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Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

Haris Akram Bhatti1,2, Tom Rientjes3, Alemseged Tamiru Haile4

  • 1Department of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, Enschede 7514 AE, The Netherlands. bhatti@neduet.edu.pk.

Sensors (Basel, Switzerland)
|June 18, 2016
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Summary
This summary is machine-generated.

Satellite rainfall estimates from NOAA

Keywords:
CMORPHGilgel Abbeybias factoroptimum window sizesatellite rainfall correction

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Area of Science:

  • Hydrology
  • Remote Sensing
  • Environmental Science

Background:

  • Satellite-based rainfall estimates are increasingly used in hydrology for rainfall-runoff modeling.
  • These estimates are prone to errors, necessitating bias correction for accurate hydrological applications.
  • The National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) is a high-resolution satellite rainfall product.

Purpose of the Study:

  • To assess the bias in CMORPH satellite rainfall estimates in the Gilgel Abbey catchment, Ethiopia.
  • To identify the optimal window size for bias correction of CMORPH data.
  • To evaluate the effectiveness of a proposed bias correction method for satellite rainfall.

Main Methods:

  • Aggregated high-resolution (8 km-30 min) CMORPH data to daily resolution for 2003-2010.
  • Calculated bias correction factors using sequential windows (SW) of 3 to 31 days.
  • Spatially interpolated station-based bias factors and applied them to CMORPH estimates.

Main Results:

  • Confirmed the presence of bias in CMORPH satellite rainfall estimates.
  • The 7-day sequential window (SW) approach demonstrated the best performance for bias correction.
  • The bias correction method significantly improved the accuracy of rainfall estimates.

Conclusions:

  • Bias correction is essential for improving the reliability of satellite rainfall data in hydrological modeling.
  • The 7-day sequential window bias correction method is effective for CMORPH data.
  • The study validates a practical approach for enhancing satellite rainfall product accuracy.