AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-35-2017HAI, a new airborne, absolute, twin dual-channel, multi-phase TDLAS-hygrometer:
background, design, setup, and first flight dataBuchholzBernhardAfchineArminhttps://orcid.org/0000-0002-7669-8295KleinAlexanderSchillerCorneliushttps://orcid.org/0000-0002-1394-3097KrämerMartinahttps://orcid.org/0000-0002-2888-1722EbertVolkervolker.ebert@ptb.dehttps://orcid.org/0000-0002-1394-3097Physikalisch-Technische Bundesanstalt Braunschweig, Braunschweig, GermanyPhysikalisch Chemisches Institut, Universität Heidelberg,
Heidelberg,
GermanyCenter of Smart Interfaces, Technische Universität
Darmstadt, Darmstadt, GermanyForschungszentrum Jülich, IEK-7, Jülich, Germanycurrently at: Department of Civil and Environmental
Engineering, Princeton University, Princeton, USAdeceasedVolker Ebert (volker.ebert@ptb.de)3January2017101355716May201620May201611October201617November2016This 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://amt.copernicus.org/articles/10/35/2017/amt-10-35-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/35/2017/amt-10-35-2017.pdf
The novel Hygrometer for Atmospheric
Investigation (HAI) realizes a unique concept for simultaneous
gas-phase and total (gas-phase + evaporated cloud particles) water
measurements. It has been developed and successfully deployed for the first
time on the German HALO research aircraft. This new instrument combines
direct tunable diode laser absorption spectroscopy (dTDLAS) with a
first-principle evaluation method to allow absolute water vapor measurements
without any initial or repetitive sensor calibration using a reference gas
or a reference humidity generator. HAI contains two completely independent
dual-channel (closed-path, open-path) spectrometers, one at 1.4
and one at 2.6 µm, which together allow us to cover the entire
atmospheric H2O range from 1 to 40 000 ppmv with a single instrument.
Both spectrometers each comprise a separate, wavelength-individual
extractive, closed-path cell for total water (ice and gas-phase)
measurements. Additionally, both spectrometers couple light into a common
open-path cell outside of the aircraft fuselage for a direct, sampling-free,
and contactless determination of the gas-phase water content. This novel
twin dual-channel setup allows for the first time multiple self-validation
functions, in particular a reliable, direct, in-flight validation of the
open-path channels. During the first field campaigns, the in-flight
deviations between the independent and calibration-free channels (i.e.,
closed-path to closed-path and open-path to closed-path) were on average in
the 2 % range. Further, the fully autonomous HAI hygrometer allows
measurements up to 240 Hz with a minimal integration time of 1.4 ms. The
best precision is achieved by the 1.4 µm closed-path cell at 3.8 Hz
(0.18 ppmv) and by the 2.6 µm closed-path cell at 13 Hz (0.055 ppmv).
The requirements, design, operation principle, and first in-flight
performance of the hygrometer are described and discussed in this work.
IntroductionAtmospheric water vapor
Water vapor is in many ways one of the most important measurand for
atmospheric investigations (Ludlam, 1980; Möller et al., 2011;
Ravishankara, 2012). Water vapor is the most important greenhouse gas (Kiehl
and Trenberth, 1997) and is known as a key atmospheric coupling element of
almost all microscopic (e.g., droplets and ice crystals formation), macroscopic
(e.g., clouds and precipitation), and global processes (e.g., hydrological cycle).
It is strongly related to numerous highly relevant topics of atmospheric
science and closely related to climate change (Held and Soden, 2000;
Houghton, 2009; Kiehl and Trenberth, 1997; Maycock et al., 2011).
Unsurprisingly, numerous water vapor studies have been carried out targeting
its atmospheric trends and variability (Lu and Takle, 2010; McCarthy et al.,
2009; Ross and Elliott, 1996; Scherer et al., 2008; Trenberth et al., 2005;
Xie et al., 2011), its influence on transport models (Kiemle et al., 2012;
Schäfler et al., 2010), or its impact on radiation balance models
(Lockwood, 1990; Ramanathan et al., 1989; Schneider, 1972). The high
complexity of atmospheric water vapor is linked to its occurrence in all
three phases, which is unique among most atmospheric molecules. Water in the
gas phase is a very strong infrared absorber and significantly impacts
atmospheric energy fluxes through latent heat transfers by the different
phase transitions. Condensation to the liquid phase or freezing to solid
particles leads to effective scattering of solar radiation, which directly
raises links to the formation process of cirrus clouds (Krämer et al.,
2009; Spichtinger et al., 2004). These few relationships show the complexity
from a theoretical as well as a modeling point of view. Today, however, the
quality (particularly accuracy and comparability) of atmospheric water
measurements frequently limits a better understanding of key atmospheric
processes (Krämer et al., 2009; Peter et al., 2006; Scherer et al., 2008;
Sherwood et al., 2014). Despite the outlined importance and the large effort
invested in the developments of hygrometers in recent years, water vapor
remains a target molecule that is very difficult to measure accurately.
Several major issues exacerbate airborne water vapor measurements.
Atmospheric water vapor encompasses a very large concentration range
(3–40 000 ppmv). The high spatial variability of H2O leads, on fast
aircraft (typically around 800 km h-1 cruising speed), to highly dynamic
H2O variations in airborne measurements of up to several 1000 ppmv s-1 in
the gas phase and several 10 000 ppmv s-1 for total water (gas phase +
evaporated ice or liquid phase). Airborne hygrometers thus require high time
resolution, precision, and accuracy at the same time. Additionally, water
vapor is very effectively absorbed from nearly any surface. This challenges
in a highly complex manner not only the entire gas sampling system but also
the calibration infrastructure which is typically required for most
hygrometers. By waiving the entire calibration process, special laser-based
hygrometers (Wolfrum et al., 2011) circumvent all calibration-related issues
efficiently which will be explained later in Sect. 3.2. Last but not least,
the measurement boundary condition, such as gas pressure, gas temperature, gas
velocity, instrument operation temperature changes, or vibrations during
airborne measurement, is defined by the environment inside and outside of the
aircraft itself causing a variety of complications.
Airborne hygrometry
This brief compilation illustrates the complex challenges associated with
developing airborne water vapor instruments, especially to measure at
tropospheric and stratospheric atmospheric conditions. A high
number of (mostly single channel and single H2O phase) hygrometers have
been developed in the last decades with various advantages and drawbacks
(Wiederhold, 1997). A non-exhaustive selection of instruments has been described
(Buck, 1985; Busen and Buck, 1995; Cerni, 1994; Desjardins et al.,
1989; Diskin et al., 2002; Durry et al., 2008; Ebert et al., 2000b; Gurlit et
al., 2005; Hansford et al., 2006; Helten et al., 1998; Hunsmann et al., 2008;
Karpechko et al., 2014; Kley and Stone, 1978; May, 1998; Meyer et al., 2015;
Ohtaki and Matsui, 1982; Roths and Busen, 1996; Salasmaa and Kostamo, 1986;
Sargent et al., 2013; Schiff et al., 1994; Silver and Hovde, 1994b, a;
Thornberry et al., 2015; Webster et al., 2004; Zöger et al., 1999a,
b; Zondlo et al., 2010). Consequently, the question should be raised from
the opposite point of view: What are the important and required properties to
be covered and combined for the near-universal Hygrometer for Atmospheric
Investigation (HAI) to serve as an innovative and cutting-edge tool to explore
open and new scientific questions related to atmospheric water vapor?
General design targets for HAIOpen-path, closed-path, or both?
It is highly desirable for an airborne system to be constructed so that
it operates on the ground in the same manner as in flight and that every
change of environment and “boundary conditions” of the instrument is
logged and supervised and impacts are minimized or even prevented by design.
This allows extensive validation as well as laboratory comparisons with other
instruments and avoids systematic, barely detectable deviations only
occurring in flight. Instruments that are calibrated/validated on the ground in
different configurations (such as in Diskin et al., 2002) or under different
working conditions (such as in Sargent et al., 2013) often fail to provide a
clear, reliable, unbroken chain of calibrations from the in-flight
measurement to a (metrological) laboratory standard, which is necessary to
guarantee accuracy.
This notion related to water vapor leads directly into the everlasting
discussion about sampling via open-path systems operation in the free flow
versus closed-path systems extracting the air to be analyzed into the
instrument. Open-path hygrometers offer numerous great benefits such as the
prevention of any sampling errors or uncertainties (caused by surface
absorbing effects) as well as the high response time that is limited by
the transfer functions of optical or electrical components but not by the
gas exchange rate. The latter circumvents the complicated and adulterant
deconvolution of smoothing effects with time-response functions caused by a
sampling system. However, the boundary conditions such as gas
pressure and gas temperature as well as possible spatial inhomogeneity of
both parameters are difficult to take accurately into account for an
open-path system. Additionally, an airborne open-path sensor has to operate
in harsh boundary conditions, i.e., over a large range of temperatures (-80
to 50 ∘C) and pressures (70 to 1000 hPa), for large ram pressures
(900 km h-1 gas velocity) and mechanical stress through accretion of ice or
liquid water.
The major problem of all present open-path systems is their highly complex
calibration, or even just validation, since realistic flight conditions, in
particular the dynamics, are extremely difficult to realize in a lab with
sufficient accuracy. A direct metrological link to test the in-flight
performance, e.g., a dynamic calibration facility for open-path hygrometers,
is therefore missing.
Closed-path systems, in contrast, are simply installed inside an
air-conditioned cabin in a much more protected environment. Gas is sampled
with a suitable inlet and led via a tubing system to an “internal”
measurement chamber, such as an optical absorption cell or a “cavity”,
e.g., for a dew-point mirror hygrometer (DPH). On one hand, it is much easier to
accurately control and maintain the physical boundary conditions of the
sample gas, e.g., temperature, pressure, and flow, in the measurement
volume. On the other hand, it is difficult to ensure and maintain a
representative sampling process and to quantify and correct sampling related
deviations. These may be caused by adsorption and desorption effects, which
occur on all surfaces of the sampling system and have to be carefully
minimized, for example by heated (HAI ≈ 80 ∘C), electro-polished
stainless steel sampling pipes, along with an instrument design ensuring
high gas flows (HAI ≈ 100 L min-1) under all flight conditions.
Gas-phase H2O measurements in clouds are often carried out via backward-facing sampling inlets. However, such inlets readily sample small liquid or solid
water particles, possibly causing systematically positive offsets. The
measurement of total water, i.e., the sum of water in the gas phase as well as
in the condensed phase (ice and droplets), relies additionally on an accurate
distribution model of the particles entering the inlet system. Usually, the
particle sampling characteristics are considered individually (Krämer and
Afchine, 2004), and sampling corrections are done during further metrological
analysis and therefore not part of an instrument description. For every
H2O measurement using airborne extractive (closed-path) instruments
on aircraft, it needs to be taken into account that the instruments response
reflects contributions from the sensor element itself as well as the sampling
system and its dynamic properties.
One major indisputable advantage of typical extractive (closed-path)
instruments is the possibility for careful tests outside of the aircraft,
i.e., in a hangar or laboratory. However, to take full advantage of this, it is
desirable for a sophisticated instrument to integrate supervising and
monitoring functions in a way that a performance comparable to the
laboratory can be ensured during any in-flight situation. This generates the
great benefit of transferring the performance from laboratory to field,
quite similar to how a metrological transfer standard is typically used. This
directly reinforces the question of how to assess the accuracy of airborne
hygrometers on ground and in particular during flight operation.
State-of-the-art instrument accuracy
In general, the highest measurement and preparation accuracy is realized by
the validated primary standards of national metrology institutes such as PTB
(Germany) or NIST (USA). The international metrological water vapor scale is
defined by traceable primary water vapor generators (Brewer et al., 2011).
The mixing ratio range, required to cover the entire tropospheric and
stratospheric range (3–40 000 ppmv), is realized by a combination of
generators based on different physical principles. Their uncertainty is on
the order of 0.5 % relative (Brewer et al., 2011; Buchholz et al., 2014a;
Mackrodt, 2012). In other words, it is not possible to validate any
hygrometer with a better accuracy due to the lack of a suitable accurate
reference.
Comparing the available metrological accuracy to some results from field
comparisons of airborne hygrometers demonstrates the large potential for
improvement. For example, long-term (> 10 years) change studies of
stratospheric H2O (Oltmans and Hofmann, 1995; Rosenlof et al., 2001;
Solomon et al., 2010) suffer from significant, difficult to quantify relative
deviations between different instruments in the range of 50–100 % (Fahey
et al., 2014; Peter et al., 2006; Vömel et al., 2007), which recent
studies (such as Rollins et al., 2014) confirm (±40–50 % for
< 3 ppmv and ±20 % for > 3 ppmv). Radiosonde
comparisons with polymer sensors and chilled DPH,
covering the entire troposphere and lower-stratospheric region (such as
Miloshevich et al., 2006), show averaged overall agreements in the 10 %
range (as well as local deviations in the 30 % range). These deviations are
quite common for many airborne campaign results (e.g., Smit et al., 2014) and
become even worse when focusing on the relative deviations in regions
containing highly variable H2O structures. Hence in 2007, an
international comparison exercise, “AquaVIT” (Fahey and Gao, 2009), was
organized to compare the world's best airborne hygrometers under
well-controlled, quasi-static, equivalent conditions to evaluate the accuracy
under well-controlled laboratory conditions, without the influence of any
typical dynamic sampling effects. AquaVIT comprised 22 hygrometers (tunable
diode laser spectrometers (TDL), dew- or frost-point mirror hygrometers (D/FPH),
Lyman alpha fluorescence and absorption hygrometers (LAFH), and other
principles) from 17 international research groups. The instruments were
categorized in well-validated “core” instruments (APicT, FISH, FLASH, HWV,
JLH, CFH; see Fahey and Gao, 2009, for details) and “younger, less mature”
non-core instruments. Even the core hygrometers deviated in the important 1
to 150 ppmv H2O concentration range by up to ±10 % from their common mean
value. In other words, core instruments differed by up to 20 % from each
other, even under quasi-static conditions. Other, less representative and
extensive, comparisons (such as Hoff, 2009; Mangold and Wodca Team, 2003)
yielded similar results.
Educated guess for accuracy deviations between state-of-the-art
hygrometers
The assessment of the required accuracy depends strongly on the purpose of
the instrument and its data. For commonly used climatologies or strongly averaged coarse validation studies, larger deviations can be acceptable. In many other cases such as
the currently often discussed atmospheric supersaturations (Peter et al.,
2006), the instrument uncertainties prevent deeper investigations and
therefore a better understanding. Reconsidering the entire situation and
seeing that after so many development efforts over the past decades these
deviations remain quite high leads to the inevitable question of concealed,
common impact factors. Contemplating the typical metrological efforts needed
at national metrology institutes (NMIs) to generate an accurately humidified
gas stream (with a sub-percent uncertainty) suggests that the uncertainties
generated by typical calibration processes under field conditions could be a
major contribution to these hygrometers deviations found in AquaVIT and other
studies. In particular, comparing the performance and strategies of
lab-based, metrological, and portable field calibration facilities (Friehe et
al., 1986; Helten et al., 1998; Podolske et al., 2003; Smit et al., 2000;
Smorgon et al., 2014; Zöger et al., 1999b) indicates three significant
discrepancies: required time for calibration, frequency of calibration, and
traceability of the humidity reference itself. Calibrations in low
concentration ranges at NMIs take several hours up to days per individual
humidity value. During airborne campaigns, however, calibrations often have
to be realized (for practical reasons) in a short time, certainly less than a
few hours for a large number of concentration steps often including several
pressure levels, thereby taking the obvious risk that the
instrument/reference is not fully stabilized or equilibrated. Ideally, the
time between two calibrations should be shorter than the expected time
required for a drift/change exceeding the boundaries of the instrument
uncertainties. Some airborne instruments require for the same reason
calibrations before and after each flight in order to interpolate between
both calibrations (Zöger et al., 1999b). Some even work with in-flight
calibrations (Kaufmann et al., 2016; Thornberry et al., 2013), sacrificing
measurement time and shifting the accuracy issue to the necessary airborne
H2O source. Undoubtedly, many of these instruments have benefits, for example in
terms of precision, space, weight, and prime cost, which justify the
calibration effort. In contrast, it is often condoned that the
calibration process is hampered and turns out to be the major influence on
the accuracy of such a sensor.
Lastly, it seems necessary to implement a traceable link to the metrological
humidity scales to improve the overall accuracy of airborne hygrometry (Joint
Committee for Guides in Metrology (JCGM), 2009). By realizing an unbroken
chain of calibrations, it is possible to link the instrument performance and
the metrological water scale to the SI system of units. This
guarantees an accurate measurement/generation value with defined
uncertainties.
To summarize, fulfilling all these demands in the field similar to an NMI laboratory is a tough task. However, as
discussed later, many of the covered issues can be circumvented using first-principle techniques like direct tunable diode laser absorption spectroscopy (dTDLAS; Ebert and Wolfrum, 1994; Schulz et al.,
2007) to realize optical, absolute hygrometers which avoid over-defined operating ranges and water vapor sensor calibration.
Precision and time response of state-of-the-art instrumentation
From a user's point of view, precision and response time of an airborne
hygrometer appear equivalent to accuracy if one is interested in fine
structure resolving data. Precision and response time are, under certain
circumstances, reciprocally correlated to each other (Allan, 1966). Typical
figures for response time of airborne hygrometers in the literature are
0.5–1 Hz (Petersen et al., 2010; Szakáll et al., 2004; Zöger et
al., 1999b); some instruments deliver faster data of 4 Hz (Weinstock et al.,
1994) and up to 25 Hz (Zondlo et al., 2010). Typical
precisions at 1 Hz are in the range of 0.1–0.2 ppmv (Sargent et al., 2013; Zöger
et al., 1999b; Zondlo et al., 2010). While in the stratosphere (< 10 ppm), the precision certainly can become a limiting factor; this is much
less the case inside clouds or within the troposphere, where frequent, very
strong, spatial variations (up to 1000 ppmv per 100 m flight path in the
gas phase or up to 20 000 ppmv per 100 m during total water phase) pose a
larger problem. An instrument with a time response of just a few hertz causes
significant undersampling, which can lead to strong aliasing effects at
standard cruising speed of research aircraft (approximately 700–900 km h-1).
Important under such conditions is the instrument's linearity and range to
accurately cover the entire H2O concentration range up to 5
magnitudes for total water vapor measurements without cutoffs or complex
deconvolution functions.
Other requirements guiding the design
HAI was designed for the German HALO aircraft (HALO, 2016; Krautstrunk and
Giez, 2012), a Gulfstream G500, similar to the American HIAPER (UCAR/NCAR,
2016), which is a Gulfstream G500. HALO offers broad spatial coverage
(> 10 000 km), high altitudes (up to 15 km), large payloads of up
to 3000 kg, and a pressurized and air-conditioned cabin. Due to the
high operation costs for aircraft and the high scientific demand, H2O
data have to be measured continuously without any interruptions. The
instrument thus has to be highly reliable, robust, require low maintenance,
and conduct an entirely automatic and autonomous startup procedure. The
restrictions in weight and space as associated with operation on aircraft
result in the necessity for a compact and lightweight construction. The
utterly complex and mandatory certification process (at least in Germany)
enforces an instrument design freeze before a campaign; this results in very
stiff constraints for improvements and repairs during a campaign.
H2O spectroscopy related to the HAI instrument
HAI consists of four independent but interconnected spectrometers. For each
individual channel an individual evaluation procedure is done following an
identical, common spectroscopic method: calibration-free dTDLAS.
Direct tunable diode laser absorption spectroscopy
The requirements for fast measurements and high chemical selectivity in
combination with a robust and small system calls for a contactless
spectroscopic (hence optical) measurement technique rather than contact
sensing methods such as DPH or capacitive
polymer sensors. The latter (e.g., HUMICAP, Vaisala, used in Salasmaa and Kostamo,
1986, and
Smit et al., 2000) is quite frequently used in meteorological environments
for weight, size, and cost reasons (Busen and Buck, 1995; Hansford et al.,
2006; Wiederhold, 1997). By choice of the spectroscopic method, optical
hygrometers can be set up to become quite immune to hydrophobic
substances (unavoidable in the vicinity of aircraft) as well as particles
(dust, soot, ice, etc.) carried by the gas to be analyzed. These capabilities
were, for example, extensively demonstrated via measurements inside of combustion
processes in industrial power plants (Ebert et al., 2000a; Schlosser et
al., 2002; Sun et al., 2013; Teichert et al., 2003).
TDLAS, especially in the near
infrared spectral range, is a powerful as well as versatile diagnostic
technique and has led to numerous applications in atmospheric hygrometry (Diskin
et al., 2002; Fahey and Gao, 2009; Gurlit et al., 2005; May, 1998; Schiff et
al., 1994; Thornberry et al., 2015). Advantageous properties of diode lasers
are very high spectral resolution (providing excellent chemical selectivity),
high power density, and continuous wavelength tunability in combination with
interesting technical features such as low cost, very low size/weight/power
consumption, long lifetime, excellent beam quality, and optical fiber
coupling to name just a few.
The typical setup and working principle of a TDLAS instrument has been
frequently described in detail (Lackner, 2011; Schiff et al., 1994; Schulz et
al., 2007; Werle, 1998). Therefore, only HAI's design relevant topics are
discussed. Important for an understanding of the novel HAI instrument is the
classification of TDLAS instruments by their optical detection schemes in
classical single- (Ebert et al., 2000b) or multi-path (Gurlit et al., 2005;
Hunsmann et al., 2008; Lübken et al., 1999; May, 1998; McManus et al.,
1995) beam setups. In general, longer path lengths – achieved by multi-path
optics with a high number of reflections – provide better sensitivity.
However,
higher reflection numbers are also more critical to align. Compared to most
airborne TDLAS instruments, HAI has a relatively low number of reflections
and thus relatively short path lengths. Further categorizing distinguishes
between wavelength-modulation schemes like single modulation frequency, called direct TDLAS or dTDLAS (Ebert and Wolfrum, 1994), or double modulation schemes
like wavelength modulation spectroscopy (WMS) (Podolske and Loewenstein, 1993;
Silver, 1992; Silver and Hovde, 1994a; Silver and Zondlo, 2006; Vance et al.,
2011; Webster et al., 2004). WMS, often used for very compact sensors,
provides on the first glance higher sensitivities by using lock-in
technologies to efficiently filter noise. This, however, sacrifices the
possibility of direct physics-based quality and reliability checks, since the
actual measured WMS raw signal contains less spectral information than a
dTDLAS raw signal. This aggravates or sometimes even prevents detailed signal
analysis based on fundamental physical explanations. Using dTDLAS instead
with a special, but less common, first-principle evaluation procedure (Ebert
and Wolfrum, 2000; Farooq et al., 2008; Mihalcea et al., 1997; Schulz et al.,
2007) allows sophisticated evaluation, characterization, quality management
and a holistic view on the physical principles behind the data. This
circumstance can even be used to avoid typical calibration procedures with
reference gas standards as demonstrated in Buchholz et al. (2013b).
“Calibration-free” TDLAS: an explanation of the term
The term “calibration-free” is often used in different communities with
dissimilar meanings. To distinguish, one should consider how calibration is
defined by metrology (JCGM 2008, 2008): “calibration (…) in a
first step, establishes a relation between the measured values of a quantity
with measurement uncertainties provided by a measurement standard
(…) [I]n a second step, this information is used to establish a
relation for obtaining a measurement result from an indication (of the device
to be calibrated)”. In other words, an instrument with a deterministic
relation between indication and measured quantity can be kind of
“brute-force” calibrated, without knowing the physical details behind the
measurement process. This is often used to compensate nonlinearities,
offsets, drifts or response changes over time as long as they are stable,
predictable, or can be extrapolated. Again, the causes and physical
explanation of this disadvantageous behavior are then neither known nor
understood – it is only corrected for.
For HAI we use the term “calibration-free” to emphasize that HAI does not
rely on such a correction process. This means that the hygrometer described
in this paper is neither initially calibrated (i.e., like the factory
calibration of a commercial instrument) nor is it regularly calibrated or
adjusted by a repetitive comparison to a water vapor primary standard.
Parameters like gas pressure and temperature that are used for the
calculation of the water vapor content via our first-principle
spectrophysics model are of course measured with classically calibrated
sensors. This is done from a practical point because (a) primary standards for
temperature and pressure are by themselves large facilities and (b) the
influence in the final uncertainty budget does not justify a first-principle
approach for pressure and temperature too. Thus, calibration-free does not
mean that all measurement parameters in the entire setup are based on
principles. The whole idea behind traceability (JCGM 2008, 2008) is to use
other units, higher in the hierarchy of the SI units to generate and analyze the
target value. In metrology, this is named “an unbroken chain of
measurements”. To visualize, this means that the first and initial
measurement result of the HAI instrument delivers the final concentration
value. There is no subsequent “adjustment process” to “tune” an
intermediate result towards a target value defined by a higher reference
standard. The HAI evaluation directly delivers final values. This requires
that the entire measurement system be characterized in advance on such
a level that, for example, all influences of the instrument temperature on the
end value are characterized and the very first measurement value is
already determined within its uncertainty limits. Therefore, since there was
no calibration, it is termed calibration free.
Simplified schematic of a standard dTDLAS setup with several
spectroscopic channels: HAI combines two lasers (one at 1.4 µm for
high, one at 2.6 µm for low water vapor concentrations),
with two locations cells (1× open-path and 2× closed-path), and supplementary
spectroscopic channels (parasitic water detector (Buchholz and Ebert, 2014b)
and reference cell for spectral stabilization). HAI has in total seven
spectroscopic channels.
The uncertainty budget of HAI's calibrated sub-sensors, e.g., pressure and
temperature, also has to contain a p-T sensor drift consideration between two
calibration cycles of these p-T sensors. Sufficient information about the
required calibration interval and maximum sensor drifts has to be acquired
from trustful sources such as the manufacturer's data sheet or in-house
validations. These drift values then have to be considered in the frame of
the entire uncertainty budget. For example, one of the pressure sensors used in HAI
(Omega PAA33X-V-1) has long-term stability of 0.5 mbar yr-1. The
uncertainty budget of HAI (see below) clearly indicates that even if this
were 5 times higher it would not require a calibration cycle of less than
a year.
The calibration-free approach used in HAI could certainly be enhanced by a
calibration of HAI (such as Muecke et al., 1994) at any time (Buchholz et
al., 2013b), even after a campaign, if this seems advantageous, since the
requirements for a calibrated instrument are lower than for a
calibration-free instrument. For a limited time section between two
calibration intervals we could therefore improve the accuracy of HAI at the
expense of additional time and cost effort for the calibration itself.
However, we also would then “downgrade” HAI by making its performance
dependent on the availability of a calibration reference and we would also
limit this performance “gain” to the time span between two calibrations. In
addition, it is also a fact that a calibration only indicates the agreement
between the reference and the sensor at the time of the calibration.
Additional uncertainty contribution would have to be taken into account for
drifts of the instrument in the time span between two calibration points. The
many “ifs” related to calibration procedures and the high additional
“costs” led us to design and realize an instrument which completely avoids
such a process and has a well-defined performance based on a physical
first-principle model.
HITRAN 2008 line strength plot (Rothman et al., 2009) for CO2 (green) and H2O (black). The turquoise spectrum in the back shows the
scaled (factor 39) CO2 line strengths for visualization of the
stratospheric situation with a low water vapor content (10 ppmv) and standard
CO2 (390 ppmv) level.
Non-calibrated dTDLAS
The spectroscopic principle of non-calibrated absolute dTDLAS is very
briefly presented in the following section. For more detailed information
regarding dTDLAS, the reader is referred to the above-mentioned literature. The
sketch in Fig. 1 shows the schematics of a dTDLAS
spectrometer with two independent channels. For low light intensities
I0(λ) in the mW range, the transmitted light I(λ) can
be described by the extended Lambert–Beer equation
(Eq. 1), including possible disturbances
by background radiation, E(t), or broadband transmission losses, Tr(t).
Iλ=Et+I0λ⋅Trt⋅exp-ST⋅gλ-λ0⋅N⋅L
By applying the ideal gas law, Eq. (1) can be
used to retrieve the H2O volume mixing ratio c.
c=-kB⋅TST⋅L⋅p∫lnIν-EtI0ν⋅Trtdνdtdt
The amount fraction c is in metrological units officially specified
as [mol/mol = mol absorber per mol gas], which is in the environmental
community better known as “volume fraction”, for example in units of ppmv or
Vol.- %. The term dνdt is called the dynamic
tuning coefficient of the used laser. It can be determined experimentally
and is directly linked to the SI units (length) by using the Airy signal of
the laser light passing through a planar, air-spaced etalon (Ebert and
Wolfrum, 2000; Schlosser et al., 2002). However, unpublished, on-going, and
long-term dνdt measurements for the 1.37 µm distributed feedback (DFB)
laser type used over several years indicate a long-term stability of its tuning
characteristics better than 1 %, which is within the current uncertainties
of the tuning characterization. The variable t represents
time, kB is the Boltzmann constant and L is the
optical path length. S(T) is the line strength of the
selected molecular transition (see chapter below) and therefore a physical
property of the molecule to be measured. The gas pressure (p) and
gas temperature (T) can be accurately acquired in a closed-path
cell (CPc); the quality of the respective measurements in the open-path cell is
discussed in the following chapter describing the construction of the
open-path sensor. Equation (2) also “explains” the term calibration-free
quantitatively as there are no other “hidden” parameters used to derive the
water vapor concentration which require a calibration.
Photos of HALO's cabin layout with installed payload for the
TACTS/ESMVal campaign (left). Photo of HALO's fuselage showing the trace gas
inlet (TGI) for HAI's closed-path cells (for total water detection) as well
as HAI's open-path cell (for gas-phase water detection;
open-path laser beams are shown in yellow).
Absorption line selection
Suitable absorption lines have to be selected for a specific application by
several criteria (Wagner et al., 2012; Wunderle et al., 2006). Besides a line
strength maximization to ensure high sensitivity, other important parameters
have to be taken into account. For atmospheric measurements, the cross
sensitivity to other gases such as CO2 needs to be minimized. This
ensures a better control of the fitting process due to the fewer degrees of
freedom. Similarly, the line should be isolated from other lines to simplify
the retrieval of the baseline function. For the open-path measurements, it is
highly important to minimize temperature dependence of the line strength in
order to minimize the influence of gas temperature uncertainties. Lastly,
sometimes the primary constraint is the availability of suitable laser diodes
and additional accessories such as fibers, optic components, lenses, or
detectors. As the certification for airborne instruments nearly prevents
improvements and repairs during a campaign, all components need to be very
reliable. For HAI, we selected two specific water lines at 2596 and
1370 nm. The latter has been used by us and others before (Buchholz et al.,
2012, 2013b; Ebert, 2006; Ebert et al., 2004; Fahey and Gao, 2009; Hovde et
al., 2001; Hunsmann et al., 2008; May, 1998; Seidel et al., 2012; Witzel et
al., 2012; Wunderle et al., 2008) and improved spectral parameters were
available (Hunsmann et al., 2006). The 2.6 µm laser is not
fiber-coupled, does not have an optical isolator, is less stable in terms of
temperature fluctuations, and has a lower beam quality, but it accesses a line that is a factor
of 20 stronger and thus ultimately promises 20× higher sensitivity.
Both lines are shown (Fig. 2) as a simulation based on the HITRAN (Rothman
et al., 2009) database. Assuming an optical resolution of 5.10-4 optical
densities (OD), the
hygrometer is expected to provide for a 1.4 µm CPc
(assuming a 1.5 m path length) under atmospheric conditions a water vapor
concentration range from 10 to 40 000 ppmv and for the 2.6 µm
closed-path channel from 0.5 to 5000 ppmv. The lower limit will finally be
defined by the capability of minimizing and compensating the effects of
parasitic H2O offsets (Buchholz and Ebert, 2014b) and their related
uncertainties. For the 2.6 µm channel, the upper limit depends
strongly on the retrieval quality of the baseline. This means, for example, under
lower pressure conditions (< 500 hPa) evaluations up to 40 000 ppmv
(similar to 1.4 µm) can be performed. At higher pressures and higher
concentrations the absorption line is strongly saturated and such broadened
that the baseline cannot be retrieved with a sufficiently low uncertainty.
The upper limit in general, i.e., also for the 1.4 µm channels, is
defined by the dew point related to the instrument temperature; 40 000 ppmv
is equal to 100 % RH at 28.5 ∘C and 1013 hPa.
HAI configuration
HAI can be installed on an aircraft in several different ways and
configurations. This paper focuses on the deployment during the first HALO
(Krautstrunk and Giez, 2012) campaigns (TACTS; Engel et al., 2013 and
ESMVal; Schlager, 2014), which targeted multiphase H2O measurements.
Figure 3 (right) shows the fuselage of HALO with the two mountings belonging
to HAI. In the very front is a trace gas inlet (TGI), where HAI uses the
second opening from the top to sample air in flight. The sampling height is
approximately 25 cm above the aircraft skin. Next to the TGI is the HAI open-path
cell (OPc), which is a fiber-coupled, White type three-mirror configuration
mounted on two pylons. These mirrors fold the 4.8 m laser light beam shown in
the middle (yellow). Both the TGI and the OPc are installed on HALO specific
aperture plates. Figure 3 (left) depicts the cabin installations with the
golden HAI main instrument in the middle. The black insulated half-inch pipes on the right side of the rack are the inlet pipes guiding the air
flow sampled by the TGI to the HAI main unit. These electro-polished
stainless steel pipes are heated (70 ∘C) to evaporate ice
particles and droplets in the gas stream at a flow rate of approximately
100 volume L min-1. The open-path sensor is not visible in this photo since
it is on the left side of the rack.
Schematic of HAI's working principle in the multi-phase
configuration. By combining selective open-path gas-phase measurements with
total water measurements in closed-path extractive cells (ice and droplets are
evaporated by heated inlet lines before reaching the cells), it is possible
to derive the ice water content from the difference between closed-path and
open-path readings.
HAI main unit
HAI's main unit comprises all laser, electronic, control, etc. modules except
for some minor electronics parts of the OPc, which are directly mounted
cabin-sidewise on the OPc to minimize noise and electromagnetic irradiation;
these components on the OPc comprise low noise preamplifiers for detectors
(InGaAs and InGaAsExt), five converters for PT-100 temperature
sensors, mirror heating controllers (20 W per mirror), and two gas
pressure sensors.
Many of the modules in the HAI main unit contain innovative developments,
which are or will be published in subsequent papers to prevent this
overview paper from being overloaded with long lists of technical details.
The important functions and modules to understand the HAI concept are as
follows:
automatic line identification and spectral locking to compensate temperature
and electrical influences as well as drifts;
internal mechanism (Buchholz and Ebert, 2014b) to minimize and compensate
effects from parasitic absorption, which usually leads to variable offset
problems in spectrometers like HAI;
gas handling system which tempers the air to be analyzed to the temperature
of the instrument and therefore avoids the risk of temperature inhomogeneity
even at high gas flows and provides detailed information about
temperature, pressure, and gas flow in and through the system;
several individual sensors and electronics to collect in total more than 120
system parameters about the HAI system's health – the so-called
“housekeeping data”;
sequence control system for steering, controlling, and exception treatment
of HAI with all its submodules.
HAI's closed-path cells
Core parts of the HAI main unit are the extractive, i.e., “closed-path”,
optical cells (Fig. 4) which are integrated in individual 1.4 and
2.6 µm optics modules. The 1.4 µm laser is a DFB fiber-coupled
diode laser at 1370 nm from NEL and the 2.6 µm diode laser is a DFB diode
laser from Nanoplus, which is collimated with a ZnSe aspheric lens designed
by our group. Both optics modules are completely independent. Both
CPcs use a new, compact, high numerical aperture miniature White
type (White, 1976) design that folds the laser beam (total approximately 1.5 m)
with three mirrors at approximately 7.5 cm distance. The 1.4 µm cell is an
improved, lens-less fiber-coupled version of the White cell described in
Kühnreich et al. (2013). It is combined with a new vacuum-tight
fiber feedthrough (Buchholz and Ebert, 2014a), which reaches a very low
leakage rate of 1.9 × 10-6 hPaL s-1 and prevents parasitic absorption
offsets due to ambient air. Three mirrors, one fiber, and one detector are
the only optical parts inside the cell. This reduced amount of optics
efficiently minimizes fringing effects down to a long-term 10-4 optical density signal-to-noise (ODSN) level. No readjustment of the
cells was needed or exercised in the approximately 400 flight hours which
have been realized over more than 3 years. The temperature is
measured via a 3 mm2 accurate (0.3 K) PT100 sensor for slow temperature
changes (t0.5 approximately 2.5 s) in addition to a
thermocouple (type T, 0.5 mm diameter) for faster gas temperature changes
(t0.5 approximately 0.5 s). Due to the above-mentioned gas handling
system and the thermal mass of the whole instrument (approximately 31 kg),
temperature fluctuations are measured to be below 0.1 K during aircraft
operation. Each cell has a volume of approximately 300 cm and is, depending on the
installation scenario, typically flushed during flight with a gas flow of 20–50 vol L min-1 cell-1.
This leads to an approximate exchange time of 0.7 s by using a “bulk flow” estimation. The 2.6 µm closed-path cell
is by its design quite similar to the 1.4 µm. The most significant
difference is the free-space beam guidance into the cell. Fibers at
2.6 µm are quite critical to handle and a suitable fiber beam splitter
was not available when HAI was developed. Therefore, the 2.6 µm
laser, the reference cell for spectral stabilization, beam splitters,
mirrors, and fiber coupling section for the open-path cell are free-space
coupled without fibers and enclosed in a specially designed gas-tight
2.6 µm optics module box. This module is internally dried by a
proprietary purging cycle containing a gas drying system based on the same
idea as that used for removing parasitic offsets in fiber-coupled hygrometer (Buchholz
and Ebert, 2014b). The 2.6 µm closed-path cell is directly coupled to
this laser module. Before reaching the cell, the laser beam is therefore
“free-space” guided in a water-vapor-free environment (in typical operation
< 0.5 ppmv) for approximately 19 cm. This efficiently avoids parasitic
effects in the beam path. The optical-path ratio between closed-path cell and
“free-space” area of 7.9 (1.5 m/0.19 m) yields in the closed-path 2.6 µm
cell an offset of less than 0.063 ppmv (< 0.5 ppm/7.9) to be
corrected. The total uncertainty of the offset determination
(realized via the so-called “two pressure separation method” (2pS); Buchholz and Ebert, 2014b) amounts
to less than 0.4 ppmv, which also defines the lower measurement limit of HAI's
2.6 µm channel.
HAI's open-path cell
HAI's open-path cell (Buchholz et al., 2013a), installed in the HALO's
fuselage, uses a similar optical White cell concept like the closed-path
cells (White, 1976) in HAI's main unit. Contrary to the closed-path cells,
both wavelengths (2.6 and 1.4 µm) are simultaneously coupled
into a single set of White arranged mirrors via two independent glass fibers
for 1.4 and 2.6 µm. The cell (made of 2 in. diameter copper mirrors) has
a mirror base distance of approximately 15 cm (2x more than the closed-path
cells) and is set to an optical path length of 4.2 m. The optical measurement
volume is located approximately 23 cm above the aircraft skin. The air
temperature within the open-path cell is measured via two surface-mounted
platinum PT100 sensors. These temperature sensors provide, together with the
HALO's temperature measurement (Giez, 2012) and our CFD simulation (to be
discussed in a subsequent publication; some preliminary data are in Buchholz, 2014), an estimated (±5 K) temperature in the actual
measurement volume of the open-path cell. Improved temperature field
simulations will be realized in the future as soon as a full CFD model of the
aircraft and HAI is accessible. To prevent a dew or frost buildup on the
mirror surface, all three mirrors are individually heated with approximately
20 W each. This raises the mirror temperature, defined by core temperature
measurements inside the copper mirrors, typically 15–20 K above the
ambient temperature. The gas pressure measurements are done by two commercial
piezo pressure transmitters. Their individual ports are located on either
side of the open-path cell. An in-flight pressure sensor validation has been
realized recently via the first optical airborne pressure measurements
(Buchholz et al., 2014b). This calibration-free approach exploits the
pressure dependence of the collisional broadening of the water absorption
line.
TDLAS-based multi-phase H2O measurements
By simultaneously performing closed-path and open-path water measurements at
high speed, HAI is the first TDLAS hygrometer allowing a calibration-free,
synchronous multi-channel, multi-phase water detection. The schematic of the
HAI configuration is shown in Fig. 4.
Both independent closed-path cells are connected via heated inlet lines
(visible in Fig. 3 left) to the forward-facing TGI sampling in clouds
gas-phase water vapor and ice particles or H2O
droplets together. Droplets and ice particles are evaporated in
heated inlet pipes, so that both closed-path cells analyze the so-called
total water content, i.e., the sum of the interstitial gas-phase
water plus the evaporated condensed-phase water. This evaporation step is
necessary since the dTDLAS signal is “blind” for direct absorption from
condensed water phases. The shifted broad band spectra of condensed-phase and ice-phase
water is thus suppressed and dTDLAS is only sensitive to the narrow spectral
structure of water vapor. The combination of the narrow absorption line width
with the narrow diode laser tuning range allows a very effective
discrimination of the broad spectra of ice or liquid water. Depending on the
final usage, the total water content has to be further corrected for the
particle sampling efficiency of the inlet system impacted by traveling
speed, inlet orientation, and other parameters causing an enhancement of
certain particle size fractions (overrepresentation of large particles due
to their momentum). A detailed sampling characterization and sampling
analysis of these HALO inlet (TGI systems) can be found in literature
(Krämer and Afchine, 2004).
HAI's self-validation possibilities under clear-sky conditions
(black) and measurement redundancies under all atmospheric conditions (red).
For details see text.
The open-path cell, directly located on the fuselage of HALO, measures the
pure interstitial gas-phase water vapor of the air flowing through the cell.
By combining the total water and the gas-phase water measurement, i.e.,
closed-path and open-path signals, it is possible to derive from the
difference of both HAI signals the pure condensed water phase, i.e., the
ice or droplet phase. To our knowledge, HAI is the first airborne instrument
that can measure all water phases with the same detection principle, the same
evaluation strategy, and with two channels, each spectroscopically and
temporally linked by one single laser. When independent devices have been
used previously to measure these water products, even with similar TDLAS
instruments like in Vance et al. (2015), this always led to the discussion
on how many of the deviations are caused by atmospheric effects and how many
are linked to discrepancies between the participating instruments caused by
different characteristics, response times, or nonlinearities of the individual
instruments. Indeed, the calibration or even validation of all other
open-path hygrometers cited above is based on laboratory measurement or
in-flight comparisons with other hygrometers with different properties, making
it nearly impossible to justify more than statistical (averaged)
statements. Especially in mid-tropospheric regions, small-scale spatial
variations of water vapor content lead, at typical HALO cruising speeds, to
local temporal gradients of up to 1000 ppmv s-1 in the gas-phase and several
10 000 ppmv s-1 in the total water phase. HAI's effective average
time of just 1.3 ms at an output data rate of 120 independent
measurements per second allows for the first time a detailed investigation of
such structures as well as a very high-temporal-resolution point-by-point
comparison of all four spectrometer signals of HAI. Pairwise comparisons of
HAI's four channels therefore allow various possibilities for self-validation
of the in-flight signals. Further, it is possible to avoid averaged
statements and facilitate discrimination/analysis of effects such as sampling
artifacts, hysteresis, vibrations, and oscillations.
Self-validation
The new self-validating capabilities are a consequence of the unique 2 × 2
multi-channel configuration, which enables six individual validation
pathways, as illustrated in Fig. 5. Under
clear-sky (cloud-free) conditions, the independent open-path and
closed-path measurements should each deliver the same value. Important to
note is that both the 1.4 and the 2.6 µm spectrometer
channels are evaluated without common parameters in calibration-free (first-principle) mode. This means, in detail, two different lasers, two
different absorption paths, two different absorption lines, different line
parameters, different electronics, different gas pressure/temperature
sensors, etc. Nevertheless, both spectrometer channels have to agree within
their uncertainty if the first-principle evaluation is applicable. Due to
the large overlapping H2O concentration range of approximately 50 to
5000 ppmv, all disturbances can be clearly identified as long as they affect
both spectrometers differently. Outside of clouds (clear-sky) – i.e., in ice-particle-free and water-droplet-free air – all four spectrometer channels have to
agree. This in particular allows us to validate (or even if necessary to
calibrate) the closed-path cells in a laboratory environment and to use them
as an airborne transfer standard to validate or even calibrate in turn the
open-path sensor in flight. This is a unique property of HAI, since each
open- and closed-path spectrometer is coupled by one single laser and all
four spectrometers employ the same evaluation procedure. Hence, HAI is
called a 2 × 2 spectrometer.
dTDLAS raw signals (spectra) of all seven spectroscopic channels
permanently acquired and saved by HAI. The x axis is already converted to
the wavelength axis via the individual dynamic tuning rate of each laser.
Each spectrum contains approximately 170 016 bit values (PA: parasitic
absorption; sStab: spectral stabilization).
ResultsSignal assessment
Figure 6 shows – at the same instant of time – all seven raw optical signals
from HAI's measurement channels at approximately 15 000 ppmv and 900 hPa. For
visualization, the signals of each laser are scaled to the same peak maximum,
since transmission changes are irrelevant according to the equation for
concentration retrieval (Eq. 2). The time axis (240 Hz laser repetition
rate) is already converted to the wavelength axis using the dynamic tuning
rate. The triangular modulation of the diode laser current yields a triangular
modulation of the beam intensity and a similar shape in the wavelength space.
The tuning range of the 2.6 µm laser is set to be broader in order to
allow the evaluation of high absorbance spectra when the peak absorption is
in saturation in the absorption line center. The Lorentzian line wings still
carry enough information to retrieve the concentration. The absorbance of the
open-path signal (e.g., the 1.4 µm signal in purple) is higher than the
one from the closed-path cell due to the 3-fold longer optical path
length in the open-path cell. The 1.4 µm reference cell signal (blue)
is used for spectral stabilization and acquired from a low pressure (approximately
20 hPa) fiber-coupled reference cell. The signal for retrieving and
compensating parasitic absorption is shown in green and the associated
evaluation is described in Buchholz and Ebert (2014b). Accordingly, the
three 2.6 µm signals are shown. The parasitic absorption compensation
of the 2.6 µm system is due to the “free-space” signal in the sealed
laser module (see description above) and is at least as important as the one for the
1.4 µm spectrometer. This compensation treatment is done by using an
ultra-low pressure (approximately 0.5 hPa) pure water reference cell which provides
information for both the spectral stabilization and the parasitic absorption
correction. The latter approach is the 2pS in Buchholz and Ebert (2014b).
The exact gas pressure or gas matrix in the reference cells is
irrelevant as we use the cell for line locking but not for concentration
calibration as other TDLAS instruments do. The requirement for such a cell is
that it contains a sufficiently high water vapor content to ensure a
sufficiently high S/N H2O absorption signal. The temperatures of both
reference cells are accurately measured (PT100 sensor) to monitor the gas
pressure increase in the reference cell caused by the temperature rise. This
pressure rise induces a significant, measurable shift of the absorption
line position, which needs to be corrected to achieve a very high line
stabilization quality. The retrieved line position thus is corrected for this
shift based on the measured temperature and the known pressure shift
coefficient of the absorption line.
Typical pre-processed absorption signals (after baseline, offset,
and transmission correction) for both closed-path spectrometer channels
(left: 1.4 µm; right: 2.6 µm) during flight. The laser
modulation frequency was 240 Hz. Fifty individual raw scans are pre-averaged
yielding 4.8 H2O measurements per second with 69 ms total integration
time. This corresponds to a spatial resolution of 15 m at 800 km h-1 cruising
speed. Without averaging, HAI achieves a maximum time resolution of 1.3 ms at a spatial resolution of 30 cm at 800 km h-1 cruising speed. (It
should be noted that the four vertical axes have different scales.)
Figures 7 and 8 show
four typical absorption signals in flight with a pre-average of 50 raw
measurements recorded at 240 Hz. This leads to 4.8 measurements per second.
In flight situations similar to that, only approximately one-sixths of the whole (up- and
down-ramp) spectral scan contains significant contributions of the water
vapor absorption line (the pressure broadened target absorption line).
The “rest” of the wavelength scan is used to retrieve the baseline and the
laser-out region and used for situations where gas pressure and water vapor
concentrations are much higher (e.g., low flights in warm areas, especially
in clouds). This small section with the narrow absorption line can thus be
interpreted as the “effective” water measurement interval, which could then
be interpreted as an effective time resolution (after averaging) of
70 ms for each reading (1/240×1/6×50).
Figure 7 allows the simultaneous comparison of both
closed-path cells. Gas pressure and temperature are slightly different due
to the different piping and installation placement inside of HAI. Both
H2O concentrations agree (Δ= 1.6 %) within the combined
uncertainties (7.9 %) of both channels (4.3 % and 5.9 %).
Each graph in Fig. 7 shows in the top the
measured data (green dots) with the fitted Voigt profile (black line). The
measurement data are shown as absorbance (ODe =-ln(I/I0)) values,
i.e., the detector (I) signal divided by the so-called “base line”
(I0). The baseline resembles the absorption spectrum in the absence of
the molecular target absorption. It is retrieved from the raw spectrum (I) by applying a synchronous polynomial fit of the baseline and a Voigt profile
for the absorption line. The residuals between measured data and the model
function are depicted below. The remaining structures visible in the
residual are optical interference fringes, caused by light scattered from
imperfect mirrors, fiber surface, detector arrangement, and wall
backscattering. The frequency of a fringe can be linked to the optical
distances between the interfering surfaces. So far, these baseline
structures as well as the cell alignment remained stable and the mirrors
have never required any cleaning during the last 4 years.
“Stability” always has to be related to the right timing context:
calibration-free dTDLAS are usually operated at relatively moderate OD
resolution levels (of about > 10-3). Otherwise impacts which
affect the long-term stability cannot be controlled or assessed well enough.
However, in the context of “short-term” precision statements the residual
structures only have to be stable on the order of minutes instead of years.
The entire fringe structures displayed in the pictures above are phase
shifted by temperature due to mechanical expansion of the cell structure.
However, the thermal mass of the cells prevents fast temperature changes,
which leads to very stable “short-term” fringes down to the 10-5 OD
levels.
Comparison of pre-processed TDLAS scans (similar to
Fig. 7) of HAI's 1.4 µm closed-path (left)
and the 1.4 µm open-path (right) cell during a HALO flight. Despite
the entirely different measurement conditions such as wind speed (cm s-1 to
100 m s-1) or temperature (+28 to -23 ∘C), both
channels are evaluated calibration-free with the exact same methods and
model.
This is a crucial point compared to WMS systems, where fringe levels have to
be controlled down to the 10-6 OD level to achieve the sensitivity
advantages WMS is known for. Our calibration-free dTDLAS evaluation is
designed to require a stable baseline only in the 10-4 OD range, which
makes it inherently robust and improves the stability of the entire system.
In other words, dTDLAS systems like HAI are usually not as sensitive to
misalignments, dirt, or optical interfering structures as WMS systems but
require higher absorbance (higher line strength or longer path length)
to allow an implementation of our calibration-free approach.
Figure 8 shows a simultaneous comparison between the open-path and the
closed-path channels. The outside gas temperature in the open-path cell is
approximately 52 K lower than in the closed-path cell. The gas pressure in the
closed-path cell is much higher than in the open-path cell due to the ram
pressure guiding the air through the TGI and piping. The measured water vapor
concentrations are slightly shifted in time due to the gas transport and the
sampling delay in the pipes. The 1σ residual is a factor of 6
higher in the open-path than in the closed-path sensor. This mainly results
from the high wind speed through the open-path cell (approximately 800 km h-1),
vibrations, and the thermal mass of the closed-path cell as mentioned above.
It is important to mention that for typical diode laser instruments
(direct as well as WMS) a change of gas temperature, pressure, and
concentration over a large range can cause large systematic offsets, which
frequently require at least a three dimensional calibration procedure
(including pressure, temperature, and concentration dependence) or the
integration of complex assumptions (Duffin et al., 2007; Goldenstein et al.,
2014; Rieker et al., 2009) to correct the data via a simulation model. WMS
closed-path systems usually avoid this problem by always keeping gas
temperature and pressure as close and constant as possible to the calibrated
level, which is obviously not a viable approach for an open-path system.
Precision
One common figure of merit to quantify the short-term optical precision of a
spectrometer is the 1σ noise level in the residuum. The idea is based
on the assumption that the residual is governed by random noise, which limits
the precision of the system. The signal-to-noise ratio (SNR) is then defined
by the signal as the ODepeak value and the noise as
the statistical standard deviation of the residual and thus equal to
ODnoise. This definition yields for the 1.4 µm (2.6 µm)
closed-path cells at 4.8 Hz time resolution a SNR of 54 (204). The
corresponding precision is then 100/54 ppmv = 1.9 ppmv (at 1.4 µm)
and 100/204 ppmv = 0.49 ppmv (at 2.6 µm). This
determination is quite consistent with Fig. 8, where the precision of the
1.4 µm closed-path can be estimated to 1.3 ppmv in the same way.
However, as the fringe structure of the closed-path cell is, as discussed
earlier, quite stable, it is better to determine the instrument precision via
the Allan variance (Allan, 1966). Figure 9 shows the Allan plot for both
closed-path cells. The measurement was done at 255 ppmv by measuring gas from
a big vessel. Under these conditions, the precision at 4.8 Hz is 0.22 ppmv
(for the 1.4 µm closed-path cell) and 0.065 ppmv (2.6 µm closed-path cell). Both values are approximately a factor of 8 lower than
the one derived from the single scan analysis, which confirms the stability
thesis for these kinds of fringes. Vice versa we can derive an actual
(unstable, random) 1σ residual OD noise level of 2.3×10-5 for the
1.4 µm closed-path and 1.7×10-4 ppmv for the 2.6 µm closed-path
cell. The best precision is achieved at 3.9 Hz (0.18 ppmv) for the
1.4 µm cell and at 13 Hz (0.055 ppmv) for the 2.6 µm cell.
Normalizing this with respect to time resolution and optical path length
yields a 1σ precision of 187 (1.4 µm) and 31
ppbv m Hz-1/2 (2.6 µm).
Comparing that precision ratio of 6 between the 1.4 and the
2.6 µm to the ratio in line strength of 20 (see line selection
Sect. 3.3) confirms the statement mentioned above
that drawbacks of the longer wavelength technology significantly constrain
the practically achievable maximum gain.
For the open-path sensor, we have not yet been able to calculate an Allan
variance so far, since we have not acquired a dataset in flight where the
atmospheric water was constant enough not to dominate the Allan variance via
natural H2O fluctuations. However, using the short-term single spectrum
evaluation method in Fig. 8 yields a SNR of 145 for the
1.4 µm path, which leads to a precision of 2.1 ppmv
(4.6 ppmv m Hz-1/2).
Uncertainty consideration
One of the major benefits of the dTDLAS evaluation done with HAI is the high
level of control over the fitting and evaluation process. In particular,
combining it with the complete storage of all raw data allows a dedicated and
highly flexible post-flight analysis or post-flight processing. Raw data
storage for HAI covers not only all raw spectra but also approximately 120
measured parameters depicting the complete status of the HAI instrument. This
“housekeeping data”, for example, encompasses 15 temperature measurements
that enable diagnostic statements, for example about temperature inhomogeneity,
electronic drift, or sensor malfunctions. Housekeeping data like these
facilitate assignments of a measurement uncertainty closer to the one in a
metrological sense (Joint Committee for Guides in Metrology (JCGM), 2008). If
measurements are done in harsh environments or under rapidly changing
conditions, typical concepts of metrological uncertainty assessments fall
short since most of their concepts are developed for laboratory applications
under quasi-static conditions. However, even in this case the housekeeping
parameters provide information for a trustful uncertainty consideration. The
uncertainty calculations below are based on a physical model (Eq. 2) and
performed with an approach independent from the actual measurement. Depending
on the individual science community, these uncertainties, errors,
reproducibility, or misreading are often determined by comparing the readings
of an instrument to reference instrument. This is contrary to a metrological
uncertainty and furthermore a nonindependent approach; we therefore call that
simply deviation or standard deviation.
According to the Eq. (2) for retrieving the
final concentration, the individual contributions to the total uncertainty
budget can be assessed as followed for both closed-path cells. The optical
path length was primarily determined with a Zemax-based ray tracing
simulation. In addition, it was calculated by the mechanical mirror distance
and compared to a test measurement with a known amount of CH4 gas in
the cell. These measurements lead to an uncertainty of the optical path
length of 15 mm (1.1 %). The uncertainty for the used H2O absorption
line strength is 3.5 % [44]. The temperature sensors (PT100 and
thermocouple type E as described above) are calibrated against metrological
transfer standards (Rosemount platinum resistance thermometer type 162CE
(PRT-25), uncertainty 1.5 mK). Due to the well-known fact that temperature
measurements in moving gases suffer from many issues, we use a conservative
estimation of 1K (0.3 %). The manufacturer of the pressure sensors (Omega
PAA33X) advertises a resolution of 0.02 hPa and a long-term accuracy of
0.5 hPa. We use 1 hPa as a conservative uncertainty estimation. A general
statement for the laser tuning uncertainty is difficult in a metrological
sense, since it depends on local effects in the spectra, pressure range,
concentration level, and number of absorption lines fitted. From our
experience, we assume that deviations related to the fitting process in
total (tuning + fit process) are in the range of below 1.5 % for
situations similar to Fig. 6 (note that our calibration-free dTDLAS systems are usually designed to work in
the > 10-3 OD level, where a clear signal assessment is
feasible). As a conservative approximation we use 2 % for the fit and
1 % for the tuning. This yields to a total uncertainty of 4.3 % for the
1.4 µm closed-path cell.
Allan variance plots for both closed-path spectrometer channels.
The measurements were done by analyzing gas from a big buffer vessel with a
H2O concentration of 255 ppmv. The optimal precision at 4.8 Hz derived
from this measurement is 0.22 ppmv for the 1.4 µm closed-path cell
and 0.065 ppmv for the 2.6 µm closed-path cell. The best – i.e.,
highest –
precision of 0.18 ppmv is achieved at 3.8 Hz for the 1.4 µm cell and
0.055 ppmv at 13 Hz for the 2.6 µm cell.
The largest relative influence to this total uncertainty budget results from
the line strength (66 %), followed by the fitting uncertainty (21 %), as
well as the optical path length (7 %). Additionally, the offset uncertainty
is defined in a calibration-free dTDLAS system by the capability of
minimizing and quantifying parasitic offset effects. It is ±3 ppmv for
the 1.4 µm closed-path cell of HAI, which uses the same parasitic
prevention treatment as SEALDH-II (Buchholz and Ebert, 2014b). Thus, the
total uncertainty of the 1.4 µm closed-path system is 4.3 % ± 3 ppmv. Similar considerations lead to an uncertainty of
5.9 % ± 0.4 ppmv for the 2.6 µm closed-path cell.
Related to the
open-path cell, similar uncertainty statements are more difficult. First of
all, a full CFD model of the boundary layer around HALO is currently missing,
which would allow a retrieval of the gas temperature in the open-path cell.
The first CFD test runs of HAI's open-path sensor and its immediate
surroundings have been realized (A. Afchine (FZJ), published in Klostermann, 2011), and they led to a first temperature uncertainty estimate of
±7 K. The full CFD model could also provide pressure field
calculations. However, in this case, we developed a method to retrieve
the pressure directly from the dTDLAS signal (Buchholz et al., 2014b) and use
that for a validation of the built-in micromechanical pressure sensor. The
pressure dependent uncertainty is in the range of 3–5 %. The uncertainty of
the fitting process reveals a much higher complexity. It depends on all
impacts on the spectral signal itself, such as background radiation on the
detector, misalignment due to the distortion of the HALO fuselage, forming of
an ice layer on the mirrors, high optical dense cloud transects, and eddies
around the aircraft. From all the data gathered and compared during the
HALO missions TACTS and ESMVal, we could not find any deviation larger than
±5 % in clear-sky conditions between the open-path and the
closed-path sensor which could not be clearly linked to explainable effects.
Besides the availability of an entire CFD model in the future, HAI will be
validated by sampling the pure water vapor gas phase with its closed-path
cells (connected to backward-facing TGI measurement) in future campaigns.
These datasets will sustainably prove whether the ±5 % deviation is valid
under all conditions, since all four channels of HAI (Fig. 5) should
permanently measure then the same atmospheric value. Compared to the
deviations, revealed in the AquaVIT (Fahey et al., 2014) comparison of state-of-the-art airborne hygrometers in 2007, this 5 % is 4-fold smaller than
the span (20 %) of those instruments there. Additionally, one has to keep
in mind that this AquaVIT comparison was done under quasi-static laboratory
conditions. Therefore, this in-flight 5 % deviation between the open-path
and the closed-path sensor shows the great performance of HAI, even if a
general metrological uncertainty is difficult to be given at this time.
First airborne measurements with HAI on the HALO aircraft
Figure 10 shows typical HAI measurements during the first HALO science
mission “TACTS/ESMVal”. The chosen flight profile shows 15 min of a slow
ascent from the lower to the upper troposphere. Depicted in Fig. 10 are
the H2O concentration measured with the 1.4 µm CPc (black), the
1.4 µm OPc (magenta), and the 2.6 µm CPc (red) channel of HAI
(left axis) as well as the ambient gas pressure (right y axis, in blue),
which is directly connected to the flight level (height). The green dotted
line shows the water vapor saturation mixing ratio (SMR) calculated from the
static ambient temperature and pressure measured by the HALO avionic system
(Giez, 2012). The SMR indicates that HALO flew in the early part of the
flight (left) in clear-sky conditions (no clouds) and in the second part of
the flight though cirrus clouds. All Fig. 10 data were plotted with 1 Hz
temporal resolution. With increasing height, the H2O concentration
declines about 2.5 orders of magnitude from approximately 2000 to 75 ppmv, in
combination with simultaneous relative pressure variation from approximately 175 to
450 hPa (Δp= 275 hPa). The H2O concentrations, measured with
HAI's two independent closed-path spectrometer channels, fit nicely to each
other and show only a small average relative deviation of 1.9 % (bottom
graph in red), which is entirely covered by the uncertainty range of each
spectrometer (4.3 resp. 5.9 %). This result is consistent with other
measurements, where the 2.6 µm path was persistently on average
approximately 2 % higher than the 1.4 µm channel. This could be easily
explained with the 2.6 µm line strength from the HITRAN database being
2 % too small. The 1.9 % deviation also needs to be related to the fact
that both hygrometer channels are evaluated completely independently, without
any calibration of the instrument and most importantly under real-world
flight conditions. Despite these demanding conditions, HAI's two closed-path
channels generate a relative deviation which is just one-tenths of the above-mentioned 20 % in AquaVIT (Fahey et al., 2014), which demonstrates the
excellent accuracy of the HAI spectrometers.
HAI measurement on board HALO during the TACTS/ESMVal
campaign. Left: the flight profile shows a 15 min section of a slow ascent
from the lower troposphere to the upper troposphere. The first part (under
clear-sky condition) can be used to perform an absolute in-flight accuracy
comparison of HAI's measurement channels; the second part demonstrates a
multi-phase H2O measurement. For further explanations of all signals
and visible effects, see text.
Right: the scatterplot displays on the x axis the water vapor concentration
measured by the 2.6 µm closed-path cell and on the y axis the
relative deviation of the 1.4 µm closed-path cell (and the
1.4 µm open-path cell) from the 2.6 µm
closed-path cell measurements. This scatterplot contains all data from 13:40
to 13:48 shown in the left graph.
The purple signal in
Fig. 10 shows the measurement of the 1.4 µm open-path sensor
(1.4 µm OPc). Compared to the 1.4 µm closed-path cell
(1.4 µm CPc), the 1.4 µm OPc shows a relative deviation of
±5 % peak-to-peak or 2 % on average. Even though this performance
seems good in comparison with the AquaVIT results, a more suitable literature
reference would be one with in-flight open-path measurements rather than
quasi-static laboratory comparisons. However, there is none like that and
therefore a “state of the art” statement is quite difficult. This holds
especially when we consider the fact that the “uncertainties” or “errors”
stated for the other the AquaVIT participants were claimed to be on such a low
level that they did not cover or explain the deviations observed. This is
strongly correlated to different and nonstandardized definitions, procedures
on how to calculate sensor accuracy, and other performance figures yielding
often a systematic underestimation of uncertainties compared to metrological
procedures such as JCGM (2008) and Joint Committee for Guides in Metrology
(2009).
However, a short incomprehensive overview on the performance of previous and
current open-path instruments should be given here to compare HAI with their
claimed accuracy level:
A calibrated, 1 m direct absorption, open-path sensor at
1.4 µm was developed in a feasibility study for the Strato 2C research
aircraft and described to cover a concentration range of 2–2000 ppmv with
100 ppbv precision at 1 Hz. It was claimed to have an accuracy of 5 %, but
this hygrometer never flew (Roths and Busen, 1996).
A calibrated open-path sensor with an optical path length of
375 cm, was folded in a Herriott cell, and a time resolution of 25 Hz has a
precision of 80 ppbv. This instrument is designed for the American HIAPER
aircraft, a Gulfstream 500 similar to the HALO aircraft. Typical deviations
to other instruments were in the range between 2 and 10 % (Zondlo et
al., 2010).
A calibrated, Herriott cell-based open-path hygrometer with a
total path length of 11 m, a measurement frequency up to 10 Hz, and a
precision of 50 ppbv at 0.5 Hz claims to have an uncertainty
of 5 to 10 % (May, 1998).
An innovative concept was deployed by Diskin et al. (2002). The
spectrometer's (2f, calibrated) hardware is attached from the inside of the
cabin to a window. The laser beam hits a retro reflector mounted on the outer
engines under the airfoil. The total absorption path is 28.5 m; the
measurements frequency is 1 Hz; a 1σ error is claimed to be 3.7 %
(Podolske et al., 2003). Measurements published in Diskin et al. (2002)
show deviations of the same order.
In summary, HAI's 2 % deviation appears to be better or at least equal to
typical statements in publications but is based for the first time on a
meteorologically evaluated, calibration-free approach.
To further evaluate the performance of HAI's open-path channels we discuss
some of the TACTS data. The dark purple line shows the data after smoothing
with a 60 s sliding window algorithm. Quite interesting are the
two dents at 13:41 and 13:44 in the relative deviation, which means the
closed-path measurement was higher than the open-path measurement. Both of
them happened after the water vapor concentration dropped sharply. Hence,
the dents might be explained by desorption processes inside of the TGI, its
piping, and the measurement cell. This idea is backed by the 1.4 to
2.6 µm closed-path comparison (red). The same behavior is also
visible at 13:44, but here the 2.6 µm closed-path cell added less
desorption water to the gas steam than the 1.4 µm one. Another
influence leading to that deviation could be a temperature measurement on
HALO affected by fast changes of humidity. The total temperature influence
in Eq. (2) is at this temperature and pressure
approximately 0.6 % K-1 caused by the temperature influence via the ideal
gas law, the line strength temperature dependence, and the Gaussian width.
Other major influences can be excluded since all relevant housekeeping data
clearly indicate that HAI operated as expected during the entire time
period. The final distinction of small local effects like that can be done
by acquiring more flight data in the troposphere for a better understanding
of the sampling system. This is planned to be realized in a future campaign more
oriented towards the mid-troposphere.
This case study and analysis of HAI's capabilities and performance
demonstrates the unique opportunities offered by HAI, which allows a deep
analysis to identify and eventually classify different measurement sections
of a flight in order to distinguish instrumental artifacts from real
atmospheric situations.
During the second part of the flight shown in Fig. 10, the aircraft flew through ice clouds (cirrus; temperature
< -40 ∘C). The evaporation of the ice crystals captured by the gas
inlet leads to a clear enhancement of the amount of water vapor in the
closed-path cells. The relative comparison shows a ±5 % noisy
structure, which is caused by the low time resolution of just 1 Hz. H2O
gradients in this particular part of the flight are in the range of several
1000 ppmv s-1. Due to SSD hard drive space limitations in the first campaign, only parts
of the flights were sampled with the highest time resolution of 240 Hz;
other parts were pre-averaged online. Like in the clear-sky region,
the average deviation between both closed-path cells during cirrus cloud
transects remains at 2 %. To further check the accuracy of the open-path
channel, we use two methods. The first is based on the physical argument
that the gas-phase measurement always has to be lower than the total water
measurement, which consists of gas-phase plus evaporated ice or liquid phase.
This requirement is entirely fulfilled. The second check compares the
absolute gas-phase measurement with the SMR. In the
case of a fully equilibrated cloud, the gas phase has to be saturated at
temperature T, meaning that the relative humidity is rH = 100 %, from
which we can derive the SMR with the help of the water vapor partial
pressure curve. This is a weaker check, since it is a well-known fact that
supersaturation can occur during strong air updrafts.
The transition region at the edge of the cloud is also very interesting,
where the closed-path cell sees an enhancement while still being below the
SMR. This means that the fringe of the cloud was not saturated but had
sublimating ice particles in it.
Performance overview of HAI: 1 lower limit is defined by
offset uncertainty and the upper limit by instrument temperature or spectroscopic
effects; 2 calculated uncertainty based on calibration-free
evaluation model; 3 calculated based on the accuracy level of the
independent parasitic absorption minimization and determination (Buchholz and
Ebert, 2014b); 4 general statements for typical airborne application;
4,5,6,7,8 statements based on several metrological method
validations which are not part of this paper; 9 precision derived from
single absorption signal as described in paper; 10 precision derived from
Allan variance approach; 11 depending on broadening of the absorption
line,
the maximum time resolution variates between 1 and 2 ms; 12 HAI reports 120
independent measurements per second; 13 the effective time resolution is
limited for the closed-path cell by the gas flow rate (exchange rate) and for
the open-path cell by an minimum average number of two raw spectra to save
SSD space; 14 the Allan variance is not a feasible approach for retrieving
reliable in-flight statements; 15 these open-path figures are inferred from
closed-path measurements, and a direct metrological validation is not viable; 16 figures are based on a variety of studies which will be published in
individual, coherent publications.
1.4 µm CP2.6 µm CP1.4 µm OPEvaluation methodCalibration-free dTDLASCalibration-free dTDLASCalibration-free dTDLASMirror configurationFiber-coupled WHITE cellFiber-coupled WHITE cellFiber-coupled WHITE cellOptical path length1.5 m1.5 m4.2 mH2O concentration range13–40 000 ppmv0.4–10 000 ppmv1–50 000 ppmvLinear uncertainty24.3 %5.9 %5 %Offset uncertainty3±3 ppmv±0.4 ppmv±1 ppmvAccuracy4< 4 %16< 4 %16< 6 %15Long-term stability50.35 %160.35 %160.35 %15Reproducibility60.1 %160.1 %162 %15Instrument temperature impact70.026 %/K160.1 %/K160.026 %/K15Instrument humidity impact8< 0.001 %/%RH16< 0.001 %/%RH16< 0.001 %/%RH15Precision A9 at 4.8 Hz & 200 ppmv1.9 ppmv0.49 ppmv2.1 ppmvPrecision A9 normalized1.3 ppmv m Hz-1/20.35 ppbv m Hz-1/24.6 ppmv m Hz-1/2Precision B10 at 4.8 Hz & 200 ppmv0.22 ppmv0.065 ppmv14Best precision B10 at 200 ppmv0.18 ppmv (3.9 Hz)0.55 ppmv (13Hz)14Best precision B10 normalized187 ppbv m Hz-1/231 ppbv m Hz-1/214Maximum optical time resolution111.3 ms1.3 ms1.3 msMaximum measurement frequency12120 Hz120 Hz120 HzEffective time resolution130.7 s0.7 s2.6 msSpatial averaging at 800 km h-1155 m155 m0.57 m
The data between 13:40 and 13:48 of Fig. 10 right are plotted in Fig. 10
left in a different way: the x axis shows the water vapor concentration
measured by the 2.6 µm closed-path cell; the y axis shows the
corresponding relative deviation between the 1.4 µm closed-path cell
(in black; the 1.4 µm open-path cell in purple) and the
2.6 µm closed-path cell. The ±10 % range of the y axis is
chosen to show HAI's independent in-flight performance in the
perspective of the results of the cited laboratory comparison
AquaVIT. As explained above, there is a small offset between both instrument
channels. This is not surprising as all HAI channels are evaluated completely
independent in the described calibration-free approach. The deviations
between the 1.4 and 2.6 µm closed-path cells of about 2 %
have consistently been seen in other measurements too, but they are within the
metrological uncertainty of the evaluation. This systematic deviation is most
likely caused by an inaccurate line strength parameter in the HITRAN (Rothman
et al., 2009) database used for this evaluation. The very tiny concentration
dependency (0.18 % per 1000 ppmv) of the relative deviation between the
1.4 and the 2.6 µm closed-path cell reflects the larger
complexity of evaluating the 2.6 µm spectra at very high ODs – e.g., approximately OD = 16 at 8000 ppmv.
Note that an evaluation of the 2.6 µm closed-path cell at
8000 ppmv is from a spectroscopic point of view similar to the evaluation of
a hypothetical 160 000 ppmv concentration in the 1.4 µm closed-path
cell. Comparing this 160 000 ppmv with HAI's (1.4 µm closed-path cell)
calculated offset uncertainty of 3 ppmv (laboratory validations show real
fluctuation of just around 0.6 ppmv) demonstrates the impressive linearity
which can be achieved with our dTDLAS systems, like HAI, over a range of 5 to 6
orders of magnitude.
These first detailed close-ups in HAI's multi-phase measurements show the potential to investigate many interesting questions in following campaigns. To really
investigate and fully understand all effects, HAI needs more data in cirrus,
mixed-phase, and other clouds in a multi-phase configuration as well as a
validation campaign in which closed- and open-path cells measure the pure
gas-phase via a backward-facing inlet. The latter is necessary to
distinguish between sampling effects and optical or spectral effects in and
outside of clouds. This knowledge, especially in combination with the
mentioned CFD model, will then allow the reevaluation of HAI's raw data, which are
always saved during flights, and extent statements even for this short cirrus
transect.
Performance summary
Table 1 shows an overview of HAI's current performance features. The
2.6 µm open-path channel is not mentioned due to low light
transmission in the 2.6 µm fiber. This problem is likely to be
related to the novel ZBLAN fiber used here which prevented the 2.6 µm
open-path channel from excelling the 1.4 µm open-path performance during the first
flight campaign. It is expected for future campaigns that the optical
transmission and thus the 2.6 µm open-path channel performance can be
improved significantly.
Conclusion and outlook
The novel Hygrometer for
Atmospheric Investigation realizes a simultaneous gas-phase and total water
measurement in a unique concept. Based on calibration-free direct tuneable diode laser
absorption spectroscopy, HAI provides a variety of unique features as
summarized in Table 1, such as accurate (< 4 %), precise
(0.065 ppmv at 4.8 Hz, channel depended), and very fast (down to 1.3 ms)
measurements with meteorologically defined uncertainties (4.3 %, channel
dependent). HAI contains four measurement channels, grouped into two
completely independent dual-channel spectrometers, one at 1.4 and
one at 2.6 µm, to cover the entire H2O concentration range of the
aircraft accessible atmosphere. Each spectrometer feeds light in a
wavelength-individual extractive, closed-path cell with an optical
absorption path length of 1.5 m for total water measurements. Additionally,
both spectrometers couple their light in a common open-path cell (optical
path of 4.8 m) located outside of the aircraft fuselage, for a sampling-free
and contactless determination of the gas-phase water content. These four
spectroscopic channels plus three additional supplementary spectroscopic
channels allow multiple self-validation strategies inside and outside of
clouds and therefore solve the current lack of an integrated approach to
validate open-path sensors in flight. HAI's complex control software
minimizes maintenance at ground and ensures almost entirely autonomous
operation. In addition, instrument health is permanently supervised by
permanent storage of more than 120 housekeeping data. This enables a novel,
holistic quality management and a sophisticated signal cross check, which
guarantees a high trust level of the final H2O values.
HAI was operated for the first time during the TACTS/ESMVal flight campaign
for more than 120 operation hours without any malfunction. The entirely
independent, never-calibrated first-principle evaluation of the closed-path
spectrometer channels yields in-flight deviations of only 1.9 % over a
large concentration (75 to 2000 ppmv) and pressure range (175 to 450 hPa).
The deviation between the open-path and the closed-path measurements in the
same flight segment was on average just 2 %, with a short-term deviation
between +3 and -5 %. Despite measuring with a single evaluation
concept, over a very broad range of conditions (i.e., temperatures
(-70 to 30 ∘C), gas speeds (cm s-1 vs. 100 m s-1), and optical
disturbances (no background light vs. sunlight; clean mirrors vs. dirty
scratched mirrors outside), HAI provided a high trust level of the data over
extensive science missions. Laboratory evaluations demonstrated the lowest
achievable precision of 0.18 ppmv (at 3.8 Hz) for the 1.4 µm
closed-path cell and 0.055 ppmv (at 13 Hz) for the 2.6 µm closed-path
cell.
In conclusion, HAI proved during its first deployment a novel, highly
complex, and demanding set of capabilities. This will enable in the future a
much more accurate and stringent evaluation of atmospheric multi-phase water
vapor data inside and outside of clouds and foster in further HALO missions
the investigation of new scientific questions in the atmospheric water
cycle. HAI can serve in the future as a major, powerful tool for
cutting-edge atmospheric water vapor measurements.
Data availability
HAI's data are available on the HALO database https://halo-db.pa.op.dlr.de/.
Acknowledgements
Parts of this work were funded by the Deutsche Forschungsgemeinschaft (DFG) via
FKZ EBE 235/3 and SCHI 872/2. Numerous metrological questions were partially
co-funded via the EMRP/EMPIR projects METEOMET-1 and METEOMET-2. The
EMRP/EMPIR is jointly funded by the EMRP/EMPIR participating countries within
EURAMET and the European Union. The authors wish to thank M. Riese
(Forschungszentrum Jülich) for the strong continued support of the
project, Mark Zondlo (University Princeton) for sharing his significant knowledge
about atmospheric water vapor measurements, A. Engel and H. Bönisch
(Goethe Universität Frankfurt) for their coordination of the TACTS
campaign, H. Schlager for organizing the ESMVal campaign, and A. Giez,
M. Zöger, V. Dreiling, K. Witte, and A. Minikin, representatives of the
Deutsche Luft und Raumfahrtzentrum Oberpfaffenhofen, for providing the HALO
avionic data.
Edited by: D. Toohey
Reviewed by: three anonymous referees
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