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 PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer obse...
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ftleibnizopen:oai:oai.leibnizopen.de:mdJDfYoBNQPDO7WIXGDn 2023-10-09T21:44:13+02:00 Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data Dai, Guangyao Althausen, Dietrich Hofer, Julian Engelmann, Ronny Seifert, Patric Bühl, Johannes Mamouri, Rodanthi-Elisavet Wu, Songhua Ansmann, Albert 2018 application/pdf https://doi.org/10.34657/822 https://oa.tib.eu/renate/handle/123456789/873 eng eng München : European Geopyhsical Union CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ Atmospheric Measurement Techniques, Volume 11, Issue 5, Page 2735-2748 AERONET calibration data set lidar measurement method mixing ratio numerical method observational method photometer precipitable water radiosonde relative humidity temperature profile uncertainty analysis water vapor 550 article Text 2018 ftleibnizopen https://doi.org/10.34657/822 2023-09-10T23:16:47Z 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 PollyXT 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 PollyXT 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 R2 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 (simulation) and ... Article in Journal/Newspaper Aerosol Robotic Network LeibnizOpen (The Leibniz Association) |
institution |
Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
AERONET calibration data set lidar measurement method mixing ratio numerical method observational method photometer precipitable water radiosonde relative humidity temperature profile uncertainty analysis water vapor 550 |
spellingShingle |
AERONET calibration data set lidar measurement method mixing ratio numerical method observational method photometer precipitable water radiosonde relative humidity temperature profile uncertainty analysis water vapor 550 Dai, Guangyao Althausen, Dietrich Hofer, Julian Engelmann, Ronny Seifert, Patric Bühl, Johannes Mamouri, Rodanthi-Elisavet Wu, Songhua Ansmann, Albert Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data |
topic_facet |
AERONET calibration data set lidar measurement method mixing ratio numerical method observational method photometer precipitable water radiosonde relative humidity temperature profile uncertainty analysis water vapor 550 |
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 PollyXT 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 PollyXT 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 R2 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 (simulation) and ... |
format |
Article in Journal/Newspaper |
author |
Dai, Guangyao Althausen, Dietrich Hofer, Julian Engelmann, Ronny Seifert, Patric Bühl, Johannes Mamouri, Rodanthi-Elisavet Wu, Songhua Ansmann, Albert |
author_facet |
Dai, Guangyao Althausen, Dietrich Hofer, Julian Engelmann, Ronny Seifert, Patric Bühl, Johannes Mamouri, Rodanthi-Elisavet Wu, Songhua Ansmann, Albert |
author_sort |
Dai, Guangyao |
title |
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_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_sort |
calibration of raman lidar water vapor profiles by means of aeronet photometer observations and gdas meteorological data |
publisher |
München : European Geopyhsical Union |
publishDate |
2018 |
url |
https://doi.org/10.34657/822 https://oa.tib.eu/renate/handle/123456789/873 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmospheric Measurement Techniques, Volume 11, Issue 5, Page 2735-2748 |
op_rights |
CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.34657/822 |
_version_ |
1779321705307570176 |