Parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans, A

2006 Summer.Includes bibliographical references (pages 82-87).Covers not scanned.Print version deaccessioned 2021.There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals

Marine Low Clouds : Radiation, Turbulence, and Forecasting

High-resolution ship-based observations of a nearly uniform stratocumulus deck in the southeast Pacific during 17 days of the 2008 VOCALS regional experiment elucidate radiation and turbulence in the marine boundary layer (MBL). A new method for prescribing observations-based cloud properties to the Rapid Radiative Transfer Model is presented and applied to the VOCALS dataset. The simulated radiative fluxes compare well with surface observations and with simulations from several other commonly employed methods, particularly during times of high drizzle content and/or precipitation. Clear-sky and cloudy scenarios illustrate the radiative effect of clouds on the MBL. Turbulence estimates are made for the stratocumulus layer using the simulated radiative fluxes and vertical velocity observations from the NOAA W-band Doppler cloud radar. The averages and diurnal cycles of the terms in the turbulent kinetic energy (TKE) budget are presented. 10-minute vertical velocity statistics and estimates of TKE budget terms reveal a robust diurnal cycle of weak turbulence during daylight hours with a rapid increase near sundown followed by sustained strong turbulence at night. Buoyant production is driven by cloud-top longwave radiative cooling during the night, but is offset and dominated by absorption of solar radiation from ~0900-1600 LT. At night, dissipation increases toward cloud base while vertical velocity variance is nearly constant with height. This suggests larger-scale turbulence occurs near cloud-top while dissipation interacts with smaller-scale turbulence near cloud base. Cloud-top entrainment rates are estimated from the entrainment heat flux calculated as a residual from the heat budget. Entrainment, with an average value of 4.6 mm s-1, has a diurnal cycle similar to turbulence with larger values at night. The third part of this dissertation presents one of the first studies using reforecast data to produce downscaled low cloud probabilistic forecasts for aviation applications. Probability of low cloud ceiling and visibility are forecast using analogs chosen from an ensemble of reforecasts, on the basis of their agreement with thermodynamic soundings. Skillful forecasts are produced for up to 30-hour leads times using reforecasts of 1° resolution at major airports across the continental United States. These probabilistic forecasts outperform climatology at all lead times and trained forecasters at nine-hour and longer lead times. These results suggest that real-time forecasts based on analog reforecasts is a useful approach for aviation applications with potential to enhance safety and commerce

Occurrence and Energy Dissipation of Breaking Surface Waves in the Nearshore Studied with Coherent Marine Radar

Wave breaking influences air-sea interactions, wave induced forces on coastal structures, sediment transport and associated coastline changes. A good understanding of the process and a proper incorporation of wave breaking into earth system models is crucial for a solid assessment of the impacts of climate change and human influences on coastal dynamics. However, many aspects are still poorly understood which can be attributed to the fact that wave breaking is difficult to observe and study because it occurs randomly and involves multiple spatial and temporal scales. Within this doctoral work, a nearshore field experiment was planned and conducted on the island of Sylt in the North Sea to investigate the dynamics of wave breaking. The study combines in-situ observations, numerical simulations and remote sensing using shore-based coherent marine radar. The field measurements are used to investigate the coherent microwave backscatter from shoaling and breaking waves. Three major developments result from the study. The first one is a forward model to compute the backscatter intensity and Doppler velocity from known wave kinematics. The second development is a new classification algorithm to identify dominant breakers, whitecaps and radar imaging artifacts within the radar raw data. The algorithm is used to infer the fraction of breaking waves over a sub- and an inter-tidal sandbar as well as whitecap statistics and results are compared to different parameterizations available in literature. The third development is a new method to deduce the energy of the surface roller from the Doppler velocity measured by the radar. The roller energy is related to the dissipation of roller energy by the stress acting at the surface under the roller. From the spatial gradient of roller energy, the transformation of the significant wave height is computed along the entire cross-shore transect. Comparisons to in-situ measurements of the significant wave height from two bottom mounted pressure gauges and a wave rider buoy show a total root-mean-square-error of 0.20 m and a bias of −0.02 m. It is the first time that measurements of the spatio-temporal variation of the bulk wave energy dissipation together with the fraction of breaking waves are achieved in storm conditions over such a large distance of more than one kilometer. The largest dissipation rates (> 300 W/m² ) take place on a short distance of less than one wave length (≈ 50 m) at the inter-tidal sandbar. However, during storm conditions 50 % of the incoming wave energy flux is already dissipated at the sub-tidal sandbar. The simultaneous measurements of the occurrence frequency and the energy dissipation facilitate an assessment of the bulk dissipation of individual breaking waves. For the spilling-type breakers in this area, the observed dissipation rate is about 30 % smaller than the dissipation rate according to the generally used bore analogy. This must be considered within nearshore wave models if accurate predictions of the breaking probability are required

Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach

This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo

Microwave scattering from surf zone waves

Wave breaking in the surf zone is an important forcing mechanism on the generation of nearshore currents and in the driving of sediment transport. At the same time, wave breaking can have significant spatial and temporal variability that needs to be accounted for in the description of nearshore processes. Remote sensors are best suited to collect wave breaking measurements due to their large footprint and synoptic capabilities, but in order to extract quantitative wave parameters a proper understanding of the imaging mechanisms is essential. Microwave sensors have been shown to be able to measure wave parameters in deep water, but in the surf zone many of the assumptions the algorithms are based upon do not hold. Additionally, the dynamics of breaking waves are different and may affect in a yet determined way the signal. This dissertation first intends to address an observational gap regarding surf zone microwave measurements. A novel combination of synchronous, large coverage marine radar, calibrated pulsed Doppler radar and video observations from a field site enable the analysis of the evolution and characteristics of the wave signature. The combined data sets yield superior discrimination rates between breaking and non-breaking waves. Discrimination also allows the study of the microwave scattering by source, where active breaking is separated from remnant foam and steepening waves. Results show that the backscattered power from breaking waves, specifically from the wave roller, is a several dB larger than that of foam and steepening waves and independent of the environmental conditions and polarization state. While similar results have been obtained for deep water waves and variety of scattering models have been proposed, it is found that none of the models can describe all the data. Additionally, most of the models neglect the roller morphology. Therefore, in the last section a scattering model is introduced, in which the roller is treated as a volume where a collection of water droplets embedded in air can scatter incoherently. Multiple interactions of the scattered fields between particles and the boundaries are also accounted for. Though the model formulation is complex, it depends on a few physical parameters (diameter, volume fraction, medium permittivity) and no calibration constants. Comparison against data shows that the model does a reasonable job in predicting the observed scattering levels, polarization response and grazing angle dependencies, although is not capable to reproduce the maximum scattered levels observed and predicts polarization ratios always less than unity

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Development of a dynamic underwater acoustic communication channel simulator with configurable sea surface parameters to explore time-varying signal distortion

A wide-band phase-coherent multi-path underwater acoustic channel simulation is developed using an approximate quantitative model of the acoustic wave response to a time-varying three-dimensional rough surface. It has been demonstrated over transmission ranges from 100 m to 8 km by experimental channel probing and comparable synthetic replication of the channel probing through the simulated channel, that the simulation is capable of reproducing fine-time-scale Doppler and delay distortions consistent with those generated in real shallow channels

Asteroid Retrieval Feasibility Study

This report describes the results of a study sponsored by the Keck Institute for Space Studies (KISS) to investigate the feasibility of identifying, robotically capturing, and returning an entire Near-Earth Asteroid (NEA) to the vicinity of the Earth by the middle of the next decade. The KISS study was performed by people from Ames Research Center, Glenn Research Center, Goddard Space Flight Center, Jet Propulsion Laboratory, Johnson Space Center, Langley Research Center, the California Institute of Technology, Carnegie Mellon, Harvard University, the Naval Postgraduate School, University of California at Los Angeles, University of California at Santa Cruz, University of Southern California, Arkyd Astronautics, Inc., The Planetary Society, the B612 Foundation, and the Florida Institute for Human and Machine Cognition