An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
i

Superseded

This Document Has Been Replaced By:

i

Retired

This Document Has Been Retired

i

Up-to-date Information

This is the latest update:

An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables
  • Published Date:

    2018

  • Source:
    Remote Sensing, 10(8), 26.
Filetype[PDF-8.09 MB]


This document cannot be previewed automatically as it exceeds 5 MB
Please click the thumbnail image to view the document.
An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables
Details:
  • Description:
    This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.
  • Document Type:
  • Main Document Checksum:
  • File Type:
  • Supporting Files:
    No Additional Files
No Related Documents.

You May Also Like: