Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data
We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer obs...
Published in: | Atmospheric Measurement Techniques |
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Main Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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Copernicus Publications
2018
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Online Access: | https://doi.org/10.5194/amt-11-2735-2018 https://doaj.org/article/eb9d8bb8cc464fa1bdf72d31ba22f483 |
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author | G. Dai D. Althausen J. Hofer R. Engelmann P. Seifert J. Bühl R.-E. Mamouri S. Wu A. Ansmann |
author_facet | G. Dai D. Althausen J. Hofer R. Engelmann P. Seifert J. Bühl R.-E. Mamouri S. Wu A. Ansmann |
author_sort | G. Dai |
collection | Directory of Open Access Journals: DOAJ Articles |
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container_start_page | 2735 |
container_title | Atmospheric Measurement Techniques |
container_volume | 11 |
description | We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the Polly XT Raman lidar is 6.56 g kg −1 ± 0.72 g kg −1 (with a statistical uncertainty of 0.08 g kg −1 and an instrumental uncertainty of 0.72 g kg −1 ). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R 2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS ... |
format | Article in Journal/Newspaper |
genre | Aerosol Robotic Network |
genre_facet | Aerosol Robotic Network |
id | ftdoajarticles:oai:doaj.org/article:eb9d8bb8cc464fa1bdf72d31ba22f483 |
institution | Open Polar |
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op_doi | https://doi.org/10.5194/amt-11-2735-2018 |
op_relation | https://www.atmos-meas-tech.net/11/2735/2018/amt-11-2735-2018.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-11-2735-2018 1867-1381 1867-8548 https://doaj.org/article/eb9d8bb8cc464fa1bdf72d31ba22f483 |
op_source | Atmospheric Measurement Techniques, Vol 11, Pp 2735-2748 (2018) |
publishDate | 2018 |
publisher | Copernicus Publications |
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spelling | ftdoajarticles:oai:doaj.org/article:eb9d8bb8cc464fa1bdf72d31ba22f483 2025-01-16T18:38:48+00:00 Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data G. Dai D. Althausen J. Hofer R. Engelmann P. Seifert J. Bühl R.-E. Mamouri S. Wu A. Ansmann 2018-05-01T00:00:00Z https://doi.org/10.5194/amt-11-2735-2018 https://doaj.org/article/eb9d8bb8cc464fa1bdf72d31ba22f483 EN eng Copernicus Publications https://www.atmos-meas-tech.net/11/2735/2018/amt-11-2735-2018.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-11-2735-2018 1867-1381 1867-8548 https://doaj.org/article/eb9d8bb8cc464fa1bdf72d31ba22f483 Atmospheric Measurement Techniques, Vol 11, Pp 2735-2748 (2018) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2018 ftdoajarticles https://doi.org/10.5194/amt-11-2735-2018 2022-12-31T14:10:54Z We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the Polly XT Raman lidar is 6.56 g kg −1 ± 0.72 g kg −1 (with a statistical uncertainty of 0.08 g kg −1 and an instrumental uncertainty of 0.72 g kg −1 ). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R 2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmospheric Measurement Techniques 11 5 2735 2748 |
spellingShingle | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 G. Dai D. Althausen J. Hofer R. Engelmann P. Seifert J. Bühl R.-E. Mamouri S. Wu A. Ansmann Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title | Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title_full | Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title_fullStr | Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title_full_unstemmed | Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title_short | Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
title_sort | calibration of raman lidar water vapor profiles by means of aeronet photometer observations and gdas meteorological data |
topic | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
topic_facet | Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
url | https://doi.org/10.5194/amt-11-2735-2018 https://doaj.org/article/eb9d8bb8cc464fa1bdf72d31ba22f483 |