Monitoring nearshore processes and understanding significant coastal change using x-band radar

Remote sensing through X-band radar can provide wave and current parameters and bathymetric maps in a 4-km radius from a land-based deployment. This paper explores the use of radar to monitor changes in nearshore bathymetry at Thorpeness, Suffolk, UK. The method presented enables significant nearshore changes to be identified based on the analysis of standard deviation of sediment volume. Seasonal changes in bathymetry can reach 4 m but depths tend to be consistent in each season. A storm power index was calculated for periods of time preceding the significant changes in bathymetry. Results indicate that impact on the nearshore is not directly linked to storm power. Storm clusters and antecedent nearshore conditions seem to be important factors, as larger volume changes were measured as a result of the first and smallest storm of a cluster

Reconstruction of the frequency-wavenumber spectrum of water waves with an airborne acoustic Doppler array for non-contact river monitoring

This work presents a novel method to reconstruct the frequency-wavenumber spectrum of water waves based on the complex acoustic Doppler spectra of scattered sound measured with an array of microphones. The reconstruction is based on a first-order small-roughness-amplitude expansion of the acoustic wave scattering equation, which is discretized and inverted by means of a singular value decomposition. An analogy of this approach to the first-order Bragg scattering problem is demonstrated by means of a stationary phase expansion. The approach enables the reconstruction of the dispersion relation of water waves when the ratio between roughness height and acoustic wavelength is less than 0.1, and when the surface wavelength is larger than 1/2 of the acoustic wavelength. The method is validated against synthetic data and data from laboratory and field experiments, to demonstrate its applicability to two-and three-dimensional complex patterns of water waves, and specifically to the surface deformations that arise naturally in a turbulent open-channel flow. Fitting the reconstructed data with the analytical dispersion relation enables the non-contact estimate of the underlying flow velocity for hydraulic conditions where the coexistence of different types of turbulence-forced and freely propagating water waves would limit the accuracy of standard non-contact Doppler velocimetry approaches, paving the way for robust and accurate non-contact river monitoring using acoustics

The Application of Proper Orthogonal Decomposition to Numerically Modeled and Measured Ocean Surface Wave Fields Remotely Sensed by Radar

Phase-resolved ocean surface wave elevation maps provide important information for many scientific research areas (e.g., rogue waves, wave-current interactions, and wave evolution/growth) as well as for commercial and defense applications (e.g., naval and shipping operations). To produce these maps, measurements in both time and space are necessary. While conventional wave sensing techniques are limited spatially, marine radar has proven to be a complex yet promising remote sensing tool capable of providing both temporal and spatial wave measurements. The radar return from the sea surface is complex because it contains contributions from many sources only part of which provide information about the ocean surface wave field. Most existing techniques used to extract ocean wave fields from radar measurements implement fast Fourier transforms (FFTs) and filter this energy spectrum using the linear dispersion relationship for ocean waves to remove non-wave field contributions to the radar signal. Inverse Fourier transforms (IFFTs) return the filtered spectrum to the spatial and temporal domain. However, nonlinear wave interactions can account for a non-negligible portion of ocean wave field energy (particularly in high sea states), which does not completely adhere to the linear dispersion relationship. Thus, some nonlinear wave energy is lost using these FFT dispersion-filtering techniques, which leads to inaccuracies in phase-resolved ocean surface wave field maps. This deficiency is significant because many of the aforementioned research areas and applications are most concerned with measurement and prediction of such anomalous wave conditions. Proper orthogonal decomposition (POD) is an empirical technique used in scientific fields such as fluid mechanics, image processing, and oceanography (Sirovich, 1987). This technique separates a signal into a series of basis functions, or modes, and time or spatial series coefficients. Combining a subset of the modes and coefficients can produce a reduced order representation of the measured signal; this process is referred to as a reconstruction. This research applies POD to radar Doppler velocity measurements of the sea surface and uses the leading modes as a filter to separate wave contributions to the radar measurement from non-wave contributions. In order to evaluate the robustness of this method, POD is applied to ocean wave radar measurements obtained using three different radar systems as well as to numerically modeled radar data for a variety of environmental conditions. Due to the empirical nature of the POD method, the basis functions have no innate physical significance, therefore the shape and content of leading POD modes is examined to evaluate the linkage between the mode functions and the wave field physics. POD reconstructions and FFT-based methods are used to compute wave field statistics that are compared with each other as well as to ground truth buoy measurements. Correlation coefficients and root mean squared error are used to evaluate phase-resolved wave orbital velocity time series reconstructions from POD and FFT-based methods relative to ground truth buoy velocity time series measurements. Results of this study show that when POD is applied to radar measurements of the sea surface: (i) the leading mode basis functions are oscillatory and linked to the physics of the measured wave field; (ii) POD performs comparably to FFT-based dispersion filtering methods when calculating wave statistics; and (iii) phase-resolved POD orbital velocity maps show higher correlations with buoy velocity time series relative to orbital velocity time series based on FFT dispersion filtering methods when high group line energy is present (i.e., in the presence of steep and breaking waves)

Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains

The Neckar Valley and the Swabian Jura in southwest Germany comprise a hotspot for severe convective storms, causing tens of millions of euros in damage each year. Possible reasons for the high frequency of thunderstorms and the associated event chain across compartments were investigated in detail during the hydro-meteorological field campaign Swabian MOSES carried out between May and September 2021. Researchers from various disciplines established more than 25 temporary ground-based stations equipped with state-of-the-art in situ and remote sensing observation systems, such as lidars, dual-polarization X- and C-band Doppler weather radars, radiosondes including stratospheric balloons, an aerosol cloud chamber, masts to measure vertical fluxes, autosamplers for water probes in rivers, and networks of disdrometers, soil moisture, and hail sensors. These fixed-site observations were supplemented by mobile observation systems, such as a research aircraft with scanning Doppler lidar, a cosmic ray neutron sensing rover, and a storm chasing team launching swarmsondes in the vicinity of hailstorms. Seven Intensive Observation Periods (IOPs) were conducted on a total of 21 operating days. An exceptionally high number of convective events, including both unorganized and organized thunderstorms such as multicells or supercells, occurred during the study period. This paper gives an overview of the Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign, briefly describes the observation strategy, and presents observational highlights for two IOPs

94 GHz Monolithic Transmitter for Weather Radar Application

This thesis was written for concluding my studies at the University of Padua. The main topic is the design of a monolithic transmitter in SiGe bipolar technology, for weather radar application at an operating frequency around 94GHz. At such a high frequency parasitic elements have to be taken into account very carefully. Appropriate matching networks become important to allow the signals to pass across the different ections of the transmitter, without reflections or attenuations. To this aim, transmission lines were used instead of inductors, in order to save size and to have a more reliable modelling of device parameters and parasitic elements. The structure of the transmitter includes a transformer (which acts as Balun), a frequency quadrupler and a buffer. The transmitter input receives a single-ended reference signal at 23.5GHz, with a power of 0dBm on a single-ended input impedance of 50Ω. The output has been designed for a differential load of 100Ω and to operate in the temperature range of 0°C - 100°C, with a typical output power above 10dBm and spurious harmonic below -25dBcopenMotivi di sicurezza e/o proprietà dei risultati e/o informazioni sensibil

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

TRMM (Tropical Rainfall Measuring Mission): A satellite mission to measure tropical rainfall

The Tropical Rainfall Measuring Mission (TRMM) is presented. TRMM is a satellite program being studied jointly by the United States and Japan which would carry out the systematic study of tropical rainfall required for major strides in weather and climate research. The scientific justification for TRMM is discussed. The implementation process for the scientific community, NASA management, and the other decision-makers and advisory personnel who are expected to evaluate the priority of the project is outlined

New hybrid neuro-evolutionary algorithms for renewable energy and facilities management problems

Esta tesis tiene como objetivo la optimización de la explotación de recursos energéticos renovables, así como la mejora en la gestión de instalaciones en ingeniería oceánica y aeropuertos, usando métodos computacionales híbridos pertenecientes a una rama de la Inteligencia Artificial (IA), denominada aprendizaje máquina, para este fin. Hoy en día, los combustibles fósiles constituyen la fuente energética más importante del planeta, sin embargo, estas formas de energía contribuyen al Cambio Climático en gran medida, afectando los ecosistemas severamente. Por esta razón, se tiende gradualmente al uso de fuentes de energía renovables que garanticen un desarrollo sostenible. Sin embargo, se observa una lenta evolución en este sentido, y la única cuestión que cabe preguntarse es cuándo las energías renovables tendrán mayor penetración en el sistema que los actuales combustibles fósiles. Para responder a esta pregunta, una buena manera es centrarse en el principal inconveniente de este tipo de energías: la variabilidad natural inherente al recurso. Esto significa que las predicciones sobre los parámetros más importantes de los que dependen las energías renovables son necesarias para conocer la cantidad de energía que será obtenida en un momento dado. El otro tema abordado en esta tesis está relacionado con los parámetros que influyen en diferentes actividades marinas y aeropuertos, cuyo conocimiento de su comportamiento es necesario para desarrollar una correcta gestión de las instalaciones en estos entornos. Por ejemplo, la altura significativa de las olas (Hs) es un parámetro básico en la caracterización de las olas, muy importante para el desarrollo de actividades marinas como el diseño y mantenimiento de barcos, estructuras marinas, convertidores energéticos de ola, etc. Por otro lado, la escasa visibilidad en los aeropuertos, normalmente causada por la niebla, es otro aspecto fundamental para el correcto desarrollo de actividades aeroportuarias, y que puede causar retrasos en los vuelos, desvíos y cancelaciones, o accidentes en el peor de los casos. En este trabajo se ha realizado un análisis del estado del arte de los modelos de aprendizaje máquina que se utilizan actualmente, con el objetivo de resolver los problemas asociados a los temas tratados con anterioridad. Diferentes contribuciones han sido propuestas: - Uno de los pilares esenciales de este trabajo está centrado en la estimación de los parámetros más importantes en la explotación de energías renovables. Con este propósito, los algoritmos Vectores Soporte para Regresión (VSR), Redes Neuronales (RN) (Perceptrones Multicapa (MLP) y Máquinas de Aprendizaje Extremo (MAE)) y Procesos Gaussianos son utilizados en diferentes problemas prácticos. El rendimiento de estos algoritmos es analizado en cada uno de los experimentos realizados, tanto la precisión de los mismos como la especificación de las características internas. - Otro de los aspectos tratados está relacionado con problemas de selección de características. Concretamente, con el uso de algoritmos evolutivos como Algoritmos de Agrupación Genética (AAG) o los algoritmos de Optimización de Arrecife de Coral (OAC) hibridizados con otros métodos de aprendizaje máquina como clasificadores y regresores. En este sentido, el AAG o OAC analizan diferentes conjuntos de características para obtener aquel que resuelva el problema con la mayor precisión, y el regresor empleado proporciona la predicción en función de las características obtenidas por el Algoritmo Genético (AG), reduciendo el coste computacional con gran fiabilidad en los resultados. La metodología mencionada es aplicada a múltiples problemas: predicción de Hs, relevante en aplicaciones energéticas y actividades marinas, estimación de eventos puntuales como son las rampas de viento (ERV), variaciones indeseables en la potencia eléctrica producidas por un parque eólico, predicción de la radiación solar global en áreas de España y Australia, realmente importante en términos de energía solar, y la estimación de eventos de baja visibilidad en aeropuertos. Los casos prácticos citados son desarrollados con el consecuente análisis previo de la base de datos empleada, normalmente, en términos de variables meteorológicas