Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

Wang, Yadong; Zhang, Jian; Chang, Pao-Liang; Cao, Qing

doi:10.1175/JHM-D-14-0136.1

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Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

2015
By Wang, Yadong ; Zhang, Jian ; Chang, Pao-Liang ; ...
Source: Journal of Hydrometeorology, 16(5), 2230-2247

[PDF-3.44 MB]

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Journal Title:

Journal of Hydrometeorology
Personal Author:

Wang, Yadong ; Zhang, Jian ; Chang, Pao-Liang ; Cao, Qing

Wang, Yadong ; Zhang, Jian ; Chang, Pao-Liang ; Cao, Qing Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)

OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) Less -
Description:

Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.
Keywords:

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Algorithms Data Processing Radars/Radar Observations Satellite Observations
Source:

Journal of Hydrometeorology, 16(5), 2230-2247
DOI:

https://doi.org/10.1175/JHM-D-14-0136.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072
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Submitted
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urn:sha256:d20940432fd877aa31f90cf3a5bbe64a9e7431215030a74c61c3869dd77b32ce
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https://staging-noaa.cdc.gov/view/noaa/32129/noaa_32129_DS1.pdf
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Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

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