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Precipitation is a dominating quantity in microwave radiometry. The large emission and scattering signals of rain and ice, respectively, introduce large contributions to the measured brightness temperature. While this allows for accurate sensing of precipitation, it also results in degraded performance when retrieving other geophysical parameters, such as near-surface ocean winds. In particular, the retrieval of wind direction requires precise knowledge of polarization, and nonspherical particles can result in a change in the polarization of incident radiation. The aim of this dissertation is to investigate the polarizing effects of precipitation in the atmosphere, including the existence of a precipitation signal in the third Stokes parameter, and compare these effects with the current sensitivities of passive wind vector retrieval algorithms. Realistic simulated precipitation profiles give hydrometeor water contents which are input into a vector radiative transfer model. Brightness temperatures are produced within the model using a reverse Monte Carlo method. Results are produced at three frequencies of interest to microwave polarimetry, 10.7 GHz, 18.7 GHz, and 37.0 GHz, for the first 3 components of the Stokes vector.

For some time now, there has been a growing awareness in society about climate change, pollution, energy and the use of natural resources. This thinking has permeated society, mainly because the extreme natural phenomena that we are experiencing nowadays are no longer outliers in our time series of meteorological records. In this regard, it has been proven that the actual high temperatures are not only unparalleled, but also consistent around the globe which is something that had not happened until now (Neukom et al., 2019). The XX century was a turning point when it comes to the increase of the landuse for crops. In a context where the population doubled, the crop production for food from 1960 to 2010 tripled, helping to reduce the hungry population. When the world’s population is expected to continue to grow up to 9 billion people (Goodfray et al., 2010) by middle XXI century, it is essential to provide ourselves with the necessary tools to maximise crop production by taking advantage of all the resources available under a sustainable point of view. Under this context, all farmers in the European Union (EU) have the possibility to benefit from the Common Agricultural Policy (CAP), which came into force in 1960. The CAP is responsible for the financing of aid to farmers on a cross-compliance basis, based on the declaration of crop types. Traditionally, the authorities have checked the veracity of declarations in person through field inspections, which is clearly a highly inefficient, impractical and very expensive system. However, in 2018 the European Commission drafted an amendment to the CAP (European Commission, 2018), to be implemented in 2020, recommending the establishment of newprocedures for checking declarations, including the use of satellite data from the Copernicus programme or other new technologies. Among the various satellite technologies, Synthetic Aperture Radar (SAR) (Brown,1967; Curlander and McDonough, 1992) has proven the most reliable,as the images are acquired with a constant pass period and they are not subject to cloud problems (as is the case with sensors working in the optical domain) and information can be acquired both day and night. They are based in a SAR microwave sensor installed on a satellite platform with a forward trajectory which offers side-looking imaging geometries. Working in a range between 300 MHz and 30 GHz, the SAR sensor is in charge of emitting electromagnetic pulses and receiving the resulting echoes from the imaged target, which can help retrieve information about its dielectric properties, geometry, orientation, shape, and its behaviour along time. For a given target, the SAR backscattering response σ0 is function of many parameters (Lee and Pottier, 2017; Dobson et al., 1985): wave frequency, polarisation, imaging configuration, roughness, geometrical structure and dielectric properties. This makes the information extraction a major problem, as identical radar responses from two different targets may lead to the same result. To cope with this problem, the main techniques are based on extending the observation space by working with the full diversity of data. Thus, the main axes of SAR data are: • Time • Polarimetry • Interferometry • Frequency. Time series of radar data constitutes a major source of information for the classification of crops and land cover, since it makes it possible to distinguish between classes by their temporal behaviour: some land covers show a uniform response along time (e.g. urban areas), whereas there are others subject to seasonal changes (e.g. crops). It may happen that different crop species give the same radar response at a given time, however, when the time window becomes larger, and consecutive acquisitions are taken over a shorter time span, the more one can detect abrupt changes in the target over a longer time interval. Polarimetry is sensitive to the shape, orientation and the scattering mechanisms of the scatterers (Boerner et al.,1981; Zyl, Zebker, and Elachi, 1987). In that sense, when using different polarisations it is possible to discern better the true nature of the target, as some features may be visible in one polarisation but not in the others. Regarding multi-spectral data, it also constitutes a major source of information which can be exploited for classification purposes. Working with sensors operating at different frequencies, or wavelengths, provides diversity in the size of the elements of the scene to which the radar is sensitive as the radar backscattering will come from elements the size of the wavelength used it. For all of the above, multifrequency data provide complementary information, as each frequency operates and interacts with elements of the same wavelength or longer, and being transparent to all others. In addition, different bands are also associated with different spatial resolutions, so a high-frequency sensor can complement the classification performance of a low-frequency sensor when there are sufficiently small details in the scene that cannot be appreciated with the spatial resolution available at the lower frequency. From all the 4 axes exposed above, Interferometry (Graham, 1974) is without a doubt the least exploited for classification purposes. While polarimetry is sensitive to the scattering mechanisms of the scene by means of the polarisation information, interferometry adds the third dimension by being sensitive to the spatial distribution of the scatterers (Treuhaft et al., 1996). Coherence and phase difference computed between two complex-valued SAR images are the main descriptors of interferometry (Bamler and Hartl, 1998), and together, can be used to derive topographic information, vegetation structure, and deformation (volcanoes, landslides, etc.). For this reason, interferometry is especially suited for classification of covers in which there is vertical distribution of elements, e.g. urban areas and vegetation (forests and crops). Polarimetric interferometric SAR (PolInSAR) (Cloude and Papathanassiou, 1998; Treuhaft and Cloude, 1999), constitutes the next step forward, and is based on the application of interferometry to all polarisation channels. Polarimetry can identify the different scattering mechanisms in the scene by using the polarisation information, whilst interferometry is able to locate the effective scattering phase centres, which are mainly dependent on frequency, the polarisation employed, the physical, geometrical structure and orientation of the scatterer. By using the combination of both we can retrieve the vertical structure of the scene, which shows a great potential for classification purposes, since classes characterised by similar backscattering or polarimetric responses can be separated if their heights are different (e.g. types of buildings, forests, crops, etc.), whereas classes with similar heights, and hence similar interferometric coherence values (e.g. grass, crops, bare soil, etc.) can be resolved using their polarimetric response. In summary, PolInSAR-based classification is attractive since polarimetric ambiguities are resolved by interferometric information and vice-versa. The lack of exploitation of the 4 data axes in the literature, plus the arrival of a new generation of SAR sensors in the near future such as ROSE-L, BIOMASS and NISAR among others, offers a new range of possibilities in terms of new types of features for classification whose results and impact must be analysed. In this context, there are many types of SAR data (i.e. features) that have not been used yet, acquired from different sensors (Sentinel-1, PAZ, TanDEMX, TerraSAR-X and ALOS-2), and whose diversity axes, either used individually or jointly, have not yet been explored for classification applications. Therefore, the exploration of these new types of SAR data, whose contribution to classification is unknown regarding crop-type mapping, is the main objective of this doctoral thesis, and consequently also its main novelty. Based on the current state of the art of the research topic the main objective of this PhD thesis is to explore the added value of new SAR features, and their potential, alone or used together, for crop type and land cover classification. In the end, several experiments will be carried out, in different test sites, in which the proposed new features will be evaluated and compared with the traditional observables used so far, with the aim of evaluating their internal potential in classification applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante UAFPU20-08.

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<p>  </p> <p>Rapid intensification of low-level rotation in non-classic tornadic storms in southeastern United States, often at time scales shorter than the volume updates from existing opera- tional radars, calls for a deeper understanding of storm-scale processes. There is growing evidence that the highly nonlinear interactions between vertical wind shear and cold pools regulate the intensity of downdrafts, low- and mid-level updrafts, and thus tornadic poten- tial in supercells. Tornado-strength circulations are more likely associated with cold pools of intermediate strength. The microphysical pathway leading to storm electrification also plays a major role in the regulation of cold pool intensity. Storm electrification and subsequent lightning initiation are a by-product of charging of ice hydrometeors in the mixed-phase updrafts. Lightning flashes frequently initiate along the periphery of turbulent updrafts and total flash rate is controlled by the updraft speed and volume.</p> <p><br></p> <p>In the first part of this work, polarimetric fingerprints like ZDR and KDP columns (proxies for mixed-phase updraft strength) are objectively identified to track rapid fluctuations in updraft intensity. We quantify the volume of ZDR and KDP columns to evaluate their utility in predicting temporal variability in lightning flash characteristics and the onset of severe weather. Using observational data from KTLX radar and Oklahoma Lightning Mapping Array, we had previously found evidence of temporal covariance between ZDR column volume and the total lightning flash rate in a tornadic supercell in Oklahoma. </p> <p><br></p> <p> Here, we extend our analysis to three high-shear low-CAPE (HSLC) cases observed during the 2016-17 VORTEX-SE field campaign in Northern Alabama. In all three scenarios (one tornadic and one nontornadic supercell, and a quasi-linear convective system), the KDP column volume had a stronger correlation with total flash rates than the ZDR column volume. We also found that all three storms maintained a normal tripole charge structure, with majority of the cloud-to-ground (CG) strikes lowering negative charge to the ground. The tornadic storm’s CG polarity changed from negative to positive at the same time it entered a region with higher surface equivalent potential temperature. In contrast to the Oklahoma storm, lightning flash initiations in HSLC storms occurred primarily outside the footprint of ZDR and KDP column objects.</p> <p><br></p> <p>Storm dynamics coupled with microphysical processes such as diabatic heating/cooling and advection/sedimentation of hydrometeors also plays a significant role in electrification of thunderstorms. Simulation of deep convection, therefore, needs to account for the feedback of microphysics to storm dynamics. In the second part of this work, the NSSL microphysics scheme is used to simulate ice mass fluxes, cold pool intensity, and noninductive charging rates. The scheme is run in its triple-moment configuration in order to provide a more realis- tic size-sorting process that avoids pathologies that arise in double-moment representations.</p> <p><br></p> <p>We examine the possible tertiary linkage between noninductive charging rates and cold pool through their dependence on mixed-phase microphysical processes. The Advanced Re- gional Prediction System (ARPS) model is used to simulate the same three HSLC cases from VORTEX-SE 2016-17 IOPs. WSR-88D radar reflectivity and Doppler velocity observations are assimilated in a 40-member ensemble using an ensemble Kalman filter (EnKF) filter.</p> <p><br></p> <p>In all three cases, the simulated charge separation is consistent with the observed normal tripole. Greater updraft mass flux, supercooled liquid water concentration, and nonprecip- itation mass flux explain the nontornadic supercell’s higher total flash rate compared to the tornadic supercell. Positive and negative graupel charging rates were found to have the greatest linear correlation with updraft mass flux, followed by precipitation mass flux in all three cases. At zero time lag, horizontal buoyancy gradients associated with a surface cold pool were not found to be correlated with either the charging rates or the updraft and precipitation mass flux. Total flash rate based on empirical relationships between simulated ice mass fluxes was lower than the observed values.</p>

Jefferson Lab's cutting-edge parity-violating electron scattering program has increasingly stringent requirements for systematic errors. Beam polarimetry is often one of the dominant systematic errors in these experiments. A new Moeller Polarimeter in Hall A of Jefferson Lab (JLab) was installed in 2015 and has taken first measurements for a polarized scattering experiment. Upcoming parity violation experiments in Hall A include CREX, PREX-II, MOLLER and SOLID with the latter two requiring &lt; 0.5% precision on beam polarization measurements, a precision which has not been achieved to date. The polarimeter measures the Moeller scattering rates of the polarized electron beam incident upon an iron target placed in a saturating magnetic field. The spectrometer consists of four quadrupoles and one momentum selection dipole. The detector is designed to measure the scattered and knock out target electrons in coincidence. Beam polarization is extracted by constructing an asymmetry from the scattering rates when the incident electron spin is parallel and anti-parallel to the target electron spin. The largest systematic errors associated with Moeller polarimetry comes from the precision that the target polarization and the detector acceptance is known will be discussed. Other errors including the Levchuk effect, beam stability, and target heating will be addressed. / Physics