more information about the scaling exponents we obtain from sea surface temperature (SST) because their information content is limited to a single value. We examine the application of empirical mode decomposition (EMD) to power-law scaling using SST from Scripps Pier, California. The daily observations we employ extend from 1920 to 2009, a period of 90 years. The annual cycle and the long-term trend were first removed. The decomposition produced a total of 15 modes. The scaling exponents were then calculated separately for each mode from the EMD. We have examined the distribution of scaling exponents with respect to the ensemble, and then with respect to the individual modes for the oceanic processes that we may infer from them. The first three modes are antipersistent and contain about one-quarter of the total variance. The pattern of modes that was obtained is continuous and relatively smooth beyond mode 3 with increasing values up to mode 8 and generally decreasing values thereafter. The pattern exhibits intramodal correlation, as expected, and intermodal correlation as well. Intermodal correlation is likely due, for the most part, to long-range persistence. The annual cycle in SST at Scripps Pier is a dominant feature in the record and contains almost 70% of the variance. A method for removing the annual cycle that is not based on removing the mean value is introduced and is recommended for future use.

Restricted access
Biao Zhang, Yiru Lu, William Perrie, Guosheng Zhang, and Alexis Mouche

Abstract

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

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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