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The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements. / Biryukov, E. Yu.; Kostsov, V. S.

In: Atmospheric and Oceanic Optics, Vol. 32, No. 5, 2019, p. 569-577.

Research output: Contribution to journal › Article › peer-review

Harvard

Biryukov, EY & Kostsov, VS 2019, 'The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements', Atmospheric and Oceanic Optics, vol. 32, no. 5, pp. 569-577. https://doi.org/10.1134/S1024856019050051

APA

Biryukov, E. Y., & Kostsov, V. S. (2019). The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements. Atmospheric and Oceanic Optics, 32(5), 569-577. https://doi.org/10.1134/S1024856019050051

Vancouver

Biryukov EY, Kostsov VS. The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements. Atmospheric and Oceanic Optics. 2019;32(5):569-577. https://doi.org/10.1134/S1024856019050051

Author

Biryukov, E. Yu. ; Kostsov, V. S. / The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements. In: Atmospheric and Oceanic Optics. 2019 ; Vol. 32, No. 5. pp. 569-577.

BibTeX

@article{d771409b9be545d4928c4e679e4fcdde,
title = "The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements",
abstract = "Estimates of the error in determining the cloud liquid water path by the multiple linear regression (MLR) technique using different regression relations obtained both by model calculations and by experimental data (for reference, results of the method based on the inversion of the radiative transfer equation were taken) are presented. It is shown that if the MLR method is trained by experimental data and measurements in seven spectral channels of the radiometer, the random component of the liquid water path error in the cloud is 0.015–0.017 kg/m2, which is half that obtained when trained by the results of model calculations. The cloud liquid water path bias does not exceed 0.005 kg/m2. The MLR results allow one to reliably identify periods of clear sky by the criterion of the minimum variance of the water content.",
keywords = "cloud liquid water path, inverse problems, linear regression, microwave radiometer, remote sensing, troposphere, VAPOR, RADIOMETER, LIQUID WATER",
author = "Biryukov, {E. Yu.} and Kostsov, {V. S.}",
year = "2019",
doi = "10.1134/S1024856019050051",
language = "English",
volume = "32",
pages = "569--577",
journal = "Atmospheric and Oceanic Optics",
issn = "1024-8560",
publisher = "Springer Nature",
number = "5",

}

RIS

TY - JOUR

T1 - The Use of Linear Regression Relations Derived from Model and Experimental Data for Retrieval of the Water Content of Clouds from Ground-Based Microwave Measurements

AU - Biryukov, E. Yu.

AU - Kostsov, V. S.

PY - 2019

Y1 - 2019

N2 - Estimates of the error in determining the cloud liquid water path by the multiple linear regression (MLR) technique using different regression relations obtained both by model calculations and by experimental data (for reference, results of the method based on the inversion of the radiative transfer equation were taken) are presented. It is shown that if the MLR method is trained by experimental data and measurements in seven spectral channels of the radiometer, the random component of the liquid water path error in the cloud is 0.015–0.017 kg/m2, which is half that obtained when trained by the results of model calculations. The cloud liquid water path bias does not exceed 0.005 kg/m2. The MLR results allow one to reliably identify periods of clear sky by the criterion of the minimum variance of the water content.

AB - Estimates of the error in determining the cloud liquid water path by the multiple linear regression (MLR) technique using different regression relations obtained both by model calculations and by experimental data (for reference, results of the method based on the inversion of the radiative transfer equation were taken) are presented. It is shown that if the MLR method is trained by experimental data and measurements in seven spectral channels of the radiometer, the random component of the liquid water path error in the cloud is 0.015–0.017 kg/m2, which is half that obtained when trained by the results of model calculations. The cloud liquid water path bias does not exceed 0.005 kg/m2. The MLR results allow one to reliably identify periods of clear sky by the criterion of the minimum variance of the water content.

KW - cloud liquid water path

KW - inverse problems

KW - linear regression

KW - microwave radiometer

KW - remote sensing

KW - troposphere

KW - VAPOR

KW - RADIOMETER

KW - LIQUID WATER

UR - http://www.scopus.com/inward/record.url?scp=85073615997&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/linear-regression-relations-derived-model-experimental-data-retrieval-water-content-clouds-groundbas

U2 - 10.1134/S1024856019050051

DO - 10.1134/S1024856019050051

M3 - Article

AN - SCOPUS:85073615997

VL - 32

SP - 569

EP - 577

JO - Atmospheric and Oceanic Optics

JF - Atmospheric and Oceanic Optics

SN - 1024-8560

IS - 5

ER -

ID: 48640860