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Satellite Observations of Oceanic Hi...

Jones, Spencer R.,

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Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval // Spencer R Jones.
Author:	Jones, Spencer R.,
Description:	1 electronic resource (63 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-06.
Contained By:	Masters Abstracts International86-06.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31559195
ISBN:	9798346869115

Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval /
Jones, Spencer R.,

Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval /Spencer R Jones. - 1 electronic resource (63 pages)

Source: Masters Abstracts International, Volume: 86-06.

The high latitude oceans are problematic for satellite estimations of precipitation due to the high frequency of occurrence of light drizzle and snowfall. Passive microwave radiometric observations are sensitive to integrated cloud water path and provide good sampling for robust statistics but have little skill in distinguishing precipitation onset from cloud water and cloud ice due to a lack of sensitivity to drop sizes when they are small. Spaceborne precipitation radars to date have lacked sensitivity to drizzle, and cloud radars have suffered from both the uncertainties inherent in Z-R relations and poor sampling due to nadir-only scans. This study combines coincident active and passive microwave observations from CloudSat's Cloud Profiling Radar (CPR) and the Advanced Scanning Microwave Radiometer (AMSR2) to resolve cloud and hydrometeor distribution parameters and to force consistency between the two independent sets of coincident observations. Consistency between the radar and radiometer is found by using an optimal estimation (OE) retrieval algorithm, a physics-based technique that simultaneously resolves the most likely atmospheric state given both radar and radiometer observations as well as a priori information.The OE algorithm uncertainties are estimated using a method that attempts to emulate the departure in observation space of retrieved states from the unknown true state. The focus on observational uncertainties and the accuracy obtained by using nondiagonal observational error covariance matrices allows the algorithm both to resolve states that are radiatively consistent and to reduce the level of nonuniqueness found in dealing with passive observations alone. The result is an estimation of drizzle frequency and intensity that are consistent with both the CPR and AMSR2 observations for the high latitude oceans. We find that zonal means of retrieved high-latitude drizzle below 0.25 mm hr-1 from these combined observations (0.263 mm day-1) falls slightly above those of CloudSat estimates (0.244 mm day-1), provided by the 2C-RAIN-PROFILE and 2C-SNOW-PROFILE products (Lebsock 2018; Wood and L'Ecuyer 2018), and far below that of radiometer-only estimates (0.920 mm day-1) provided by GPROF (Kummerow et al. 2015).

English

ISBN: 9798346869115Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

High-latitude drizzle

Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval /
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245 1 0 $a Satellite Observations of Oceanic High-Latitude Drizzle Using a Combined Radar-Radiometer Retrieval / $c Spencer R Jones.
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500 $a Source: Masters Abstracts International, Volume: 86-06.
500 $a Advisors: Kummerow, Christian Committee members: Chiu, Christine; Grassotti, Christopher; Chandrasekaran, Venkatachalam.
502 $b M.S. $c Colorado State University $d 2024.
520 $a The high latitude oceans are problematic for satellite estimations of precipitation due to the high frequency of occurrence of light drizzle and snowfall. Passive microwave radiometric observations are sensitive to integrated cloud water path and provide good sampling for robust statistics but have little skill in distinguishing precipitation onset from cloud water and cloud ice due to a lack of sensitivity to drop sizes when they are small. Spaceborne precipitation radars to date have lacked sensitivity to drizzle, and cloud radars have suffered from both the uncertainties inherent in Z-R relations and poor sampling due to nadir-only scans. This study combines coincident active and passive microwave observations from CloudSat's Cloud Profiling Radar (CPR) and the Advanced Scanning Microwave Radiometer (AMSR2) to resolve cloud and hydrometeor distribution parameters and to force consistency between the two independent sets of coincident observations. Consistency between the radar and radiometer is found by using an optimal estimation (OE) retrieval algorithm, a physics-based technique that simultaneously resolves the most likely atmospheric state given both radar and radiometer observations as well as a priori information.The OE algorithm uncertainties are estimated using a method that attempts to emulate the departure in observation space of retrieved states from the unknown true state. The focus on observational uncertainties and the accuracy obtained by using nondiagonal observational error covariance matrices allows the algorithm both to resolve states that are radiatively consistent and to reduce the level of nonuniqueness found in dealing with passive observations alone. The result is an estimation of drizzle frequency and intensity that are consistent with both the CPR and AMSR2 observations for the high latitude oceans. We find that zonal means of retrieved high-latitude drizzle below 0.25 mm hr-1 from these combined observations (0.263 mm day-1) falls slightly above those of CloudSat estimates (0.244 mm day-1), provided by the 2C-RAIN-PROFILE and 2C-SNOW-PROFILE products (Lebsock 2018; Wood and L'Ecuyer 2018), and far below that of radiometer-only estimates (0.920 mm day-1) provided by GPROF (Kummerow et al. 2015).
546 $a English
590 $a School code: 0053
650 4 $a Remote sensing. $3 535394
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Meteorology. $3 542822
650 4 $a Atmospheric sciences. $3 3168354
653 $a High-latitude drizzle
653 $a Ocean
653 $a Precipitation
653 $a Satellite
653 $a Optimal estimation
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690 $a 0404
690 $a 0557
690 $a 0725
710 2 $a Colorado State University. $b Atmospheric Science. $e degree granting institution. $3 3816976
720 1 $a Kummerow, Christian $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-06.
790 $a 0053
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31559195