Precipitation data merging using general linear regression

Anish C. Turlapaty; Nicolas H. Younan; Valentine Anantharaj

doi:10.1109/IGARSS.2009.5417769

Precipitation data merging using general linear regression

Anish C. Turlapaty, Nicolas H. Younan, Valentine Anantharaj

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.

Original language	English
Title of host publication	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
Pages	III259-III262
DOIs	https://doi.org/10.1109/IGARSS.2009.5417769
State	Published - 2009
Externally published	Yes
Event	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa Duration: Jul 12 2009 → Jul 17 2009

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	3

Conference

Conference	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/Territory	South Africa
City	Cape Town
Period	07/12/09 → 07/17/09

Keywords

Data merging
Error propagation
Linear regression
Precipitation

Access to Document

10.1109/IGARSS.2009.5417769

Cite this

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title = "Precipitation data merging using general linear regression",

abstract = "Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.",

keywords = "Data merging, Error propagation, Linear regression, Precipitation",

author = "Turlapaty, {Anish C.} and Younan, {Nicolas H.} and Valentine Anantharaj",

year = "2009",

doi = "10.1109/IGARSS.2009.5417769",

language = "English",

isbn = "9781424433957",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

pages = "III259--III262",

booktitle = "2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings",

note = "2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 ; Conference date: 12-07-2009 Through 17-07-2009",

}

Turlapaty, AC, Younan, NH & Anantharaj, V 2009, Precipitation data merging using general linear regression. in 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings., 5417769, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 3, pp. III259-III262, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Cape Town, South Africa, 07/12/09. https://doi.org/10.1109/IGARSS.2009.5417769

Precipitation data merging using general linear regression. / Turlapaty, Anish C.; Younan, Nicolas H.; Anantharaj, Valentine.
2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings. 2009. p. III259-III262 5417769 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Anantharaj, Valentine

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N2 - Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.

AB - Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.

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KW - Error propagation

KW - Linear regression

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M3 - Conference contribution

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BT - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings

T2 - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

Y2 - 12 July 2009 through 17 July 2009

ER -

Precipitation data merging using general linear regression

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Keywords

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