Advances in numerical analysis of precipitation remote sensing with polarimetric radar

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Tanvir Islam",
  title =        "Advances in numerical analysis of precipitation remote
                 sensing with polarimetric radar",
  school =       "Civil Engineering, University of Bristol",
  year =         "2012",
  address =      "UK",
  note =         "University Prize for Best Thesis in Faculty of
                 Engineering in 2012-13",
  keywords =     "genetic algorithms, genetic programming, polarimetric
                 radar, dual polarisation radar, microphysics of
                 precipitation, drop size distribution (DSD), clutter
                 and anomalous propagation identification, attenuation
                 correction, rainfall estimators, microphysical DSD
                 retrievals, melting layer and bright band detection,
                 hydrometeor classification",
  broken =       "",
  URL =          "",
  abstract =     "Since the early use of ground radar for precipitation
                 detection in post-world war II, the radar has evolved
                 on its own in precipitation remote sensing research and
                 applications. The recent advances in radar remote
                 sensing is, the development of polarimetric radar, also
                 known as dual polarization radar, which has the
                 capability of transmitting electromagnetic spectra in
                 both horizontal (H) and vertical (V) polarisation
                 states, thus providing additional information of the
                 target precipitation particles by measuring
                 polarimetric signatures, the reflectivity factor at H
                 polarisation (ZH) , differential reflectivity (ZDR) ,
                 differential propagation phase (Delta Phi DP) ,
                 specific differential phase (KDP) , cross-correlation
                 coefficient (PHV) and linear depolarization ratio
                 (LDR). In commensurate with new era in precipitation
                 remote sensing, this thesis explores the potential of
                 polarimetric radar on the improvements in precipitation
                 remote sensing in the UK context. All major area of the
                 improvements aided by the polarimetry and polarimetric
                 signatures are addressed. These include the clutter and
                 anomalous propagation identification, attenuation
                 signal correction, polarimetric rainfall estimators,
                 drop size distribution retrievals, bright band/melting
                 layer recognition and hydrometeor classification.
                 Several novel approaches and investigations dealing
                 with the polarimetric improvements are scrutinised and
                 proposed in terms of numerical analysis, while some of
                 them employ artificial intelligence (AI) techniques.
                 Key original contributions in synergy with polarimetric
                 radar signatures on precipitation remote sensing are:
                 1) long-term disdrometer DSD analysis to support the
                 development of polarimetry based algorithms and models,
                 2) the use of several AI techniques such as support
                 vector machine, artificial neural network, decision
                 tree, and nearest neighbour system for clutter
                 identification, 3) the sensitivity associated with
                 total differential propagation phase constraint (delta
                 phi DP) on ZH correction for attenuation, 4) the
                 exploration of polarimetric rainfall estimators [R(ZH,
                 ZDR, Knp)] for rainfall estimation, 5) a genetic
                 programming approach for drop size distribution
                 retrievals [Do(ZH, ZDR) , Nw(ZH, ZDR, Do), mu(ZH, ZDR,
                 Do)], and its use for convective/stratiform rain
                 indexing, and 6) a fuzzy logic based system for
                 automatic melting layer/bright band recognition and
                 hydrometeor classification as well as appraisal with a
                 numerical weather prediction (NWP) model and radio
                 soundings observations. In fact, the radar polarimetry
                 has been proved not only to improve data quality and
                 precipitation estimation, but also characterising the
                 precipitation particles, thus has a great potential on
                 fostering the precipitation remote sensing research and
  notes =        "EThOS Persistent ID:",

Genetic Programming entries for Tanvir Islam