Reduced major axis approach for correcting GPM/GMI radiometric biases to coincide with radiative transfer simulation

Tanvir Islam, Prashant K. Srivastava, George P. Petropoulos, Sudhir K. Singh

Research output: Contribution to journalArticlepeer-review

4 Citations (SciVal)
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Abstract

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm – RARMA (Radiometric Adjustment using Reduced Major Axis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer model. The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach. NOAA’s Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission’s GPM Microwave Imager (GMI). Experimental results demonstrate that radiometric biases are apparent in the GMI instrument. The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm. The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model, with the help of a microwave emissivity model (FASTEM-5). Document embargo 04/09/2016.
Original languageEnglish
Pages (from-to)40-45
Number of pages6
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Volume168
Early online date04 Sept 2015
DOIs
Publication statusPublished - 01 Jan 2016

Keywords

  • bias correction and estimation
  • radiometric adjustment
  • radiative transfer model
  • radiance assimilation
  • systematic error
  • satellite calibration
  • global precipitation measurement (GPM)

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