AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-2485-2017Comparison of Vaisala radiosondes RS41 and RS92 launched over the oceans
from the Arctic to the tropicsKawaiYoshimiykawai@jamstec.go.jphttps://orcid.org/0000-0002-1186-6809KatsumataMasakihttps://orcid.org/0000-0001-5138-6415OshimaKazuhirohttps://orcid.org/0000-0001-7580-4490HoriMasatake E.InoueJunhttps://orcid.org/0000-0001-7738-6480Research and Development Center for Global Change, Japan Agency for
Marine–Earth Science and Technology, Yokosuka 237-0061, JapanInstitute of Arctic Climate and Environment Research, Japan Agency for
Marine–Earth Science and Technology, Yokosuka 237-0061, JapanArctic Environment Research Center, National Institute of Polar
Research, Tachikawa 190-8518, JapanYoshimi Kawai (ykawai@jamstec.go.jp)14July20171072485249815February201729March201731May20175June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/2485/2017/amt-10-2485-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/2485/2017/amt-10-2485-2017.pdf
To assess the differences between the RS92 radiosonde and its
improved counterpart, the Vaisala RS41-SGP radiosonde version with a
pressure sensor, 36 twin-radiosonde launches were made over the Arctic Ocean,
Bering Sea, western North Pacific Ocean, and the tropical Indian Ocean during
two cruises of R/V Mirai in 2015. The biases, standard
deviations, and root mean squares (rms's) of the differences between the RS41
and RS92 data over all flights and altitudes were smaller than the nominal
combined uncertainties of the RS41, except that the rms of the differences of
pressure above 100 exceeded 0.6 hPa. A comparison between daytime and
nighttime flights in the tropics revealed that the pressure difference was
systematically larger during the day than at night above an altitude of 4.5 km, suggesting that there was some effect of solar heating on the pressure
measurements, but the exact reason is unclear. The agreement between the RS41
and RS92 temperature measurements was better than the combined uncertainties.
However, there were some noteworthy discrepancies presumably caused by the
“wet-bulbing” effect on the RS92 radiosonde and the stagnation of the
balloon. Although the median of the relative humidity differences was only a
little more than 2 % of the relative humidity at all altitudes, the
relative humidity of the RS92 was much lower than that of the RS41 at
altitudes of about 17 km in the tropics. This dry bias might have been caused
by the incomplete solar radiation correction of the RS92, and a correction
table for the daytime RS92 humidity was calculated. This study showed that
the RS41 measurements were consistent with the specifications of the
manufacturer in most cases over both the tropical and polar oceans. However,
further studies on the causes of the discrepancies are needed.
Introduction
Radiosonde observations are operationally conducted twice a day at about 800
sites throughout the world. Radiosondes measure temperature, humidity, wind
velocities, and pressure (or height) in the troposphere and stratosphere.
They ascend through the atmosphere attached to balloons filled with helium
or hydrogen gas. The data are sent to the Global Telecommunication System
and are used for data assimilation in real-time operational weather forecast
systems, atmospheric reanalyses, and climate models. In situ aerological
observations are also indispensable for validating satellite-derived
meteorological data (e.g., Fujita et al., 2008); for assessing long-term
trends in the upper atmosphere (e.g., Thorne et al., 2005; Maturilli and
Kayser, 2016); and for other meteorological research, including assimilation
experiments and air–sea interaction studies (e.g., Inoue et al., 2013, 2015;
Kawai et al., 2014). Efforts to improve the quality of radiosonde data have
continued to the present time (e.g., Ciesielski et al., 2014; Bodeker et al.,
2016). One consequence of the technological advancements has been the need
to account for accuracy differences following radiosonde upgrades in the
long-term continuous datasets (Wang et al., 2013).
Positions of the twin-radiosonde launches during the (a) MR15-03
cruise and (b) MR15-04 cruise. (c) Time–latitude diagram of the launches.
Black and red dots represent daytime and nighttime soundings, respectively.
The red dots in December are plotted with a shift of 3 ∘ latitude.
The model RS92 radiosonde manufactured by Vaisala Ltd., which was first
introduced in 2003, has been used throughout the world, and it is now being
replaced with a successor model, the RS41 (Table 1). To clarify the
differences between the RS41 and RS92 radiosondes, intercomparison
experiments have already been carried out at several sites on land from high
latitudes to the tropics (Motl, 2014; Jauhiainen et al., 2014; Jensen et
al., 2016). Jauhiainen et al. (2014) have reported results of comparisons in
several countries, including Finland, the United Kingdom, the Czech
Republic, and Malaysia. They reported that the RS41 radiosonde was a
consistent improvement over the RS92 in terms of reproducibility with
respect to temperature and humidity under both day and night conditions. A
different intercomparison study was carried out at a site in Oklahoma, USA,
by Jensen et al. (2016). They showed that the RS92 and RS41 measurements
agreed much better than the manufacturer-specified combined uncertainties.
Their results also indicated that the RS41 measurements of temperature and
humidity appeared to be less sensitive to solar heating than those made with
the RS92.
Nominal accuracies of the radiosondes according to the
manufacturer.
a Ascent rate above 3 m s-1.
b Wind speed above 3 m s-1.
The accuracy of the pressure measured with the model RS41-SGP, however, has
not yet been examined, nor has a comparison been made between the RS41 and
RS92 radiosondes in the marine atmosphere. Unlike the atmosphere over land,
the marine atmosphere is less affected by topography and the greater
temperature variations of the land surface. As a result, phenomena such as
convection and precipitation and their diurnal cycles over the oceans are
different from those over land (e.g., Yang and Slingo, 2001; Minobe and
Takebayashi, 2015). We performed a total of 36 intercomparison flights
during two cruises of R/V Mirai of the Japan Agency for Marine–Earth Science and
Technology (JAMSTEC) in 2015. Our observations covered a wide range of
latitudes over the oceans, an important consideration from the standpoint of
confirming the performance of the RS41. We describe the cruises and the
methodology of the intercomparison observations in Sect. 2. Section 3 shows
the results of the comparisons. In Sect. 4, we focus on the data obtained in
the tropics and further discuss the reasons for the differences between the
RS41 and RS92 results. Section 5 is a summary of the study.
Photographs of (a) the RS92 and RS41 radiosondes directly
attached to each other and (b) a launch on R/V Mirai.
Intercomparison experimentCruises
The intercomparison observations were performed by launching both the RS41
and RS92 radiosondes tied to one balloon (referred to as a
“twin-radiosonde” flight) during the MR15-03 and MR15-04 cruises of R/V Mirai.
In the case of the MR15-03 cruise, the vessel departed from Hachinohe,
Japan, on 26 August; cruised the Arctic Ocean from 6 September to 3 October
(JAMSTEC, 2015); and returned to Hachinohe on 21 October. The
twin-radiosonde flights were launched nine times in the Chukchi Sea, four times in
the Bering Sea, and five times in the western North Pacific (Fig. 1a and Table 2).
The MR15-04 cruise was for tropical meteorological research, and the
vessel stayed near 4∘04′ S, 101∘54′ E
off Bengkulu, west of Sumatra Island, in the Indian Ocean during 23 November
to 17 December for stationary observations, including 16 twin-radiosonde
flights (JAMSTEC and BPPT, 2015). We also conducted intercomparison
observations twice in the western Pacific on the way from Japan to the site
off Sumatra (Fig. 1b and Table 2).
Date, position (latitude and longitude), surface meteorological
state (pressure, temperature, relative humidity, wind direction, and wind
speed), CAPE, CIN, and PW when each twin-radiosonde flight was launched. Italic font for UTC time denotes nighttime.
We used radiosonde models RS92-SGPD and RS41-SGP in this study. Their
nominal accuracies are summarized in Table 1. Whereas the RS41-SG radiosonde
used in the previous studies (Motl, 2014; Jauhiainen et al., 2014; Jensen et
al., 2016) derived pressure from Global Positioning System (GPS) data with
no pressure sensor, the RS41-SGP has a pressure sensor consisting of a
silicon capacitor. The pressure and height data analyzed in this study were
measured directly and derived from the hypsometric equation, respectively.
Note that GPS-derived pressure and height were not used, unlike in the
previous studies. Two different DigiCORA systems were used on R/V Mirai for the
simultaneous RS92 and RS41 soundings. The receiving system (MW41) used for
the RS41 included a processor (SPS331); processing and recording software
(MW41 v2.2.1); GPS antenna (GA20); and ultra-high-frequency (UHF) antenna (RB21), which was part of
the ASAP sounding station permanently installed on R/V Mirai. The RS41 sensors
were calibrated with a new calibrator (RI41) and a barometer (PTB330). In
contrast, we used a previous-generation system for the RS92: the receiving
system (MW31) included a processor (SPS311), software (DigiCORA v3.64), GPS
antenna (GA31), and UHF antenna (RM32). The instrumentation was temporarily
placed in or on the aft wheelhouse. The RS92 sensors were calibrated with a
calibrator (GC25) and a PTB330 barometer. Because version 3.61 of DigiCORA
was incorrectly used during the cruises, all RS92 sounding data were
simulated with DigiCORA v3.64 after the cruises.
The RS41 and RS92 radiosondes were directly attached to each other with
sticky tape (Fig. 2), instead of hanging them from the two ends of a rod
(Jensen et al., 2016), to facilitate the launching operations on the rocking
ship deck. The two radiosondes were hung from a single 350 g Totex balloon
with the cord of the RS41 radiosonde. The ascent rates were approximately 5 and
4 m s-1 during the MR15-03 and MR15-04 cruises,
respectively (Table 2). Whereas nighttime twin-radiosonde flights could be
carried out only once during the MR15-03 cruise owing to operations
associated with oceanographic observations, we performed eight nighttime
flights during the MR15-04 cruise (Fig. 1c and Table 2). In addition
information about surface meteorological state, Table 2 lists convective
available potential energy (CAPE), convective inhibition (CIN), and
precipitable water (PW) calculated from RS41 data. CAPE and CIN were
calculated for an air parcel corresponding to an average over the lowest 50 hPa.
A number of issues were addressed in post-processing the sounding data.
During flight no. 33 (02:50 UTC on 16 December), the radiosondes oscillated
vertically about the 0 ∘C level likely due to icing on the
balloon, and hence only the data before the up-and-down motion were analyzed
in this study. In the case of flight no. 9 (05:30 UTC on 16 September), we
delayed the measurement time of the RS41 by 17 s in the analysis because the
twin radiosondes flew horizontally just after launching, and the automatic
determinations of the starting times disagreed between the RS92 and RS41.
Because the pressure values measured with the PTB330 barometer for the
calibration of the RS92 had a bias of 0.18 hPa before the launch of the
no. 5 radiosondes, we subtracted 0.18 hPa from the observed pressure values of
the RS92 no. 1–4 radiosondes when the data were analyzed. The balloon
release detection mode was changed from automatic to manual during the
MR15-04 cruise, and the starting times of the RS92 and RS41 radiosondes
during the MR15-04 cruise generally appeared to differ slightly. Therefore,
the measurement times of all the RS92 radiosonde data during the MR15-04
cruise were delayed by 1.7 s in the analysis.
Results
To facilitate comparison with the results of Jensen et al. (2016), we
interpolated the RS92 radiosonde profiles to the same time step as the RS41
profiles and calculated differences between them at each 10 m vertical grid
based on the RS41 radiosonde heights (Fig. 3). The vertical axis of Fig. 3
is therefore nearly equivalent to the passage of time. The biases, standard
deviations, and root mean square (rms) differences were all smaller than the
combined uncertainties, except that the rms differences of pressure above
100 exceeded 0.6 hPa (Table 3). For temperature and wind speeds, the
biases and rms differences in our experiments were nearly the same as those
of Jensen et al. (2016), but the differences of pressure and relative
humidity (RH) were much larger in our study.
Vertical profiles of the median (black), 25–75th percentile
(green), 10–90th percentile (gray), and mean ± standard deviation
(cyan) of all differences between the RS92 and RS41 observations (RS92–RS41)
for (a) pressure, (b) geopotential height, (c) relative humidity,
(d) temperature, (e) zonal wind, and (f) meridional wind.
Biases, rms differences, and standard deviations (SDs) of the
variables between the RS92 and RS41 radiosondes. The bias is the mean of
RS92–RS41 differences.
The pressure difference between the RS41 and RS92 radiosondes increased as
the radiosondes rose to an altitude of about 5 km but averaged an almost
constant 0.5–0.6 hPa above that altitude (Fig. 3a). The 90th-percentile
line revealed that the sensor-measured RS41 pressure was lower than the RS92
for more than 90 % of the measurements above 5 km. The percentage of the
pressure differences that exceeded the combined uncertainty (Table 1) was
13.7 % below 100 hPa but 50.9 % above 100 hPa. The bias of pressure
causes the bias of geopotential height (Fig. 3b). The height difference
increased with the altitude: The median of the RS41 height was greater than
that of the RS92 by approximately 35 m at an altitude of 15 km, and 100 m at
22 km.
We also checked the GPS-derived pressure of the RS41 radiosondes. Figure 4
shows the difference between the RS92 pressure and the RS41 GPS-derived one.
The use of the GPS-derived pressure reduced the bias by approximately 0.2 hPa
above an altitude of 15 km, but there was still a bias of 0.4 hPa or
more at most altitudes. The median of the difference in Fig. 4 was almost
the same as in Fig. 3a around an altitude of 5 km. The use of the GPS did not
essentially improve the pressure bias. This is different from the results of
Jensen et al. (2016).
As in Fig. 3a but for between the RS41 GPS-derived and RS92
pressures (RS92–RS41).
Relative humidity
The median of the relative humidity differences peaked at approximately
2 % RH
near 10 km (Fig. 3c), a result consistent with the data of Jensen
et al. (2016). The humidity difference was also large near the sea surface in
our analysis. For 13.0 % of the measurements, the absolute value of the
difference exceeded 4.0 % RH, which is the combined uncertainty of the
RS41-SGP. One noteworthy feature of Fig. 3c is that there were quite large
differences of relative humidity at a height of about 17 km, although the
median difference was less than 0.5 % RH. Figure 5 shows the relationship
between the humidity difference and temperature for each category of
relative humidity. During both the MR15-03 and MR15-04 cruises, the RS41
radiosonde recorded a higher mean relative humidity than the RS92 for
all humidity ranges. The humidity difference peaked at around
-40 ∘C, a pattern similar to Fig. 17 of Jensen et al. (2016). The
differences were relatively small, in the range of -50 to
-70 ∘C, but the RS41 humidity was much higher than the RS92 at
temperatures below -80 ∘C (Fig. 5b). The atmosphere associated
with temperatures below -80 ∘C corresponds to the tropopause in
the tropics, where the greatest differences were apparent at altitudes of
about 17 km (Fig. 3c).
Mean difference in relative humidity between the RS92 and RS41
radiosondes (RS92–RS41) as a function of the RS41 temperature for
relative humidity ranges of 0–20 % (blue), 20–40 % (red), 40–60 % (green),
60–100 % (black), and 0–100 % (gray). (a) MR15-03 and (b) MR15-04.
Temperature
In the case of temperature, although there was a slight positive bias below
an altitude of 10 km, the median of the differences was within ±0.12 ∘C
below an altitude of 26 km (Fig. 3d). The median exceeded
0.5 ∘C above 27 km, but only four flights reached that height, and
the large median was attributable to differences on two of the flights (nos. 23
and 24). The percentages of the temperature difference that exceeded the
combined uncertainty were 4.0 % below 16 km and 5.9 % above 16 km.
Figure 3d also shows that the standard deviation of the temperature
differences was smaller at altitudes below 16 km, but there were quite large
standard deviations near the surface and at altitudes of about 1.3 and
5.3 km because of some outliers. The extreme temperature difference, which
reached 2.75 ∘C at an altitude of 1.27 km, was observed on 10
December in the tropics (Fig. 6a). The RS92 temperature became much lower
than the RS41 just after the radiosondes passed through a saturated layer
into a dry layer. The greater reduction of the RS92 temperature was probably
due to the “wet-bulbing” effect mentioned by Jensen et al. (2016), who
indicated that the sequential pulse heating method with relatively long
non-heating periods may not be sufficient to eliminate icing/wetting of the
RS92 sensor. A large temperature difference that was likely caused by the
wet-bulbing effect was also observed in a sounding in the Arctic, although
the maximum difference was less than 0.75 ∘C (Fig. 6b).
Vertical profiles of the RS41 temperature (red), RS92 temperature (blue),
RS41 relative humidity (magenta), and RS92 relative humidity (cyan).
(a) Flight no. 29 launched at 17:27 UTC on 10 December 2015 in the tropics,
and (b) flight no. 9 launched at 05:30 UTC on 16 September 2015 in the
Arctic.
Figure 7 shows the cases of extreme temperature differences that contributed
to the greater standard deviation and cannot be explained by the wet-bulbing
effect. For the flight on 11 December (Fig. 7a), there was a large
temperature discrepancy inside the saturated layer. In that case, the
radiosondes were launched in heavy rain, and the ascent rate dropped to
nearly zero at approximately 5.4 km, probably because of rain or snow and
freezing of the balloon. Furthermore, the horizontal wind speed was less
than 3.0 m s-1 around this altitude. As a result, the temperature
sensors were presumably not ventilated sufficiently. In the case of the
flights on 1 and 3 December (Fig. 7b and c), the RS41 temperatures were
higher than the RS92 by more than 1.0 ∘C near the surface. Because
the surface reference air temperatures were close to the RS92 temperatures
at the lowest level, we suspect that the RS41 temperatures were too high.
These large temperature differences led to enormous discrepancies in CAPE:
864.6 J kg-1 for no. 22 and 1819.0 J kg-1 for no. 23.
Yoneyama et al. (2002) have indicated that ship body heating can affect radiosonde
sensors. However, that effect was restricted to within several tens of
meters of the sea surface in their experiments. Although we cannot
completely exclude the possibility that the temperature sensors of the two
RS41 radiosondes were improperly heated by the body of the ship or direct
insolation or improper handling near the surface, the reason for these large
discrepancies remains unclear.
As Fig. 6 but for (a) flight no. 30 launched at 14:20 UTC on 11
December 2015, (b) flight no. 22 launched at 05:30 UTC on 1 December 2015,
and (c) flight no. 23 launched at 05:29 UTC on 3 December 2015. All launches
in the tropics.
Wind speed
Vertical profiles of the wind speed differences are shown in Fig. 3e and f.
The percentages of the differences in the zonal and meridional wind speeds
that exceeded 0.5 m s-1 were 1.9 and 1.5 %, respectively.
Although both the zonal and meridional wind speeds agreed to within 0.5 m s-1
for almost all measurements, several spikes can be seen in the
standard deviations and percentiles. In half of all flights, the magnitude
of the difference of the horizontal wind speed exceeded 1.0 m s-1 for a
brief moment. The wind speed data in our soundings were noisier than those
reported by Jensen et al. (2016).
DiscussionDay–night differences
Figure 8 compares the differences between daytime (10 flights) and nighttime
(8 flights) for the soundings during the MR15-04 cruise. The median of the
pressure difference was greater in the day than at night above an altitude
of 4.5 km (Fig. 8a). The median of the nighttime differences was close to
that of the daytime flights in the Arctic cruise below an altitude of 15 km,
the implication being that the day–night difference might reflect some
effect of solar heating.
Differences between the RS92 and RS41 radiosonde (RS92–RS41)
results for daytime (blue) and nighttime (red) flights during the MR15-04
cruise for (a) pressure, (b) temperature, and (c) relative humidity.
Relative difference between the RS92 and RS41 relative humidity
obtained during the daytime on the MR15-04 cruise (blue dots, %).
Relative difference is defined as the relative humidity difference expressed
as a percentage of the RS41 relative humidity. Green line denotes the median
of the relative difference. (b) shows an enlargement of part (a).
The ratio of the RS41 to the RS92 PW as a function of solar
altitude angle. Blue and red dots represent soundings in the MR15-03 and
MR15-04 cruises, respectively.
The median profiles of temperature differences in the day and night were
close to each other, with slightly larger differences in the night at
altitudes of 5–15 km (Fig. 8b). The daytime difference became greater above
approximately 24 km, a pattern similar to the results of Jensen et al. (2016).
According to them, the difference in the radiation correction
schemes between the RS92 and RS41 may be the dominant cause of these
temperature differences, particularly at high solar elevation angles and low
pressures.
The median of the relative humidity difference was larger during the day
than at night from the surface to an altitude of 20 km and was especially
large at an altitude of about 17 km (Fig. 8c). The very large difference
(RS41 > RS92) in relative humidity around the tropopause shown in
Figs. 3c and 5b occurred in the daytime. This pattern is consistent with the
results of Jauhiainen et al. (2014), who indicated that the difference was
largely due to the dissimilar approaches used to compensate for the heating
effect of solar radiation on the humidity sensor. Similar dry biases were
reported for the RS92 radiosonde with the earlier version of DigiCORA
(Vömel et al., 2007; Yoneyama et al., 2008), although the dry bias was
generally absent from later observations (Ciesielski et al., 2014; Yu et
al., 2015) because the bias due to solar heating was removed by a correction
scheme included in the v3.64 software or developed by Wang et al. (2013).
Figure 9 shows the relative difference of relative humidity in the daytime
between the RS92 and RS41 radiosondes. The relative difference is defined to
be the relative humidity difference expressed as a percentage of the RS41
relative humidity. The relative difference was small in the lower
troposphere and became greater as the radiosondes rose higher. Its median
peaked at -36.9 % at an approximate altitude of 19 km. This pattern of
the vertical profile of relative difference is similar to that between the
RS92 radiosonde and a reference instrument shown by Vömel et al. (2007),
but the values in Fig. 9 are less than half of those in Fig. 6 of Vömel
et al. (2007) because the RS92 DigiCORA v3.64 and RS41 relative humidity
data are already inherently better.
We evaluated how the differences between the two types of radiosonde
affected CAPE, CIN, and PW (Table 4). CAPE tended to be larger when the RS92
was used in the nighttime. This was due to slightly higher temperature of
RS92 near the surface (Fig. 8b). On the other hand, in the daytime the RS41
CAPE was larger than the RS92 and the RS41 CIN was smaller than the RS92. The
day–night differences in the CAPE and CIN biases were caused by the
difference in the humidity bias between daytime and nighttime. The
near-surface humidity of the RS41 was larger than that of the RS92 in the
daytime (Fig. 8c). The larger pressure bias in daytime (Fig. 8a), which
means an atmospheric layer in the RS41 observation, also may contribute
to the daytime bias of CAPE. Although the bias of PW was less than 1.0 mm,
the daytime humidity difference between the RS41 and RS92 affected PW. The
ratio of the RS41 to the RS92 PW was dependent on solar altitude angle
(Fig. 10), similar to the general shape of the dependence indicated by
Miloshevich et al. (2009) (their Fig. 4a), suggesting that the humidity bias
was mainly related to solar heating.
Humidity correction
Figures 8c, 9, and 10 imply that a small dry bias still remains in the RS92
radiosonde observations. We attempted to correct the RS92 relative humidity
obtained during the MR15-04 cruise by using the RS41 as a reference
instrument. However, this is not based on an assertion that the RS42
measurements must be true values. There is no independent evidence to judge
which radiosonde was more accurate. The RS41 relative humidity was larger
than the RS92 at an altitude between 3 and 13 km (Fig. 8c), suggesting that the
RS41 humidity also has a slight moist bias that is unrelated to the
radiation correction scheme. The correction attempted in this subsection is
a proposal to bridge the gap in relative humidity between the RS41 and RS92
radiosondes.
We used the cumulative distribution function (CDF) matching method proposed
by Nuret et al. (2008) and Ciesielski et al. (2009) to make the correction.
The details of this method can be found in Ciesielski et al. (2009). We
first created CDFs of relative humidity for the RS92 and RS41 using
temperature bins of 20 between +30 and
-90 ∘C (10 to 30, -10 to 10, -30 to
-10, -50 to -30, -70 to -50, and
-90 to -70 ∘C) using 5 hPa radiosonde data in 5 % RH intervals.
Figure 11 shows the CDFs of the RS92 and RS41 in the temperature range -90
to -70 ∘C as an example. The frequency of lower relative humidity
was greater for the RS92 in this temperature range, which includes the
tropopause (Fig. 11a). We then, for example, paired the RS92 value of 27.50 % RH
at the 71.23th percentile with the corresponding RS41 value at this
same percentile. The RS41 relative humidity at the 71.23th percentile was
36.43 % RH, and the difference between 36.43 and 27.50 % RH (=+8.93 % RH)
was the bias correction for the RS92 value of 27.5 % RH.
Figure 11b shows the bias correction over the entire relative humidity range
for temperatures of -90 to -70 ∘C.
(a) CDFs of relative humidity for the RS92 (bold dashed line) and
RS41 (bold solid line) data in the temperature range of –90 to
-70 ∘C. The daytime data obtained during the MR15-04 cruise were
used. Thin solid lines illustrate the CDF matching technique (see text). (b) Bias
correction of relative humidity for the same temperature range.
Biases and standard deviations of CAPE, CIN, and PW between the RS92
and RS41 radiosondes. The bias is the mean of RS92–RS41 differences.
Values in parentheses are the statistics without the two outliers shown in
Fig. 7b–c (flight nos. 22 and 23).
Table 5 shows the daytime bias correction for the entire range of
temperatures and relative humidities. The correction was seldom more than
5 % RH when the RS92 temperature exceeded -60 ∘ C. The correction
was large for RS92 radiosonde values in the range 15–50 % RH and
temperatures of -80∘ C, with a maximum of +8.93 % RH. This
pattern is similar to that of the correction table for the RS80 radiosonde
in the daytime reported by Ciesielski et al. (2010) (their Fig. 7b), but the
values in Table 5 are much smaller. We corrected the daytime RS92 relative
humidity values obtained during the MR15-04 cruise using Table 5. The
correction value for an arbitrary RS92 measurement can be obtained by linear
two-dimensional interpolation using Table 5 and the RS92 temperature and
relative humidity. Figure 12 shows median profiles of the differences
between the RS92 and RS41 radiosondes before and after the correction.
Although the median of the magnitude of the differences still exceeded 2.0 % RH
around 120, 150, and 560 hPa, most of the medians were within ±1.0 % RH.
The mean of the relative humidity difference of the 5 hPa
interval data was -2.02 % RH if no correction was made; this difference
was reduced to -0.01 % RH after the correction.
Medians of the relative humidity difference between the RS92 and
RS41 radiosondes obtained during the daytime on the MR15-04 cruise. Blue and
black lines show the profiles before and after the bias correction of the
RS92 data.
Bias correction table of relative humidity that was created by
matching the CDFs from the RS92 data to the RS41 data (% RH) based on the
daytime data obtained during the MR15-04 cruise.
To examine differences between the RS41 and RS92 radiosondes, a total of 36
twin-radiosonde flights were performed over the Arctic Ocean, Bering Sea,
western North Pacific Ocean, and the tropical Indian Ocean during two cruises
of R/V Mirai in 2015. We used the model RS41-SGP radiosonde, which has a pressure
sensor, unlike previous studies that used the RS41-SG, which has no pressure
sensor.
The biases, standard deviations, and rms of the differences between the RS41
and RS92 over all flights and heights were smaller than the nominal combined
uncertainties of the RS41, except that the rms differences of pressure above
100 hPa exceeded 0.6 hPa. Whereas the biases and the rms differences of
temperature and wind speeds were close to those reported by Jensen et al. (2016),
the differences of pressure and relative humidity were greater in
our experiments. The pressure difference increased as the radiosondes rose
higher; the median and mean were 0.5–0.6 hPa at altitudes above 5 km. This
pressure difference corresponded to a geopotential height difference of more
than 35 m above an altitude of 15 km. A comparison between daytime and
nighttime flights in the tropics revealed that the pressure difference was
systematically larger in the day than at night at altitudes above 4.5 km,
the suggestion being that there was some effect of solar heating on the
pressure measurements. The exact reason, however, is unclear.
The RS41 and RS92 temperature measurements in general agreed better than the
combined uncertainties, but there were some noteworthy exceptions. One
possible reason for the noteworthy discrepancies is the wet-bulbing effect
described by Jensen et al. (2016). In a dry layer just above a saturated
layer, the RS92 temperature sensor was cooled too much by evaporation. The
RS41 temperature appeared to be less sensitive to this wet-bulbing effect.
This phenomenon was confirmed in both the tropics and Arctic. During heavy
rain and weak wind conditions, the stagnation of the balloon probably
suppressed the ventilation around the temperature sensors, the result being
an extreme temperature difference.
The median of the relative humidity differences at all altitudes was only a
little more than 2 % RH. However, there were quite large differences at an
altitude of about 17 km. These large differences occurred in the daytime
around the tropical tropopause, where the temperature was below
-80 ∘C. The reason for this dry bias may be that there was some
remnant of the error of the RS92 radiosonde solar radiation correction. The
differences in humidity affected the calculation of CAPE, CIN, and PW, and
we confirmed the day–night difference of these variables. We attempted to
correct the RS92 relative humidity data obtained in the daytime during the
MR15-04 cruise by using the CDF matching method, and the corrected RS92
relative humidity agreed well with the RS41 values.
Our results showed that measurements with the RS41 radiosonde satisfied the
performance specifications of the manufacturer in most cases over both the
tropical and polar oceans. The RS41 temperature and humidity sensors
appeared to be unaffected by the solar radiation correction error and the
wet-bulbing effect. Some concerns, however, do remain. Specifically, the
reasons for the pressure bias in the upper layer and the two cases of
extreme temperature discrepancies that occurred below an altitude of several
hundred meters are unknown. Further experiments will be necessary to address
these issues, and users should be cognizant of these concerns.
The sounding dataset and the ship-observed surface meteorology are expected
to be released just 2 years after the cruises (October 2017 for the
MR15-03, and December 2017 for the MR15-04) from the website of the Data
Research System for Whole Cruise Information (DARWIN) in JAMSTEC
(http://www.godac.jamstec.go.jp/darwin/e) in accord with the cruise data
policy of JAMSTEC.
All co-authors contributed to designing the experiments and preparing for
the observation cruises. YK, MK, and KO participated
in the R/V Mirai cruises and carried out the radiosonde soundings. KO
reprocessed the RS92 data. MK calculated CAPE, CIN, and PW. YK
mainly analyzed the data and prepared the manuscript with
contributions from all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors sincerely thank the captains, crews, and observation technicians
of R/V Mirai and all colleagues who helped with the experiments. The authors
are also grateful to Kunio Yoneyama of JAMSTEC for valuable advice, especially
for advice concerning the humidity correction. This study was supported by
the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for
Scientific Research (A), (B), and (C) (KAKENHI) grant numbers 24241009,
16H04046, and 16K05563.
Edited by: Joanna Joiner
Reviewed by: three anonymous referees
ReferencesBodeker, G. E., Bojinski, S., Cimini, C., Dirksen, R. J., Haeffelin, M.,
Hannigan, J. W., Hurst, D. F., Leblanc, T., Madonna, F., Maturilli, M.,
Mikalsen, A. C., Philpona, R., Reale, T., Siedel, D. J., Tan, D. G. H.,
Thorne, P. W., Vömel, H., and Wang, J.: Reference upper-air observations
for climate: From concept to reality, B. Am. Meteorol. Soc., 97, 123–135,
10.1175/BAMS-D-14-00072.1, 2016.Ciesielski, P. E., Johnson, R. H., and Wang, J: Correction of humidity
biases in Vaisala RS80-H sondes during NAME, J. Atmos. Ocean. Tech., 26,
1763–1780, 10.1175/2009JTECHA1222.1, 2009.Ciesielski, P. E., Chang, W.-M., Huang, S. -C., Johnson, R. H., Jou, B. J.-D., Lee,
W.-C., Lin, P.-H., Liu, C.-H., and Wang, J.:
Quality-controlled upper-air sounding dataset for TiMREX/SoWMEX: Development
and corrections, J. Atmos. Ocean. Tech., 27, 1802–1821,
10.1175/2010JTECHA1481.1, 2010.Ciesielski, P. E., Yu, H., Johnson, R. H., Yoneyama, K., Katsumata, M.,
Long, C. N., Wang, J., Loehrer, S. M., Young, K., Williams, S. F., Brown,
W., Braun, J., and Van Hove, T.: Quality-controlled upper-air sounding
dataset for DYNAMO/CINDY/AMIE: Development and corrections, J. Atmos. Ocean.
Tech., 31, 741–764, 10.1175/JTECH-D-13-00165.1, 2014.Fujita, M., Kimura, F., Yoneyama, K., and Yoshizaki, M.: Verification of
precipitable water vapor estimated from shipborne GPS measurements, Geophys.
Res. Lett., 35, L13803, 10.1029/2008GL033764, 2008.Inoue, J., Enomoto, T., and Hori, M. E.: The impact of radiosonde data over
the ice-free Arctic Ocean on the atmospheric circulation in the Northern
Hemisphere, Geophys. Res. Lett., 40, 864–869, 10.1002/grl.50207, 2013.Inoue, J., Yamazaki, A., Ono, J., Dethloff, K., Maturilli, M., Neuber, R.,
Edwards, P., and Yamaguchi, H.: Additional Arctic observations improve
weather and sea-ice forecasts for the Northern Sea Route, Sci. Rep., 5,
16868, 10.1038/srep16868, 2015.JAMSTEC: R/V
Mirai Cruise Report MR15-03, Cruise Rep., Japan Agency for Marine-Earth
Science and Technology, Yokosuka, Japan, available from:
http://www.godac.jamstec.go.jp/catalog/data/doc_catalog/media/MR15-03_leg1_all.pdf
(last access: 10 July 2017), 297 pp., 2015.JAMSTEC and BPPT: R/V Mirai Cruise Report MR15-04, Cruise Rep., Japan Agency for
Marine-Earth Science and Technology, Yokosuka, Japan, Agency for the Assessment
and Application of Technology, Indonesia, available from:
http://www.godac.jamstec.go.jp/catalog/data/doc_catalog/media/MR15-04_all.pdf (last access: 10 July 2017), 241 pp., 2015.
Jauhiainen, H., Survo, P., Lehtinen, R., and Lentonen, J.: Radiosonde RS41
and RS92 key differences and comparison test results in different locations
and climates. TECO-2014, WMO Technical Conference on Meteorological and
Environmental Instruments and Methods of Observations, Saint Petersberg,
Russian Federation, 7–9 July 2014, P3(16), 2014.Jensen, M. P., Holdridge, D. J., Survo, P., Lehtinen, R., Baxter, S., Toto,
T., and Johnson, K. L.: Comparison of Vaisala radiosondes RS41 and RS92 at
the ARM Southern Great Plains site, Atmos. Meas. Tech., 9, 3115–3129,
10.5194/amt-9-3115-2016, 2016.Kawai, Y., Tomita, H., Cronin, M. F., and Bond, N. A.: Atmospheric pressure
response to mesoscale sea surface temperature variations in the Kuroshio
Extension: In situ evidence, J. Geophys. Res.-Atmos., 119, 8015–8031,
10.1002/2013JD021126, 2014.Maturilli, M. and Kayser, M.: Arctic warming, moisture increase and
circulation changes observed in the Ny-Ålesund homogenized radiosonde
record, Theor. Appl. Climatol., 10.1007/s00704-016-1864-0, 2016.Miloshevich, L. M., Vömel, H., Whiteman, D. N., and Leblanc, T.:
Accuracy of assessment and correction of Vaisala RS92 radiosonde water vapor
measurement, J. Geophys. Res., 114, D11305, 10.1029/2008JD011565, 2009.Minobe, S. and Takebayashi, S.: Diurnal precipitation and high cloud
frequency variability over the Gulf Stream and over the Kuroshio, Clim.
Dynam., 44, 2079–2095, 10.1007/s00382-014-2245-y, 2015.
Motl, M.: Vaisala RS41 trial in the Czech Republic, Vaisala News, 192,
14–17, 2014.Nuret, M., Lafore, J.-P., Bock, O., Guichard, F., Agusti-Panareda, A.,
N'Gamini, J.-B., and Redelsperger, J.-L.: Correction of humidity bias for
Vaisala RS80-A sondes during the AMMA 2006 observing period, J. Atmos. Ocean.
Tech., 25, 2152–2158, 10.1175/2008JTECHA1103.1, 2008.Thorne, P. W., Parker, D. E., Tett, S. F. B., Jones, P. D., McCarthy, M.,
Coleman, H., and Brohan, P.: Revisiting radiosonde upper air temperatures
from 1958 to 2002, J. Geophys. Res., 110, D18105, 10.1029/2004JD005753,
2005.Vömel, H., Selkirk, H., Miloshevich, L., Valverde-Canossa, J.,
Valdés, J., Kyrö, E., Kivi, R., Stolz, W., Peng, G., and Diaz, J. A.:
Radiation dry bias of the Vaisala RS92 humidity sensor, J. Atmos. Ocean.
Tech., 24, 953–963, 10.1175/JTECH2019.1, 2007.
Wang, J., Zhang, L., Dai, A., Immler, F., Sommer, M., and Vömel, H.:
Radiation dry bias correction of Vaisala RS92 humidity data and its impacts
on historical radiosonde data, J. Atmos. Ocean. Tech., 30, 197–214,
10.1175/JTECH-D-12-00113.1, 2013.Yang, G.-Y. and Slingo, J.: The diurnal cycle in the tropics, Mon. Weather Rev.,
129, 784–801,
10.1175/1520-0493(2001)129<0784:TDCITT>2.0.CO;2, 2001.Yoneyama, K., Hanyu, M., Sueyoshi, S., Yoshiura, F., and Katsumata, M.:
Radiosonde observation from the ship in the tropical region, Report of Japan
Marine Science and Technology Center, available from:
http://www.jamstec.go.jp/res/ress/yoneyamak/PDFs/Yoneyama-etal_2002_JAMSTECR.pdf (last access: 10 July 2017), 45, 31–39, 2002.Yoneyama, K., Fujita, M., Sato, N., Fujiwara, M., Inai, Y., and Hasebe, F.:
Correction for radiation dry bias found in RS92 radiosonde data during the
MISMO field experiment, SOLA, 4, 13–16, 10.2151/sola.2008-004, 2008.Yu, H., Ciesielski, P. E., Wang, J., Kuo, H.-C., Vömel, H.,
and Dirksen, R.: Evaluation of humidity correction methods for Vaisala RS92
tropical sounding data, J. Atmos. Ocean. Tech., 32, 397–411,
10.1175/JTECH-D-14-00166.1, 2015.