scholarly journals Comparisons of Raman Lidar Measurements of Tropospheric Water Vapor Profiles with Radiosondes, Hygrometers on the Meteorological Observation Tower, and GPS at Tsukuba, Japan

2007 ◽  
Vol 24 (8) ◽  
pp. 1407-1423 ◽  
Author(s):  
Tetsu Sakai ◽  
Tomohiro Nagai ◽  
Masahisa Nakazato ◽  
Takatsugu Matsumura ◽  
Narihiro Orikasa ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Temporal Variations ◽  
Radiosonde Data ◽  
Calibration Coefficient ◽  
Lidar Data ◽  
Raman Lidar ◽  
Meteorological Observation ◽  
Meteorological Research Institute ◽  
Water Vapor Mixing Ratio

Abstract The vertical distribution profiles of the water vapor mixing ratio (w) were measured by Raman lidar at the Meteorological Research Institute, Japan, during the period from 2000 to 2004. The measured values were compared with those obtained with radiosondes, hygrometers on a meteorological observation tower, and global positioning system (GPS) antennas near the lidar site. The values of w obtained with the lidar were lower than those obtained with the corrected Meisei RS2-91 radiosonde by 1.2% on average and higher than those obtained with the corrected Vaisala RS80-A radiosonde by 17% for w ≥ 0.5 g kg−1. The lidar data were higher than those radiosondes’ data by 19% or 33% for w < 0.5 g kg−1. The vertical variations of w obtained with the lidar differed from those obtained with the Meisei RS-01G radiosonde and Meteolabor Snow White radiosonde by 5% on average for w ≥ 0.5 g kg−1. The lidar data were lower than those radiosondes’ data by 37% or 39% for w < 0.5 g kg−1. The temporal variations of w obtained with the lidar and the hygrometers on the meteorological tower agreed to within 0.4% at a height of 213 m, although the absolute values differed systematically by 9%–14% due to the incomplete overlap of the laser beam and the receiver’s field of view at heights between 50 and 150 m. The precipitable water vapor obtained with the lidar indicated a mean positive bias of 2 mm (9%–11%) relative to those obtained with GPS. The lidar water vapor calibration coefficient that was calculated using RS2-91 radiosonde data varied by 11% during an 18-month period. Therefore, it is necessary to develop an accurate, yet convenient, method for determining the calibration coefficient for the use of the lidar.

Calibration of water vapor Raman lidar measurements by use of radiosonde and microwave radiometer measurements

2021 ◽  
Author(s):  
Dietrich Althausen ◽  
Clara Seidel ◽  
Ronny Engelmann ◽  
Hannes Griesche ◽  
Martin Radenz ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
The Arctic ◽  
Radiosonde Data ◽  
Lidar Data ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Vertical Column ◽  
Linear Fit ◽  
Water Vapor Mixing Ratio

<p>Water vapor profiles with high vertical and temporal resolution were determined by use of the Raman lidar PollyXT within the MOSAiC campaign in the Arctic during the winter time 2019 – 2020. These measurements need a calibration. Usually, radiosonde data are utilized to calibrate the lidar data by the profile or the linear fit method, respectively. The radiosonde is drifting with the wind; thus, it is often measuring different atmospheric volumes compared to the lidar observations.</p> <p>The period 5-7 February 2020 is used to demonstrate the results. The correlation coefficient of the linear fit between the radiosonde and the lidar data varies with the different atmospheric conditions. The calibration results from the profile method coincide with those of the linear fit method, but the selection of the appropriate calibration setup is not straightforward. The varying correlation of the calibration results is attributed to the partly too low data-variability of the water vapor mixing ratio in the respective heights.  Moreover, the drift of the radiosondes with the wind and hence measurements of atmospheric volumes with lateral distances will have decreased the correlation between the lidar and the radiosonde measurements.</p> <p>During MOSAiC a microwave radiometer was collocated close to the lidar. This system was measuring the same atmospheric vertical column. Its product, the integrated water vapor, might be useful for the calibration of the lidar.</p> <p>Hence, the contribution will analyze the error of the lidar retrieved water vapor mixing ratio that includes the calibration with the radiosonde data and the microwave radiometer product.</p> <p> </p>

scholarly journals Calibration of a Multichannel Water Vapor Raman Lidar through Noncollocated Operational Soundings: Optimization and Characterization of Accuracy and Variability

2010 ◽  
Vol 27 (1) ◽  
pp. 108-121 ◽  
Author(s):  
Davide Dionisi ◽  
Fernando Congeduti ◽  
Gian Luigi Liberti ◽  
Francesco Cardillo
Keyword(s):  
Water Vapor ◽  
National Research Council ◽  
Calibration Procedure ◽  
System Stability ◽  
Calibration Coefficient ◽  
Spatial And Temporal Variability ◽  
Raman Lidar ◽  
Measurement Session ◽  
Definition Of ◽  
Water Vapor Mixing Ratio

Abstract This paper presents a parametric automatic procedure to calibrate the multichannel Rayleigh–Mie–Raman lidar at the Institute for Atmospheric Science and Climate of the Italian National Research Council (ISAC-CNR) in Tor Vergata, Rome, Italy, using as a reference the operational 0000 UTC soundings at the WMO station 16245 (Pratica di Mare) located about 25 km southwest of the lidar site. The procedure, which is applied to both channels of the system, first identifies portions of the lidar and radiosonde profiles that are assumed to sample the same features of the water vapor profile, taking into account the different time and space sampling. Then, it computes the calibration coefficient with a best-fit procedure, weighted by the instrumental errors of both radiosounding and lidar. The parameters to be set in the procedure are described, and values adopted are discussed. The procedure was applied to a set of 57 sessions of nighttime 1-min-sampling lidar profiles (roughly about 300 h of measurements) covering the whole annual cycle (February 2007–September 2008). A calibration coefficient is computed for each measurement session. The variability of the calibration coefficients (∼10%) over periods with the same instrumental setting is reduced compared to the values obtained with the previously adopted, operator-assisted, and time-consuming calibration procedure. Reduction of variability, as well as the absence of evident trends, gives confidence both on system stability as well as on the developed procedure. Because of the definition of the calibration coefficient and of the different sampling between lidar and radiosonde, a contribution to the variability resulting from aerosol extinction and to the spatial and temporal variability of the water vapor mixing ratio is expected. A preliminary analysis aimed at identifying the contribution to the variability from these factors is presented. The parametric nature of the procedure makes it suitable for application to similar Raman lidar systems.

scholarly journals Water Vapor Mixing Ratio Distribution Inversion by Raman Lidar in Beijing

2020 ◽  
Vol 237 ◽  
pp. 06020
Author(s):  
SiQi Yu ◽  
Dong Liu ◽  
JiWei Xu ◽  
ZhenZhu Wang ◽  
DeCheng Wu ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiosonde Data ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Ratio Distribution ◽  
Water Vapor Mixing Ratio ◽  
Detection Instrument ◽  
Active Detection ◽  
The Vertical Distribution ◽  
Good Agreement

Water Aerosol Raman Lidar-II is an active detection instrument with high temporal and spatial resolution at Nanjiao observation station, and that could continuous water vapor mixing ratio (WVMR) measurements. WVMR profiles inversion from lidar data and water ratio retrieved from radiosonde data are in good agreement. The statistical results of the vertical distribution of WVMR indicate that WVMR seasonal mean distribution is consistent with precipitation. In addition, WVMR in Nanjiao station is related to total cloud cover.

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 %.

scholarly journals Raman Lidar for Meteorological Observations, RALMO – Part 2: Validation of water vapor measurements

2013 ◽  
Vol 6 (5) ◽  
pp. 1347-1358 ◽  
Author(s):  
E. Brocard ◽  
R. Philipona ◽  
A. Haefele ◽  
G. Romanens ◽  
A. Mueller ◽  
...  
Keyword(s):  
Water Vapor ◽  
Ground Level ◽  
High Temporal Resolution ◽  
Lidar Data ◽  
Raman Lidar ◽  
Gps Measurements ◽  
Regional Center ◽  
Meteorological Observations ◽  
Water Vapor Mixing Ratio ◽  
Humidity Profiles

Abstract. The Raman Lidar for Meteorological Observations (RALMO) was installed at the MeteoSwiss Regional Center of Payerne, Switzerland, in summer 2008. One of its aims is to provide continuous vertical profiles of tropospheric water vapor during day and night at a high temporal resolution. Twelve months (October 2009–September 2010) of lidar data are analyzed. During this period of time, the lidar produced 9086 profiles, representing 52.6% of the time (this figure reached 63.2% for the first 6 months of 2011). Under cloud-free conditions, half of the profiles reached more than 8610 m above ground level at night, and 4050 m during the day. In order to validate the capabilities of the instrument, the year of lidar data was compared to the collocated radiosondes. On average, lidar water vapor mixing ratio was found to be within 5 to 10% of radiosonde values up to 8 km at night, and within 3% up to 3 km during the day. Relative humidity results show an agreement within 2 and 5% for day and night, respectively. An integrated water vapor comparison also shows a good correlation with both radiosondes and GPS measurements: the lidar had a 4.2% dry bias compared to radiosondes and a 5.3% wet bias compared to GPS. These results validate the performance of the lidar and the humidity profiles with a 30 min time resolution.

scholarly journals Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

2018 ◽  
Author(s):  
Peng Jiang ◽  
Shirong Ye ◽  
Yinhao Lu ◽  
Yanyan Liu ◽  
Dezhong Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Statistical Tests ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Temporal Variations ◽  
Radiosonde Data ◽  
High Temporal Resolution ◽  
Time Varying ◽  
Weighted Mean Temperature ◽  
Significance Levels

Abstract. Water-vapor-weighted mean temperature, Tm, is the key variable to estimate mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). In near real-time GPS-PWV retrieving, estimating Tm from surface air temperature Ts is a widely used method because of its high temporal resolution and a fair degree of accuracy. Based on the Tm estimates and the extracted Ts parameters at each reanalysis grid node, analyses of the relationship between Tm and Ts were performed without smoothing of data which will produce superior results than other similar studies. Analyses demonstrate that Ts–Tm relationship has significant spatial and temporal variations. Then static and time-varying global gridded Ts–Tm equations were established and evaluated by comparisons with radiosonde data at radiosonde 758 stations in the Integrated Global Radiosonde Archive (IGRA). Results show that our global gridded Ts–Tm equations have prominent advantages than other globally applied models. Large biases of Bevis equation or latitude-related linear model at considerable stations are removed in gridded Ts–Tm estimating models. Multiple statistical tests at 5 % significance levels show that time-varying global gridded model is superior to other Ts–Tm models at 83.64 % of all radiosonde stations, while no model is significantly better at 5.54 % of sites and others superior at only 10.82 % of sites. GPS-PWV retrievals using different Tm estimates were compared at a number of IGS stations. By application of time-varying global gridded Ts–Tm equations, the relative differences of GPS-PWVs at most sites are within 1 %. Such results are obviously superior to other Ts–Tm models. The differences between GPS-PWVs and radiosonde PWVs are influenced by other comprehensive factors instead of single Tm parameter. However evident improvements still exist at special site by using more precise Ts–Tm equations. PWV errors could decrease by more than 30 % during wetter seasons.

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

2017 ◽  
Author(s):  
Guangyao Dai ◽  
Dietrich Althausen ◽  
Julian Hofer ◽  
Ronny Engelmann ◽  
Patric Seifert ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Pressure Profiles ◽  
Temperature And Pressure ◽  
Water Vapor Mixing Ratio ◽  
Lidar Observations ◽  
Humidity Profiles

Abstract. We present a practical method to continuously calibrate Raman lidar observations of the water vapor mixing ratio profile. 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 is obtained from the lidar observation. During CyCARE, 9 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. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with good accuracy.

scholarly journals Raman Lidar for Meteorological Observations, RALMO – Part 2: Validation of water vapor measurements

2012 ◽  
Vol 5 (5) ◽  
pp. 6915-6948 ◽  
Author(s):  
E. Brocard ◽  
R. Philipona ◽  
A. Haefele ◽  
G. Romanens ◽  
D. Ruffieux ◽  
...  
Keyword(s):  
Water Vapor ◽  
Ground Level ◽  
High Temporal Resolution ◽  
Lidar Data ◽  
Raman Lidar ◽  
Gps Measurements ◽  
Regional Center ◽  
Meteorological Observations ◽  
Water Vapor Mixing Ratio ◽  
Humidity Profiles

Abstract. The Raman Lidar for Meteorological Observations (RALMO) was installed at the MeteoSwiss Regional Center of Payerne, Switzerland, in Summer 2008. One of its aim is to provide continuous vertical profiles of tropospheric water vapor during day and night at a high temporal resolution. Twelve months (October 2009–September 2010) of lidar data are analyzed. During this period of time, the lidar produced 9086 profiles, representing 52.6% of the time (this figure reached 63.2% for the first 6 months of 2011). Under cloud-free conditions, half of the profiles reached more than 8610 m above ground level at night, and 4050 m during the day. In order to validate the capabilities of the instrument, the year of lidar data was compared to the collocated radiosondes. On average, lidar water vapor mixing ratio was found to be within 5 to 10% of radiosonde values up to 8 km at night, and within 3% up to 3 km during the day. Relative humidity results show an agreement within 2 and 5% for day and night, respectively. Integrated water vapor comparison also shows a good correlation with both radiosondes and GPS measurements: the lidar had a 4.2% dry bias compared to radiosondes and a 5.3% wet bias compared to GPS. These results validate the performance of the lidar and the humidity profiles with a 30-min time resolution.

scholarly journals Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Raquel Perdiguer-López ◽  
José Luis Berné-Valero ◽  
Natalia Garrido-Villén
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
The North ◽  
Radiosonde Station ◽  
Observation Method ◽  
North Of Spain

A processing methodology with GNSS observations to obtain Zenith Tropospheric Delay using Bernese GNSS Software version 5.2 is revised in order to obtain Precipitable Water Vapor (PWV). The most traditional PWV observation method is the radiosonde and it is often used as a standard to validate those derived from GNSS. For this reason, a location in the north of Spain, in A Coruña, which has a GNSS station with available data and also a radiosonde station, was chosen. Two GPS weeks, in different weather conditions were calculated. The result of the comparison between the GNSS- retrieved PWV and Radiosonde-PWV is explained in the last section of this paper.

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