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...
Published in: | Atmospheric Measurement Techniques |
---|---|
Main Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2018
|
Subjects: | |
Online Access: | https://doi.org/10.5194/amt-11-2735-2018 |
id |
ftcyprusunivt:oai:ktisis.cut.ac.cy:20.500.14279/11862 |
---|---|
record_format |
openpolar |
spelling |
ftcyprusunivt:oai:ktisis.cut.ac.cy:20.500.14279/11862 2024-04-21T07:43:51+00:00 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 2018-05-08 pdf https://doi.org/10.5194/amt-11-2735-2018 en eng Atmospheric Measurement Techniques, 2018, vol. 11, no. 5, pp. 2735-2748 18671381 doi:10.5194/amt-11-2735-2018 2735 2748 open AERONET Calibration Lidar Measurement method Mixing ratio Earth and Related Environmental Sciences Natural Sciences article 2018 ftcyprusunivt https://doi.org/10.5194/amt-11-2735-2018 2024-03-27T01:15:28Z 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 Ktisis Cyprus University of Technology Atmospheric Measurement Techniques 11 5 2735 2748 |
institution |
Open Polar |
collection |
Ktisis Cyprus University of Technology |
op_collection_id |
ftcyprusunivt |
language |
English |
topic |
AERONET Calibration Lidar Measurement method Mixing ratio Earth and Related Environmental Sciences Natural Sciences |
spellingShingle |
AERONET Calibration Lidar Measurement method Mixing ratio Earth and Related Environmental Sciences Natural Sciences 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 Lidar Measurement method Mixing ratio Earth and Related Environmental Sciences Natural Sciences |
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 |
publishDate |
2018 |
url |
https://doi.org/10.5194/amt-11-2735-2018 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
Atmospheric Measurement Techniques, 2018, vol. 11, no. 5, pp. 2735-2748 18671381 doi:10.5194/amt-11-2735-2018 2735 2748 |
op_rights |
open |
op_doi |
https://doi.org/10.5194/amt-11-2735-2018 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
11 |
container_issue |
5 |
container_start_page |
2735 |
op_container_end_page |
2748 |
_version_ |
1796933825034452992 |