Lidar and S-band radar profiling of the atmosphere: adaptive processing for boundary-layer monitoring, optical-parameter error estimation, and application cases

• Autores: Diego Lange Vega
• Directores de la Tesis: Francisco Rocadenbosch Burillo (dir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2014
• Idioma: inglés
• Tribunal Calificador de la Tesis: Agustín Sánchez-Arcilla Conejo (presid.), Alberto Aguasca Sole (secret.), Oriol Jorba Casella (voc.), Stephen J. Frasier (voc.), Doina Nicoleta Nicolae (voc.)
• Materias:
  • Matemáticas
    • Análisis numérico
      • Construcción de algoritmos
  • Ciencias de la tierra y del espacio
    • Ciencias de la atmósfera
      • Óptica atmosférica
    • Meteorología
      • Meteorología por radar
  • Ciencias tecnológicas
    • Tecnología electrónica
      • Dispositivos láser
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • This Ph.D. thesis addresses remote sensing of the atmosphere by means of lidar and S-band clear-air weather radar, and related data signal processing. Active remote sensing by means of these instruments offers unprecedented capabilities of spatial and temporal resolutions for vertical atmospheric profiling and the retrieval of key optical and physical atmospheric products in an increasing environmental regulatory framework. The first goal is this Ph.D. concerns the estimation of error bounds in the inversion of the profile of the atmospheric backscatter coefficient from elastic lidar signals (i.e., without wavelength shift in reception when interacting with atmospheric scatterers) by means of the two-component inversion algorithm (the so-called Klett-Fernald-Sasano¿s algorithm). This objective departs from previous works at the Remote Sensing Lab. (RSLab) of the Universitat Politècnica de Catalunya (UPC) and derives first-order error-propagated bounds (approximate) and total-increment bounds (exact). As distinctive feature in the state of the art, the error bounds merge into a single body both systematic (i.e., user-calibration inputs) and random error sources (finite signal-to-noise ratio, SNR) yielding an explicit mathematical form. The second goal, central to this Ph.D., tackles retrieval of the Atmospheric Boundary Layer Height (ABLH) from elastic lidar and S-band Frequency-Modulated Continuous-Wave (FMCW) radar observations by using adaptive techniques based on the Extended Kalman Filter (EKF). The filter is based on morphological modelling of the Mixing-Layer-to-Free-Troposphere transition and continuous estimation of the noise covariance information. In the lidar-EKF realization the proposed technique is shown to outperform classic ABLH estimators such as those based on derivative techniques, thresholded decision, or the variance centroid method. The EKF formulation is applied to both ceilometer and UPC lidar records in high- and low-SNR scenes. The lidar-EKF approach is re-formulated and successfully extended to S-band radar scenes (Bragg¿s scattering) in presence of interferent noise sources (Rayleigh scattering from e.g., insects and birds). In this context, the FMCW feature enables the range-resolved capability. EKF-lidar and EKF-radar ABLH estimates are cross-examined from field campaign results. Finally, the third goal deals with exploitation of the existing UPC lidar station: In a first introductory part, a modified algorithm for enhancing the dynamic range of elastic lidar channels by ¿gluing¿ analog and photon-counting data records is formulated. In a second part, two case examples (including application of the gluing algorithm) are presented to illustrate the capabilities of the UPC lidar in networked atmospheric observation of two recent volcano eruption events as part of the EARLINET (European Aerosol Research Lidar Network). The latter is part of GALION (Global Atmospheric Watch Atmospheric Lidar Observation Network)-GEOSS (Global Earth Observation System of Systems) framework.