Comparison of Cimel Sun-Photometer and Ground-Based Gnss Integrated Water Vapor Over South-Western European Sites

2022 ◽  
Author(s):  
Javier Vaquero-Martínez ◽  
André Felipe Bagorrilha ◽  
Manuel Antón ◽  
Juan Carlos Antuña-Marrero ◽  
Victoria E. Cachorro
Keyword(s):  
Water Vapor ◽  
Sun Photometer ◽  
Western European

Raman Lidar and Sun Photometer Measurements of Aerosols and Water Vapor

1997 ◽  
pp. 23-26 ◽  
Author(s):  
R. A. Ferrare ◽  
S. H. Melfi ◽  
D. Whiteman ◽  
K. D. Evans ◽  
G. Schwemmer ◽  
...  
Keyword(s):  
Water Vapor ◽  
Raman Lidar ◽  
Sun Photometer

scholarly journals Precipitable water vapor characterization in the Gulf of Cadiz region (southwestern Spain) based on Sun photometer, GPS, and radiosonde data

2010 ◽  
Vol 115 (D18) ◽  
Author(s):  
B. Torres ◽  
V. E. Cachorro ◽  
C. Toledano ◽  
J. P. Ortiz de Galisteo ◽  
A. Berjón ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Gulf Of Cadiz ◽  
Gulf Of Cádiz ◽  
Sun Photometer

Total water vapor content retrieval from sun photometer data

2013 ◽  
Vol 26 (4) ◽  
pp. 281-284 ◽  
Author(s):  
K. M. Firsov ◽  
T. Yu. Chesnokova ◽  
E. V. Bobrov ◽  
I. I. Klitochenko
Keyword(s):  
Water Vapor ◽  
Water Vapor Content ◽  
Total Water ◽  
Content Retrieval ◽  
Sun Photometer

Ceilometer, sun photometer and ozonometer measurements of the aerosol optical depth,angstrom coefficients, water vapor and total ozone content over Sofia (Bulgaria)

2013 ◽  
Author(s):  
Nikolay Kolev ◽  
Tsvetina Evgenieva ◽  
Nikolay Miloshev ◽  
Plamen Muhtarov ◽  
Doino Petkov ◽  
...  
Keyword(s):  
Water Vapor ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Total Ozone ◽  
Ozone Content ◽  
Total Ozone Content ◽  
Sun Photometer

scholarly journals Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

2018 ◽  
Vol 11 (5) ◽  
pp. 2735-2748 ◽  
Author(s):  
Guangyao Dai ◽  
Dietrich Althausen ◽  
Julian Hofer ◽  
Ronny Engelmann ◽  
Patric Seifert ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
The Sun ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Pressure Profiles ◽  
Sun Photometer ◽  
Water Vapor Mixing Ratio ◽  
Lidar Observations

Abstract. 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 radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.

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