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Validation of Satellite Radar Snowfa...

Siew, Ian,

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Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations // Ian Siew.
Author:	Siew, Ian,
Description:	1 electronic resource (74 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
Subject:	Meteorology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30693009
ISBN:	9798381410259

Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations /
Siew, Ian,

Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations /Ian Siew. - 1 electronic resource (74 pages)

Source: Masters Abstracts International, Volume: 85-07.

Precise satellite snowfall and rainfall measurements are crucial for the global water budget since there are immense climate and water resource applications. The validation of these satellite measurements is therefore necessary for determining the accuracy of global precipitation. In this study, satellite snowfall from CloudSat's Cloud Profiling Radar (CPR) and rainfall from the Global Precipitation Measurement Mission's (GPM) Dual-Frequency Precipitation Radar (DPR) are validated using ground station measurements from the Global Historical Climatology Network Dataset (GHCND). However, since the GHCND snowfall measurements were in solid form, it was necessary to convert them to their liquid equivalent before satellite comparison. Using a normalized frequency bin method, a relation equation was found between the snow density and the daily average temperature, which is the average of the maximum and minimum daily temperature. This relation equation was then implemented for the satellite snowfall comparison, with results showing a satellite overestimation near 10%. Rainfall satellite validation against GHCND data found a satellite underestimation of 20%. Regional patterns for both snowfall and rainfall exhibited enhanced overestimation and underestimation, respectively. Finally, performing satellite validation of satellite snowfall plus rainfall resulted in an underestimation of close to 5%, which suggests rainfall dominance.

English

ISBN: 9798381410259Subjects--Topical Terms:

542822
Meteorology.
Subjects--Index Terms:

Rainfall dominance

Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations /
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100 1 $a Siew, Ian, $e author. $0 (orcid)0009-0004-9257-5668 $3 3829848
245 1 0 $a Validation of Satellite Radar Snowfall and Rainfall Retrievals by Surface Observations / $c Ian Siew.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (74 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-07.
500 $a Advisors: Liu, Guosheng Committee members: Misra, Vasubandhu; Atwood, Alyssa.
502 $b M.S. $c The Florida State University $d 2023.
520 $a Precise satellite snowfall and rainfall measurements are crucial for the global water budget since there are immense climate and water resource applications. The validation of these satellite measurements is therefore necessary for determining the accuracy of global precipitation. In this study, satellite snowfall from CloudSat's Cloud Profiling Radar (CPR) and rainfall from the Global Precipitation Measurement Mission's (GPM) Dual-Frequency Precipitation Radar (DPR) are validated using ground station measurements from the Global Historical Climatology Network Dataset (GHCND). However, since the GHCND snowfall measurements were in solid form, it was necessary to convert them to their liquid equivalent before satellite comparison. Using a normalized frequency bin method, a relation equation was found between the snow density and the daily average temperature, which is the average of the maximum and minimum daily temperature. This relation equation was then implemented for the satellite snowfall comparison, with results showing a satellite overestimation near 10%. Rainfall satellite validation against GHCND data found a satellite underestimation of 20%. Regional patterns for both snowfall and rainfall exhibited enhanced overestimation and underestimation, respectively. Finally, performing satellite validation of satellite snowfall plus rainfall resulted in an underestimation of close to 5%, which suggests rainfall dominance.
546 $a English
590 $a School code: 0071
650 4 $a Meteorology. $3 542822
650 4 $a Remote sensing. $3 535394
650 4 $a Climate change. $2 bicssc $3 2079509
653 $a Rainfall dominance
653 $a Cloud Profiling Radar
653 $a Dual-Frequency Precipitation Radar
653 $a Rainfall satellite validation
653 $a Rainfall measurements
690 $a 0557
690 $a 0404
690 $a 0799
710 2 $a The Florida State University. $b Earth, Ocean & Atmospheric Science. $e degree granting institution. $3 3816815
720 1 $a Liu, Guosheng $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-07.
790 $a 0071
791 $a M.S.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30693009