Radiosonde observations (RAOBs) have provided the only long-term global
in situ temperature measurements in the troposphere and lower
stratosphere since 1958. In this study, we use consistently reprocessed
Global Positioning System (GPS) radio occultation (RO) temperature data
derived from the COSMIC and Metop-A/GRAS missions from 2006 to 2014 to
characterize the inter-seasonal and interannual variability of temperature
biases in the upper troposphere and lower stratosphere for different
radiosonde sensor types. The results show that the temperature biases for
different sensor types are mainly due to (i) uncorrected solar-zenith-angle-dependent errors and (ii) change of radiation correction. The mean
radiosonde–RO global daytime temperature difference in the layer from 200 to
20 hPa for Vaisala RS92 is equal to 0.20 K. The corresponding difference is
equal to
Stable, long-term atmospheric temperature climate data records (CDRs) with accurate uncertainty estimates are critical for understanding climate variability and change in both the troposphere and stratosphere and their feedback mechanisms (Thorne et al., 2011; Seidel et al., 2011). Radiosonde observations (RAOBs) have provided the only long-term global in situ temperature, moisture, and wind measurements in the troposphere and lower stratosphere since 1958. Several groups have used multiple years of RAOB temperature measurements to construct long-term CDRs (e.g., Durre et al., 2005; Free et al., 2004, 2005; Sherwood et al., 2008; Haimberger et al., 2008, 2011; Thorne et al., 2011; Seidel et al., 2009). However, it has long been recognized that the quality of the RAOB observations varies for different sensor types and height (e.g. Luers and Eskridge, 1995, 1998; Luers, 1997). Therefore, except for some sensor types where a relatively objective radiation correction had been applied (i.e., Vaisala RS90), it is difficult to objectively identify, trace, and remove most of the sensor-dependent biases for the historical sonde data and use the corrected RAOB temperatures to construct consistent temperature CDRs. The large uncertainties among temperature CDRs constructed from satellite and in situ measurements are still one of the most challenging issues for climate change research (IPCC AR5).
The causes of temperature errors in RAOB sensors include the changing of instruments and practices (Gaffen, 1994) and errors occurring due to the influence of solar and infrared radiation on the thermistor. In the past decade, many homogenization methods have been proposed to identify and correct errors due to changing of instruments and practice (Luers and Eskridge, 1998; Lanzante et al., 2003; Andrae et al., 2004; Free et al., 2004, 2005; Sherwood et al., 2008; Haimberger et al., 2008, 2011; Thorne et al., 2011; Seidel et al., 2009). Possible errors due to changes of instruments were identified by comparing with temperature measurements from adjacent weather stations. However, this approach is limited by the low number of co-located observations and large atmospheric variability. In addition, due to lack of absolute references, the remaining radiation temperature biases from adjacent stations may not be completely removed. As a result, only relative temperature differences of a possibly large uncertainty among stations are identified.
To correct possible RAOB temperature errors due to radiative effects, Andrae et al. (2004) and Haimberger et al. (2007, 2008, 2011) calculated temperature differences between observations and reanalyses data which were then used to minimize the differences between daytime and nighttime temperature differences. Nevertheless, because changes of reanalysis systems and possible incomplete calibration of satellite instruments may complicate the temperature bias correction, long-term stability of the derived temperature trends is still of great uncertainty. To correct the RAOB solar/infrared radiation errors, radiation correction tables (for example, RSN96, RSN2005, and RSN2010 tables from Vaisala) were introduced by manufacturers. However, when and how exactly different countries start to apply these corrections and whether there are remaining uncorrected radiative effects over different geographic regions is still unknown. It is important to use stable and accurate temperature references to characterize these errors from multiple sensors in different geographical regions over a long period of time.
The fundamental observable (time delay) for the Global Positioning System
(GPS) radio occultation (RO) satellite remote sensing technique can be traced
to ultra-stable international standards (atomic clocks) on the ground. While
time delay and bending angles are traceable to the international standard of
units (SI traceability), the derived temperature profiles are not. To
investigate the structural uncertainty of RO temperature profiles, Ho et al., (2009a, 2011) compared CHAMP (CHAllenging Minisatellite Payload)
temperature profiles generated from multiple centers when different inversion
procedures were implemented. Results showed that the mean RO temperature
biases for one center relative to the all center mean is within
The mean temperature difference between the collocated soundings of COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) and CHAMP was within 0.1 K from 200 to 20 hPa (Ho et al., 2009b; Anthes et al., 2008; Foelsche et al., 2009). At 20 hPa, the mean temperature difference between COSMIC and CHAMP was within 0.05 K (Ho et al., 2009b). Schreiner et al. (2014) compared reprocessed COSMIC and Metop-A/GRAS (Meteorological Operational Polar Satellite A/Global Navigation Satellite System (GNSS) receiver for Atmospheric Sounding) bending angles and temperatures produced at COSMIC Data Analysis and Archive Center (CDAAC). The mean layer temperature difference between 200 to 10 hPa was within 0.05 K where the mean temperature difference at 20 hPa is equal to 0.03 K. These results demonstrate the consistency of COSMIC and Metop-A/GRAS temperatures.
The precision of RO temperature is
RO-derived atmospheric variables have been used as reference to identify RAOB sensor-dependent biases. For example, Kuo et al. (2004) used RO data to identify sensor-type-dependent refractivity biases. Ho et al. (2010a) demonstrated that RO-derived water vapor profiles can be used to distinguish systematic biases among humidity sensors. He et al. (2009) and Sun et al. (2010, 2013) used RO temperature data in the lower stratosphere to quantify the temperature biases for several sensor types. While He et al. (2009) used the COSMIC post-processed temperature profiles from August 2006 to February 2007 to quantify the radiosonde radiation temperature biases for different sensor types, Sun et al. (2010, 2013) used COSMIC real-time processed temperature profiles to identify radiosonde temperature biases for numerical weather prediction (NWP) analysis. Because complete GPS orbital information is not available in real time, approximate GPS orbital information was used in the real-time inversion processing. The differences between real-time and post-processed RO temperatures in the lower stratosphere range from 0.3 to 0.1 K depending on the comparison period. Although real-time COSMIC data, which are processed by using periodically revised inversion packages, may be suitable for weather analysis, they may not be suitable for climate studies. Both of these RAOB–RO comparisons are constructed from a relatively limited period of time. A consistent validation of the variability of inter-seasonal and interannual RAOB temperature biases over a longer time period (close to 10 years) for different temperature sensor types has not yet been done.
Recently, the UCAR CDAAC has developed an improved reprocessing package, which is used to consistently process RO data from multiple years of multiple RO missions including COSMIC (launched in April 2006) and Metop-A/GRAS (launched in October 2006). A sequence of processing steps is used to invert excess phase measurement to retrieve atmospheric variables including bending angle, refractivity, pressure, temperature, and geopotential height.
The new inversion package uses improved precise orbit determination (POD) and excess phase processing algorithm, where a high-precision, multiple GNSS data processing software (i.e., Bernese Version 5.2; Dach et al., 2015) is applied for clock estimation and time transfer. In the reprocessing package, the POD for COSMIC and Metop-A/GRAS is implemented separately (Schreiner et al., 2011). The reprocessed RO data produce more consistent and accurate RO variables than those from post-processed (periodically updated inversion packages were used) and real-time processed datasets.
The objectives of this study are to use consistently reprocessed GPS RO temperature data to characterize (i) temperature biases dependent on solar zenith angle (SZA), (ii) potential residual temperature errors due to incomplete radiation correction, (iii) temperature biases due to change of radiation correction over different geographical regions, (iv) the inter-seasonal and interannual variability of these temperature biases, and (v) the trends of these biases and their uncertainty for different sensor types in the upper troposphere and lower stratosphere. In contrast to previous studies (i.e., He et al., 2009, and Sun et al., 2010, 2013) that used shorter time periods, close to 8 years (from June 2006 to April 2014) of consistently reprocessed temperature profiles derived from COSMIC and Metop-A/GRAS are used. Because the quality of RO data does not change during the day or night and is not affected by clouds (Anthes et al., 2008), the RO temperature profiles co-located with RAOBs are useful for identifying the variation of temperature biases over time of different temperature sensors.
In Sect. 2, we describe the RO and RAOB data and the comparison method. The global comparison of RAOB–RO pairs for different temperature sensor types for daytime and nighttime is summarized in Sect. 3. The global SZA-dependent temperature biases for various sensor types at different geographical regions are also compared in this section. The inter-seasonal variations of RAOB–RO temperature biases are assessed in Sect. 4. We conclude our study in Sect. 5.
The radiosonde data used in this study were downloaded from CDAAC
(
There are more than 1100 radiosonde stations globally. Figure 1 depicts the
geophysical locations for all RAOB data from June 2006 to April 2014. These
include Vaisala RS80, RS90, RS92, AVK-MRZ (and other Russian sondes), VIZ-B2,
Sippican MARK II A, Shanghai (from China), and Meisei (Japan). Table 1
summarizes the availability for different instrument types. In total,
17 different types of radiosonde systems were used. The solar
absorptivity (
Global distribution of radiosonde stations colored by radiosonde types. Radiosonde types updated from June 2006 to April 2014 are used. The percentage of each type of radiosonde used among all stations is listed. For those stations that radiosonde types are changed during this period, the latest updated radiosonde type is used in this plot. Vaisala RS92 ship observations contain less than 3 % of the total RS92 profiles.
Summary of the availability for different instrument types and their
solar absorptivity (
Because the Vaisala RS80 sensor was never changed and should be the same for
all RS80 models and the software uses the same radiation correction table
that should not show any differences, we do not further separate Vaisala RS80
sensors (i.e., ID
Mean and standard deviation (SD) of temperature differences (K) from the
layer from 200 to 20 hPa between eight types of
radiosonde
The reprocessed COSMIC (Version 2013.3520) and Metop-A/GRAS (Version
2016.0120) dry temperature profiles downloaded from UCAR CDAAC
(
The RO atmPrf data from COSMIC and Metop-A/GRAS were first interpolated to
the mandatory pressure level of the radiosondes (i.e., 200, 150, 100, 50, and
20 hPa). To account for the possible temporal and spatial mismatches between
RO data and RAOBs, the RO data within 2 h and 300 km of the radiosonde data
were collected for different RAOB instrument types. These matching criteria
are similar to the criteria used by He et al. (2009). However, in contrast to He et al. (2009),
positions of RO measurements at the corresponding heights are used in the
RAOB–RO ensembles. We compute temperature differences between RO atmPrf and
the corresponding RAOB pairs in the same pressure level
In addition, we compare the monthly mean temperature biases
Mean RAOB–RO temperature biases at 50 hPa for the RAOB–RO ensembles
from June 2006 to April 2014 for
RS92 (ID
In total, we have more than 600 000 RAOB–RO pairs. Using Eq. (2), we compute the temperature biases of radiosonde measurements for each individual sensor type. The mean temperature bias for ensembles of the RAOB–RO pairs from June 2006 to April 2014 for the layer between 200 and 20 hPa for different RAOB sensor types is summarized in Table 2. The standard deviations for each radiosonde type are also shown. The radiosonde temperature biases vary for different sensor types. All biases are less than 0.25 K except for AVK and VIZ-B2, which reach 0.66 and 0.71 K, respectively, during the day.
The solar radiation effect on sensors is the dominant error source of RAOB temperature biases (Luers et al., 1998; He et al., 2009). We assume that all operational data have a radiation correction already applied. The global temperature biases relative to the co-located RO temperature at 50 hPa for various radiosonde sensor types for daytime and nighttime are shown in Fig. 2. Only those stations containing more than 50 RAOB–RO pairs are plotted. Figure 2a shows biases for different sensor types, which vary with geographical region. Most of the sensor types contain positive temperature biases ranging from 0.1 to 0.6 K during the daytime. This bias during daytime may be a result of the residual error of the systematic radiation bias correction. Although we only include stations containing more than 50 RAOB–RO pairs, some level of heterogeneity (i.e., Fig. 2a over Brazil) may be due to low sample sizes. For example, stations with temperature biases larger than 0.5 K in eastern Brazil contain only about 60 RAOB–RO pairs. The cause of the heterogeneity in temperature bias between North and South China is not certain at this point.
The mean nighttime biases are very different from those in the daytime for
the same sensors. Figure 2b shows that most of the sensor types show a cold
bias at night except for Vaisala in South America, Australia, and Europe.
The mean biases at night for the two sonde types with the largest warm bias
at daytime (AVK and VIZ-B2) are equal to
The global mean
More than 50 % of RAOB data are from Vaisala sondes, from a number of
different countries. In total, 267 597 RS92 (ID
Figure 3 indicates that RS92 measurements in different regions have a similar quality in terms of mean differences from RO with a small warm bias above 100 hPa, as well as similar standard deviations relative to the mean biases of approximately 1.5 K. Because some stations in the United States are only interested in the tropospheric profiles and use smaller balloons, fewer RS92–RO samples are available above 70 hPa compared to those in other countries.
Comparisons of temperature between RS92 and RO for daytime over
Figure 4 depicts the mean RS92–RO temperature differences from 200 to 20 hPa for nighttime. The nighttime RS92 data over different regions show similar standard deviations of about 1.5 K compared to those at daytime. In most of the regions, the mean nighttime temperature biases are similar to those in the daytime results, with small (0.1–0.2 K) warm biases above 100 hPa. These residual nighttime warm biases are not seen in the RAOB–RO ensemble pairs for Sippican MARK, VIZ-B2, AVK, and Shanghai sondes (see Sect. 3.3). This 0.1–0.2 K warm bias for RS92 at night could be due to calibration of the RS92 temperature sensor (see Dirksen et al., 2014).
Comparisons of temperature between RS92 and RO for nighttime over
Because the quality of RO temperature is not affected by sunlight, the small but obvious geographic-dependent biases are most likely due to the residual radiation correction for RS92 and when and how different countries apply the radiation correction (see Sect. 4.1).
To consider a possible SZA dependence of the temperature bias due to residual
radiation errors for Vaisala RS92, we bin the computed temperature
differences in 5
The mean temperature biases (RS92 minus RO) at 50 hPa varying for
SZA from 0 degrees to 180 degrees for
Unlike Vaisala sondes, which are distributed in almost all latitudinal zones, other sonde types are distributed mainly in the northern midlatitudes. Figure 6 depicts the mean temperature differences from 200 to 20 hPa in the daytime for Sippican, VIZ-B2, AVK, and Shanghai. The biases for VIZ-B2 and AVK-MRZ are positive everywhere above 200 hPa, with means of about 0.7 K. The biases are smaller for Sippican and Shanghai. These mean biases are similar to those from He et al. (2009). The small differences between these and He et al. (2009) results are likely due to the sampling differences between He et al. (2009) (August 2006 to February 2007, or 7 months) and this study (95 months).
Comparisons of temperature between radiosonde and RO during the
daytime for
Figure 7 depicts the mean temperature differences from 200 to 20 hPa in the nighttime also for Sippican, VIZ-B2, AVK-MRZ, and Shanghai. The nighttime biases are generally less than 0.1 K except from VIZ-B2 above 100 hPA where they exceed 0.5 K. The small positive values for VIZ-B2 and AVK-MRZ, which were present in the daytime (Fig. 6), are not present during the night (Fig. 7)
Comparisons of temperature between radiosonde and RO during the
nighttime for
We also bin the temperature differences for these four sonde types in
5
The mean temperature biases at 50 hPa varying for SZA from 0
to 180
The VIZ-B2 sonde has a large warm bias (as high as 2.0 K) during daytime and
a cold bias (as low as
Since there is some residual radiation error, we characterize the long-term stability of RAOB temperature measurements for different RAOB sensor types by quantifying their seasonal temperature biases relative to those of co-located RO data.
The temperature differences between RS92 – RO from January 2007 to
December 2010 (
The Vaisala RS92 radiosonde was introduced in 2003 and is scheduled to be
replaced by the Vaisala RS41 in 2017. Vaisala included a reinforcement of the
RS92 sensor in 2007, which affected the radiation error. To account for this
sensor update, the radiation correction tables were updated in 2011 (RSN2010,
software version 3.64), which is used to replace the original radiation
correction table. Between 200 and 20 hPa, the correction in RSN2010 is about
0.1 K larger than in RSN2005 (see
To identify possible RS92 temperature biases due to changes of the radiation
correction table (i.e., RSN2010), we compare the mean
The Deutscher Wetterdienst (DWD), Germany's Meteorological Service,
implemented the updated radiation correction for the Vaisala RS92 in the
spring of 2015 rather than in 2011 to avoid inconsistencies with
corrections already implemented in their data assimilation system. This may
in part explain the greater consistency of
In this section we look at time series and trends in the de-seasonalized radiosonde–RO temperature differences from 2006 to 2014 in order to determine the long-term stability of these differences. Ideally, if both radiosondes and RO were free of biases, the time series would be stable and show small differences near zero with small standard deviations and no trends. We choose 50 hPa for showing these time series because the biases tend to be larger at this level than at lower levels. We also computed time series for 150 hPa, but except for lower biases the results were similar to those at 50 hPa (not shown).
Figure 10 shows daytime and nighttime time series of monthly mean temperature
biases at 50 hPa for Vaisala RS92 for the United States, Australia, Germany,
Canada, United Kingdom, and Brazil. Table 3 summarized the mean and standard deviation of
the monthly mean temperature differences for RS92 and RO at 50 hPa.
Figure 10 indicates that there is little variation over time in the monthly
mean temperature differences at 50 hPa in all six regions, with little
difference between day and night values. The magnitudes of the mean biases
range from
The time series of monthly mean temperature differences from RO at
50 hPa for RS92 for
Mean, standard deviation (SD) of monthly temperature differences (K), de-seasonalized trend of temperature differences (K/5 yr), and root mean square (RMS) of de-seasonalized RS92–RO temperature difference time series at 50 hPa over United States, Australia, Germany, Canada, United Kingdom, and Brazil.
Figure 11 shows the daytime and nighttime time series of monthly mean temperature biases for Sippican
MARK IIA, VIZ-B2, AVK-MRZ, and Shanghai in northern hemispheric midlatitude (60–20
The time series of temperature difference at 50 hPa for
Mean, standard deviation (SD), de-seasonalized trend of temperature
differences (K/5 yr), and root mean square (RMS) of de-seasonalized time
series of RAOB minus RO temperature difference at 50 hPa for global Vaisala
(RS80, RS90, and RS92), and other sensor types in the northern hemispheric
midlatitude (60–20
Figure 12 shows daytime and nighttime time series of monthly mean de-seasonalized temperature biases at 50 hPa for Vaisala RS92 for the United States, Australia, Germany, Canada, United Kingdom, and Brazil. Table 3 summarizes the trends of the de-seasonalized temperature differences, and shows the de-seasonalized trends in RO temperatures for comparison. The root mean square (RMS) of the de-seasonalized time series (RMS of difference) in Table 3 indicates the trend uncertainty of the time series.
The time series of de-seasonalized temperature differences at
50 hPa for RS92 for
The de-seasonalized temperature differences are computed from
Figure 12 indicates the de-seasonalized trends in daytime temperature
differences for RS92 are within
The time series of de-seasonalized temperature differences at
50 hPa for
The de-seasonalized trends in RO temperatures are generally larger than those
for the radiosonde–RO differences (Table 3). A maximum de-seasonalized trend
of 1.14 K/5 yr is found for nighttime temperatures over the United Kingdom.
A minimum de-seasonalized trend of
We compare the global trend of radiosonde–RO temperature differences for
the Vaisala and other radiosondes at 50 hPa in Table 4. The Vaisala RS92
biases are 0.22 (day) and 0.12 K (night). The trends in global
de-seasonalized temperature differences for Vaisala RS92 for daytime and
nighttime are equal to 0.07 and
Figure 13 depicts the de-seasonalized temperature differences for Sippican
MARK IIA, VIZ-B2, AVK-MRZ, and Shanghai in northern
hemispheric midlatitude
(60–20
The corresponding nighttime de-seasonalized trends in the biases vary from
In this study, we used consistently reprocessed GPS RO temperature data to
characterize radiosonde temperature biases and the inter-seasonal and
interannual variability of these biases in the upper troposphere and lower
stratosphere for different radiosonde types. We reach the following
conclusions.
SZA-dependent biases: the solar radiative effect on different
sensors is the dominant error source of RAOB temperature biases during
daytime. With the consistent precision of RO temperature data between COSMIC
and Metop-A, we are able to identify the mean temperature biases from the 200
to 20 hPa layer among older sensors (i.e., Vaisala RS80 sensors) and new
sensors (i.e., RS92 sensors), as well as the daytime and nighttime biases for
the same sensor types which are usually distributed in the same countries
(i.e., Shanghai sensor in China, AVK in Russia, VIZ-B2 in United States).
Because the quality of RO temperature is not affected by sunlight, those
daytime/nighttime biases mainly originate from uncorrected radiation biases
for each individual sensor types. Most of the sensor types contain positive
temperature biases from 200 to 20 hPa. The mean temperature difference (K)
from the layer from 200 to 20 hPa for Vaisala RS92 during the daytime is
equal to 0.2 K, which is statistically insignificant. The corresponding
difference is equal to 0.71 K for VIZ-B2, 0.66 K for Russian AVK-MRZ, which
is statistically significant. Most of the sensor types show a cold bias at
night, where the VIZ-B2 bias is as large as Residual SZA-dependent biases: after applying the solar
radiation correction, most of the RS92 daytime biases are removed. However, a
small residual radiation bias for RS92 remains, which varies with different
geographical region or operating organization. Similar to the results of
He et al. (2009) and Sun et al. (2010, 2013), we find that there exists a small SZA-dependent biases among different sensor types. The daily mean difference for
RS92 varies from 0.09 (Canada) to 0.31 K (Brazil), with a slightly larger
warm bias for low SZA (near noon) than that at higher SZA (late afternoon and
in the night). These biases are less than the uncertainty described in
Dirksen et al. (2014). Changes of the radiation correction and RAOB temperature uncertainty due
to when and how the radiative correction was implemented: the correction for
RSN2010 is about 0.1 K higher than those from RSN2005. To identify the
possible RS92 temperature biases due to changes of radiation correction
table, we compared the mean RS92 temperature differences from January 2007 to
December 2010 to those from January 2011 to April 2014. Results show that
there is no consistent pattern of differences in these two periods over the
six regions, with mean differences ranging from We used time series of RAOB–RO differences to indicate the long-term
stability for each sonde type. The uncertainties are from the combined
effects of (i) uncorrected SZA-dependent biases, (ii) change
of radiation correction, (iii) when and how the radiation correction was
implemented, and (iv) small samples used in the time series and trend
analysis. Results show that the time series of the RS92 differences at all
regions are, in general, stable in time with a small day–night difference in
each region. Other sensors have much larger variation than those of Vaisala
RS92. We found that the variation of mean radiosonde–RO temperature differences
in different regions is closely related to the corresponding variation of
SZA, especially for VIZ-B2 and AVK-MRZ during the daytime. The Sippican MARK
IIA over the United States and the Shanghai sondes do not show significant
seasonal variation. The de-seasonalized trend in RS92 and RO differences from
June 2006 to April 2014 is within
Note that the analyses we performed here do not include other error sources (i.e., cloud radiative effect, ventilation, and sensor orientation, metadata errors) mentioned by Dirksen et al. (2014). Since it is not possible to investigate these errors, we assume these errors introduce more or less random errors when a relative large sample is used. In addition, although RO-derived dry temperature data are not directly traceable to the international standard of units (SI traceability), it has been shown that the high precision nature of the basic RO observations of time delay and bending angle are preserved through the inversion procedures (Ho et al., 2009a, 2011). This makes RO-derived dry temperature uniquely useful for assessing the radiosonde temperature biases and their long-term stability including the seasonal and interannual variability in the lower stratosphere. Results from this study also demonstrate the potential usage of RO data to identify RAOB temperature biases for different sensor types.
The RAOB data used in this study are downloaded from
The authors declare that they have no conflict of interest.
We thank Douglas Hunt from COSMIC team at UCAR for providing COSMIC and Metop-A/GRAS reprocessed temperature data. We also thank reviewers' valuable comments. Special thanks go to the first reviewer, Rick Anthes. His review and comments improve the quality and readability of this paper dramatically. This work is supported by the NSF CAS AGS-1033112. Edited by: R. Müller Reviewed by: R. Anthes and one anonymous referee