Ozone plays a significant role in the chemical and radiative
state of the atmosphere. For this reason there are many instruments used to
measure ozone from the ground, from space, and from balloons. Balloon-borne
electrochemical cell ozonesondes provide some of the best measurements of the
ozone profile up to the mid-stratosphere, providing high vertical resolution,
high precision, and a wide geographic distribution. From the mid-1990s to the
late 2000s the consistency of long-term records from balloon-borne
ozonesondes has been compromised by differences in manufacturers, Science
Pump (SP) and ENSCI (EN), and differences in recommended sensor solution
concentrations, 1.0 % potassium iodide (KI) and the one-half dilution:
0.5 %. To investigate these differences, a number of organizations
have independently undertaken comparisons of the various ozonesonde types and
solution concentrations, resulting in 197 ozonesonde comparison profiles. The
goal of this study is to derive transfer functions to allow measurements
outside of standard recommendations, for sensor composition and ozonesonde
type, to be converted to a standard measurement and thus homogenize the data
to the expected accuracy of 5 % (10 %) in the stratosphere (troposphere).
Subsets of these data have been analyzed previously and intermediate transfer
functions derived. Here all the comparison data are analyzed to compare (1) differences in sensor solution composition for a single ozonesonde type,
(2) differences in ozonesonde type for a single sensor solution composition, and
(3) the World Meteorological Organization's (WMO) and manufacturers'
recommendations of 1.0 % KI solution for Science Pump and 0.5 % KI for
ENSCI. From the recommendations it is clear that ENSCI ozonesondes and
1.0 % KI solution result in higher amounts of ozone sensed. The results
indicate that differences in solution composition and in ozonesonde type
display little pressure dependence at pressures
Ozone is one of the critical atmospheric trace gases. Ozone contributes to the oxidizing capacity of the troposphere, to the absorption of terrestrial IR radiation, and to the absorption of solar UV in the stratosphere. An overabundance of ozone in the troposphere causes air quality problems, while a deficit in the stratosphere leads to enhanced exposure to UV. Ozone measurements are thus required to maintain our understanding of these processes, and they are required over times scales of hours to years and from single point measurements to vertical profiles to the mid-stratosphere. Measurements are required over hours at single locations to characterize air quality, while regular profiles over decades are required to characterize stratospheric ozone loss and to contribute to climate modeling.
Historically, the first ozone profile information was extracted from the
Dobson measurements with the discovery of the Umkehr effect in the 1930s
(Götz et al., 1934). In optimal (blue sky) conditions at sunrise and at
sunset two coarse-resolution (
Ozone is recognized as an essential climate variable (ECV), and target
observation requirements for satellite-based products for climate are
defined by the Global Climate Observing System (GCOS), which is a joint
undertaking of the World Meteorological Organization (WMO), the United
Nations Environmental Programme, and others (GCOS, 2010). The measurement
requirements for an ECV represent a challenge even for ground-based
instruments:
accuracy: 10 % (troposphere), 5 % (stratosphere); spatial resolution: Horizontal: 5–50 km (troposphere), 50–100 km
(stratosphere); vertical resolution: 0.5 km (troposphere), 0.5–3 km (stratosphere); 3-hourly observing cycle everywhere; stability: 1 % (troposphere), 0.6 % (stratosphere).
Since the late 1960s the vast majority of vertical ozone profile information
has been from individual ozonesonde flights. The instruments used are all based on
measurements of an electrical current from an electrochemical galvanic cell,
which is a measure of the amount of ozone sampled. The current is generated
when ozone in the air, which is bubbled through an electrolytic solution,
reacts with iodide ions in the electrolyte in the cell. Variations of this
principle, described in detail in Sect. 2.1, led to the Brewer–Mast (BM)
ozonesonde (Brewer and Milford, 1960), the Japanese KC ozonesonde (Komhyr
and Harris, 1965; Kobayashi and Toyama, 1966), and the electrochemical
concentration cell (ECC) ozonesonde (Komhyr, 1969). The BM ozonesonde
consists of a single electrochemical cell with a potential applied across
the silver anode and platinum cathode immersed in an alkaline potassium
iodide (KI) solution. The KC ozonesonde has a platinum cathode and carbon
anode immersed in a pH-neutral KI solution. The ECC ozonesonde consists of
two half cells, each containing a platinum electrode, and differing
concentrations of iodide I
While the ECC was under development, different concentrations of KI in the cathode were investigated, and the results compared with corresponding total column measurements. In the 1980s solution concentrations of 1.5 and 1.0 % were in use (Barnes et al., 1985; Komhyr et al., 1995a). By the mid-1980s a 1.0 % solution of KI became the standard recommendation for Science Pump (SP) ozonesondes (Komhyr, 1986). SP was the only manufacturer of ECC ozonesondes until the mid-1990s, when the company ENSCI (EN) was formed, which began manufacturing an alternate ECC ozonesonde. Initially ENSCI also recommended a KI concentration of 1.0 % for the cathode; however, this was changed to 0.5 % after unpublished comparisons of EN and SP ozonesondes using 1.0 % KI indicated that EN ozonesondes recorded more ozone than the SP ozonesondes at the same solution concentration.
These changes created some confusion as recommendations in the preparation of ECC ozonesondes changed. The first results comparing ozonesondes flown with 1.0 and 0.5 % KI cathode solution were based on only a few comparisons (Boyd et al., 1998). More extensive results were obtained from comprehensive intercomparisons in the laboratory (Smit et al., 2007) and in the field (Kivi et al. 2007; Deshler et al., 2008). These comparisons led to the current WMO recommendations for ECC ozonesonde preparations (Smit and ASOPOS Panel, 2014); however, between the mid-1990s and late 2000s the ozonesonde community was using several combinations between 1.0 and 0.5 % KI cathode cell concentrations in SP and EN ozonesondes. In some cases, other concentrations were also used, such as 2.0 %, but we focus here on the manufacturer-recommended concentrations of 1.0 and 0.5 %.
Data sets used in the analysis giving experiment or location, years of the comparisons, location latitude and longitude, ozonesonde and solution strengths compared, number of comparisons, sampling frequency of the data (Hz), and the platform. The multi-sonde platforms contained from 4 to 12 ozonesondes.
The Montreal Protocol, signed in 1991, established the publication every 4 years of an ozone assessment (e.g., WMO, 2010, 2014). One of the most comprehensive reports regarding measurement techniques was the SPARC/IOC/GAW (Stratospheric Processes And their Role in Climate/International Ozone Commission/Global Atmospheric Watch) study (WMO, 1998). An update of this study, the SI2N (SPARC/IGACO (Integrated Global Atmospheric Chemistry Observations)/IOC/NDACC (Network for the Detection of Atmospheric Composition Change)) initiativeto report the present state of knowledge of the different techniques and to reprocess long time series accordingly, is being covered in a special issue of Atmos. Chem. Phys., Atmos. Meas. Tech., and ESSD. A parallel European Space Agency–CCI (Climate Change Initiative) project was established in 2011 to improve the satellites' products for the prominent ECVs, one being ozone. Comparisons with current satellite measurements of ozone, and future instrumental improvements for new satellite generations, require more accurate ground-based data series for validation (Liu et al., 2006; Hubert et al., 2016). Such comparisons have a rich heritage in previous field campaigns comparing various methods to measure ozone (Hilsenrath et al., 1986; Kerr et al., 1994; Margitan et al., 1995; Komhyr et al., 1995b; Meijer et al., 2004).
The MOZAIC data sets (e.g., Thouret et al., 1998, 2006), obtained from in-service aircraft provide a comparison to tropospheric ozonesonde measurements especially at the tropopause, where ozone profiles are at their minimum values. Staufer et al. (2013, 2014) found a systematic difference with ozonesondes when aircraft measurements were compared to ozonesonde measurements determined by matching balloon and aircraft measurements via air parcel trajectory calculations, concluding an overestimation by the ozonesondes on the order of 5–10 % in the upper-troposphere–lower-stratosphere region. Logan et al. (2012) extensively analyzed tropospheric ozonesonde data by comparison to MOZAIC aircrafts' ascent/descent profiles and to high-altitude ground-based measurements, pointing out biased and suspicious data sets.
Clear improvement of ozonesonde measurement precision for properly prepared and analyzed instruments is shown in recent comparison experiments for both the EN and SP instruments (Smit et al., 2007; Deshler et al., 2008; Thompson et al., 2012). A good sign of the stability of these results in the last 10 years has been confirmed in recent studies, e.g., Logan et al. (2012). These results are not the case for the accuracy of measurements with “provider–solution” combinations which differ from the recommendations. Such combinations typically deviate from trusted ozone measurements by 5–10 %. These latter deviations are now reasonably well characterized by a large set of comparison measurements (Table 1). Thus, it is time to apply corrections to ozonesonde data measured with provider–solution combinations differing from the standard WMO recommendations. Such applications will homogenize these data sets and thereby improve data quality, usefulness for trend analysis, global homogeneity, and references for satellites and models. The appropriate corrections to apply, using the large comparison data set available, are developed in Sects. 3 and 4.
To homogenize these records to a single standard requires transfer functions to convert measurements made with any of the various combinations to one of the two WMO recommended standard preparations: 1.0 for SP and 0.5 % for EN for the KI concentrations of the cathode electrolyte. Obtaining these transfer functions is the goal of this paper.
The transfer functions will be derived from published and unpublished measurements which compare directly the response of SP and EN ECC ozonesondes using 1.0 and 0.5 % concentrations of KI in the cathode cells under identical environments of ozone, pressure, and temperature. These comparisons were done in an environmental simulation chamber in Jülich, Germany (Smit et al., 2007), Jülich Ozone Sonde Intercomparison Experiment 2009, henceforth JOSIE09; on a multiple-ECC-ozonesonde gondola (Deshler et al., 2008), Balloon Experiment on Standards for OzoneSondes, henceforth the BESOS experiment; on other multiple-ozonesonde balloon flights (Kivi et al., 2007); and on unpublished dual-ozonesonde flights from Payerne, Switzerland; McMurdo Station, Antarctica; Sodankylä, Finland; Wallops Island, Virginia, USA; and Laramie, Wyoming, USA. Together 197 comparisons of the different possible combinations have been made at these sites with the goal of developing transfer functions to convert measurements made with either manufacturer and with either 1.0 or 0.5 % KI concentration to one of the WMO recommendations: SP with 1.0 % KI, EN with 0.5 % KI.
The basic ECC principles, operation, and uncertainties are described in the next section. Section 3 describes the database and the methods to establish the transfer functions. Section 4 describes the methods and the results for the derivation of the transfer functions. The results are discussed in Sect. 5, and then conclusions appear in Sect. 6.
Boxplot of the background current (
Ozonesondes are based on an electrochemical cell where the chemical
potential difference is maintained by differences in the iodide (I
Each of the terms in Eq. (4) has an uncertainty deduced from the measurement method, but they also have an additional uncertainty which is more difficult to quantify, especially at high altitude (low pressure) levels. The background current, flow rate, pump temperature, and stoichiometry of ozone to iodine require special mention.
Ozonesonde pump flow rates (cm
The background current
The flow rate is well characterized in the laboratory preparation at
surface pressure as shown in the box-and-whisker plot of the flow rates
(Fig. 2) measured at the different stations. The median values for each
ozonesonde type are within 0.3 mL s
The effective stoichiometric factor,
All these factors are eventually time and pressure dependent, and they
superpose each other, which makes their individual contribution particularly
difficult to determine. The factors which are of particular concern for the
issue of varying ozonesonde manufacturer and cathode solution concentration
are contained in the terms
The details of the differences in ozonesonde manufacturer and how they
affect these terms can only be speculated on at this time. The SP and EN
ozonesondes are in principle the same, yet the EN ozonesondes consistently
indicate a higher ozone amount when compared to an SP ozonesonde with the
same sensor KI electrolyte concentration. The differences in cell
manufacturer may affect the efficiency of electron release at the anode and
electron gain by iodine at the cathode, which could be related to
differences in the platinum electrode sensitivity and the ion bridge
conductance. The inner surface proprieties of the cell walls are different
and may affect reactions of ozone and the cell walls. Either of these would
affect
Differences in KI concentration in the dilute electrolytic solution likely
affects the efficiency of the conversion of O
The preparation before the flight of an ECC ozonesonde is crucial for its
performance. The standard operating procedures (SOPs) for ECC ozonesondes
have been established by a group of experts under the guidance of the World
Calibration Center for Ozonesondes (WCCOS) of the WMO (Smit and the ASOPOS
panel, 2014). These results are from a 10-year coordinated process to
improve the different aspects of ECC ozonesonde preparation and data
processing. Operating the ECC ozonesondes under these prescribed rules has
been tested extensively in different JOSIE experiments at the Jülich
Research Center (Smit et al., 2007). The large balloon experiment BESOS was
designed as an extension in the real atmosphere of these laboratory
developments (Deshler et al., 2008). In addition to 18 ozonesondes, the
BESOS gondola included also the Jülich reference UV spectrometer
(Proffitt and McLaughlin, 1983; Smit et al., 2007) to replicate as far as possible
the test procedure of the JOSIE experiments. The two experiments agree on
the following conclusions:
ECC ozonesondes prepared according to the SOPs provide very reproducible
(< 2–3 %) measurements. The results depend on the ozonesonde manufacturer (e.g., EN vs. SP) and on
the sensing solution concentration (e.g., 0.5 % vs. 1.0 %). The order of
magnitude of the differences is 5 %. The combinations EN-0.5 % and SP-1.0 % as provider–solution agree
with each other to within 2 % but overestimate the reference UV
photometer in the stratosphere by 5–10 %. However, the total ozone column
estimated from these combinations on the BESOS gondola agreed with a
collocated Dobson spectrophotometer. It is possible to reconcile the measurements made with other
provider–solution combinations and the photometer with the help of a
simple linear in ln
JOSIE and BESOS were completed under conditions not reflecting directly the
diversity of the operational services around the world. Each sounding
station has specific instrumentation and operators even though they follow
the same procedures. It is therefore important to verify that the
conclusions from JOSIE and BESOS are also reflected in results of several
operational stations. At the stations, it is not possible to fly a reference
UV instrument so only relative differences can be derived from dual- or
multiple-ozonesonde flights. In the present analysis the JOSIE and BESOS
results will be analyzed in a similar way to the other dual- and multi-instrument
flights.
In the mid-1990s the problems of differences in provider–solution combinations were still of marginal importance (SPARC, 1998). The ENSCI company had entered the market only a few years earlier, and the disparate preparation procedures prevented clearly identifying problems. The conclusions at that time were that the effect of changes in ECC KI solution concentrations were complex and required further study before clear recommendations could be provided.
McPeters et al. (1999) report a 2 % consistency from five triple ECC flights during a validation campaign at Mauna Loa in 1995 using EN-1.0 % ozonesondes. The authors report that the ozonesondes overestimate the Dobson measurements by an average of 5 %. In profile, above 25 km, the ozonesonde measurements are greater than the lidar and microwave measurements by a similar amount. Boyd et al. (1998) presented ozone profile differences from EN ozonesondes with 1.0 and 0.5 % KI solutions at Lauder, New Zealand. A 5–6 % systematic overestimation of ozone by the 1 % solution compared to the 0.5 % with the EN ozonesondes is evidenced by comparison of the ozone profile and total column collocated lidar and Dobson measurements.
For an analysis of the transition from Brewer–Mast to ECC (EN-1.0 %) ozonesondes, the ECC data were normalized to the Dobson column to be consistent with the Brewer–Mast SOPs (Stübi et al., 2008). Stübi et al. found that the ECC ozonesondes systematically overestimated the total ozone column with a mean normalization factor of 0.95 for more than 100 dual-ozonesonde flights between ECC and BM, indicating an overestimation of 5 % of the ozone column by EN-1.0 % ozonesondes.
Kivi et al. (2007) analyzed a series of dual- and multiple-ozonesonde flights with SP and EN instruments using 0.5 and 1.0 % sensing solutions. For the homogenization of ozone profiles from the northern high-latitude stations the authors derived a third-order polynomial correction based on altitude to correct the overestimation of ozone from EN-1.0 % compared to SP-1.0 %.
Mean and standard deviations of the various comparison measurements. The mean ratio is equivalent to the slope of a linear fit to the data which passes through the origin. For the data sets with the most data and the most varied comparisons the means and standard deviations are given for the pressure intervals indicated at the top. For the other data sets the means and standard deviations are given for all the data without regard to pressure.
The laboratory work (Smit et al., 2007) and the several field measurements (Boyd et al., 1998; McPeters et al., 1999; Stübi et al., 2008; Kivi et al., 2007; Deshler et al., 2008) all indicate a relatively consistent systematic bias, on the order of 5 %, between the different ozonesonde manufacturers with the same electrolytic concentration and between different electrolyte concentrations in ozonesondes from a single manufacturer.
The early stoichiometric work on the yield of iodine from ozone showed varying results, with much of the uncertainty arising from the variety of KI sensing solutions, pH buffers, and sensors used (Saltzman and Gilbert, 1959; Boyd et al., 1970; Dietz et al., 1973; Pitts et al., 1976; Lanting, 1979). Common to many of the references was the suggestion of a secondary reaction producing additional iodine perhaps from reactions of iodide with the phosphate buffers. Johnson et al. (2002) showed that the same type of ECC ozonesonde operated with differing amounts of KI, and corresponding changes in the phosphate buffers, provides slightly different stoichiometric ratios of iodine to ozone. In fact these differences were very apparent in the initial development of the ECC ozonesondes (Komhyr, 1969, 1986).
The present analysis of dual-ozonesonde measurements is an extension of the JOSIE and BESOS experiments to link short-term instrument comparison campaigns to routine operations at regular sounding stations. The JOSIE experiments were conducted in a controlled atmospheric chamber. BESOS and the other comparisons were conducted via balloon flights under real atmospheric conditions. JOSIE and BESOS used the same reference UV photometer (Proffitt et al., 1983) for the final comparisons, and the results of those comparisons confirmed the high precisions and good accuracy of well-prepared ozonesondes. For the extensive additional data presented here an independent (e.g., photometric) reference is not available; rather the ozonesondes are compared pairwise. The JOSIE and BESOS data are included here also pairwise (Table 2). The first of these dual-ozonesonde comparisons began in the late 1990s at different locations. Although there was no coordinated effort, the motivation at each station was similar: the need for homogenization of the long-term ozonesonde record at the station. Table 1 summarizes the data sets used for the present analysis. Differences in the details of these comparisons at the different stations are described below.
While the differences are described below, the comparisons were the same in
following the SOPs established by Smit and the ASOPOS panel (2014) in the
preparation of each ozonesonde and in using only 0.5 % or 1.0 % KI
buffered cathode solutions. The 0.5 % KI cathode solutions were created by
diluting the standard buffered 1.0 % KI solution by 50 % with distilled
deionized water. The standard 1.0 % KI solution is created by dissolving
the following in distilled deionized water: 10 g KI, 25 g KBr, 1.25 g
NaH
The JOSIE experiments have been described by Smit and Kley (1998), Smit and
Sträter (2004a, b), and Smit et al. (2007), so only the experimental
principles are reviewed here. Four ozonesondes can be placed simultaneously
in the atmospheric simulator. Pressure and temperature can be regulated from
surface conditions to 10 hPa and
The BESOS experiment was described fully by Deshler et al. (2008). A collaborative team of ozonesonde experts prepared a balloon gondola (100 kg) with 16 ozonesondes, the Jülich UV photometer (Proffitt et al., 1983), a Vaisala radiosonde, and a data acquisition system. Dobson and Brewer spectrophotometers were available at the launch site. The flight to 32 km was completed on 13 April 2004 from Laramie, Wyoming. The data from this flight are used here similarly to the JOSIE data by considering the ozonesondes pairwise. The payload had a set of 12 standard ozonesondes, six from EN and six from SP; out of each set of six ozonesondes, three had a 0.5 % and three a 1.0 % KI solution concentration. Thus, a set of nine pairs is available for comparison for each provider–solution combination (Table 1). The correlation matrix for any one of the three pairs of a comparison indicated little variation in the correlation coefficient for any combination of the two types of ozonesondes being compared. This suggests that covariance does not negate the independence of the nine comparisons for any combination of ozonesonde type or solution concentration.
Mean profile of the measured pump temperature for the different dual-ozonesonde flight data sets. The higher pump temperature at the upper part of the profiles for the McMurdo and Sodankylä stations is due to a heat source to avoid freezing of the solution. Error bars are 1 standard deviation of the measurements.
The Payerne station is run under the responsibility of MeteoSwiss, and the
radiosondes used were the SRS model from the Swiss company Meteolabor. SRS
radiosondes are not capable of interfacing two ozonesondes, so for the
dual-ozonesonde flights two independent receiving systems were used. These were synchronized
at the time of the launch to better than 1 s, and the sampling
frequency was about 7 s. For the analysis, the data are interpolated
to a common timescale to avoid any problems related to a difference of the
pressure readings from the two ozonesondes. The data sets consist of two
campaigns embedded in the operational service as a dual-ozonesonde flight for the
Wednesday sounding, with the reference (operational) ozonesonde being the
EN-0.5 %. The data sets consist of
48 pairs of EN-0.5 % and EN-1.0 % during June 2002–July 2003, 26 pairs of EN-0.5 % and SP-1.0 % during May 2005–December 2006.
Measurements from McMurdo Station, Antarctica, were conducted by the
University of Wyoming during the ozone hole period, August–November
1986–2010 (Mercer et al., 2007). From this record 18 flights with two EN
ozonesondes interfaced to a single microprocessor and Vaisala RS80
radiosonde were completed. The years (number of flights) are 1996 (3), 1999
(1), 2000 (1), 2002 (6), and 2006 (7). In each case EN-1.0 % and EN-0.5 % KI concentration solutions were compared. The low-temperature
conditions in Antarctica require a heater near the cells to prevent the
solution freezing at high altitude. This preventive action is visible in
Fig. 3 with a mean pump temperature which stays close to 24
The Sodankylä station is run by the Finnish Meteorological Institute.
The radiosondes used for the dual-sonde and multiple-sonde measurements were
the Vaisala RS80. In the data set used here there is a mix of five dual-ozonesonde flights
and four larger balloon flights with “six-sonde” payloads. The larger balloon
payloads were recovered and flown again the next day with reused
ozonesondes. The mean pump temperature profile shown in Fig. 3 is
characterized by the leveling of pump temperature at about
22 six pairs of EN-0.5 % and EN-1.0 % during September 2003–July 2004, five pairs of EN-0.5 % and SP-1.0 % during May 2003–March 2005, eight pairs of EN-1.0 % and SP-1.0 % during September 2003–July 2004.
Resources for ozonesonde measurements, with Sippican radiosondes, from
Wallops Island have been, and continue to be, provided by NASA
Headquarters. The Wallops Island practice is to use the background current
measured during the day-of-flight preparation prior to exposing the ECC to
moderate ozone (5 7 pairs of SP 5A-ECCs 0.5 % vs. 1.0 % in 1996, 11 pairs of SP 6A-ECCs 0.5 % vs. 1.0 % in 2004.
These results were obtained from a collaboration between the Climate Monitoring and Diagnostic Laboratory (CMDL) of the National Oceanic and Atmospheric Administration and the University of Wyoming. CMDL prepared the gondola, and the University of Wyoming conducted the flight operation. The measurements were obtained from a gondola containing six EN ozonesondes, three with 0.5 % KI and three with 1.0 % KI. The instruments were synchronized to a common data system and an RS80 Vaisala radiosonde. The flight occurred on 20 June 1996 and reached an altitude of 32 km. Post-flight analysis eliminated one of the 0.5 % KI EN ozonesondes from the comparison. Results from this ozonesonde were significantly different from the other two 0.5 % KI ozonesondes, which agreed quite well.
The ozone data processing from the measured current is based on Eq. (4) with little variability among the data sets. The major difference is in the sampling frequency of the measurements, which ranged from 0.2 to 1.0 Hz. The typical e-folding response of an ozonesonde is on the order of 0.05 Hz (Smit and Kley, 1998). All the sampling rates here are faster than this but are the same for every pairwise comparison so the sampling rate will not affect a comparison. However, since the data analysis is based on the individual pair differences, it is necessary to average the high-frequency measurements to a common timescale to avoid unduly weighting the high-frequency data relative to the lower-frequency measurements. Ultimately the high-frequency data were averaged to a frequency of 0.2 Hz so that when the data-weighted means of the comparisons were calculated each comparison profile was weighted about equally.
In Fig. 1, the background currents measured for the different data sets
are summarized as box-and-whisker plots. For all sites except Wallops
Island, these are the background currents after exposure to moderate ozone
and just prior to flight. The
The pump flow rate is the second parameter measured in the laboratory
preparation of each ozonesonde. Figure 2 shows the coherency of the pump
flow rates at the five field measurements sites. In about half the measurement
sets, the interquartiles of the variations amongst the instruments measured
are less than 3 % of the median, and in all cases except one the
interquartiles are less than 6 % of the median. The figure also shows a
systematically 0.2–0.3 mL s
Example of the results of a dual-ozonesonde flight profile from Payerne. The only difference between the ozonesondes is the KI solution concentration.
The application of pump efficiency corrections varies amongst the data sets. In general Komhyr (1986) is used for SP and Komhyr et al. (1995a) is used for EN ozonesondes. Since the comparisons analyzed are amongst pairs of identical ozonesondes, the pump efficiency does not play an important factor unless significantly different pump efficiencies were applied separately to the ozonesondes in a measurement pair. In most cases the same efficiency factors were applied to both ozonesondes of a pair. The one exception is the Wallops Island data, where individual pump efficiency curves were applied prior to mid-2000, when the system failed. The pairwise comparisons of these data, however, were quite similar to the Wallops Island data, where identical pump efficiencies were used, and to the pairwise comparisons from the other data sets.
For the data processing, individual pump temperatures are used as
illustrated in Fig. 3 for the mean pump temperature profiles for each
data set. It should be noted that the SP 5A ozonesondes used at Wallops
Island did not have an explicit hole to insert the pump temperature sensor.
The standard deviations of the temperature range from 1
Scatterplots of ozone partial pressures measured with KI
concentrations of 1.0 % (
In Fig. 4, an example of a dual-ozonesonde flight from Payerne is illustrated. The two ozonesondes separated by a 1.5 m long boom were hanging under the same balloon, and the data transmitted to two independent receiving systems on the ground. The ozone profiles have identical structures, and differences increase near the ozone maximum at pressures less than 50 hPa, indicating some dependence on both ozone partial pressure and atmospheric pressure. The increased sensitivity of the 1.0 % solution is clear throughout the profile.
The simplest way to analyze the data is to compare ozone partial pressures
measured by ozonesonde pairs operated simultaneously, either in the
atmosphere or in the simulation chamber. Scatterplots of ozone partial
pressure measured with ECC ozonesondes with 1.0 % solution (
Table 2 also shows additional analysis in three other comparison groups: boxes 3 and 4 correspond to a change of provider keeping the same concentration, and the final box, 5, corresponds to a fit to a mix of SP-1.0 % and EN-0.5 %. The tendency of a decrease of the linear term at lower pressures is present in most data sets except for this last group, where the linear fit is not statistically different than the fits at pressures above 30 hPa. The correlation coefficients for the data are all above 0.998. There are four cases in Table 2, two in the Payerne (boxes 1 and 5) and two in the BESOS (boxes 3 and 5) data sets, with standard deviations > 0.1. These are all in the column for pressures > 500 hPa. The origin of the large standard deviations for Payerne, EN-1.0 % vs. EN-0.5 %, probably lies in the outliers apparent in Fig. 5a at pressures > 500 hPa. Such discrepancies are less obvious in the other Payerne comparison and in the BESOS data. The cause of these larger standard deviations was not investigated further in light of small standard deviations in all data sets at pressures less than 500 hPa.
Transfer function parameters summary. The second and third columns
are for
Considering the strong linear relationship of the dual measurements for the
differences in concentration in the same ozonesonde type, and differences in
ozonesonde type with the same sensor concentration, it is natural to simply
use a single ratio to characterize the relationship of the two measurements
at pressures above a certain threshold pressure and then to use a linear
relationship in log
Vertical profiles of ratios of ozone partial pressure using
ozonesondes with different KI concentrations (0.5, 1.0 %) in
ozonesondes of the same manufacturer, EN or SP.
These comparisons suggest that measurements from ozonesondes using a 1.0 % KI concentration in the cathode can be used to derive measurements which would have been obtained from measurements with a 0.5 % KI solution. To do this, measurements using the 1.0 % KI solution are modified using a pressure-independent ratio at pressures above some threshold pressure and a pressure-dependent ratio below the threshold pressure. Different values for the threshold pressure to switch from a single ratio to a pressure-dependent ratio were tested, but the results were not very sensitive to this value, and it has been fixed at 30 hPa.
With the threshold pressure level established, each data set was used to
calculate a mean concentration ratio at
Vertical profiles of ratios of ozone partial pressure using
ozonesondes of different manufacturer (SP, EN) with the same KI
concentrations in both ozonesondes, either 1.0 %
The coefficients for the transfer function representing the ratio of ozone
sensed at the differing concentrations were calculated as a weighted mean
and standard deviation (according to sample size) of the individual
parameters given in Table 3 for all data sets considered in the analysis.
These values comprise the final row in each box in Table 3. Not all data
from each data set were used due to unstable ratios at particularly low ozone
concentrations, or during clearly deficient ozonesonde performance. The
primary regions where some of the data were excluded are displayed as the
gray areas in Fig. 6. Data in these regions were excluded for the
following specific reasons:
McMurdo Station: some of the dual-ozonesonde measurements were completed in ozone hole
conditions, and in these cases ozone drops to near zero, producing highly
divergent ratios. JOSIE: at three points during the simulated profiles, the ozone flow was
stopped to measure the residual signal and the response time, producing very
low ozone levels and thus likewise ratio divergences. BESOS: in the first minutes of the flight, the data acquisition unit was
unstable and too noisy to consider in the present analysis.
The common transfer function to analyze differences in KI concentration,
OZ
In Fig. 7, profiles of the ratio of SP to EN ozonesondes with the same KI
solution concentration are shown in the same format as Fig. 6. The upper
panels show the difference between the SP and EN ozonesondes with a 1.0 %
solution concentration, while the lower panels are for the 0.5 % solution
concentration. Figure 7a is from multiple dual-ozonesonde flights at Sodankylä
over the period 1995–2002, while the other panels present the analysis of the
JOSIE and BESOS experiments. Using these data, and following the procedure
used to reconcile the two solution concentrations in the same ozonesonde
provider (Sect. 4.1), the common transfer function to correct a change from
one provider to the other was derived. Similar to the analysis in Sect. 4.1 the results from fits to each data set and their weighted mean are
provided in the third and fourth box in Table 3. Combining the results from
the boxes comparing EN and SP ozonesondes at 1.0 % and EN and SP
ozonesondes at 0.5 % results in the transfer function, OZ
Ratios of SP-1.0 % to EN-0.5 % ozonesondes. The measurements
are from
Also included in the last row in each box in Table 3 is the weighted standard deviation of the comparison ratios. These provide an indication of the uncertainty in the ratios, and thus in the corresponding transfer functions, ranging from 0.037 to 0.084. To establish a single uncertainty which could be applied to all the transfer functions, the individual comparison standard deviations were averaged and found to be near 0.05. Thus 0.05 was adopted as the single uncertainty which could be applied to all the transfer functions proposed. This value is used to apply the error bars shown in Figs. 6 and 7 and shows how well the transfer functions, including the uncertainty, represent the data. Clearly 0.05 both under and over estimates the ratios, depending on the particular comparison; however, over all does a reasonable job.
With the similarity of the two transfer functions, OZ
The present conclusion is that the interchange between the EN-0.5 % and SP-1.0 % combinations would not have a negative impact on the continuity of a time series. It may increase the variability, but no noticeable break should appear at the transition between these two systems.
Ratios of total column ozone at Nairobi, Kenya, measured either
with the OMI satellite instrument or a Dobson spectrophotometer, compared to
EN ozonesondes using 1.0 and 0.5 % KI concentrations. The ratios are
also shown after correction of the EN-1.0 % to EN-0.5 % (OZ
Ozone time series from the Nairobi station at three pressure levels: 500, 30, and 10 hPa. The black symbols correspond to the data from 1996 to 2010 (EN-1.0 % solution), and the red symbols from 2010 onwards (EN-0.5 %). The horizontal segments are the mean values over the two periods. No corrections to the EN-1.0 % data have been made for this figure.
The Kenyan Meteorological Department (KMD), in collaboration with
MeteoSwiss, operates the Nairobi aerological station, within the SHADOZ
(Southern Hemisphere ADditional OZone station) network (Thompson et al.,
2012). Weekly ozone soundings began in 1996. In summer 2010, due to
interruption of the Vaisala RS80 radiosonde production, new equipment based
on the RS92 was installed at Nairobi. Coincidently, the ozonesonde solution
concentration was changed from 1.0 to 0.5 %, keeping the same
ozonesonde provider, EN. This data set is used here to illustrate the
application of the transfer function OZ
To quantify the concentration change, the mean ozone profiles before and
after 2010 have been calculated and appear in Fig. 10, with black squares
for 1996 to 2010 and blue circles after 2010. Red triangles correspond to
the 1996–2010 data after correction of each profile with the transfer
function OZ
For a total ozone column comparison three estimations are available for the Nairobi station: the ozonesonde integrated profile, a Dobson D018 colocated spectrophotometer, and the OMI (Ozone Monitoring Instrument) satellite overpass measurements. The change of sensing solution and the corrections shown in Fig. 10 have affected the ratios of the total column ozone as shown in Table 4.
There has been a significant effort to reconcile ozonesonde measurements completed with instruments from the two ozonesonde providers, Science Pump and ENSCI, with various combinations of the recommendations for the KI sensor solution concentrations 1.0 and 0.5 %. The motivation for this effort rests on characterizations of the precision and accuracy achievable with well-prepared ozonesondes through laboratory tests (Smit and Sträter, 2004a, b; Smit et al., 2007) and field tests (Kivi et al., 2007; Deshler et al., 2008). These results have shown that the precision of an ECC ozonesonde is better than observed systematic differences between ozonesonde type or solution concentration. The results presented here demonstrate that the differences in ozonesonde type, with the same solution concentration, are quite systematic and thus can be characterized, to within experimental uncertainties, with a single relationship for both 0.5 and 1.0 % KI concentrations. Similarly, systematic differences between sensor solution concentrations in the same ozonesonde for both SP and EN ozonesondes can also be characterized by a single relationship. These results attest to the consistency in ozonesonde manufacturing for both companies and that both ozonesonde types have similar differences in performance when the KI solution concentration is varied, pointing again to the strength of the instrumental technique and the instruments.
The rationale employed in this analysis was to find a simple set of relationships which could be applied throughout all the data analyzed. Clearly there are differences in the various data sets as shown in Fig. 6. In this case the recommended relationship (Eq. 5) for the relationship between 0.5 and 1.0 % KI does not optimally fit the BESOS SP data (Fig. 6c), but it does fit quite well with the Payerne and McMurdo Station data (Fig. 6a, b). The overestimation of the BESOS SP data is counterbalanced by the underestimation of the JOSIE09 SP data (Fig. 6d). This relationship does well against the BESOS EN-0.5–1.0 % and the Wallops Island SP-0.5–1.0 % comparisons (not shown). The relationship is not steep enough for the Sodankylä measurements at pressures < 30 hPa. Differences such as these led to the alternate transfer functions using first- to third-order polynomials in log pressure derived by Kivi et al. (2007) and Deshler et al. (2008). Neither of these relationships, however, would do well across the full data set analyzed here. In particular the third-order polynomial provided by Kivi et al. was required due to the significant ratio decrease at pressures below 50 hPa.
On the left (upper scale), mean ozone profile for the EN-1.0 %
period in black, EN-0.5 % period in blue, and corrected EN-1.0 % profile
using OZ
Similar comments may arise from the analysis of the ozonesonde type comparisons (Fig. 7), although in general the proposed relationship requiring a more significant decrease in the ozonesonde type ratio at pressures below 30 hPa is better at reproducing the ozonesonde type comparisons. The only data set not shown in this comparison is from JOSIE09 comparing the ozonesonde types at 1.0 %. That ratio profile compared to the relationships recommended is quite similar to the comparison at 0.5 % (Fig. 7c).
The reasons behind the increase in ozone sensed with an increase in KI concentration have not been fully explored and are beyond the scope of this paper. The discussions of this effect have centered on the importance of the sodium phosphate hydrate buffers used to maintain the pH of the solution. These buffers, which vary in proportion to the KI concentration, may lead to secondary reactions between iodide ions and the buffer leading to excess iodine, thus indicating additional ozone (Saltzman and Gilbert, 1959; Johnson et al., 2002). Similarly there have been discussions on the reasons behind the increased sensitivity of the EN ozonesondes compared to SP ozonesondes. Speculation has centered on the efficiencies of the platinum electrode in scavenging the iodine, the conductance of the ion bridge, or the surface properties of the SP Teflon cells versus the EN molded plastic cells, but there has been no systematic investigation of this effect. This also remains beyond the scope of the work presented here.
For a transfer function to have wide acceptance within the community it must have reasonable application to the widest possible set of comparisons. Specialized transfer functions have been derived for particular subsets of the data (Kivi et al., 2007; Deshler et al., 2008), but it has not been demonstrated that these functions are useful beyond the specific data from which they were derived. The analysis here sought to develop as simple a relationship as possible based on the full comparison data set available. This was achieved through weighting of the ratio fits by the number of profile comparisons to arrive at the final four relationships described in Eqs. (5) and (6). Once derived, the individual data sets were compared against the derived transfer functions, and a subset of these is shown in Figs. 6 and 7. While Kivi et al. (2007) did not show such a comparison, Deshler et al. (2008) did. Figure 5 from Deshler et al. could be compared here against Figs. 6c, 7b, and 7d. Compared to Deshler et al. the fits proposed here improve the comparisons of ozonesonde type while not significantly diminishing the comparisons of sensor concentration. Coupling this with the ability of the fits to reproduce nearly all data sets within the uncertainty of the fits provides strong support for the validity of the proposed transfer functions. This is not to argue that the relationships proposed here should be used instead of results of an individual investigation of a particular comparison data set; however, such an individual transfer function must be supported by the appropriate measurement set and made available publicly through the refereed literature. For investigators without access to the resources to conduct such a study, the transfer functions proposed here will do an adequate job of data homogenization.
The final comparison investigated here is between the two manufacturer's recommendations. This was done through 43 comparison profiles summarized in Fig. 8. There was no attempt to derive a fitting function for these data, and as the figure illustrates such an exercise would be difficult. Figure 8 suggests some bias in the smaller data sets investigated, with ratios > 1 for Sodankylä and BESOS but < 1 for JOSIE09, while Payerne, by far the largest data set, shows no systematic bias. The objective analysis shown in Tables 2 and 3 not only quantifies these differences but also shows that the differences are on average generally not different than 1.0, in contrast to the results of the solution concentration and ozonesonde type comparisons. Thus the data here suggest that the two, manufacturer and WMO, recommended ozonesonde type and solution concentration packages can be used directly and should be widely comparable.
Measurements with various combinations of ozonesonde type, Science Pump or ENSCI, and with differing combinations of the KI solution concentration, 1.0 or 0.5 %, have led to variations in ozonesonde preparation at a number of ozonesonde stations throughout the world. These changes began in the mid-1990s and played a role in the analysis of ozonesonde data between then and the late 2000s (Mercer et al., 2007; Tarasick et al., 2016). Recognizing that these differences exceeded the accuracy and precision that is possible from ozonesondes (Smit et al., 2007; Deshler et al., 2008) led many investigators to independently explore the differences that occur when the same ozonesonde is operated with differing solution concentrations and when differing ozonesonde types are operated with the same solution concentrations (Johnson et al., 2002; Kivi et al., 2007; Smit et al., 2007; Deshler et al., 2008). Measurements from these investigators and other unpublished comparisons have been analyzed in this paper. The analysis has focused on three basic comparisons: (1) sensor solution composition differences in ozonesondes of the same type; (2) ozonesonde type differences using the same sensor solution concentration; and (3) differences between the manufacturer and WMO recommendations, Science Pump 1.0 and ENSCI 0.5 % KI solution concentrations. Using the published and unpublished data has resulted in the analysis here of 116 profile comparisons for solution concentration differences, 38 profile comparisons for ozonesonde type differences, and 43 profile comparisons of the manufacturer's solution concentration recommendations. The data sets used in the comparisons have been obtained from the laboratory (JOSIE09), multi-sonde balloon-borne gondolas (BESOS, Sodankylä), and dual-ozonesonde balloon-borne gondolas (Payerne, McMurdo Station, Sodankylä, Wallops I., Laramie), involving at least six different scientific groups.
Overall the measurements display a satisfying coherence when solution
concentrations or ozonesonde type are compared. At pressures above 30 hPa,
the surface to 30 hPa, the two measurements can be characterized with a
simple ratio displaying almost no pressure dependence. In addition this
ratio is, within experimental uncertainty, the same, 0.96, whether the
difference is in solution concentration with the same ozonesonde type or
ozonesonde type with the same solution concentration. Ozone concentrations
are higher for 1.0 % than 0.5 % KI and for ENSCI than Science
Pump ozonesondes. At pressures below 30 hPa there is a pressure dependence
which is linear in log
The conclusions arrived at from the analysis described here are the following.
For differences in solution concentration independent of ozonesonde type,
OZ OZ OZ OZ
For differences in ozonesonde type independent of solution concentration, but with both ozonesondes using the same solution
concentration,
We recommend that all ozonesonde measurements completed with 1.0 % KI in
ENSCI ozonesondes or 0.5 % KI in Science Pump ozonesondes should adjust
their data according to the relationships shown above such that the final
data product would be representative of 0.5 % KI ENSCI or 1.0 % KI Science
Pump. This should be done for any data prepared for analysis and for public
availability. An uncertainty of 0.05 can be ascribed to the application of
these transfer functions.
The investigation of 43 profiles comparing 1.0 % KI in Science Pump ozonesondes and 0.5 % KI in ENSCI ozonesondes found that the dispersion in the comparisons was centered on a ratio of 1.0. Thus there is no recommendation to alter data obtained from instruments using the recommended concentrations.
If these recommendations are followed, it can be expected that data sets
experiencing variations in the use of ozonesonde type and solution
concentration will see their long-term data converge to within the expected
These recommendations have been implemented in the WMO/GAW's guidelines for the homogenization of ozonesonde data (Smit and O3S-DQA-Panel, 2012) and recommended to the ozone sounding stations of the NDACC and SHADOZ (Thompson et al., 2012; Witte et al., 2017). The effort for the ozonesonde investigators to accomplish these corrections will be significant, but in the end the health of the network is dependent on such quality control measures being implemented, and it will greatly add to the value of the measurements. All future measurements should use the WMO/GAW recommendations for solution composition. Any deviation from these recommendations should be justified and carefully researched prior to a change.
The ozonesonde measurements used in this analysis are available from the
individual investigators, and the majority of these data are on the NDACC
database. These data and the code used to create Figs. 4–8 are also
compiled at
The authors declare that they have no conflict of interest.
This work was encouraged by the ozonesonde working groups of the WMO/GAW, NDACC, and SHADOZ. The following agencies have supported the generation of these data sets: MeteoSwiss, Finish Meteorological Institute, World Ozonesonde Calibration Facility, US National Science Foundation, US National Aeronautics and Space Administration, and US National Oceanic and Atmospheric Administration. Edited by: H. Maring Reviewed by: two anonymous referees