Naïve Bayesian Precipitation Type Retrieval from Satellite Using a Cloud-Top and Ground-Radar Matched Climatology

Grams, Heather M.; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.

doi:10.1175/JHM-D-16-0058.1

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Naïve Bayesian Precipitation Type Retrieval from Satellite Using a Cloud-Top and Ground-Radar Matched Climatology

2016
By Grams, Heather M. ; Kirstetter, Pierre-Emmanuel ; Gourley, Jonathan J.
Source: Journal of Hydrometeorology, 17(10), 2649-2665

[PDF-3.06 MB]

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Journal Title:

Journal of Hydrometeorology
Personal Author:

Grams, Heather M. ; Kirstetter, Pierre-Emmanuel ; Gourley, Jonathan J.

Grams, Heather M. ; Kirstetter, Pierre-Emmanuel ; Gourley, Jonathan J. 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:

Satellite-based precipitation estimates are a vital resource for hydrologic applications in data-sparse regions of the world, particularly at daily or longer time scales. With the launch of a new generation of high-resolution imagers on geostationary platforms such as the Geostationary Operational Environmental Satellite series R (GOES-R), an opportunity exists to advance the detection and estimation of flash-flood-scale precipitation events from space beyond what is currently available. Because visible and infrared sensors can only observe cloud-top properties, many visible- and infrared-band-based rainfall algorithms attempt to first classify clouds before deriving a rain rate. This study uses a 2-yr database of cloud-top properties from proxy Advanced Baseline Imager radiances from GOES-R matched to surface precipitation types from the Multi-Radar Multi-Sensor (MRMS) system to develop a naïve Bayesian precipitation type classifier for the four major types of precipitation in MRMS: stratiform, convective, tropical, and hail. Evaluation of the naïve Bayesian precipitation type product showed a bias toward classifying convective and stratiform at the expense of tropical and hail. The tropical and hail classes in MRMS are derived based on the vertical structure and magnitude of radar reflectivity, which may not translate to an obvious signal at cloud top for a satellite-based algorithm. However, the satellite-based product correctly classified the hail areas as being convective in nature for the vast majority of missed hail events.
Keywords:

[+]

Bayesian Methods Classification Clouds Precipitation Remote Sensing Satellite Observations
Source:

Journal of Hydrometeorology, 17(10), 2649-2665
DOI:

https://doi.org/10.1175/JHM-D-16-0058.1
Document Type:

Journal Article
Funding:

Grant no. NA11OAR4320072
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:ccf5046cc3750d215500173490b9dfcbd439eb25c07afdd79f0b5734c3ad7940
Download URL:

https://repository.library.noaa.gov/view/noaa/32146/noaa_32146_DS1.pdf
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Naïve Bayesian Precipitation Type Retrieval from Satellite Using a Cloud-Top and Ground-Radar Matched Climatology

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