[1] The paper presents the current status of the Maritime Aerosol Network (MAN), which has been developed as a component of the Aerosol Robotic Network (AERONET). MAN deploys Microtops handheld Sun photometers and utilizes the calibration procedure and data processing (Version 2) traceable to AERONET. A web site dedicated to the MAN activity is described. A brief historical perspective is given to aerosol optical depth (AOD) measurements over the oceans. A short summary of the existing data, collected on board ships of opportunity during the NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project is presented. Globally averaged oceanic aerosol optical depth (derived from island-based AERONET measurements) at 500 nm is $0.11 and Angstrom parameter (computed within spectral range 440-870 nm) is calculated to be $0.6. First results from the cruises contributing to the Maritime Aerosol Network are shown. MAN ship-based aerosol optical depth compares well to simultaneous island and near-coastal AERONET site AOD.

ISI Document Delivery No.: 170BP Times Cited: 0 Cited Reference Count: 53 Cited References: Alvarez-Salgado XA, 2001, PROG OCEANOGR, V51, P321, DOI 10.1016/S0079-6611(01)00073-8 Alvarez-Salgado XA, 2007, LIMNOL OCEANOGR, V52, P1287 AlvarezSalgado XA, 1997, CONT SHELF RES, V17, P1247, DOI 10.1016/S0278-4343(97)00014-9 Aminot A, 2007, BREST FRANCE Aristegui J, 2009, PROG OCEANOGR, V83, P33, DOI 10.1016/j.pocean.2009.07.031 Barbosa AB, 2001, PROG OCEANOGR, V51, P339, DOI 10.1016/S0079-6611(01)00074-X Barton ED, 1998, PROG OCEANOGR, V41, P455, DOI 10.1016/S0079-6611(98)00023-8 Batteen ML, 2007, OCEAN MODEL, V18, P1, DOI 10.1016/j.ocemod.2007.02.006 Batten SD, 2001, PROG OCEANOGR, V51, P385, DOI 10.1016/S0079-6611(01)00076-3 Borges AV, 2001, PROG OCEANOGR, V51, P283, DOI 10.1016/S0079-6611(01)00071-4 Brink K., 1991, J GEOPHYS RES, V14, P637 Capet X, 2008, J PHYS OCEANOGR, V38, P29, DOI 10.1175/2007JPO3671.1 Castro CG, 2000, CONT SHELF RES, V20, P189, DOI 10.1016/S0278-4343(99)00071-0 Cermeno P, 2006, ESTUAR COAST SHELF S, V67, P251, DOI 10.1016/j.ecss.2005.11.027 Cravo A, 2010, CONT SHELF RES, V30, P1601, DOI 10.1016/j.csr.2010.06.007 Duro A, 2000, J PLANKTON RES, V22, P339, DOI 10.1093/plankt/22.2.339 Fileman E, 2001, PROG OCEANOGR, V51, P361, DOI 10.1016/S0079-6611(01)00075-1 FIUZA AFD, 1982, OCEANOL ACTA, V5, P31 Freon P, 2009, PROG OCEANOGR, V83, P1, DOI 10.1016/j.pocean.2009.08.001 GARCIA HE, 1992, LIMNOL OCEANOGR, V37, P1307 Garcia-Munoz M, 2005, J MARINE SYST, V54, P83, DOI 10.1016/j.jmarsys.2004.07.005 Gasparini S, 2007, PLANKTON IDENTIFIER Gorsky G, 2010, J PLANKTON RES, V32, P285, DOI 10.1093/plankt/fbp124 Halvorsen E, 2001, PROG OCEANOGR, V51, P399, DOI 10.1016/S0079-6611(01)00077-5 HAYNES R, 1993, J GEOPHYS RES-OCEANS, V98, P22681, DOI 10.1029/93JC02016 Heukelem L.V., 2001, J CHROMATOGR A, V910, P31 Joint I, 2001, PROG OCEANOGR, V51, P303, DOI 10.1016/S0079-6611(01)00072-6 Joint I, 2002, J MARINE SYST, V32, P219, DOI 10.1016/S0924-7963(02)00037-4 Labasque T, 2004, MAR CHEM, V88, P53, DOI 10.1016/j.marchem.2004.03.004 Landry MR, 2004, ICES J MAR SCI, V61, P501, DOI 10.1016/j.icesjms.2004.03.011 Landry MR, 2012, J PLANKTON RES, V34, P739, DOI 10.1093/plankt/fbs025 Lorenzo LM, 2005, J MARINE SYST, V54, P157, DOI 10.1016/j.jmarsys.2004.07.010 Meunier T, 2010, OCEAN MODEL, V35, P277, DOI 10.1016/j.ocemod.2010.08.004 Motoda S, 1959, DEVICES SIMPLE PLANK Otero P, 2008, J MARINE SYST, V72, P238, DOI 10.1016/j.jmarsys.2007.05.016 PAULY D, 1995, NATURE, V374, P255, DOI 10.1038/374255a0 Peliz A, 2005, DEEP-SEA RES PT I, V52, P621, DOI [10.1016/j.dsr.2004.11.005, 10.1016/j.dsar.2004.11.005] Peliz A, 2002, J MARINE SYST, V35, P61, DOI 10.1016/S0924-7963(02)00076-3 Quaresma LS, 2007, MAR GEOL, V246, P123, DOI 10.1016/j.margeo.2007.04.019 Queiroga H, 2005, HYDROBIOLOGIA, V545, P195, DOI 10.1007/s10750-005-2675-y Raimund S, 2011, BIOGEOSCIENCES, V8, P1551, DOI 10.5194/bg-8-1551-2011 Ras J, 2008, BIOGEOSCIENCES, V5, P353 Relvas P, 2007, PROG OCEANOGR, V74, P149, DOI 10.1016/j.pocean.2007.04.021 Resende P, 2007, ACTA OECOL,...

Measurement of suspended particulate matter concentration (SPMC) spanning large time and geographical scales have become a matter of growing importance in recent decades. At many places worldwide, complex observation platforms have been installed to capture temporal and spatial variability over scales ranging from cm (turbulent regimes) to whole basins. Long-term in situ measurements of SPMC involve one or more optical and acoustical sensors and, as the ground truth reference, gravimetric measurements of filtered water samples. The estimation of SPMC from optical and acoustical proxies Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. generally results from the combination of a number of independent calibration measurements, as well as regression or inverse models. Direct or indirect measurements of SPMC are inherently associated with a number of uncertainties along the whole operation chain, the autonomous field deployment, to the analyses necessary for converting the observed proxy values of optical and acoustical signals to SPMC. Controlling uncertainties will become an important issue when the observational input comprises systems of sensors spanning large spatial and temporal scales. This will be especially relevant for detecting trends in the data with unambiguous statistical significance, separating anthropogenic impact from natural variations, or evaluating numerical models over a broad ensemble of different conditions using validated field data. The aim of the study is to present and discuss the benefits and limitations of using optical and acoustical backscatter sensors to acquire long-term observations of SPMC. Additionally, this study will formulate recommendations on how to best acquire quality-assured SPMC data sets, based on the challenges and uncertainties associated with those long-term observations. The main sources of error as well as the means to quantify and reduce the uncertainties associated with SPMC measurements are also illustrated. Highlights ► Errors associated with optical and acoustical sensors for SPMC are quantified. ► A strict protocol limits the uncertainties. ► Systematic errors may reach up to ±20% and errors due to biofouling to 100% or more. ► Changes of the inherent particle properties may result in uncertainties up to 200%. ► A model based on the R 2 quantifies the uncertainty of the sensor derived SPMC. ► Hach-turbidities could be a cheap alternative for sample SPMC.

Regions Of Freshwater Influence (ROFI) are of particular interest in a source-to-sink approach in terms of sediment advection, settling, and deposition in the coastal zone. An experiment was carried out in the ROFI of the Rhône River in February 2016 to describe the properties of suspended particulate matter (SPM) during a flood event. A digital holographic camera (LISST-HOLO, 20-2000 μm) was used to estimate the variability of fine sediment floc properties (size, nature and shape) formed in the Rhône mouth. An automatic image toolbox was developed to classify the different constituents of the SPM (as diatoms, bubbles and flocs) and to describe the diversity of floc shapes existing in the material in suspension. We estimated the fractal dimension (DF3D), the aspect ratio (AR) and the settling velocity of flocs (Ws). The estimated DF3D ranged between 2.0 and 2.5 highlights the complexity of floc shape, which was used as a proxy of the flocculation mechanism functioning in the Rhône mouth. Additionally, we performed a sensitivity analysis on the estimate of Ws using different shape-related coefficients (α/β) and primary particle size (dP). The results highlighted the impact of the flocculation of fine sediments on the increase of Ws from 0.01 to 3 mm s−1 when floc sizes increase from 30 to 500 μm. Ws showed a decrease of 41% considering the sphericity of flocs that emphasized the need to consider the floc shape to properly estimate their settling velocity. We showed that an increase of dP from 1 to 12 μm induces a fivefold increase of Ws that showed the need for an adequate system to properly estimate the size of primary particles. These results emphasized the need to take into account such variability in future model of floc dynamics in ROFI to properly estimate plume sinking rate and SPM dynamics. Highlights ► Description of fine sediment floc properties in the Rhône River ROFI. ► Evidence of the diversity of floc shapes existing in the material in suspension. ► Flocculation increases of two orders the settling velocity of fine sediments. ► Evidence of the decrease of floc settling velocities considering floc shapes.

A pair of self-contained acoustic Doppler current profilers (SC-ADCPs) operating with different frequencies were moored on a muddy sea bottom at about 20 m depth in the Bay of Vilaine off the French Atlantic coast. With their acoustic beams oriented upwards, the SC-ADCPs ensonified most of the water column. The results of several months of in situ recorded echo intensity data spanning 2 years (2003 to 2004) from the dual-frequency ADCPs are presented in this paper. The aim was to estimate suspended particle mass concentration and mean size. A concentration index CI is proposed for the estimation of particle concentration. Based on theory the CI-unlike the volume backscatter strength-does not depend on particle size. Compared with in situ optical data, the CI shows reasonable precision but not increased with respect to that of the highest-frequency backscatter strength. Concerning the mean particle size, despite a lack of quantitative validation with optical particle-size measurements, the method yielded a qualitative discrimination of mineral (small) and organic (large) particles. This supports the potential of dual-frequency ADCPs to quantitatively determine particle size. A cross-calibration of the transducers of each ADCP shows that a specific component of the precision of the backscatter strength measured by ADCP depends on the acoustic frequency, the cell thickness and the ensemble integration time. Based on these results, the use of two ADCPs operating with distinctly different frequencies (two octaves apart) or a single dual-frequency ADCP is recommended.

Many different instruments directly or indirectly observe the oceanic tidal movements. Among them, we consider here the coastal tide gauge and deep‐sea pressure recorder which provide at least the principal spectral constituents of the tidal height, the gravimeter which indirectly observes the oceanic tides through the gravitational effect generated by the deformation of the solid Earth under the ocean tidal mass load, and the satellite altimeter which samples the tides in the time domain and over the nearly whole ocean. Our present paper deals with an empirical method for the recovery of the oceanic tides by inversion of heterogeneous data sets. The aim is to take advantage of the different kinds of information coming from the measurement instruments in order to complete more accurate empirical solutions. First, tide gauge and gravity loading data are separately and jointly inverted to provide global charts of the major semidiurnal M2 wave. The results prove that the tide gauge data have a major contribution in the tidal mapping but also suggest that the gravity loading data distort the information. This is certainly due to unmodelled geophysical phenomena and instrumental errors. Second, tide gauge and satellite altimeter data are considered in a separate and a joint inversion computation to provide tidal estimations along a predefined geographical line in the North Atlantic. The results are accurate enough to make provision for the boundary conditions of an hydrodynamical tidal model. As an intrinsic advantage of the inversion method, a posteriori standard deviations and error covariances for all the solutions displayed are provided and analyzed.

Due to complex natural and anthropogenic interconnected forcings, the dynamics of suspended sediments within the ocean water column remains difficult to understand and monitor. Numerical models still lack capabilities to account for the variabilities depicted by in situ and satellite-derived datasets. Besides, the irregular space-time sampling associated with satellite sensors make crucial the development of efficient interpolation methods. Optimal Interpolation (OI) remains the state-of-the-art approach for most operational products. Due to the large increase of both in situ and satellite measurements more and more available information is coming from in situ and satellite measurements, as well as from simulation models. The emergence of data-driven schemes as possibly relevant alternatives with increased capabilities to recover finer-scale processes. In this study, we investigate and benchmark three state-of-the-art data-driven schemes, namely an EOF-based technique, an analog data assimilation scheme, and a neural network approach, with an OI scheme. We rely on an Observing System Simulation Experiment based on high-resolution numerical simulations and simulated satellite observations using real satellite sampling patterns. The neural network approach, which relies on variational data assimilation formulation for the interpolation problem, clearly outperforms both the OI and the other data-driven schemes, both in terms of reconstruction performance and of a greater ability to recover high-frequency events. We further discuss how these results could transfer to real data, as well as to other problems beyond interpolation issues, especially short-term forecasting problems from partial satellite observations.

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled "Hidden" and "Observable". The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) from the Iberian-Biscay-Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error-NRMSE of less than 16%) is the first step in demonstrating the robustness of the method.