Evaluation of GPM DPR Rain Parameters with North Taiwan Disdrometers

Balaji Kumar Seela, Jayalakshmi Janapati, Pay Liam Lin, Chen Hau Lan, Mu Qun Huang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h21), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m23 mm21) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual-or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.

Original languageEnglish
Pages (from-to)47-64
Number of pages18
JournalJournal of Hydrometeorology
Volume25
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • In situ atmospheric observations
  • Radars/Radar observations
  • Remote sensing
  • Statistics

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