Buoy Data Research Articles

The effects of ocean temperature gradients on bigeye tuna (Thunnus obesus) distribution in the equatorial eastern Pacific Ocean

The water temperature at 100–300 m depth layer impact on bigeye tuna distribution has been studied from data collected by 15 fishery survey vessels, surveying every day, in the Equatorial Pacific Ocean from February to March and June to September 2010. The in situ ocean temperature measurements at four depths (100, 150, 200, and 300 m), satellite data and TAO/TRITON moored buoy data were also augmented in this study. The results reveal stereoscopic temperature factors for the prediction of tuna fishing ground: horizontal temperature gradient (HTG), vertical temperature gradient (VTG) in the 100–300 m layer. Maximal catches were observed where (1) vertically there has a strong subsurface temperature front in the 150–200 m layer, with horizontal temperature gradient up to near 0.020 °C/km; (2) there have vertical temperature gradient between −0.088 and −0.066 °C/m. Meanwhile, the monthly tuna fishery catches decrease with the synchronized reduction of 20 °C isotherm depth in the ocean.

Evaluating wind datasets for wave hindcasting in the NW Iberian Peninsula coast

ABSTRACT The available wind datasets can be exploited to support the setup of accurate wave models, able to reproduce and forecast extreme event scenarios. It is of utmost importance in the actual context of climate change. This study focuses on evaluating the performance of a numerical wave model, using different wind datasets, helping to create a tool to assess coastal risks, and further on to support the future implementation of reliable warning systems based on numerical models. The numerical model SWAN was implemented, configured and validated for the NW Iberian Peninsula coast, as a test case region. A period of two months, from December 2013 to January 2014, was simulated due to the winter storms that crossed the area. Six distinct wind datasets were selected to test their suitability in regional wave modelling. The results were validated against several sets of wave buoy data, considering wave parameters such as significant wave height, mean wave period and peak direction. The implemented wave model configuration allowed the representation of the wave evolution with relatively good accuracy. All the wind datasets were able to produce reasonably good wave condition estimates. The dataset that best represented the wave properties varied from one wave parameter to another, but the most reliable for the selected region was the reanalysis product generated at the European Centre for Medium-Range Weather Forecasts.

An Efficient Method for Simulating Typhoon Waves Based on a Modified Holland Vortex Model

A combination of the WAVEWATCH III (WW3) model and a modified Holland vortex model is developed and studied in the present work. The Holland 2010 model is modified with two improvements: the first is a new scaling parameter, bs, that is formulated with information about the maximum wind speed (vms) and the typhoon’s forward movement velocity (vt); the second is the introduction of an asymmetric typhoon structure. In order to convert the wind speed, as reconstructed by the modified Holland model, from 1-min averaged wind inputs into 10-min averaged wind inputs to force the WW3 model, a gust factor (gf) is fitted in accordance with practical test cases. Validation against wave buoy data proves that the combination of the two models through the gust factor is robust for the estimation of typhoon waves. The proposed method can simulate typhoon waves efficiently based on easily accessible data sources.

Simulation of the atmospheric parameters during passage of a tropical storm over the South China Sea: a comparison with MetOcean buoy and ERA‐Interim data

AbstractThe accuracy of Weather Research and Forecasting (WRF) model derived meteorological fields during the passage of a tropical storm, Pabuk, over the southern South China Sea is investigated. The European Centre for Medium‐Range Weather Forecasts’ ERA‐Interim data were also used for comparison. Data from a MetOcean buoy which captured tropical storm Pabuk were used to evaluate the accuracy of the WRF model. The period of study was from January 1, 2019 to January 5, 2019. Simulated parameters (surface temperature, wind speed and pressure) showed agreement with the buoy data before and after passage of the tropical storm. In contrast, model output shows differences from observations during the passage of the storm near 0600 UTC on January 3, 2019. Overall, surface pressure was simulated with a maximum correlation of 0.93 while the wind speed showed a minimum correlation of 0.76. The surface temperature was simulated with a correlation of 0.78. Weaker correlations arise due to differences in simulated and observed parameters during the storm. During the storm, model estimates overestimated the buoy wind speed and pressure data. The results show the inaccuracy of the simulation due to the vigorous conditions prevailing during the storm. The location of the centre of the storm differing between the model and the ERA‐Interim data could also contribute to the discrepancy of the WRF data. The MetOcean buoy provided a rare opportunity for validation of the WRF model during the storm over the understudied shallow tropical South China Sea.

Three-dimensional thermohaline anomaly structures of rings in the Kuroshio Extension region

Using AVISO satellite altimeter observations during 1993–2015 and a manual eddy detection method, a total of 276 anticyclonic rings and 242 cyclonic rings shed from the Kuroshio Extension (KE) were identified, and their three-dimensional (3D) anomaly structures were further reconstructd based on the Argo float data and the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) cruise and buoy data through an interpolation method. It is found that the cyclonic (anticyclonic) rings presented consistent negative (positive) anomalies of potential temperature; meanwhile the relevant maximum anomaly center became increasingly shallow for the cyclonic rings whereas it went deeper for the anticyclonic rings as the potential temperature anomaly decreased from the west to the east. The above deepening or shoaling trend is associated with the zonal change of the depth of the main thermocline. Moreover, the composite cold ring between 140° and 150°E was found to exhibit a double-core vertical structure due to the existence of mode water with low potential vorticity. Specifically, a relatively large negative (positive) salinity anomaly and a small positive (negative) one appeared for the composite cyclonic (anticyclonic) ring at the depth above and below 600 m, respectively. The underlying driving force for the temperature and salinity anomaly of the composite rings was also attempted, which varies depending on the intensity of the background current and the temperature and salinity fields in different areas of the KE region, and the rings’ influences on the temperature and salinity could reach deeper than 1 000 m on average.

Buoy observation data verification and application

The buoy automatic observation is greatly influenced by the marine environment. The quality of buoy observation data needs to be tested before it is applied. In this paper, buoy observation data is verified with the ocean station and ocean section observation data, and the reliability of buoy observation is analyzed. On this basis, the hydrometeorological condition in the central Bohai Sea is analyzed for surface water temperature, surface salinity, sea surface air temperature and sea surface pressure. The results show: The reliability and continuity of buoy observation of sea surface water temperature, sea surface air temperature and sea surface pressure are high; the reliability of sea surface salinity, average wind speed, average wind direction, significant wave height, significant wave period, average wave height and average wave period are low. The hydrometeorological condition in the central Bohai Sea analyzed by buoy observation data is basically consistent with the existing research conclusions. The lowest water temperature in the central Bohai Sea occurs in February.

Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors

Land and Sea Surface Temperatures (LST and SST) are both recognized as Essential Climate Variables, and are routinely retrieved by a wealth of satellites. However, for validated approaches, the latest data are usually not available to the general public. We offer to bridge this gap, by using Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI), with its 15 min temporal resolution. Here, we present generic algorithms for the retrieval of both LST and SST, valid for the SEVIRI instrument onboard MSG platforms 8–11, which we validate using hourly data of 4 ground stations and 11 buoys in Spain over the years 2015 to 2018. These validations show that in the best conditions of surface homogeneity (cloud-free summer nights), errors in our LST estimation are below 1.5 K for stations with good thermal homogeneity. Comparison with LSA-SAF (Land Surface Analysis - Satellite Application Facility) LST shows differences below 2 K for most of SEVIRI disk, with higher differences in arid areas and during daytime. As for SST retrieval, the average error amount to 0.67 K for cloud-free buoy data. These algorithms have been implemented in a near-real time processing chain, which provide actualized LST and SST maps every 15 min within 5 min of image reception. These maps, along with other products, can be freely consulted from a dedicated webpage (https://www.uv.es/iplsat).

Design of containerized marine knowledge system based on IoT-Cloud and LoRaWAN

Recently, the research of marine knowledge and information technology has grown rapidly by the increasing interest in the rich repository of natural resources in the sea. For marine knowledge services, accurate marine environmental data must be continuously collected to deeply understand and analyze the marine circumstances. However, there is an insufficiency of research on the observation of marine circumstances in South Korea for the marine knowledge services. The ocean data buoy, a marine environmental monitoring equipment currently operating in South Korea, is large in size and high in production cost because it consumes a lot of power for communication. Aso, it provides only marine data and lacks information on marine knowledge. In this paper, we have proposed containerized marine knowledge system by means of IoT-Cloud and LoRaWAN to improve the marine environment monitoring. The proposed system enables flexible construction of the system and can analyze the marine knowledge through visualizing the gathered data and the knowledge processing with respect to the prediction of red tide events. LoRaWAN-based IoT devices are able to collect long-range marine environmental data in an energy efficient manner. Our proposed method is helpful for researching low-cost marine monitoring buoy and flexible marine knowledge system.

Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data

Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 °C higher than the surrounding water and exceeds 6 °C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide.

Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching

Sea ice drift detection has the key role of global climate analysis and waterway planning. The ability to detect sea ice drift in real-time also contributes to the safe navigation of ships and the prevention of offshore oil platform accidents. In this paper, an Enhanced Delaunay Triangulation (EDT) algorithm for sea ice tracking was proposed for dual-polarization sequential Synthetic Aperture Radar (SAR) images, which was implemented by combining feature tracking with pattern matching based on integrating HH and HV polarization feature information. A sea ice retrieval algorithm for feature detection, matching, fusion, and outlier detection was specifically developed to increase the system’s accuracy and robustness. In comparison with several state-of-the-art sea ice drift retrieval algorithms, including Speeded Up Robust Features (SURF) and the Oriented FAST and Rotated BRIEF (ORB) method, the results of the experiment provided compelling evidence that our algorithm had a higher accuracy than the SURF and ORB method. Furthermore, the results of our method were compared with the drift vector and direction of buoys data. The drift direction is consistent with buoys, and the velocity deviation was about 10 m. It was proved that this method can be applied effectively to the retrieval of sea ice drift.

Experimental Study of the Statistical Properties of Directionally Spread Ocean Waves Measured by Buoys

AbstractWave-following buoys are used to provide measurements of free surface elevation across the oceans. The measurements they produce are widely used to derive wave-averaged parameters such as significant wave height and peak period, alongside wave-by-wave statistics such as crest height distributions. Particularly concerning the measurement of extreme wave crests, these measurements are often perceived to be less accurate. We directly assess this through a side-by-side laboratory comparison of measurements made using Eulerian wave gauges and model wave-following buoys for randomly generated directionally spread irregular waves representative of extreme conditions on deep water. This study builds on the recent work of McAllister and van den Bremer (2019, https://doi.org/10.1175/JPO-D-19-0170.1), in which buoy measurements of steep directionally spread focused waves groups were considered. Our experiments confirm that the motion of a wave-following buoy should not significantly affect the measured wave crest statistics or spectral parameters and that the discrepancies observed for in situ buoy data are most likely a result of filtering. This filtering occurs when accelerations that are measured by the sensors within a buoy are converted to displacements. We present an approximate means of correcting the resulting measured crest height distributions, which is shown to be effective using our experimental data.

Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight

Offshore aquaculture of giant kelp (Macrocystis pyrifera) has been proposed by the US Department of Energy for large scale biofuel production along the west coast of the California. The Southern Californian Bight provides an ideal area for offshore kelp aquaculture as the upwelling and advection of cool, nutrient-rich waters supports the growth of vast native giant kelp populations. However, concentrations of nutrients vary greatly across space, can be limiting for kelp growth over seasonal to interannual time scales, and inputs of nutrients to surface waters may be subject to local circulation processes. Therefore, it is important to understand both the spatiotemporal variability of seawater nitrate concentrations and the appropriate scale of observation in order for offshore kelp aquaculture to be successful. Here, we use a combination of satellite sea surface temperature imagery, in situ measurements, and modeling to determine seawater nitrate fields across multiple spatial and temporal scales. We then combine this information with known giant kelp physiological traits to develop a Kelp Stress Index for the optimal siting of offshore kelp aquaculture over seasonal to decadal scales. Temperature to nitrate relationships were determined from in situ measurements using generalized additive models and validated with buoy data. Summer and winter relationships were significantly different, and satellite-derived products compared well to buoy validations. Surface nitrate patterns, as derived from satellite temperature products, reveal the spatial variability in nitrate concentrations and indicate areas that that may cause nutrient stress seasonally and during the negative phase of the North Pacific Gyre Oscillation. As the spatial scale of the surface nitrate product decreased, the negative bias increased and fine scale spatial variability was lost. Similarly, the averaging of daily nitrate concentration determinations over longer time scales increased the negative bias. We found that daily, 1 km spatial resolution nitrate products were most sufficient for identifying localized upwelling and areas of consistently high surface nitrate concentrations, and that areas in the northern and western-most portions of the Southern California Bight are the most suitable for sustained offshore kelp aquaculture.

Inversion of Evaporation and Water Vapor Transport Using HY-2 Multi-Sensor Data

HY-2 satellite is the first marine dynamic environment satellite of China. In this study, global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30, 2014. The algorithm of evaporation and water vapor transport is discussed in detail, and results are compared with other reanalysis data. The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data. Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation. To improve the calculation accuracy, we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project (GPCP) data. In calculating the water vapor transport, the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.

An update of proposed Sri Lanka warning system for east and west coast tsunamis

PurposeBased on the existing provisions/operations of tsunami warning in the Indian Ocean, authors observed that detection as well as arrival time estimations of regional tsunami service providers (RTSPs) could be improved. In particular, the detection mechanisms have been eccentrically focussed on Sunda and Makran tsunamis, although tsunamis from Carlsberg ridge and Chagos archipelago could generate devastating tsunamis for which inadequate provisions exist for detection and arrival time/wave height estimation. RTSPs resort to assess estimated arrival time/wave heights from a scenario-based, pre-simulated database. These estimations in terms of Sri Lanka have been found inconsistent. In addition, current warning mechanism poorly manages non-seismic tsunamis. Thus, the purpose of this study is to investigate these drawbacks and attempt to carve out a series of suggestions to improve them.Design/methodology/approachThe work initiated with data retrieved from global earthquake and tsunami databases, followed by an estimation of probabilities of tsunamis in the Indian Ocean with particular emphasis on Carlsberg and Chagos tsunamis. Second, probabilities of tsunami detection in each sub-region have been estimated with the use of available tide gauge and tsunami buoy data. Third, the difficulties in tsunami detection in the Indian Ocean are critically assessed with case studies, followed by recommendations to improve the detection and warning.FindingsProbabilistic estimates show that given the occurrence of a significant earthquake, both Makran and Carlsberg/Chagos regions possess higher probabilities to harbour a tsunami than the Sunda subduction zone. Meanwhile, reliability figures of tsunami buoys have been declined from 79-92 to 68-91 per cent over the past eight years. In addition, a Chagos tsunami is left to be detected by only one tide gauge prior to it reaching Sri Lankan coasts.Research limitations/implicationsThe study uses an averaged tsunami speed of 882 km/h based on 2004 Asian tsunami. However, using exact bathymetric data, Tsunamis could be simulated to derive speeds and arrival times more accurately. Yet, such refinements do not change the main derivations and conclusions of this study.Practical implicationsTsunami detection and warning in the Indian Ocean region have shown room for improvement, based on the inadequate detection levels for Carlesberg and Chagos tsunamis, and inconsistent warnings of regional tsunami service providers. The authors attempted to remedy these drawbacks by proposing a series of suggestions, including a deployment of a new tsunami buoy south of Maldives, revival of offline buoys, real-time tsunami simulations and a strategy to deal with landslide tsunamis, etc.Social implicationsIndian Ocean is prone to mega tsunamis as witnessed in 2004. However, more than 50 per cent of people in the Indian Ocean rim countries dwell near the coast. This is verified with deaths of 227,898 people in 14 countries during the 2004 tsunami event. Thus, it is of paramount importance that sufficient detection levels are maintained throughout the Indian Ocean without being overly biased towards Sunda tsunamis. With respect to Sri Lanka, Makran, Carlesberg or Chagos tsunamis could directly hit the most populated west coast and bring about far worse repercussions than a Sunda tsunami.Originality/valueThis is the first instance where the threats from Carlesberg and Chagos tsunamis to Sri Lanka are discussed, probabilities of tsunamis are quantified and their detection levels assessed. In addition, reliability levels of tsunami buoys and tide gauges in the Indian Ocean are recomputed after eight years to discover that there is a drop in reliability of the buoy data. The work also proposes a unique approach to handle inconsistencies in the bulletins of regional tsunami service providers, and to uphold and improve dwindling interest on tsunami buoys.

СИСТЕМА МОРСКИХ РЕТРОСПЕКТИВНЫХ РАСЧЕТОВ И ПРОГНОЗОВ ГИДРОМЕТЕОРОЛОГИЧЕСКИХ ХАРАКТЕРИСТИК АЗОВСКОГО МОРЯ И КЕРЧЕНСКОГО ПРОЛИВА

The paper presents Marine Hindcast and Forecasts System (MHFS) of hydrometeorological and ice characteristics for the Sea of Azov and Kerch Strait. It is being developed at the Federal State Budgetary Institution “N.N. Zubov State Oceanographic Institute” (FSBI “SOI”). The main components of this system include regional models of the atmosphere, marine circulation, dynamics-thermodynamics of ice cover and wind waves. For the Sea of Azov and Kerch Strait, marine circulation models of the MHFS were implemented with a high spatial resolution (500 m and 120–200 m for the Sea of Azov and the Kerch Strait marine circulation models respectively and 100 m for the Kerch Strait wind waves mode). The MHFS allows both hindcast calculations and operational analyzes and forecasts of hydrometeorological conditions in the study areas. Additionally, this system allows to obtain an almost complete list of hydrometeorological characteristics which are necessary for solving practical problems in planning economic or recreational activities, organizing navigation, warning of hazardous events, planning operations in case of emergencies, etc. For the Sea of Azov, the MHFS was implemented in cooperation with the Federal State Budgetary Institution “North-Caucasian Administration for Hydrometeorology and Environmental monitoring” (FSBI “North-Caucasian AHEM”). In 2019 the system was recommended for the practical implementation by the Roshydromet Central Methodical Commission on Hydrometeorological and Heliogeophysical Forecasts. The Kerch Strait MHFS is being implemented in cooperation with the Federal State Budget Educational Institution of Higher Education “Admiral F.F. Ushakov” and now at the stage of tests and accumulation of the forecast database for the purpose of their subsequent verification using hydrological buoy data, calibration of models’ parameters and increasing the accuracy of the obtained results. The Kerch Strait MHFS results are planned to be used for navigation in the Kerch Strait.

Principal-component estimates of the Kuroshio Current axis and path based on the mathematical verification between satellite altimeter and drifting buoy data

We used satellite altimetry data to investigate the Kuroshio Current because of the higher resolution and wider range of observations. In previous studies, satellite absolute geostrophic velocities were used to study the spatio-temporal variability of the sea surface velocity field along the current, and extraction methods were employed to detect the Kuroshio axes and paths. However, sea surface absolute geostrophic velocity estimated from absolute dynamic topography should be regarded as the geostrophic component of the actual surface velocity, which cannot represent a sea surface current accurately. In this study, mathematical verification between the climatic absolute geostrophic and bin-averaged drifting buoy velocity was established and then adopted to correct the satellite absolute geostrophic velocities. There were some differences in the characteristics between satellite geostrophic and drifting buoy velocities. As a result, the corrected satellite absolute geostrophic velocities were used to detect the Kuroshio axis and path based on a principal-component detection scheme. The results showed that the detection of the Kuroshio axes and paths from corrected absolute geostrophic velocities performed better than those from satellite absolute geostrophic velocities and surface current estimations. The corrected satellite absolute geostrophic velocity may therefore contribute to more precise day-to-day detection of the Kuroshio Current axis and path.

Computer Prediction of Seawater Sensor Parameters in the Central Arctic Region Based on Hybrid Machine Learning Algorithms

In recent years, with the large-scale reduction of Arctic sea ice, the supplement of chlorophyll sensor data in seawater has become an essential part of environmental assessment. Accurately predicting the chlorophyll sensor data in seawater is of great significance to protect the Arctic marine ecological environment. A machine learning prediction method combined with wavelet transform is proposed. This process uses data from upper ocean observation buoys placed in the Arctic Ocean (A.O.) to predict the sensor analogue of chlorophyll-a (C.A.) in the upper ocean of the Arctic Ocean. Choose the best wavelet transform method and prevent the LSTM gradient from disappearing. A model combining SAE (stacked autoencoder) Bi (bidirectional) LSTM (long short-term memory) and wavelet transform is proposed. Experiments were conducted to compare the predictive performance of buoy data input as univariate at two different times and locations in the Arctic Ocean. The results show that compared with other models (such as LSTM), in the SAE Bi LSTM model, the data of the two sites have the highest prediction accuracy. The best wavelet transform methods are fourth-order four-layer and first-order four-layer. The observational data of the Chukchi Sea from 2018 to 2019 obtained the best prediction results. The root mean square error (RMSE) value is 0.02003 volts; the average absolute error (MAE) is 0.0841 volts. This research provides a new method for predicting the chlorophyll sensor parameters in the upper ocean through the sea ice buoy observed at a given point, which helps to improve the accuracy of the ocean sensor parameter prediction on the Arctic ice buoy.

An Adaptive GaoFen-3 SAR Wind Field Retrieval Algorithm Based on Information Entropy

Chinese GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery acquired in standard strip mode can capture streak features caused by wind over the sea surface, which are commonly known as wind streaks. To retrieve the sea surface wind field from a GF-3 SAR image, an adaptive wind retrieval algorithm based on information entropy is proposed in this paper. This algorithm can adaptively extract the optimal wavenumber range of each subimage based on maximum information entropy. After all subimages are processed for possible wind streak direction solutions, a circle median filter is used to determine the unique wind streak direction solution for each subimage. Twelve GF-3 standard strip SAR images are inverted to validate the proposed algorithm. The experiments show that when the GF-3 image data are compared to the European Centre for Medium-Range Weather Forecasts reanalysis wind field data, the root mean square error of wind speed and direction retrieved by the algorithm are 1.29 m/s and 15.33°, respectively, and when they are compared to the National Data Buoy Center buoy wind data, the mean absolute deviation errors of wind speed and direction are 0.57 m/s and 9.95°, respectively. These comparisons demonstrate that the proposed algorithm can effectively retrieve wind field information from GF-3 SAR images.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Localized hypoxia may have caused coral reef mortality at the Flower Garden Banks

On July 25, 2016, turbid water and dead corals, sponges and other invertebrates were discovered at the East Bank (EB) of the Flower Garden Banks (FGB) National Marine Sanctuary. Mortality was spread over 0.06 km2, with up to 80% coral mortality reported in some areas. Within days, response efforts were underway to investigate the potential mechanisms leading to the mortality event. Hydrographic surveys, moored buoy data, and a regional hydrodynamic model were used to characterize water chemistry, hydrography, and microbial communities within the FGB. Low salinity (~ 31–33), total alkalinity (~ 2284–2330 µmol kg−1), and dissolved inorganic carbon (DIC, ~ 1968–2011 µmol kg−1) were detected in surface waters over the EB and eastern stations, revealing the presence of river-derived water. The Mississippi/Atchafalaya rivers were the primary sources of freshwater during the event, although Texas rivers, all of which had unusually high discharge during 2016, contributed approximately one-fifth to the total freshwater mass. At 75 m depth, high density, salinity, DIC, ammonium, and abundance of microbial taxa associated with deep waters were coincident with low temperature and aragonite saturation state at the northern and eastern stations, indicating a deeper source water at these stations. Cross-slope density gradients were also consistent with an upwelling circulation pattern. Using these observations and data, we hypothesize that the mortality event was most likely caused by the combination of two processes. The turbid freshwater layer inhibited photosynthesis, leading to net respiration of coral reef organic matter. Additionally, deep, dense waters upwelled onto the bank and formed a stratified bottom layer, which prevented re-oxygenation from the overlying water column and led to localized areas of hypoxia within pockets on the reef. Hypoxia likely formed rapidly, within two days. Moving forward, high-frequency temporal measurements of oxygen and carbonate chemistry are critical for monitoring risks (e.g., hypoxia and acidification) associated with freshwater discharge and upwelling, since these processes may adversely affect coral reef health.