ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7753-2015Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPEFothA.andreas.foth@uni-leipzig.dehttps://orcid.org/0000-0002-1164-3576BaarsH.https://orcid.org/0000-0002-2316-8960Di GirolamoP.PospichalB.https://orcid.org/0000-0001-9517-8300Leipzig Institute for Meteorology, University of Leipzig, Leipzig, GermanyLeibniz Institute for Tropospheric Research, Leipzig, GermanyScuola di Ingegneria, Università degli Studi della Basilicata, Potenza, ItalyA. Foth (andreas.foth@uni-leipzig.de)16July20151514775377638December20145March201511June201515June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/7753/2015/acp-15-7753-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7753/2015/acp-15-7753-2015.pdf
In this paper, we present a method to derive water vapour profiles
from Raman lidar measurements calibrated by the integrated water
vapour (IWV) from a collocated microwave radiometer during the
intense observation campaign HOPE in the frame of the HD(CP)2
initiative. The simultaneous observation of a microwave radiometer
and a Raman lidar allowed an operational and continuous measurement
of water vapour profiles also during cloudy conditions. The
calibration method provides results which are in a good agreement with
conventional methods based on radiosondes. The calibration factor
derived from the proposed IWV method is very stable with a relative
uncertainty of 5 %. This stability allows for the calibration of the lidar
even in the presence of clouds using the calibration factor
determined during the most recent clear sky interval. Based on
the application of this approach, it is possible to retrieve water
vapour profiles during all non-precipitating
conditions. A statistical analysis shows a good agreement between
the lidar measurements and collocated radiosondes. The relative
biases amount to less than 6.7 % below 2 km.
Introduction
Water vapour plays a key role in the description of the thermodynamic state of
the atmosphere and it is the most important
greenhouse gas . Its amount in the atmosphere is controlled
mostly by the air temperature rather than by
emissions . Therefore, tropospheric water vapour is
considered a feedback agent more than a forcing to climate change. Due to its
spatio-temporal variability and its involvement in many atmospheric processes
(e.g. cloud formation), it is difficult to properly implement water vapour in
climate models . Uncertainties in both
observations and modelling of water vapour strongly affect the representation of
clouds and precipitation in climate models and predictions. For that reason the
German research project High Definition Clouds and Precipitation for advancing
Climate Prediction (HD(CP)2) was initiated aiming to improve the clouds and
precipitation representation in models and to quantify the errors associated.
One part within the HD(CP)2 initiative was the intense observation campaign
HD(CP)2 Observational Prototype Experiment (HOPE) in Jülich. During HOPE
different remote-sensing instruments to measure water vapour, both active and
passive, were deployed. An active method is given by the Raman lidar
technique . Raman
lidars enable high vertical resolution measurements of water vapour and were
therefore applied in several field studies such as
IHOP or
COPS . However, Raman lidars
provide no water vapour information from inside the cloud or above (for
optically thick clouds) so that lidar measurements are limited from the surface
to the cloud base. Furthermore, daytime measurements are limited in height due
to the presence of scattered solar radiation. In addition, water vapour Raman
lidars need to be calibrated with an instrument measuring simultaneously for
example a microwave radiometer (MWR) or radiosonde
(RS) .
Passive microwave radiometry provides atmospheric water vapour
observations with high temporal resolution, but limited vertical
information . However, the integrated water
vapour (IWV) can be retrieved very accurately. In addition, microwave
radiometers can be operated during all weather conditions except for
precipitation.
In contrast to the already presented remote-sensing observations water,
vapour profiles can be measured in situ using radiosondes. RS
launches are mostly performed by the national weather services and
usually twice a day. Therefore, the horizontal and temporal resolution
of routine measurements is rather low.
As described above it is a challenge to provide continuous high-resolution water
vapour profiles with a single instrument. In recent years several supersites,
like the Jülich Observatory for Cloud Evolution (JOYCE), the Leipzig Aerosol
and Cloud Remote Observations System (LACROS) and the Richard Assmann
Observatory (RAO), installed a combination of remote-sensing systems. The
synergy of these instruments provides complementary information on the water
vapour structure. Thus, when both Raman lidar and MWR are measuring collocated
and simultaneously at supersites, continuous water vapour profiles can be
obtained operationally . The major objective of this paper
is to apply a Raman lidar calibration method which is only based on IWV from MWR
in a very straightforward way to offer a broad application. In previous
approaches the total precipitable water from MWR in combination with RS has been
used to calibrate the water vapour Raman lidar . We
demonstrate that the lidar calibration with MWR is as accurate as conventional
methods based on RS allowing operational applicability.
Using the presented calibration method water vapour profiles can be retrieved
from ground up to cloud base. In future studies these
operationally generated water vapour profiles will
serve as input into an optimal estimation technique
to retrieve water vapour from within or above the cloud.
Therefore, we will develop a two-step algorithm
combining Raman lidar mass mixing ratio and MWR
brightness temperatures. Both steps, the Kalman filter and the one
dimensional-variational retrieval method will improve the accuracy of the
water vapour measurement and will provide reliable data under most conditions
except during rainy periods.
Instrumentation
In the framework of the HD(CP)2 initiative HOPE was conducted around
Jülich in western Germany during April and May 2013
. The goal of HOPE was to probe the atmosphere with
a specific focus on the development of clouds and precipitation. HOPE was
further conceived for a critical model evaluation and to provide information
on sub-grid variability and microphysical properties. Two observatories were
set up in addition to JOYCE. The LACROS site was temporarily built up in
Krauthausen which is about 4 km south of JOYCE. Both JOYCE and LACROS
observatories are equipped with a set of active and passive remote-sensing
instruments such as lidars and MWRs. Radiosondes were launched in Hambach
which is about 4 km away from JOYCE and LACROS
(Fig. 1). In the following subsections we present the
instruments used for this study.
LidarsPollyXT
The lidar measurements at the LACROS site were conducted with the fully
automatic portable multiwavelength Raman and
polarization lidar PollyXT by the
Leibniz Institute for Tropospheric Research (TROPOS). PollyXT
measures backscattered light at wavelengths of 355,
532 and 1064 nm and Raman scattered light at 387, 407 and 607 nm
wavelengths. From that, water vapour profiles can be retrieved (see
Sect. ). In the lowermost heights the overlap of the
laser beam with the receiver field-of-view of the bistatic system is
incomplete. However, the overlap of both Raman channels is assumed to be
identical and for that reason the overlap effect is negligible
when regarding water vapour measurements. During
daytime, no water vapour measurements can be performed due to the high
daylight background. The vertical and temporal resolution of the raw data is
30 m in height and 30 s in time. The smoothing lengths amount to 90 m and
90 s.
BASIL
During the HOPE campaign the University of Basilicata lidar system (BASIL) was
deployed at the JOYCE site. BASIL has been developed by the Engineering School
(formerly the Department of Environmental Engineering and Physics) of the
University of Basilicata in Potenza. BASIL performs high resolution and accurate
measurements of atmospheric water vapour, both during daytime and nighttime.
A thorough description of the technical characteristics, measurement
capabilities and performances is given in . For water
vapour measurements BASIL uses the same wavelengths as PollyXT. The
maximum vertical and temporal resolution are 7.5 m in height and 1 s in time,
respectively, and can be traded-off to improve the measurement precision. The
time resolution used in this study amounts to 1 min and 7.5 s. In contrast to
PollyXT, the more powerful laser of BASIL enables measurements also
during daytime. Due to the use of a non-paralyzable counting system and the high
count rate of the BASIL measurements, a dead time correction is applied. For an
automatic analysis of the BASIL data only the digital signals are used. For that
reason a dry bias in the lowermost 500 m is expected. The accuracy of the
profiles could be improved by gluing the analogue and the digital
signals , but this approach is not considered
in this work, which is only focused on demonstrating the automated calibration
procedure. This is to simplify the data analysis procedure and to allow for an
easier implementation of the automated calibration procedure.
Map of the area around Jülich with westward (blue) and eastward
(red) RS trajectories. The darker area in the east indicates the open-cast
mining and the brighter area in the north indicates a hill named
Sophienhöhe.
Microwave radiometer HATPRO
The humidity and temperature profiler (HATPRO) is a passive instrument that
measures atmospheric emission at two frequency bands in the microwave spectrum.
Seven channels are along the 22.235 GHz H2O absorption line. From these
observations humidity information can be retrieved. The seven channels of the
other band from 51 to 58 GHz along the O2 absorption complex contain
the vertical temperature profile information. The fully automatic microwave
radiometer HATPRO allows to derive temperature and humidity profiles as well as
integrated quantities such as IWV and liquid water path (LWP) with a high
temporal resolution up to 1 s . Observations are possible
during nearly all weather conditions except precipitation.
To retrieve atmospheric quantities from the measured brightness temperatures,
statistical algorithms were used by means of a multi-linear regression between
modelled brightness temperatures and atmospheric profiles. Both MWRs from JOYCE
and LACROS use the same retrieval algorithms which are based on a long-term
data set of De Bilt radiosondes . The accuracy of the
temperature information in the planetary boundary layer can be enhanced through
measurements at different elevation angles . The scan mode
requires horizontally homogeneous atmospheric conditions in the direct
horizontal vicinity (∼3 km).
Radiosonde
Radiosondes (RS) were launched minimum twice a day (11:00 and 23:00 UTC) and more
often during intensive observation periods (IOP) at the KITCube site in Hambach.
The RS (type Graw DFM-09) measures temperature, humidity, pressure and wind
velocity . Due to the vicinity of the RS station to
the open-cast mining and its depth of nearly 400 m, horizontal inhomogeneities
between the RS and the lidar locations are
likely (Fig. ).
Methodology
The Raman lidar technique enables the determination of water vapour mixing
ratio profiles using the inelastic backscatter from nitrogen at 387 nm and
from water vapour at 407 nm . The
Raman lidar equation for inelastic signals can be written as
PλR(z)=P0,λ0KλROλR(z)z2βλR(z)×exp-∫0z[αλ0(ξ)+αλR(ξ)]dξ
and describes the Raman signals PλR(z) from distance
z measured with a lidar at the Raman wavelength
λR. P0,λ0 is the emitted laser
power at λ0. KλR denotes the
system constant and combines all the range-independent system parameters.
OλR(z) is the overlap factor.
βλR(z) represents the molecular backscatter
coefficient as a function of range. The exponential term characterises the
extinction of light on the way from the lidar to the backscattering molecule
(αλ0(z)) at the emitted wavelength λ0 and on
the way back to the lidar (αλR(z)) at the Raman
wavelength λR. One has to consider that the wavelength is
shifted after the Raman scattering process.
The Raman backscatter coefficient
βλR(z)=NλR(z)dσλR(π)dΩ,
is given by the molecule number density NλR(z)
of the Raman-active gas and the differential cross section for the
backward direction dσλR(π)/dΩ.
The mixing-ratio of water vapour to dry air mH2O is defined as:
mH2O=ρH2O(z)ρair(z)∼NH2O(z)NN2(z),
with ρH2O and ρair as density of water vapour and
dry air, respectively. mH2O is also proportional to the ratio of
the molecular number densities of water vapour and nitrogen. Inserting
Eqs. () and () in Eq. ()
mH2O is determined by:
mH2O=CH2OPH2O(z)PN2(z)exp-∫0zαλN2(ξ)d(ξ)exp-∫0zαλH2O(ξ)d(ξ).
In Eq. () identical overlap factors were assumed.
Differences in the range-independent Raman backscatter cross sections for
both channels are absorbed within the calibration factor CH2O
whereon we focus in this paper. The second term indicates the signal ratio
which is directly measured. The third term describes the difference between
the atmospheric transmission at λN2 and
λH2O. The extinction coefficients
αλN2(z) and αλH2O(z)
consist of a molecular (superscript m) and a particle part (superscript p).
Figure a and b displays the vertical profiles
of the molecular and particle extinction coefficients for both Raman channels
on 5 May 2013, 23:10 UTC measured with PollyXT.
αλN2m(z) and
αλH2Om(z) are calculated by using
temperature profiles from the MWR and standard atmosphere pressure profiles
.
αλN2p(z) and
αλH2Op(z) can be determined by the
Raman method using the particle extinction coefficient at 355 nm and
a certain Ångström-exponent, but they are strongly influenced by the
overlap effect. In contrast, the particle backscatter coefficient from the
Raman method is the ratio between the elastic signal
at 355 nm and the inelastic signal at 387 nm and is
therefore not affected by the overlap
. Hence, the particle extinction coefficients are
calculated from the particle backscatter coefficients multiplied by a certain
height-independent lidar ratio (i.e. extinction-to-backscatter ratio) of
50 sr over the full height range. The
particle extinction coefficients are strongly smoothed, therefore there is no
strong decrease in the lowermost layers. For the calculation of the particle
backscatter coefficient at the Raman wavelengths, a spectral dependence with
a backscatter-related Ångström-exponent of 1 is assumed. The
determined aerosol optical depth (AOD) at 355 nm amounts to 0.22 on
5 May 2013, 23:10 UTC.
The resulting differential transmission ratios are illustrated in
Fig. c. The black line indicates
the influence by the differences in the atmospheric transmission at
both Raman wavelengths. With a longer path through the atmosphere the
influence of the differential transmission increases. By completely
neglecting the differences in the atmospheric transmission, the error
is less than 2.9 % below 2 km where most of the water vapour is
located. In 10 km the value is 6.8 % but in this altitude the
amount of water vapour is rather low. Since it is quite an effort to
retrieve aerosol extinction profiles operationally, we neglect the
particle contribution to the transmission. The resulting error amounts
to 1.3 % at 2 km (blue line). These values are in a good
agreement with studies on a modelled
atmosphere .
The temperature dependence of the water vapour Raman spectrum portion that is
selected by the interference filter is not considered in this paper. For the
optical setup of both lidars used here, the effect is negligible in the lower
troposphere according to .
(a) Calculated profiles of the molecular extinction coefficient at 387
and 407 nm. (b) Determined particle extinction coefficient at 387 and 407 nm
from a PollyXT measurement on 5 May 2013, 23:10 UTC.
(c) Resulting transmission ratio considering the molecular (red), the particle
(blue) contribution and the the sum of both (black).
For PollyXT and BASIL the lowermost 400 and 100 m, respectively, of
the signal ratio are assumed to be well mixed and are set constant to account
for the overlap problem. The associated errors amount to a maximum of 0.6 and
0.1 gkg-1 at the surface for PollyXT and BASIL,
respectively. These errors are estimated using the average over nighttime
radiosonde profiles during HOPE.
Calibration methods
After considering the uncertainties explained above, the calibration
factor CH2O can be determined by comparison
with simultaneous measurements from a reference instrument. In the
following subsections three different methods with two instruments
(MWR and RS) are presented in detail for a clear sky night from
a PollyXT measurement on 5 May 2013 (HOPE
IOP 12). Afterwards, the stability of the IWV method during the 2 month period of HOPE is presented.
Calibration methods for a clear sky night from
a PollyXT measurement on 5 May 2013 (HOPE IOP 12):
(a) regression method. Water vapour mixing ratio of the
radiosonde (RS) as function of the signal ratio from the lidar
averaged over 20 min from 23:00 to 23:20 UTC. CH2O is
the slope of the regression line, σCH2O is the
standard error of the slope and R2 is the coefficient of
determination. (b) Profile method. The calibration factor
for each considered height bin. The numbers indicate the mean
calibration factor and its standard deviation. (c) IWV
method. Time series of the calculated calibration factor (black
line). The black numbers denote the mean and the standard deviation
of the whole time range, whereas the grey numbers correspond to the
time range of the RS ascent (grey area).
Regression method
The regression method can be used to calibrate the lidar profile with an RS
by performing a linear regression between the water
vapour mixing ratio from the RS and the signal ratio
PH2O/PN2 from the lidar
(see Fig. a). The calibration factor
CH2O is defined as the slope of the regression line. In our
case, the calibration factor amounts to 12.32 gkg-1. The
standard error of the slope (σCH2O) is
0.17 gkg-1 and the correlation coefficient R2=0.98 shows
a good correlation between the lidar signal and the mixing ratio from the RS.
This results in a relative error of 1.4 %. The signal ratio is corrected
for differential transmission and is averaged over 20 min from 23:00 to
23:20 UTC. The vertical smoothing amounts to 270 m. Only an altitude region
from 2 up to 5 km is regarded for the regression to exclude boundary layer
inhomogeneities in the water vapour structure and to avoid differences due to
the RS drift in higher altitudes. Using this method,
found a variability in the calibration factor of about 10 %.
Profile method
Another method to calibrate the lidar with an RS is the profile method with an
associated uncertainty of about 5 % .
CH2O is calculated by the temporal mean of the water
vapour mixing ratio measured with RS and the signal ratio from the lidar for
each considered height bin. This ratio varies with altitude resulting in a mean
calibration factor of 12.18 gkg-1 and a standard deviation of
1.71 gkg-1 (Fig. b). The relative
error amounts to 14 %. Here, the same time range, altitude region and vertical
smoothing as for the regression method are applied.
Comparison between IWV from MWR and RS during
HOPE. Grey and black triangles indicate all weather
conditions and only clear sky conditions, respectively. The solid lines
notify the according regression lines. The numbers in the upper left corners
denote the bias and the standard deviation, respectively.
IWV method
In previous experiments , radiosondes showed
a significant sonde-to-sonde variability
as well as a dry bias . For that reason, water vapour Raman
lidars were often calibrated based on the IWV or integrated precipitable water
retrieved from a MWR resulting in a relative uncertainty
of 5 % , 7 % and
3 % , respectively.
Before using the MWR for the calibration of the lidar it is necessary to
estimate the accuracy of the IWV.
Figure displays the IWV comparison between MWR and
RS. On average the bias during all weather conditions and clear sky is very
low with values of -0.01±0.96 and 0.02±0.92 kg m-2
(mean ± SD), respectively. However, during drier
(IWV < 7 kg m-2) or more humid
(IWV > 20 kg m-2) clear sky conditions the relative
difference can amount to 10 %. These relative differences have to be
considered when calibrating lidar profiles.
Using the IWV method, CH2O is defined as the ratio of the IWV
measured with the MWR and the integrated signal ratio from the lidar. For
simultaneous measurements from MWR and lidar, CH2O can be
calculated from the mean of its time series during clear sky. To determine
clear sky periods, two criteria have to be fulfilled. First, the
standard deviation of LWP from MWR within a 20 min interval should amount to
less than a threshold of 1.5 g m-2. The second one is based on the
detection of a potential cloud base with the lidar signal at 1064 nm. Profiles
with cloud bases higher than 6 km are treated as clear sky profiles. For that
reason, the integrated signal ratio of the lidar is calculated by integrating
the profiles from the ground to 6 km. Water vapour above this height is mostly
negligible. In that way, the lidar can be calibrated in the presence of high
clouds.
The time series of the calculated CH2O is presented in
Fig. c. The mean and the standard deviation
are 12.77 and 0.36 gkg-1, respectively. Regarding only the time range
which is used for the calibration with the RS, the mean amounts to
12.78 gkg-1 and the standard deviation to 0.3 gkg-1. The
relative error thus does not exceed 3 %.
Calibration factors and errors of the regression, profile and IWV method.
To give an overview, the calibration factors and errors of all presented methods
are summarized in Table . The relative difference between
these methods amounts to less than 5 %. The IWV method is well suited to avoid
errors due to the RS drift.
Stability of the calibration factor
Having demonstrated that the calibration factors of all three methods are in
a good agreement, we will here discuss the stability of the IWV method.
Figure presents the time series of the calibration
factor of PollyXT using the IWV method (black and blue lines).
The grey areas denote the standard deviation during each 20 min interval.
Rearrangements in the optical setup of PollyXT,
more specifically adjustments of the overlap or
cleanups of the quartz plate in the roof of the PollyXT cabinet
can cause changes in the calibration factor. Such rearrangements or time
leaps of more than 4 h are indicated by dotted lines. The means and standard
deviations amount to 15.2±0.4 and 12.4±0.6gkg-1 before
and after the major rearrangement in the optical setup on 15 April 2013,
10:06 UTC, respectively. These values correspond to relative errors of 3 and
5 % and are comparable to studies of and
. Without any strong rearrangements in the optical setup,
the calibration factor is very stable, enabling an operational applicability.
This is particularly important during cloudy conditions when no calibration
can be performed. In those cases, the calibration factor from the last
20 min clear sky interval can be applied. This is explained in more detail
in Sect. .
Furthermore, the calibration factors determined by the regression method (red
points and error bars) and the profile method (green plus signs) are added to
Fig. . Their uncertainties amount to
11.9±1.3gkg-1 (11 %) and 13.3±1.3gkg-1
(10 %), respectively. The error bars of the profile method are too large and
are omitted for clarity.
Calibration factor of PollyXT using the IWV method as
function of time given in number of 20 min interval. The black and the blue
solid lines indicate the calibration factor before and after the major
rearrangement of the optical setup on 15 April 2013, 10:06 UTC, respectively.
The grey areas denote the standard deviation during each 20 min interval. The
numbers represent the according means and standard deviations over the time. The
grey dotted lines demonstrate rearrangements on PollyXT especially
adjustments of the overlap or cleanups of the quartz plate in the roof of the
PollyXT cabinet or they indicate leaps in the time of more than
4 h. The calibration factors of the regression and the profile method are
indicated by red points with error bars and green plus signs, respectively.
Water vapour measurements
The availability of two Raman lidar systems as well as frequent RS launches
allow a statistical analysis of the water vapour profile accuracy. This
section starts with an overview over the PollyXT water vapour
observation during HOPE. Afterwards, a case study comparing water vapour
measurements of PollyXT, BASIL and RS is presented. This part is
followed by an extensive statistical analysis showing the accuracy of the IWV
method for the whole experimental periody. Finally, this section ends with an
example of a water vapour measurement in the presence of clouds.
Overview over PollyXT water vapour observations during HOPE
Using the IWV method, it was possible to obtain calibrated water
vapour profiles by PollyXT during almost every night from
4 April to 29 May 2013 (Fig. ). The water
vapour content in the planetary boundary layer (PBL) is quite variable
ranging from about 3 gkg-1 on 7 April up to about
8 gkg-1 on 8 May 2013. The PBL contains more water vapour
than the layers above. However, the water vapour in the top layers was
often not observed due to the presence of clouds (e.g. the night from 11 to
12 April 2013). A method to derive water vapour also in cloudy cases
is presented in Sect. .
Overview over the water vapour profiles observed by
PollyXT during HOPE: (a) April and (b) May 2013.
(a) Comparison of the mixing ratio profiles from
PollyXT (black), BASIL (red) and radiosonde (blue) on 5 May 2013,
23:00 UTC. The lidar profiles are averaged over 20 min.
(b) Differences in mixing ratio between radiosonde and
PollyXT or BASIL, respectively.
Comparison of water vapour measurements on 4 May 2013
During the night of 4 to 5 May 2013, clear sky conditions were present over
the area and all measurement systems were running. Figure a
shows a comparison of water vapour mixing ratio profiles from
PollyXT, BASIL and RS at 23:00 UTC. The lidar profiles are
averaged over 20 min starting at 23:00 UTC. The vertical smoothing lengths
are 90 and 22.5 m for PollyXT and BASIL, respectively. Due to
the different vertical resolution, the lidar profiles are interpolated to the
RS height grid. All three curves show a similar behavior, except within the
PBL up to 1.6 km. Above the PBL top a strong decrease in the water vapour
mixing ratio can be observed. The differences between
the RS as independent reference and the lidars are illustrated in
Fig. b. It can be seen that the
discrepancies are quite large in the PBL. The mean
difference and its standard deviation in the PBL amount to
-0.14±0.31gkg-1 (relative error -3.2±8.2%) and
-0.46±0.45gkg-1 (-11.4±12%) for
PollyXT (black) and BASIL (red), respectively. These differences
are expected due to the normal water vapour variability in the PBL. Negative
values indicate drier RS values.
The largest differences occur at the PBL top with
-1gkg-1 (PollyXT) and -1.37gkg-1
(BASIL) which can be caused by small-scale variability of the PBL height.
Above the PBL in the free troposphere (FT) between 2 and 5 km the
differences are smaller with values of 0.17±0.17gkg-1
(8.5±10.5%) for PollyXT and
0.08±0.17gkg-1 (4.8±8.6%) for BASIL.
Statistical analysis of lidar profiles determined by the IWV method:
(a) Absolute bias between the radiosonde (RS) and PollyXT
(black), RS and PollyXT calibrated with a constant calibration
factor of 12.4 (blue), between RS and BASIL (red) and between
PollyXT and BASIL (green). (b) Root-mean-square error
(RMSE) of the water vapour mixing ratio. The numbers indicate the sample size.
Statistical analysis
For a statistical analysis of the developed calibration method, the absolute
bias between RS and PollyXT, as well as between RS and BASIL is
presented. For PollyXT also a constant calibration factor of 12.4
is used, being the average from the IWV method. In the analysis only clear
sky nighttime measurements within less than 2 h before or after the RS
launch time are considered. The sample size amounts
to 53 and 33 observations, respectively. The profiles
are interpolated to the height grid of the lidar and are averaged over
20 min. For the comparison between both lidars only simultaneous 20 min
averages are investigated (19 cases). One has to consider that several lidar
profiles were compared to one RS profile (e.g. lidar profiles from 21:20,
21:40 and 22:00 UTC to the RS from 23:00 UTC). The PollyXT and
BASIL cases are compared to 15 and 6 radiosondes, respectively.
The absolute bias between RS and PollyXT, as well as the
absolute bias between RS and BASIL are largest in the lowermost layer from 0 to
0.5 km (Fig. a). These biases are induced by the different
measurement locations and the missing gluing in the BASIL data. This can have an
impact on the mixing ratio of up to 1 gkg-1 in the lowermost 500 m. In
the PBL up to about 2 km, the absolute bias between RS and BASIL and between
PollyXT and BASIL shows negative values indicating that BASIL
measures a higher amount of water vapour. These higher biases in the PBL can be
explained by the higher variability of water vapour due to the different
measurement locations, since the RS launch site (KIT) is directly situated at
the open-cast mining.
Absolute and relative bias for water vapour mixing ratio
(mean ± SD). Values are represented for
the layers from 0 to 2 km, from 2 to 4 km and from 4 to 10 km.
0–2 km 2–4 km 4–10 km Abs. biasRel. biasAbs. biasRel. biasAbs. biasRel. bias(g kg-1)(%)(g kg-1)(%)(g kg-1)(%)RS-PollyXT-0.03±0.15-0.6±2.80.14±0.17.5±5.10.01±0.040.6±15.2RS-PollyXT(const)-0.09±0.34-1.0±4.80.24±0.368.3±13.8-0.06±0.11-15±.5RS-BASIL-0.2±0.4-5.3±8.2-0.15±0.11-7.2±5.10.01±0.040.9±26.5PollyXT-BASIL-0.3±0.3-6.7±6.6-0.13±0.08-7.7±2.9-0.02±0.0415.4±148.5
Absolute bias and standard deviation (error bars) between the RS and
PollyXT distinguished by different trajectories. The black line
indicates the bias considering all trajectories.
The trajectories of the RS up to an altitude of 2 km are shown in
Fig. split into the trajectories west and east.
Figure depicts the biases between RS and
PollyXT distinguished by the direction of the RS trajectories. When
the RS drifts to the east (red), the RS rises in an air mass which is not
affected by the pit. In these cases, the bias is close to zero at altitudes from
0.5 to 1 km. During the weaker easterly wind conditions, the RS drifts in an
air mass which is strongly affected by the pit, whereas the air sounded by the
lidar passes the pit southwards and is therefore not disturbed. Here the lidar
and the RS do not profile the same air masses resulting in a higher bias down to
-0.4gkg-1. However, the differences between the biases are in the
range of their standard deviations.
Above the PBL the biases converge to zero (Fig. a). The bias
between the RS and PollyXT shows a small increase at about
2.5 km caused by four cases in which the atmosphere changes so fast that the lidar
and the RS do not measure the same air mass. In high altitudes no significant
biases are noticeable. Obviously, the water vapour amount decreases with
altitude and therefore the RMSE also decreases with height
(Fig. b). The coefficient of variation (CV) also known as
relative RMSE increases with height due to the decreasing water vapour
amount. In high altitudes the CV is more noisy for all four comparisons.
(a) Height-time display of the water vapour mixing ratio from
a PollyXT measurement on 16 April 2013, 00:40 UTC. White areas
are regions in or above clouds without any water vapour information. The bars on
the top indicate which profiles are calibrated (green) based on the current IWV
from MWR. The red bars denote profiles which are calibrated with the averaged
calibration factor from the last clear sky 20 min interval (red). (b) Profiles
of the 20 min intervals at 01:00 UTC (black) and 02:20 UTC (red).
The bias of the previously described comparisons is summarised in
Table . It can be seen that the absolute bias is larger in
the lower layers up to 4 km than in the upper layer (4 to 10 km). However,
large relative biases can occur in the upper layer due to the lower water vapour
mixing ratio. In addition, the bias is larger using a constant calibration
factor instead of calibration factors determined by the IWV method.
found relative biases within 3 % up to 3 km during the
day, and within 5 to 10 % up to 8 km during the night. Values of about
0.6±0.6gkg-1 in the altitude range 1.5 to 5.5 km were
identified by .
Water vapour measurements below clouds
After showing the stability and accuracy of the calibration factor from the IWV
method we can calibrate the lidar profiles during all non-precipitating
conditions. Figure a shows the height-time display of
the water vapour mixing ratio from a PollyXT measurement on 16 April 2013, 00:40 UTC. The white area indicates regions inside or above clouds
without any water vapour information. The cloud base was determined by the
gradient method on the range-corrected signal at 1064 nm .
The green marked profiles until 01:20 UTC are calibrated with the IWV method,
whereas the red marked after 01:20 UTC indicate cloudy conditions. These
profiles are calibrated using the averaged calibration factor of the last
20 min clear sky interval (01:00 to 01:20 UTC). Both profiles of the water
vapour mixing ratio at 01:00 and 02:20 UTC are in a good agreement below the
cloud base (Fig. b). With this technique it is possible to
provide continuous water vapour profiles up to the cloud base in all
non-precipitating night cases.
Conclusions
In this study, we present water vapour profiles from Raman lidar automatically
calibrated by microwave radiometer enabling an operational applicability. It is
shown that the calibration factors during HOPE were very stable with a relative
error of 5 %. This allowed us to retrieve water vapour profiles in all
non-precipitating weather conditions. During clear sky cases, the lidar can be
calibrated simultaneously with the IWV from the MWR, whereas in cloudy cases the
calibration factor from the last 20 min clear sky interval can be applied.
Therefore, the lidar setup should only be changed during clear sky conditions.
The presented case study and the statistical analysis show a good agreement
between measurements with RS and two different lidar systems calibrated by MWR.
This results in rather accurate profiles. The biases between the lidars and the
RS can be explained by the different measurement locations and a possible
systematic bias in the RS. This could be investigated in further studies.
Particularly with regard to the increasing amount of ground-based remote-sensing supersites that are equipped with Raman lidar and MWR without
operational RS launches (e.g. LACROS in Leipzig), water vapour profiles can be
retrieved on a routine basis.
Acknowledgements
This work was funded by the Federal Ministry of Education and
Research in Germany (BMBF) through the research programme “High
Definition Clouds and Precipitation for Climate Prediction –
HD(CP)2” (FKZ: 01LK1209D).
The authors acknowledge the BASIL, the LACROS and the radiosonde team for
conducting the measurements and like to acknowledge all other supporters of the
HOPE campaign.
Edited by: H. Russchenberg
ReferencesAdam, M. and Venable, D. D.: Systematic distortions in water vapor mixing
ratio and aerosol scattering ratio from a Raman lidar, in: SPIE Proceedings,
Vol. 6750, Lidar Technologies, Techniques, and Measurements for Atmospheric
Remote Sensing III, Florence, Italy, 17–19 September 2007, 67500S,
10.1117/12.738205, 2007.Adam, M., Demoz, B. B., Whiteman, D. N., Venable, D. D., Joseph, E.,
Gambacorta, A., Wei, J., Shephard, M. W., Miloshevich, L. M.,
Barnet, C. D., Herman, R. L., Fitzgibbon, J., and Connell, R.: Water
Vapor Measurements by Howard University Raman Lidar during the WAVES 2006
Campaign, J. Atmos. Ocean. Tech., 27, 42–60,
10.1175/2009JTECHA1331.1, 2010.Althausen, D., Engelmann, R., Baars, H., Heese, B., Ansmann, A.,
Müller, D., and Komppula, M.: Portable Raman lidar PollyXT for
automated profiling of aerosol backscatter, extinction, and depolarization,
J. Atmos. Ocean. Tech., 26, 2366–2378, 10.1175/2009JTECHA1304.1,
2009.Ansmann, A., Wandinger, U., Riebesell, M., Weitkamp, C., and
Michaelis, W.: Independent measurement of extinction and backscatter
profiles in cirrus clouds by using a combined Raman elastic-backscatter
lidar, Appl. Opt., 31, 7113–7131, 10.1364/AO.31.007113, 1992.Baars, H., Ansmann, A., Engelmann, R., and Althausen, D.: Continuous
monitoring of the boundary-layer top with lidar, Atmos. Chem. Phys., 8,
7281–7296, 10.5194/acp-8-7281-2008, 2008.Bhawar, R., di Girolamo, P., Summa, D., Flamant, C., Althausen, D.,
Behrendt, A., Kiemle, C., Bosser, P., Cacciani, M., Champollion,
C., di Iorio, T., Engelmann, R., Herold, C., Müller, D., Pal,
S., Wirth, M., and Wulfmeyer, V.: The water vapour intercomparison
effort in the framework of the Convective and Orographically-induced
Precipitation Study: airborne-to-ground-based and airborne-to-airborne lidar
systems, Q. J. R. Meteorol. Soc., 137, 325–348, 10.1002/qj.697,
2011.Brocard, E., Philipona, R., Haefele, A., Romanens, G., Mueller, A., Ruffieux,
D., Simeonov, V., and Calpini, B.: Raman Lidar for Meteorological
Observations, RALMO – Part 2: Validation of water vapor measurements, Atmos.
Meas. Tech., 6, 1347–1358, 10.5194/amt-6-1347-2013, 2013.Bucholtz, A.: Rayleigh-scattering calculations for the terrestrial
atmosphere, Appl. Opt., 34, 2765–2773, 10.1364/AO.34.002765, 1995.Crewell, S. and Löhnert, U.: Accuracy of Boundary Layer Temperature
Profiles Retrieved With Multifrequency Multiangle Microwave Radiometry.,
IEEE Trans. Geosci. Remote Sens., 45, 2195–2201,
10.1109/TGRS.2006.888434, 2007.Di Girolamo, P., Summa, D., and Ferretti, R.: Multiparameter Raman lidar
measurements for the characterization of a dry stratospheric intrusion
event, J. Atmos. Ocean. Tech., 26, 1742–1762,
10.1175/2009JTECHA1253.1, 2009.Dionisi, D., Congeduti, F., Liberti, G. L., and Cardillo, F.:
Calibration of a Multichannel Water Vapor Raman Lidar through Noncollocated
Operational Soundings: Optimization and Characterization of Accuracy and
Variability, J. Atmos. Ocean. Tech., 27, 108–121,
10.1175/2009JTECHA1327.1, 2010.England, M. N., Ferrare, R. A., Melfi, S. H., Whiteman, D. N., and
Clark, T. A.: Atmospheric water vapor measurements: Comparison of
microwave radiometry and lidar, J. Geophys. Res., 97, 899–916,
10.1029/91JD02384, 1992.Ferrare, R. A., Melfi, S. H., Whiteman, D. N., Evans, K. D.,
Schmidlin, F. J., and Starr, D. O.: A Comparison of Water Vapor
Measurements Made by Raman Lidar and Radiosondes, J. Atmos. Ocean.
Tech., 12, 1177–1195,
10.1175/1520-0426(1995)012<1177:ACOWVM>2.0.CO;2, 1995.Ferrare, R., Turner, D., Clayton, M., Schmid, B., Redemann, J., Covert, D.,
Elleman, R., Ogren, J., Andrews, E., Goldsmith, J. E. M., and Jonsson, H.:
Evaluation of daytime measurements of aerosols and water vapor made by an
operational Raman lidar over the Southern Great Plains, J. Geophys. Res.
Atmos., 111, D05S08, 10.1029/2005JD005836, 2006.
Hartmann, D., Klein Tank, A., Rusticucci, M., Alexander, L.,
Brönnimann, S., Charabi, Y., Dentener, F., Dlugokencky, E.,
Easterling, D., Kaplan, A., Soden, B., Thorne, P., Wild, M., and
Zhai, P.: Observations: Atmosphere and Surface, in: Climate Change 2013:
The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Stocker, T., Qin, D., Plattner, G., Tignor, M., Allen, S.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2013.Held, I. and Soden, B.: Water vapor Feedback and Global Warming, Annu. Rev.
Energy Environ., 25, 441–475, 10.1146/annurev.energy.25.1.441, 2000.Herold, C., Althausen, D., Müller, D., Tesche, M., Seifert, P.,
Engelmann, R., Flamant, C., Bhawar, R., and di Girolamo, P.:
Comparison of Raman Lidar Observations of Water Vapor with COSMO-DE
Forecasts during COPS 2007, Weather Forecast., 26, 1056–1066,
10.1175/2011WAF2222448.1, 2011.Löhnert, U. and Crewell, S.: Accuracy of cloud liquid water path from
ground-based microwave radiometry 1. Dependency on cloud model statistics,
Radio Sci., 38, 8041, 10.1029/2002RS002654, 2003.
Macke, A.: HOPE – A German intensive field campaign to capture the
spatiotemporal variability of the thermodynamics, energetics, and
microphysics of the cloudy troposphere with high resolution, in: 14th
Conference on Atmospheric Radiation, J6.4, Boston, MA, USA, 07–11 July
2014.Madonna, F., Amodeo, A., Boselli, A., Cornacchia, C., Cuomo, V., D'Amico, G.,
Giunta, A., Mona, L., and Pappalardo, G.: CIAO: the CNR-IMAA advanced
observatory for atmospheric research, Atmos. Meas. Tech., 4, 1191–1208,
10.5194/amt-4-1191-2011, 2011.Mattis, I., Ansmann, A., Althausen, D., Jaenisch, V., Wandinger, U.,
Müller, D., Arshinov, Y. F., Bobrovnikov, S. M., and Serikov, I. B.:
Relative-humidity profiling in the troposphere with a Raman lidar., Appl.
Opt., 41, 6451–6462, 10.1364/AO.41.006451, 2002.Mona, L., Cornacchia, C., D'Amico, G., Di Girolamo, P., Pappalardo, G.,
Pisani, G., Summa, D., Wang, X., and Cuomo, V.: Characterization of the
variability of the humidity and cloud fields as observed from a cluster of
ground-based lidar systems, Q. J. R. Meteorol. Soc., 133, 257–271,
10.1002/qj.160, 2007.Müller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U.,
Althausen, D., and Pisani, G.: Aerosol-type-dependent lidar ratios
observed with Raman lidar, J. Geophys. Res., 112, D16202,
10.1029/2006JD008292, 2007.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt,
J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B.,
Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang,
H.: Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013:
The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Stocker, T., Qin, D., Plattner, G., Tignor, M., Allen, S.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2013.
Nash, J., Smout, R., Oakley, T., Pathack, B., and Kurnosenko, S.: WMO
Intercomparison of Radiosonde Systems, Vacoas, Mauritius, 02–25 February
2005, Instruments and observing methods report No. 83, WMO/TD-No. 1303,
Geneva, Switzerland, 2006.
Nash, J., Oakley, T., Vömel, H., and Wei, L.: WMO Intercomparison of High
Quality Radiosonde Systems, Yangjiang, China, 12 July–03 August 2010,
Instruments and observing methods report No. 107, WMO/TD-No. 1580, Geneva,
Switzerland, 2011.Navas-Guzmán, F., Fernández-Gálvez, J., Granados-Muñoz, M.
J., Guerrero-Rascado, J. L., Bravo-Aranda, J. A., and Alados-Arboledas, L.:
Tropospheric water vapour and relative humidity profiles from lidar and
microwave radiometry, Atmos. Meas. Tech., 7, 1201–1211,
10.5194/amt-7-1201-2014, 2014.Newsom, R. K., Turner, D. D., Mielke, B., Clayton, M., Ferrare, R.,
and Sivaraman, C.: Simultaneous analog and photon counting detection for
Raman lidar, Appl. Opt., 48, 3903–3914, 10.1364/AO.48.003903, 2009.Reichardt, J., Wandinger, U., Klein, V., Mattis, I., Hilber, B., and Begbie,
R.: RAMSES: German Meteorological Service autonomous Raman lidar for water
vapor, temperature, aerosol, and cloud measurements., Appl. Opt., 51,
8111–8131, 10.1364/AO.51.008111, 2012.Rose, T., Crewell, S., Löhnert, U., and Simmer, C.: A network
suitable microwave radiometer for operational monitoring of the cloudy
atmosphere, Atmos. Res., 75, 183–200, 10.1016/j.atmosres.2004.12.005,
2005.Sakai, T., Nagai, T., Nakazato, M., Matsumura, T., Orikasa, N., and
Shoji, Y.: Comparisons of Raman Lidar Measurements of Tropospheric Water
Vapor Profiles with Radiosondes, Hygrometers on the Meteorological
Observation Tower, and GPS at Tsukuba, Japan, J. Atmos. Ocean. Tech.,
24, 1407–1423, 10.1175/JTECH2056.1, 2007.Tompkins, A. M.: A Prognostic Parameterization for the Subgrid-Scale
Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to
Diagnose Cloud Cover., J. Atmos. Sci., 59, 1917–1942,
10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2, 2002.Turner, D. D. and Goldsmith, J. E. M.: Twenty-Four-Hour Raman Lidar Water
Vapor Measurements during the Atmospheric Radiation Measurement Program's
1996 and 1997 Water Vapor Intensive Observation Periods, J. Atmos. Ocean.
Tech., 16, 1062–1076,
10.1175/1520-0426(1999)016<1062:TFHRLW>2.0.CO;2, 1999.Turner D. D., Ferrare R. A., Brasseur L. A. H., Feltz W. F., and
Tooman T. P.: Automated Retrievals of Water Vapor and Aerosol Profiles
from an Operational Raman Lidar, J. Atmos. Ocean. Tech., 19, 37–50,
10.1175/1520-0426(2002)019<0037:AROWVA>2.0.CO;2, 2002.Turner, D. D., Lesht, B. M., Clough, S. A., Liljegren, J. C.,
Revercomb, H. E., and Tobin, D. C.: Dry Bias and Variability in Vaisala
RS80-H Radiosondes: The ARM Experience, J. Atmos. Ocean. Tech., 20,
117–132, 10.1175/1520-0426(2003)020<0117:DBAVIV>2.0.CO;2, 2003.Twomey, S.: Aerosols, clouds and radiation, Atmos. Environ., 25,
2435–2442, 10.1016/0960-1686(91)90159-5, 1991.
Wandinger, U.: Raman Lidar, in: Lidar – Range-Resolved Optical Remote
Sensing of the Atmosphere, edited by: Weitkamp, C., vol. 102 of Springer Series in Optical Sciences, 241–271, Springer
Berlin/Heidelberg, 2005.Weckwerth, T. M., Parsons, D. B., Koch, S. E., Moore, J. A., Lemone,
M. A., Demoz, B. B., Flamant, C., Geerts, B., Wang, J., and Feltz,
W. F.: An Overview of the International H2O Project (IHOP_2002) and
Some Preliminary Highlights, B. Am. Meteorol. Soc., 85, 253–277,
10.1175/BAMS-85-2-253, 2004.
Westwater, E. R., Crewell, S., Mätzler, C., and Cimini, D.:
Principles of surface-based microwave and millimeter wave radiometric remote
sensing of the troposphere, Quad. Soc. Ital. Elettromagnetismo, 1, 50–90,
2005.Whiteman, D. N.: Examination of the traditional Raman lidar technique. II.
Evaluating the ratios for water vapor and aerosols, Appl. Opt., 42,
2593–2608, 10.1364/AO.42.002593, 2003.Whiteman, D. N., Melfi, S. H., and Ferrare, R. A.: Raman lidar system
for the measurement of water vapor and aerosols in the earth's atmosphere,
Appl. Opt., 31, 3068–3082, 10.1364/AO.31.003068, 1992.Whiteman, D. N., Demoz, B., di Girolamo, P., Comer, J., Veselovskii,
I., Evans, K., Wang, Z., Cadirola, M., Rush, K., Schwemmer, G.,
Gentry, B., Melfi, S. H., Mielke, B., Venable, D., and van Hove,
T.: Raman Lidar Measurements during the International H2O Project. Part I:
Instrumentation and Analysis Techniques, J. Atmos. Ocean. Tech., 23,
157–169, 10.1175/JTECH1838.1, 2006a.
Whiteman, D. N., Demoz, B., di Girolamo, P., Comer, J., Veselovskii,
I., Evans, K., Wang, Z., Sabatino, D., Schwemmer, G., Gentry, B.,
Lin, R.-F., Behrendt, A., Wulfmeyer, V., Browell, E., Ferrare, R.,
Ismail, S., and Wang, J.: Raman Lidar Measurements during the
International H2O Project. Part II: Case Studies, J. Atmos. Ocean.
Tech., 23, 170–183, 10.1175/JTECH1839.1, 2006b.Wulfmeyer, V., Behrendt, A., Kottmeier, C., Corsmeier, U.,
Barthlott, C., Craig, G. C., Hagen, M., Althausen, D., Aoshima, F.,
Arpagaus, M., Bauer, H.-S., Bennett, L., Blyth, A., Brandau, C.,
Champollion, C., Crewell, S., Dick, G., di Girolamo, P., Dorninger,
M., Dufournet, Y., Eigenmann, R., Engelmann, R., Flamant, C.,
Foken, T., Gorgas, T., Grzeschik, M., Handwerker, J., Hauck, C.,
Höller, H., Junkermann, W., Kalthoff, N., Kiemle, C., Klink,
S., König, M., Krauss, L., Long, C. N., Madonna, F., Mobbs, S.,
Neininger, B., Pal, S., Peters, G., Pigeon, G., Richard, E.,
Rotach, M. W., Russchenberg, H., Schwitalla, T., Smith, V.,
Steinacker, R., Trentmann, J., Turner, D. D., van Baelen, J., Vogt,
S., Volkert, H., Weckwerth, T., Wernli, H., Wieser, A., and Wirth,
M.: The Convective and Orographically-induced Precipitation Study (COPS):
the scientific strategy, the field phase, and research highlights, Q. J. R.
Meteorol. Soc., 137, 3–30, 10.1002/qj.752, 2011.