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 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 Characterization of high temporal resolution prr acquisition by fast comtec card: Deadtime, PRR desaturation, temperature calibration and retrieval.

2018 ◽  
Vol 176 ◽  
pp. 01017 ◽  
Author(s):  
Giovanni Martucci ◽  
Valentin Simeonov ◽  
Ludovic Renaud ◽  
Alexander Haefele
Keyword(s):  
Water Vapor ◽  
Temporal Resolution ◽  
Dead Time ◽  
Temporal Stability ◽  
Acquisition System ◽  
High Temporal Resolution ◽  
Temperature Calibration ◽  
Raman Lidar ◽  
Meteorological Observations

RAman Lidar for Meteorological Observations (RALMO) is operated at MeteoSwiss and provides continuous measurements of water vapor and temperature since 2010. While the water vapor has been acquired by a Licel acquisition system since 2008, the temperature channels have been migrated to a Fastcom P7888 acquisition system, since August 2015. We present a characterization of this new acquisition system, namely its dead-time, desaturation, temporal stability of the Pure Rotational Raman signals and the retrieval of the PRR-temperature.

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 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 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.

Variations in the water vapor distribution and the associated effects on fog and haze events over Xi’an based on Raman lidar data and back trajectories

2017 ◽  
Vol 56 (28) ◽  
pp. 7927 ◽  
Author(s):  
Yufeng Wang ◽  
Jing Zhang ◽  
Qiang Fu ◽  
Yuehui Song ◽  
Huige Di ◽  
...  
Keyword(s):  
Water Vapor ◽  
Lidar Data ◽  
Raman Lidar ◽  
Back Trajectories ◽  
Vapor Distribution

scholarly journals Temperature profiling of the atmospheric boundary layer with rotational Raman lidar during the HD(CP)<sup>2</sup> Observational Prototype Experiment

2015 ◽  
Vol 15 (5) ◽  
pp. 2867-2881 ◽  
Author(s):  
E. Hammann ◽  
A. Behrendt ◽  
F. Le Mounier ◽  
V. Wulfmeyer
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Temperature Gradient ◽  
Atmospheric Boundary Layer ◽  
Average Power ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Backscatter Signal ◽  
Low Background ◽  
Water Vapor Mixing Ratio

Abstract. The temperature measurements of the rotational Raman lidar of the University of Hohenheim (UHOH RRL) during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observation Prototype Experiment (HOPE) in April and May 2013 are discussed. The lidar consists of a frequency-tripled Nd:YAG laser at 355 nm with 10 W average power at 50 Hz, a two-mirror scanner, a 40 cm receiving telescope, and a highly efficient polychromator with cascading interference filters for separating four signals: the elastic backscatter signal, two rotational Raman signals with different temperature dependence, and the vibrational Raman signal of water vapor. The main measurement variable of the UHOH RRL is temperature. For the HOPE campaign, the lidar receiver was optimized for high and low background levels, with a novel switch for the passband of the second rotational Raman channel. The instrument delivers atmospheric profiles of water vapor mixing ratio as well as particle backscatter coefficient and particle extinction coefficient as further products. As examples for the measurement performance, measurements of the temperature gradient and water vapor mixing ratio revealing the development of the atmospheric boundary layer within 25 h are presented. As expected from simulations, a reduction of the measurement uncertainty of 70% during nighttime was achieved with the new low-background setting. A two-mirror scanner allows for measurements in different directions. When pointing the scanner to low elevation, measurements close to the ground become possible which are otherwise impossible due to the non-total overlap of laser beam and receiving telescope field of view in the near range. An example of a low-level temperature measurement is presented which resolves the temperature gradient at the top of the stable nighttime boundary layer 100 m above the ground.

scholarly journals Study and mitigation of calibration factor instabilities in a water vapor Raman lidar

2017 ◽  
Vol 10 (7) ◽  
pp. 2745-2758 ◽  
Author(s):  
Leslie David ◽  
Olivier Bock ◽  
Christian Thom ◽  
Pierre Bosser ◽  
Jacques Pelon
Keyword(s):  
Water Vapor ◽  
Lower Troposphere ◽  
Satellite System ◽  
Calibration Factor ◽  
Raman Lidar ◽  
Lidar System ◽  
Short Term ◽  
Term Stability ◽  
Water Vapor Mixing Ratio

Abstract. We have investigated calibration variations in the Rameau water vapor Raman lidar. This lidar system was developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It aims at calibrating Global Navigation Satellite System (GNSS) measurements for tropospheric wet delays and sounding the water vapor variability in the lower troposphere. The Rameau system demonstrated good capacity in retrieving water vapor mixing ratio (WVMR) profiles accurately in several campaigns. However, systematic short-term and long-term variations in the lidar calibration factor pointed to persistent instabilities. A careful testing of each subsystem independently revealed that these instabilities are mainly induced by mode fluctuations in the optic fiber used to couple the telescope to the detection subsystem and by the spatial nonuniformity of the photomultiplier photocathodes. Laboratory tests that replicate and quantify these instability sources are presented. A redesign of the detection subsystem is presented, which, combined with careful alignment procedures, is shown to significantly reduce the instabilities. Outdoor measurements were performed over a period of 5 months to check the stability of the modified lidar system. The calibration changes in the detection subsystem were monitored with lidar profile measurements using a common nitrogen filter in both Raman channels. A short-term stability of 2–3 % and a long-term drift of 2–3 % per month are demonstrated. Compared to the earlier Development of Methodologies for Water Vapour Measurement (DEMEVAP) campaign, this is a 3-fold improvement in the long-term stability of the detection subsystem. The overall water vapor calibration factors were determined and monitored with capacitive humidity sensor measurements and with GPS zenith wet delay (ZWD) data. The changes in the water vapor calibration factors are shown to be fairly consistent with the changes in the nitrogen calibration factors. The nitrogen calibration results can be used to correct the overall calibration factors without the need for additional water vapor measurements to within 1 % per month.

scholarly journals Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

2017 ◽  
Vol 10 (11) ◽  
pp. 4303-4316 ◽  
Author(s):  
Maria Filioglou ◽  
Anna Nikandrova ◽  
Sami Niemelä ◽  
Holger Baars ◽  
Tero Mielonen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Limited Area ◽  
Mixing Ratio ◽  
Atmospheric Conditions ◽  
Raman Lidar ◽  
Mixing Ratios ◽  
Seasonal Analysis ◽  
Relative Errors ◽  
Water Vapor Mixing Ratio

Abstract. We present tropospheric water vapor profiles measured with a Raman lidar during three field campaigns held in Finland. Co-located radio soundings are available throughout the period for the calibration of the lidar signals. We investigate the possibility of calibrating the lidar water vapor profiles in the absence of co-existing on-site soundings using water vapor profiles from the combined Advanced InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) satellite product; the Aire Limitée Adaptation dynamique Développement INternational and High Resolution Limited Area Model (ALADIN/HIRLAM) numerical weather prediction (NWP) system, and the nearest radio sounding station located 100 km away from the lidar site (only for the permanent location of the lidar). The uncertainties of the calibration factor derived from the soundings, the satellite and the model data are  < 2.8, 7.4 and 3.9 %, respectively. We also include water vapor mixing ratio intercomparisons between the radio soundings and the various instruments/model for the period of the campaigns. A good agreement is observed for all comparisons with relative errors that do not exceed 50 % up to 8 km altitude in most cases. A 4-year seasonal analysis of vertical water vapor is also presented for the Kuopio site in Finland. During winter months, the air in Kuopio is dry (1.15±0.40 g kg−1); during summer it is wet (5.54±1.02 g kg−1); and at other times, the air is in an intermediate state. These are averaged values over the lowest 2 km in the atmosphere. Above that height a quick decrease in water vapor mixing ratios is observed, except during summer months where favorable atmospheric conditions enable higher mixing ratio values at higher altitudes. Lastly, the seasonal change in disagreement between the lidar and the model has been studied. The analysis showed that, on average, the model underestimates water vapor mixing ratios at high altitudes during spring and summer.

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