GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition

The Global Navigation Satellite System-Reflections (GNSS-R) signal has been confirmed to be used to retrieve sea level height. At present, the GNSS-Interferometric Reflectometry (GNSS-IR) technology based on the least square method to process signal-to-noise ratio (SNR) data is restricted by the satellite elevation angle in terms of accuracy and stability. This paper proposes a new GNSS-IR model combining variational mode decomposition (VMD) for sea level height estimation. VMD is used to decompose the SNR data into intrinsic mode functions (IMF) of layers with different frequencies, remove the IMF representing the trend item of the SNR data, and reconstruct the remaining IMF components to obtain the SNR oscillation item. In order to verify the validity of the new GNSS-IR model, the measurement data provided by the Onsala Space Observatory in Sweden is used to evaluate the performance of the algorithm and its stability in high elevation range. The experimental results show that the VMD method has good results in terms of accuracy and stability, and has advantages compared to other methods. For the half-year GNSS SNR data, the root mean square error (RMSE) and correlation coefficient of the new model based on the VMD method are 4.86 cm and 0.97, respectively

Real-Time Water Vapor Maps from a GPS Surface Network: Construction, Validation, and Applications

In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a twodimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth¿Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m-2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m-2 for the NWP analysis to 4 kg m-2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air

Application of the variational method for correction of wet ice attenuation for X-band dual-polarized radar

2011 Fall.Includes bibliographical references.In recent years there has been a huge interest in the development and use of dual-polarized radar systems operating at X-band (~10 GHz) region of the electromagnetic spectrum. This is due to the fact that these systems are smaller and cheaper allowing for a network to be built, for example, for short range (typically < 30-40 km) hydrological applications. Such networks allow for higher cross-beam spatial resolutions while cheaper pedestals supporting a smaller antenna also allows for higher temporal resolution as compared with large S-band (long range) systems used by the National Weather Service. Dual-polarization radar techniques allow for correction of the strong attenuation of the electromagnetic radar signal due to rain at X-band and higher frequencies. However, practical attempts to develop reliable correction algorithms have been cumbered by the need to deal with the rather large statistical fluctuations or "noise" in the measured polarization parameters. Recently, the variational method was proposed, which overcomes this problem by using the forward model for polarization variables, and uses iterative approach to minimize the difference between modeled and observed values, in a least squares sense. This approach also allows for detection of hail and determination of the fraction of reflectivity due to the hail when the precipitation shaft is composed of a mixture of rain and hail. It was shown that this approach works well with S-band radar data. The purpose of this research is to extend the application of the variational method to the X-band dual-polarization radar data. The main objective is to correct for attenuation caused by rain mixed with wet ice hydrometeors (e.g., hail) in deep convection. The standard dual-polarization method of attenuation-correction using the differential propagation phase between H and V polarized waves cannot account for wet ice hydrometeors along the propagation path. The ultimate goal is to develop a feasible and robust variational-based algorithm for rain and hail attenuation correction for the Collaborate Adaptive Sensing of the Atmosphere (CASA) project

UNSUPERVISED LEARNING FOR ANOMALY DETECTION IN REMOTE SENSING IMAGERY

Landfill fire is a potential hazard of waste mismanagement, and could occur both on and below the surface of active and closed sites. Timely identification of temperature anomalies is critical in monitoring and detecting landfill fires, to issue warnings that can help extinguish fires at early stages. The overarching objective of this research is to demonstrate the applicability and advantages of remote sensing data, coupled with machine learning techniques, to identify landfill thermal states that can lead to fire, in the absence of onsite observations. This dissertation proposed unsupervised learning techniques, notably variational auto-encoders (VAEs), to identify temperature anomalies from aerial landfill imagery. Twenty years of Landsat satellite observations at a number of landfills were examined for hotspots that may be associated with or leading to subsurface fires. The main contribution of this dissertation is to detect temperature anomalies in landfills using the state-of-the-art unsupervised deep learning technique of VAE based on both model reconstruction error and encoder module feature extraction. Additionally, a simple framework for assessing the health state of the landfill at any given time was established by using the clustering findings to generate a past behavior for each location in the landfill and eventually assigning it to one of four risk categories (No Risk, Low Risk, Moderate Risk, and High Risk). This framework can function as a monitoring system, inferring information such as past landfill temperature profiles, predicting possible heat elevation or smoldering events as new observations are added, and identifying the percentage of each of the four risk categories and how they increase or decrease over the lifetime of the landfill

Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection

We propose a method for detecting significant interactions in very large multivariate spatial point patterns. This methodology develops high dimensional data understanding in the point process setting. The method is based on modelling the patterns using a flexible Gibbs point process model to directly characterise point-to-point interactions at different spatial scales. By using the Gibbs framework significant interactions can also be captured at small scales. Subsequently, the Gibbs point process is fitted using a pseudo-likelihood approximation, and we select significant interactions automatically using the group lasso penalty with this likelihood approximation. Thus we estimate the multivariate interactions stably even in this setting. We demonstrate the feasibility of the method with a simulation study and show its power by applying it to a large and complex rainforest plant population data set of 83 species

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure

Pattern Identification and Analysis in Urban Flows

abstract: Two urban flows are analyzed, one concerned with pollutant transport in a Phoenix, Arizona neighborhood and the other with windshear detection at the Hong Kong International Airport (HKIA). Lagrangian measures, identified with finite-time Lyapunov exponents, are first used to characterize transport patterns of inertial pollutant particles. Motivated by actual events the focus is on flows in realistic urban geometry. Both deterministic and stochastic transport patterns are identified, as inertial Lagrangian coherent structures. For the deterministic case, the organizing structures are well defined and are extracted at different hours of a day to reveal the variability of coherent patterns. For the stochastic case, a random displacement model for fluid particles is formulated, and used to derive the governing equations for inertial particles to examine the change in organizing structures due to ``zeroth-order'' random noise. It is found that, (1) the Langevin equation for inertial particles can be reduced to a random displacement model; (2) using random noise based on inhomogeneous turbulence, whose diffusivity is derived from $k$-$\epsilon$ models, major coherent structures survive to organize local flow patterns and weaker structures are smoothed out due to random motion. A study of three-dimensional Lagrangian coherent structures (LCS) near HKIA is then presented and related to previous developments of two-dimensional (2D) LCS analyses in detecting windshear experienced by landing aircraft. The LCS are contrasted among three independent models and against 2D coherent Doppler light detection and ranging (LIDAR) data. Addition of the velocity information perpendicular to the lidar scanning cone helps solidify flow structures inferred from previous studies; contrast among models reveals the intramodel variability; and comparison with flight data evaluates the performance among models in terms of Lagrangian analyses. It is found that, while the three models and the LIDAR do recover similar features of the windshear experienced by a landing aircraft (along the landing trajectory), their Lagrangian signatures over the entire domain are quite different - a portion of each numerical model captures certain features resembling those LCS extracted from independent 2D LIDAR analyses based on observations. Overall, it was found that the Weather Research and Forecast (WRF) model provides the best agreement with the LIDAR data. Finally, the three-dimensional variational (3DVAR) data assimilation scheme in WRF is used to incorporate the LIDAR line of sight velocity observations into the WRF model forecast at HKIA. Using two different days as test cases, it is found that the LIDAR data can be successfully and consistently assimilated into WRF. Using the updated model forecast LCS are extracted along the LIDAR scanning cone and compare to onboard flight data. It is found that the LCS generated from the updated WRF forecasts are generally better correlated with the windshear experienced by landing aircraft as compared to the LIDAR extracted LCS alone, which suggests that such a data assimilation scheme could be used for the prediction of windshear events.Dissertation/ThesisDoctoral Dissertation Applied Mathematics 201