This PDF file contains the front matter associated with SPIE Proceedings Volume 7678, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Optical properties of oceanic and coastal waters are not only important for describing subsurface light field, but also useful indexes of environmental status. To meet the demand of various users, optical data products of global waters are now generated from ocean color satellite sensors (e.g. SeaWiFS, MODIS, MERIS). These products, due to imperfect sensor technology and retrieval algorithms, inherently contain some degrees of uncertainties. Traditionally, an averaged difference (or so-called error) for a dataset is usually provided via comparing retrieved values with in situ measurements. This averaged "error" is good at providing an overall picture between the retrieved and measured properties, but cannot indicate uncertainties for a specific product or a pixel, because that uncertainties in these products are not spatially uniform. Here, using optical properties derived from the Quasi-Analytical Algorithm as an example, we present an approach to quantify pixel-wise uncertainties of remote-sensing derived properties. Further, we quantitatively evaluated the uncertainties of the derived inherent optical properties (IOPs) and water-clarity products with a simulated dataset, and found that the relative uncertainty is generally within 10% for total absorption coefficients of oceanic waters. This presentation shows the theoretical basis to evaluate and understand the impacts of the various components on the analytically derived optical properties, and that a practical means to quantify the uncertainties of inverted properties for each reflectance spectrum is now available. This effort lays the groundwork for generating quality maps of optical properties derived from satellite ocean color images.

An acquisition system was developed to measure the above water polarized radiance. This system consists of one irradiance sensor for downwelling irradiance, one radiance sensor oriented at 40° from the zenith to measure sky radiance and three radiance sensors looking down at 40° from the nadir to measure above water radiance. In order to obtain the polarized radiance, polarizers with orientation of 0°, 90° and 45° respectively were placed in front of the three radiance sensors. The whole system was installed on the bow of the boat for continuous observations of above water polarized radiance along the ship's track during a recent cruise in the NY Bight area. Water optical properties were measured by an optical package towed from a small R/V. In order to obtain the degree of polarization (DOP) of the water body, the contribution of the sky radiance must be first removed and this process has to be done for all components of the Stokes vector. Using a model employing the polarized Fresnel coefficients of the interface the polarized component of reflection is estimated from the direct measurement of sky radiance and downwelling irradiance data. These components are then subtracted from the measured values to obtain the water contribution. The DOP of the ocean body is then related to the in - water IOPs.

The detection and monitoring of harmful algal blooms using in-situ field measurements is both labor intensive and is practically limited on achievable temporal and spatial resolutions, since field measurements are typically carried out at a series of discrete points and at discrete times, with practical limitations on temporal continuity. The planning and preparation of remedial measures to reduce health risks, etc., requires detection approaches which can effectively cover larger areas with contiguous spatial resolutions, and at the same time offer a more comprehensive and contemporaneous snapshot of entire blooms as they occur. This is beyond capabilities of in-situ measurements and it is in this context that satellite Ocean Color sensors offer potential advantages for bloom detection and monitoring. In this paper we examine the applications and limitations of an approach we have recently developed for the detection of K. brevis blooms from satellite Ocean Color Sensors measurements, the Red Band Difference Technique, and compare it to other detection algorithm approaches, including a new statistical based approach also proposed here. To achieve more uniform standards of comparisons, the performance of different techniques for detection are applied to the same specific verified blooms occurring off the West Florida Shelf (WFS) that have been verified by in-situ measurements.

The dynamic nature of littoral regions requires a reconnaissance approach that can rapidly quantify environmental conditions. Inadequate estimation of these conditions can have substantial impacts on the performance of Naval systems. Given that expeditionary warfare operations can occur over timescales on the order of hours, exploitation of video imagery from tactical vehicles such as Unmanned Aircraft Systems (UAS) has proved to be a reliable and adaptive solution. Tactical littoral products that can be created by exploiting UAS imagery include estimates of surf conditions, dominant wave period, wave direction, nearshore currents, and bathymetry. These vehicles can fly for durations of 1-2 hours at altitudes of less than 1000 m (beneath typical cloud cover) to obtain imagery at pixel resolutions better than 1 m. The main advantage of using imaging sensors carried by these vehicles is that the data is available in the region of operational interest where other data collection approaches would be difficult or impossible to employ. The through-the-sensor exploitation technique we have developed operates in two phases. The first step is to align individual image frames to a common reference and then georegister the alignment into a mapped image sequence. The second phase involves signal processing of pixel intensity time series (virtual sensors) to determine spatial relationships over time. Geophysical relationships, such as linear wave dispersion, can then be applied to these processed data to invert for environmental parameters such as bathymetry.

Multispectral imagery (MSI) taken with high-spatial resolution systems provides a powerful tool for mapping kelp in water. MSI are not always available, however, and there are systems which provide only panchromatic imagery which would be useful to exploit for the purpose of mapping kelp. Kelp mapping with MSI is generally done by use of the standard Normalized Difference Vegetation Index (NDVI). In broadband panchromatic imagery, the kelp appears brighter than the water because of the strong response of vegetation in the NIR, and can be reliably detected by means of a simple threshold; overall brightness is generally proportional to the NDVI. Confusion is caused by other bright pixels in the image, including sun glint. This research seeks to find ways of mitigating the number of false alarms using spatial image processing techniques. Methods developed in this research can be applied to other water target detection tasks.

Three models were used to estimate primary productivity (PP) in the Southern Ocean for the summer of 2003-2007. They are the widely accepted model VGPM, a carbon-based model CbPM and a new type of model which uses phytoplankton absorption coefficient as input variable in stead of chlorophyll concentration. It was found that the degree of agreement among the results from three models was low, but the difference appeared relatively small with regard to previous reports. Nevertheless, the results were comparable to that from a PP model parameterized specifically for use in Southern Ocean waters. Among the three models, the output from CbPM differed the most from that estimated by the other two models. The different PP estimates were primarily attributed to the different ways these models treat phytoplankton physiology, along with the difference in input variables.

In this paper, the routine monitoring water sampling data of Taihu Lake from June to September in 2004 was used to validate the three-band chlorophyll-a remote sensing inversion model(TBCM) proposed by Gitelson in 2008. The chlorophyll-a concentration (Chla) in the lake changed from 5 to 374μg/L and the average is 49μg/L. The spectrum above water surface was measured by ASD FieldPro. The result shows, (1) TBCM in other lakes cannot be used directly in the Taihu Lake; (2) TBCM of Taihu Lake built by single month data has lower error, its RMSE changed between 8.0μg/L and 17.6μg/L; The model of all data in four months has higher error, and RMSE is 26.8μg/L; (3) When data collected in August and September was used to validate the model built by data in June and July, RMSE increased from 17.2μg/L of modeling data to 50.7μg/L of validation data; (4)Samples with higher or lower Chla have higher error. When samples in alga bloom (Chla>120μg/L usually) and samples with lower Chla (<30μg/L) were not used, RMSE of model built by June and July data decrease to 6.3μg/L, and RMSE of estimated Chla in August and September will be 28.9μg/L. This paper points out that TBCM can be used to build Chla inverse model with higher precise in Taihu Lake, but model parameters must be refined according to the water area and the season before it is used in practical water quality monitoring work.

Fraunhofer lines and atmospheric absorption bands interfere with the spectral location of absorption bands of photosynthetic pigments in plankton. Hyperspectral data were used to address this interference on identifying absorption bands by applying derivative analysis of radiance spectra. Algal blooms show elevated radiance data even at longer wavelengths compared to oligotrophic water and may reach radiance values of around 800 W/m²/micrometer/sr at a wavelength of about 0.8 μm. Therefore, the use of a spectral range beyond 0.55 μm is useful to describe bloom characteristics. In particular, the slope between 0.55 μm to 0.80 μm shows an advantage to depict gradients in plankton blooms. Radiance spectra in the region from 0.4 to 0.8 μm for oligotrophic water and near coastal water show similar location of absorption bands when analyzed with derivative analysis but with different amplitudes. For this reason, radiance spectra were also analyzed without atmospheric correction, and various approaches to interpret radiance data over plankton blooms were investigated. Cluster analysis and ratio techniques at longer wavelengths were found to assist in the separation of ocean color gradients and distinguish bio-geochemical provinces in near-coastal waters. Furthermore, using the slope of spectra from plankton blooms, in connection with scatter diagrams at various wavelengths, shows that details can be revealed that would not be recognized in single channels at lower wavelength.

The focus of our study is the extinction and optical effects due to aerosol in a specific coastal region. The aerosol microphysical model of the marine and coastal atmosphere surface layer is considered. The model is made on the basis of the long-term experimental data received at researches of aerosol sizes distribution function (dN/dr) in the band particles sizes in 0.01 - 100 μk. The model is developed by present time for the band of heights is 0 - 25 m. Bands of wind speed is 3 - 18 km/s, sizes fetch is up to 120 km, RH = 40 - 98 %. Key feature of model is parameterization of amplitude and width of the modes as functions of fetch and wind speed. In the paper the dN/dr behavior depending at change meteorological parameters, heights above sea level, fetch (X), wind speed (U) and RH is show. On the basis of the developed model with usage of Mie theory for spheres the description of last version of developed code MaexPro (Marine Aerosol Extinction Profiles) for spectral profiles of aerosol extinction coefficients α(λ) calculations in the wavelength band, equal λ = 0.2 - 12 μm is presented. The received results are compared models NAN and ANAM. Also α(λ) profiles for various wind modes (combinations X and U) calculated by MaexPro code are given. The calculated spectrums of α(λ) profiles are compared with experimental data of α(λ) received by a transmission method in various geographical areas.

In this paper the results of simultaneous and spatially coincident, multi-frequency, polarimetric, spatio-temporally collocated measurements of waved pool water surface microwave reflective (radar backscattering coefficient) and emissive (brightness temperature) characteristics angular dependences at 5.6GHz and 15GHz will be represented. Angular measurements were carried out for various water surface roughness parameters at clear air, cloudy and rain conditions. For these measurements C-, and Ku-band, polarimetric, combined scatterometric-radiometric systems were used, set jointly on a mobile buggy moving along the measuring platform. Structures, operational features and the main technical characteristics of the utilized systems are presented too. The paper has an aim as well to attract attention of interested researchers and to invite them to perform their own or joint researches using available devices and facilities.

In this paper the structure and operational features of Ka-band, combined scatterometric-radiometric system and the results of spatio-temporally collocated measurements of perturbed pool water surface microwave reflective (radar backscattering coefficient) and emissive (brightness temperature) characteristics angular dependences at ~37GHz are presented, curried out under clear air, heavy clouds and rain conditions.

The U.S. National Oceanic and Atmospheric Administration's (NOAA) National Data Buoy Center (NDBC) has three major real-time ocean observing networks: (1) Weather and Ocean Platform (WxOP) Network, (2) Tropical Atmosphere/Ocean (TAO) Buoy Network, and (3) Tsunameter Buoy Network. The WxOP Platform network includes 111 moored buoys and 49 land-based Coastal-Marine Automated Network (C-MAN) stations. NDBC's moored buoys are deployed in the coastal and offshore waters from the Western Atlantic to the Pacific Ocean around Hawai'i, and from the Bering Sea to the South Pacific (including Great Lakes). C-MAN stations are usually located near the U.S. coastal water. The TAO buoy network, designed for the study of year-to-year climate variations related to El Niño and the Southern Oscillation (ENSO), consists of 55 moored ocean surface buoys and 4 sub-surface moorings along the equatorial Pacific Ocean region extending from 9°N Latitude to 8°S Latitude and 95°W Longitude to 165°E Longitude. The Tsunameter Buoy Network consists of 39 tsunameter buoy systems in the Pacific Ocean, Gulf of Mexico, and Atlantic. This paper describes NDBC's 250+ ocean observing platforms/systems and presents some examples of data collected by these platforms and systems.

Unmanned underwater vehicles are becoming an increasingly important platform in oceanographic research and operational oceanography, where continuous in situ sampling throughout the water column is essential to understanding the ocean circulation and related biological, chemical, and optical activity. The latter directly affects field operations and remote sensing capabilities from space. A unified approach is necessary for data quality control (QC), access, and storage, considering the vast amount of data collected from gliders continuously deployed across large areas and over long durations. The Binary Universal Form for the Representation of meteorological data (BUFR) maintained by the World Meteorological Organization (WMO) is adapted to include physical and optical parameters from a variety of sensor suites onboard underwater vehicles. The provisional BUFR template and related BUFR descriptors and table entries have been developed by the U.S. Navy for ocean glider profile data and QC results. Software written in FORTRAN using the ECMWF BUFRDC library has been implemented to perform both the encoding and decoding of BUFR files from and to Network Common Data Form (NetCDF) files. This presentation also discusses data collected from sensors on gliders deployed both in deep water and shallow water environments, including issues specific to optical sensors at various depths.

This work presents an electro-optical multispectral capability that detects and monitors marine mammals. It is a continuance of Whale Search Radar SBIR program funded by PMA-264 through NAVAIR. A lightweight, multispectral, turreted imaging system is designed for airborne and ship based platforms to detect and monitor marine mammals. The system tests were conducted over the Humpback whale breeding and calving area in Maui, Hawaii. The results of the tests and the system description are presented. The development of an automatic whale detection algorithm is discussed as well as methodology used to turn raw survey data into quantifiable data products.

The objective of this work was to develop and validate approaches to accurately and efficiently model channel characteristics in a range of environmental and operational conditions for underwater laser communications systems that use high frequency amplitude modulation (AM) or coded pulse trains. Two approaches were investigated: 1) a Monte Carlo model to calculate impulse responses for a particular system hardware design over a large range of environmental and operational conditions, and 2) a semi-analytic model which has the potential to be more computationally efficient than the Monte Carlo model. The formulation of the Monte Carlo code is presented in this paper, together with test results used to evaluate the range of accuracy of the model against 500ps laser-pulse propagation measurements from well-controlled and characterized particle suspensions in a 12.5m test tank. A multiple scattering study using the Monte Carlo simulation code was also performed and some results are presented. Results from the semi-analytic model will be compared with these test tank measurements and the Monte Carlo model in a later paper.

In this paper we examine the impact of measured underwater polarization characteristics on visibility. Underwater characteristics were measured both in the principal plane and outside the principal plane, with data collected during several cruises in the Chesapeake/Virginia and New York Harbor/Hudson River areas using a multi-angular hyperspectral sensor system. This system, recently developed by us, consists of three hyperspectral Satlantic radiance sensors, each with a polarizer positioned in front of it, and with polarization axes aligned at 0, 90 and 45 deg. Underwater measurements are made with scattering angles from 0-180 degrees with respect to the solar illumination. At the same time as the hyperspectral measurements are made, the inherent optical properties such as absorption and attenuation were also recorded. The waters studied varied from clear open ocean water with attenuation of less than 0.25m^-1 at 550nm c (550), to turbid coastal waters with a c(550) of more than 4m^-1. In order to examine the extent that polarization techniques can help to improve underwater visibility in these types of field conditions, we computed the related modulation transfer functions from the polarized field measurements, and included the examination of the impact of scattered polarized veiling light, inherent in the field data. Various water parameters are then explored to examine the impact of the polarization of the background light in the principal plane on underwater visibility.

Multiangular, hyperspectral measurements of the underwater polarization light field, as well as comprehensive measurements of IOPs were collected in several cruise campaigns in the Chesapeake/Virginia area and New York Harbor/Hudson River areas. The waters examined were mostly eutropic water with Chlorophyll a concentration up to approximately 57 μg/L. It is found that Chlorophyll a fluorescence markedly impacts (reduces) the underwater degree of polarization (DOP) in the 650 - 700 nm spectral region. By taking note of the unpolarized nature of algal fluorescence and the partially polarized properties of elastic scattering, particularly by non-algal particles, we were able to separate the Chlorophyll a fluorescence signal from the total radiance. The analysis is based on comparisons of the underwater multiangular, hyperspectral polarization measurements which include fluorescence, compared with adding - doubling polarized radiative transfer simulations of elastic scattering which use measured IOPs as input, and which do not include fluorescence. The difference between the two shows the impact of fluorescence. These relationships are examined in detail, and the efficacy of using DOP measurements for underwater fluorescence retrieval is evaluated for different scattering geometries and water conditions.

A theoretical model on light scattering by water was developed from the thermodynamic principles and was used to evaluate the effects of temperature and salinity. The results agreed with the measurements by Morel within 1%. The scattering increases with salinity in a non-linear manner and the empirical linear model underestimate the scattering by seawater for S < 40 psu. Seawater also exhibits an 'anomalous' scattering behavior with a minimum occurring at 24.64 °C for pure water and this minimum increases with the salinity, reaching 27.49 °C at 40 psu.

The main challenge in underwater imaging and image analysis is to overcome the effects of blurring due to the strong scattering of light by the water and its constituents. This blurring adds complexity to already challenging problems like object detection and localization. The current state-of-the-art approaches for object detection and localization normally involve two components: (a) a feature detector that extracts a set of feature points from an image, and (b) a feature matching algorithm that tries to match the feature points detected from a target image to a set of template features corresponding to the object of interest. A successful feature matching indicates that the target image also contains the object of interest. For underwater images, the target image is taken in underwater conditions while the template features are usually extracted from one or more training images that are taken out-of-water or in different underwater conditions. In addition, the objects in the target image and the training images may show different poses, including rotation, scaling, translation transformations, and perspective changes. In this paper we investigate the effects of various underwater point spread functions on the detection of image features using many different feature detectors, and how these functions affect the capability of these features when they are used for matching and object detection. This research provides insight to further develop robust feature detectors and matching algorithms that are suitable for detecting and localizing objects from underwater images.

In this paper, we explore the use of optical correlation-based recognition to identify and position underwater man-made objects (e.g. mines). Correlation techniques can be defined as a simple comparison between an observed image (image to recognize) and a reference image; they can be achieved extremely fast. The result of this comparison is a more or less intense correlation peak, depending on the resemblance degree between the observed image and a reference image coming from a database. However, to perform a good correlation decision, we should compare our observed image with a huge database of references, covering all the appearances of objects we search. Introducing all the appearances of objects can influence speed and/or recognition quality. To overcome this limitation, we propose to use composite filter techniques, which allow the fusion of several references and drastically reduce the number of needed comparisons to identify observed images. These recent techniques have not yet been exploited in the underwater context. In addition, they allow for integrating some preprocessing directly in the correlation filter manufacturing step to enhance the visibility of objects. Applying all the preprocessing in one step reduces the processing by avoiding unnecessary Fourier transforms and their inverse operation. We want to obtain filters that are independent from all noises and contrast problems found in underwater videos. To achieve this and to create a database containing all scales and viewpoints, we use as references 3D computer-generated images.

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Undersea communication channels are filled with acoustic emissions of various kinds. From sonar to the signals used in acoustic communications, man-made noise, and biological signals generated by marine life, the ocean is a complex conduit for diverse emissions. In this work we propose an algorithm in which an acoustic emission such as a sonar signal is transparently and securely embedded with signatures known as a digital watermark. Extracting the watermark helps to distinguish, for example, a friendly sonar from other acoustic emissions that may exist as part of the natural undersea environment, or from pings that may have originated from hostile forces or echoes fabricated by an adversary. We have adopted spread spectrum as an embedding technique. Spread spectrum allows for matching the watermark to propagation, multipath, and noise profiles of the channel. The sonar is first characterized by its spectrogram and divided up into non-overlapping blocks in time. Each block is individually embedded with a single bit drawn from the watermark payload. The seeds used to generate the spreading codes are the keys used by authorized receivers to recover the watermark. The detector is a maximum likelihood detector using test statistics obtained by integrating a correlation detector output over the entire sonar pulse width. Performance of the detector is controlled by signal-to-watermark ratio, specific frequency bands selected for watermarking, watermark payload, and processing gain. For validation, we use Sonar Simulation Toolset (SST). SST is a software tool that is custom-made for the simulation of undersea channels using realistic propagation properties in oceans. Probabilities of detection and false alarm rates, as well as other performance boundaries, are produced for a shallow water channel subject to multipath and additive noise.

Increased emphasis on maritime domain awareness and port security has led to the development of unmanned underwater vehicles (UUVs) capable of extended missions. These systems rely most frequently on well-developed side scan sonar and acoustic methods to locate potential targets. The Naval Research Laboratory (NRL) is developing biosensors for underwater explosives detection that complement acoustic sensors and can be used as UUV payloads to monitor areas for port and harbor security or in detection of underwater unexploded ordnance (UXO) and biochemical threats. The prototype sensor has recently been demonstrated to detect explosives in seawater at trace levels when run in a continuous sampling mode. To overcome ongoing issues with sample preparation and facilitate rapid detection at trace levels in a marine environment, we have been developing new mesoporous materials for in-line preconcentration of explosives and other small molecules, engineering microfluidic components to improve the signal, and testing alternative signal transduction methods. Additional work is being done to optimize the optical components and sensor response time. Highlights of these current studies and our ongoing efforts to integrate the biosensor with existing detection technologies to reduce false positives are described. In addition, we present the results of field tests that demonstrate the prototype biosensor performance as a UUV payload.

The capability to accurately estimate strain and orientation of cables in an undersea environment is important for a multitude of applications. One way to estimate the positional location of a submersed cable is to utilize a network of distributed bend sensors providing inputs to a curve fitting algorithm. In this work commercially available bend sensors are characterized for small deflections. In addition proto-type devices are presented which can potentially improve device sensitivity. Commercially available bend sensors are based upon electro-active materials and variable resistance materials. Electro-active materials (EAM) are known for their actuator functionality but certain EAMs are capable of sensing as well. New advances in materials such as Ionic Polymer Metal Composites (IPMC) are proving suitable for quasi-static sensor applications. These sensors are low power, conformal and produce directionally dependent output voltages which are linearly proportional to deflection, with voltage polarity representative of the deflection direction. IPMCs are capable of being morphed for increased sensitivity. Variable resistivity sensors are based on smart epoxy polymer and carbon loaded inks. These sensors are inexpensive and conformal and unlike EAMs provide static measurements.

ICx Nomadics has developed the first known real-time sensor system that is capable of detecting chemical signatures emanating from underwater explosives, based upon the same amplifying fluorescent polymer (AFP) fluorescence-quenching transduction mechanism that the Fido® family of explosives detectors utilizes. The SeaPup is capable of real-time detection of the trace chemical signatures emanating from submerged explosive compounds and has been successfully tested on various marine platforms, including a crawler robot, an autonomous underwater vehicle (AUV), and a remotely operated underwater vehicle (ROV). The present work is focused on advances in underwater in-situ chemical sensing; wherein trace amounts of dissolved explosive compounds may be detected and discriminated from other chemical species found in the marine environment. Recent progress with the SeaPup platform have focused on increasing the sensitivity of the AFP matrix through the development of a preconcentration system designed to harvest explosive analytes from a larger sample volume over a predetermined period of time. This permits real time monitoring of chemical plumes during the approach to a potential source, combined with the lowered limit of detection from extended sampling of targeted items. SeaPup has been shown to effectively map "explosive scent plumes" emanating from an underwater source of TNT, and the preconcentration system has previously been demonstrated to enhance sensitivity be over 2 orders of magnitude in a time window of minutes.

There is a strong national interest in the observation of ocean surface winds with high spatial and temporal resolution for understanding tropical cyclones and their effects on weather and climate. In this paper, we will describe the details of an end-to-end simulation to support the development of the future airborne microwave Hurricane Imaging Radiometer (HIRAD). This new instrument will extend the measurements of the Stepped Frequency Microwave Imager (SFMR) from nadir looking only to a wide swath storm coverage of ± 60° earth incidence angel (EIA). A comprehensive simulation of the instrument radiances measurements during a hurricane overflight was developed based on realistic 3-D hurricane atmosphere and surface wind field using numerical weather models especially tunes to characterize hurricane environment. Afterwards, the simulated measurements were perturbed with instrument errors and input to the Maximum Likelihood Estimation (MLE) retrieval algorithm. Results will show statistical analysis and comparisons of the retrieved wind speeds and rain rates for different swath locations.

A growing number of publications in the field of particle and dispersion science create an immediate need for a facilitated access by scientists to the latest relevant findings. An online monograph Topics in Particle and Dispersion Science (TPDSci.com) implements an original approach to this problem. This peer-review publication continually builds a comprehensive review from conclusion-oriented "very brief abstracts" (VBAs) as the building blocks. The review is complemented by an extensive concept-based index and a free-form search as major components of the TPDSci navigation system.