AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-1613-2016Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias
correction algorithmsDzamboAndrew M.https://orcid.org/0000-0003-3789-8435TurnerDavid D.dave.turner@noaa.govhttps://orcid.org/0000-0003-1097-897XMlawerEli J.Cooperative Institute for Mesoscale Meteorological Studies, University
of Oklahoma, Norman, OK, USANational Severe Storms Laboratory/NOAA, Norman, OK, USAAtmospheric and Environmental Research, Inc., Lexington, MA, USADavid D. Turner (dave.turner@noaa.gov)12April2016941613162628August201520October201528March201630March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/1613/2016/amt-9-1613-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/1613/2016/amt-9-1613-2016.pdf
Solar heating of the relative humidity (RH) probe on Vaisala RS92
radiosondes results in a large dry bias in the upper troposphere. Two
different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et
al., 2013, WANG hereafter) have been designed to account for this solar
radiative dry bias (SRDB). These corrections are markedly different with
MILO adding up to 40 % more moisture to the original radiosonde profile
than WANG; however, the impact of the two algorithms varies with height. The
accuracy of these two algorithms is evaluated using three different
approaches: a comparison of precipitable water vapor (PWV), downwelling
radiative closure with a surface-based microwave radiometer at a
high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the
space-based Atmospheric Infrared Sounder (AIRS).
The PWV computed from the uncorrected and corrected RH data is compared
against PWV retrieved from ground-based microwave radiometers at tropical,
midlatitude, and arctic sites. Although MILO generally adds more moisture
to the original radiosonde profile in the upper troposphere compared to
WANG, both corrections yield similar changes to the PWV, and the corrected
data agree well with the ground-based retrievals.
The two closure activities – done for clear-sky scenes – use the radiative
transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde
profiles to compare against spectral observations. Both WANG- and
MILO-corrected RHs are statistically better than original RH in all cases
except for the driest 30 % of cases in the downwelling experiment, where
both algorithms add too much water vapor to the original profile. In the
upwelling experiment, the RH correction applied by the WANG vs. MILO
algorithm is statistically different above 10 km for the driest 30 % of
cases and above 8 km for the moistest 30 % of cases, suggesting that the
MILO correction performs better than the WANG in clear-sky scenes. The cause
of this statistical significance is likely explained by the fact the WANG
correction also accounts for cloud cover – a condition not accounted for in
the radiance closure experiments.
Introduction
Water vapor (WV) is an important driver of weather and climate phenomena.
Numerous studies have focused on modeling processes associated with water
vapor and evaluating and improving water vapor observations (e.g., Ferrare et
al., 1995, 2006; Revercomb et al., 2003; Suortti et al.,
2008; Krämer et al., 2009; Moradi et al., 2013a, b).
Accurate measurements of water vapor are especially crucial in the upper
troposphere; although very little water vapor is present in this part of the
atmosphere (e.g., Ferrare et al., 2004), processes such as cirrus cloud
formation and maintenance (Liou, 1986) and maintenance of stratospheric
water vapor (e.g., Jensen et al., 1996a, b; Hartmann et
al., 2001) require very accurate knowledge of the upper-tropospheric water
vapor budget. Our understanding of dynamic, thermodynamic, and radiative
processes, and even cloud water vapor budget, is impacted by the quality of
water vapor measurements (Starr and Cox, 1985; Guichard et al., 2000; Wang
and Zhang, 2008).
Vaisala RS92 radiosondes have been launched by research and operational
centers for over a decade and, compared to most ground and space-based
instruments, provide very high (∼ 10 m) vertical resolution.
The RS92 radiosonde utilizes two thin-film capacitive elements to measure
water vapor, wherein the capacitance measured by the radiosonde is
proportional to the number of water vapor molecules that are in contact with
the sensor. The resulting relative humidity (RH) measurement is taken as a
function of this capacitance and the air temperature, which is measured by a
separate thin capacitive wire sensor. While in flight, one of the RH sensors
measures WV while the other RH sensor is artificially warmed to prevent ice
buildup on the sensor; this process alternates between sensors. Unlike its
predecessors (such as the RS80 radiosonde), the RH sensor is not shielded
from solar radiation. If the RH sensor is warmer than the ambient
environment due to solar heating, then the measured RH (as computed by
Vaisala's DigiCORA® software) will be lower than its actual
value. Many correction algorithms have been developed (e.g., Vömel et
al., 2007b; Cady-Pereira et al., 2008; Yoneyama et al., 2008; Miloshevich et
al., 2009; Wang et al., 2013) to correct for this solar radiative dry bias
(SRDB). Nearly all of the aforementioned algorithms correct RH as a function
of pressure, solar elevation (zenith) angle, and/or RH itself.
Two of the most widely used correction algorithms come from the work of Wang
et al. (2013) and Miloshevich et al. (2009); for brevity, these will be
referred to as WANG and MILO hereafter. WANG used Global Climate Observing
System (GCOS) Reference Upper-Air Network (GRUAN) data (Seidel et al., 2009;
Dirksen et al., 2014) to develop and test their RS92 correction algorithm.
This physically based correction uses the following form:
RHCORR=RHesT+hf⋅ΔTCORResT,ΔTCORR=cf⋅ΔTCORRRSN,
where T is the sonde-measured air temperature, hf is a heating factor (set to
13), cf is a correction factor (set to 0.4 below 500 hPa and 0.6 above 500 hPa) that accounts for both clear skies and cloud cover, and ΔTCORRRSN is a temperature correction given by Vaisala
(http://www.vaisala.com/en/products/soundingsystemsandradiosondes/soundingdatacontinuity/RS92DataContinuity/Pages/revisedsolarradiationcorrectiontableRSN2010.aspx).
Note that ΔTCORRRSN accounts for pressure and
solar zenith angle.
The MILO correction was developed using cryogenic frost-point hygrometer
(CFH), microwave radiometer (MWR), and reference humidity probes during the
2006 Water Vapor Validation Experiment Satellite/Sondes (WAVES) campaign
(Vömel et al., 2007a). MILO consists of an empirically developed
correction:
RHCORR=GP,RH×RHTLAG,SREα=SRE66∘×fractionα,
where G(P,RH) is an empirically derived function and given as a “look-up” table
of coefficients in Miloshevich et al. (2009), and RHTLAG is the
original RH data that have been corrected for time lag
Although the
time-lag correction was developed for RS80 radiosondes, RS92 radiosondes
also require a time-lag correction. See Miloshevich et al. (2009) and
Dirksen et al. (2014) for more information.
. The MILO correction also
includes a correction based on solar zenith angle (Eq. 4), which is
applied to Eq. (3): solar radiation error (SRE) is dependent on solar
altitude angle (α) and expressed as a fraction of the SRE at
66∘, which represents the mean solar zenith angle for the daytime
CFH/RS92 soundings during WAVES (Miloshevich et al., 2009). A comparison of
these two correction algorithms in a typical atmospheric sounding is given
in Fig. 1. In 2011, Vaisala upgraded its DigiCORA® software to
version 3.64, which included their own time-lag and SRDB correction
algorithm. Although the details of this algorithm are not freely available
to the public, it is possible to deactivate the time-lag and SRDB
corrections during configuration of the sonde. We note that for results
shown later in this study, the RS92 RH data are not corrected for time-lag
error
We note that the time-lag correction is easier to apply if
the RS92 data are stored with 0.1 % precision (the so-called FLEDT file);
Miloshevich et al. (2009) has recommended that this be done as “best
practices.”
because the average change in RH between time-lag corrected
and non-time-lag corrected data is almost always around 0 % and at most
around 2 % for 25 hPa bins (results not shown). This study focuses on RS92
radiosondes collected before this change to the DigiCORA software was made.
A comparison of the WANG- and MILO-corrected RH profiles (left
plot; red and green, respectively) compared to the original RH profile
(black). The light blue line represents the saturation RH with respect to
ice. The right plot shows the difference between the original RH profile and
the WANG/MILO RH profiles (red/green), respectively. This example is the 18Z
sounding for the SGP site on 15 June 2006.
We evaluate the WANG and MILO SRDB corrections at sites maintained by the
Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) program
(Ackerman and Stokes, 2003; Mather and Voyles, 2013), at which numerous
instruments are deployed that will aid in this evaluation. We use data from
the ARM sites in the Southern Great Plains (SGP) in Lamont, OK, USA, North
Slope Alaska (NSA) in Barrow, AK, USA, and the tropical western Pacific
(TWP) on Nauru Island, Republic of Nauru (Stokes and Schwartz, 1994). We
also use ARM data collected during a 3-month experiment at a 5300 m m.s.l. site
at Cerro Toco (CJC) in northern Chile (Turner and Mlawer, 2010). Utilizing
several distinct climate locations ensures a more accurate and in-depth
analysis of the two correction algorithms.
Comparing the correction algorithms directly
The two correction algorithms were applied to RS92 data launched at the SGP,
NSA, TWP, and CJC sites. These data spanned all months of the year. The mean
change in water vapor mixing ratio as a function of height (relative to the
original radiosonde measurement) for each site is shown in Fig. 2. The
largest difference between the two correction algorithms is in the
middle and upper troposphere above 7 km, where the MILO algorithm moistens
the original radiosonde much more than the WANG correction; the difference
between MILO and WANG approaches a factor of 1.8 by 14 km. Given the
sensitivity of the outgoing long-wave radiation to changes in upper-tropospheric water vapor (e.g., Ferrare et al., 2004), understanding which
of these corrections is more appropriate is very important. However, a close
inspection of Fig. 2 also shows that the WANG correction moistens the
radiosonde slightly more than the MILO correction in the lowest 2 km for the
moister tropic and midlatitude sites.
The mean relative increase in the water vapor mixing ratio caused
by the two correction algorithms for RS92 radiosondes launched at the SGP,
NSA, TWP, and CJC sites (left) and the standard deviation (right) as a
function of height. The MILO (WANG)-corrected data are shown with dotted
(solid) lines. The number of comparisons for each site is shown in the
figure. NSA results are only shown up to the mean tropopause height (10 km).
The inset plot on the main figure is the mean relative increase in the water
vapor mixing ratio caused by the two correction algorithms, but only from 0
to 4 km.
We compare the precipitable water vapor (PWV) values derived from
integrating the moisture profiles from the original and corrected radiosonde
profiles with those retrieved from the ARM two-channel MWRs using the so-called “MWRRET” algorithm (Turner et al., 2007). ARM
has used the MWR-retrieved PWV as a “standard” for correcting for
first-order radiosonde biases (Turner et al., 2003; Cady-Pereira et al.,
2008), calibrating its Raman lidar (Turner and Goldsmith, 1999), and
evaluating infrared radiative transfer models (e.g., Turner et al., 2004).
A comparison between the PWV derived from the original radiosonde
data (top), WANG-corrected (middle), and MILO-corrected (bottom) radiosonde
data with the PWV derived from the collocated MWR at the SGP site (panels
a1, a2, and a3), NSA site (panels b1, b2, and b3), and TWP Darwin site
(panels c1, c2, and c3). The solid black line superimposed on the data
denotes the mean values for each PWV bin, and the vertical lines represent
the standard deviations.
The comparisons of the radiosonde PWV values with those from the MWR (Fig. 3) show that the original uncorrected radiosondes have a dry bias that
increases as the PWV increases. Table 1 summarizes the median and standard
deviations; in an effort to remove outliers, values that were below/above
the 5th/95th percentile were removed before computing the PWV
biases. Figure 3a1 shows that the mean PWV from the original radiosondes at
SGP are approximately 0.35 cm drier than the MWR-retrieved value in the
4.25–4.75 cm bin; however, the Wang-corrected radiosonde, while moister than
the original radiosonde, still has a slight dry bias of 0.10 cm relative to
the MWR in this bin (Fig. 3a3). The magnitude of the PWV bias generally
increases when more PWV is present in the atmosphere. Both the WANG and MILO
corrections increase the sonde's derived PWV and result in much better
agreement with the MWR. This result is consistent with the findings in Yu et al. (2015), where MWR retrievals of PWV and PWV derived from WANG-corrected
RH data were found to be within the uncertainty of the MWR instrument (which
is ∼ 0.07 cm; Turner et al., 2007).
A summary of the microwave radiometer and radiosonde un/corrected
PWV biases (in mm) with ±1 σ uncertainty from the ARM's SGP, NSA,
and TWP (Darwin) site.
The PWV results (Fig. 3, Table 1), especially when we consider all three
sites (SGP, NSA, and TWP), demonstrate that both algorithms greatly improve
the accuracy of the PWV relative to the MWR but do not distinguish which of
the two corrections may be better. The WANG's drier correction (relative to
MILO) in the upper troposphere is slightly offset by its wetter correction
near the surface and thus yields similar PWV values. A close inspection of
Table 1, however, suggests that the MILO correction seems to add more PWV
compared to WANG in the tropics, whereas WANG adds more PWV in drier
climates such as SGP and NSA. Regardless of the climate, PWV is mainly
contained in the lowest 1–2 km of the atmosphere; thus corrected RH in the
middle and upper troposphere influences the results shown here very little.
To evaluate the accuracy of the two SRDB corrections as a function of
height, we first considered comparing the corrected radiosondes with water
vapor measurements made by the ARM Raman lidars (Goldsmith et al., 1998;
Ferrare et al., 2006) at the SGP and TWP/Darwin sites. Unfortunately, during
the daytime the Raman lidar observations are limited to altitudes below 5 km and thus unable to provide any insight into the accuracy of the two
corrections in the upper troposphere.
Instead we use two radiance closure experiments to evaluate the two
corrections in the upper troposphere: one downwelling experiment and one
upwelling experiment. Radiance closure studies have been used in prior
studies to validate sonde-derived brightness temperature (TB)
measurements (e.g., Turner et al., 2003; Soden et al., 2004; Mattioli et al.,
2008; Kottayil et al., 2012; Moradi et al., 2013a, b) and
offer another method for detecting systematic biases in radiosonde RH
measurements. In each experiment, a radiative transfer model is used to
transform the original RH data, along with the WANG- and MILO-corrected RH
data, into simulated brightness temperatures. The model-derived TB data
are directly compared to an appropriate reference spectral radiance
measurement, which will be described more thoroughly in the respective
experiment sections. Statistical significance (for p=0.05) is computed,
where appropriate, to show the significance of the difference between WANG,
MILO, and the original data.
Downwelling experiment
The ARM program conducted the second phase of the Radiative Heating in
Underexplored Bands Campaign (RHUBC-II) in CJC in
August through October 2009 (Turner and Mlawer, 2010). The CJC site is
located approximately 5.3 km above sea level in the Atacama Desert; this
site can be considered a mid-tropospheric site due to its altitude and water
vapor conditions. Also, during RHUBC-II, there was a high frequency
occurrence of clear-sky and dry conditions, making it optimal for studying
the accuracy of upper-tropospheric water vapor measurements.
Our reference instrument is the G-band water vapor radiometer profiler
(GVRP). The GVRP measures downwelling radiation in 15 channels at 170.0,
171.0, 172.0, …, 182.0, 183.0, and 183.31 GHz. Cimini et al. (2009) showed that the GVRP (in that paper, referred to as “MP-183”)
agreed within uncertainty with two other collocated 183 GHz radiometers
during RHUBC-I, which was held at the NSA site in February–March 2007. The
lower frequency channels (e.g., below 178 GHz) are more sensitive to the
total PWV, while the higher frequency channels are more sensitive to
middle/upper-tropospheric water vapor (Fig. 4; Cimini et al., 2009). The
GVRP has an uncertainty of 1.5 K for TB measurements (Cadeddu, 2010;
Cadeddu et al., 2013).
The corrected and uncorrected RH data from the 144 RS92 radiosondes launched
during RHUBC-II were used as input into version 4.1 of the MonoRTM radiative
transfer model (Payne et al., 2008, 2011; Clough et al., 2005)
to compute monochromatic downwelling radiance at high spectral resolution
(10 MHz) from 168 to 185 GHz. Since the Cerro Toco site almost always has clear
skies, the model was run to compute clear-sky radiances (methodology for
identifying cases with environmental inhomogeneity or clouds is described in
the next paragraph). These computed clear-sky monochromatic spectra were
convolved with the GVRP's instrument response function to calculate
brightness temperatures corresponding to each GVRP channel. These
model-derived radiances, which were converted to TB, were directly
compared to the TB measurements made by the GVRP.
To reduce the complexity of the analysis, we restricted our comparisons to
clear-sky conditions only. To identify cloudy-sky conditions as well as
inhomogeneous environments (i.e., when there was a horizontal gradient in
water vapor across the RHUBC-II site), the standard deviation of the GVRP
TB measurements at 174 GHz over a 30 min window centered at the
radiosonde launch time at both 30 and 150∘ was computed. When the
standard deviation at either angle (where 90∘ corresponds to zenith)
was more than 2.25 K, the sky conditions were not considered uniform and the
sonde was removed from subsequent analysis. This additional screening also
accounts for inhomogeneity created by localized mountain-scale circulations
and a thermally driven circulation across the Cerro Toco site (Marín et
al., 2013).
The water vapor Jacobian computed for mean conditions at Cerro
Toco (surface altitude is 5.3 km m.s.l.) at the GVRP frequencies. The PWV for
this case was 1.1 mm.
The comparison of the MonoRTM TB calculations using the MILO- and
WANG-corrected radiosondes as input demonstrated a different spectral
character based upon the PWV in the profile. For the moistest 30 % of the
CJC radiosondes (i.e., where the PWV > 0.57 mm, where the maximum
PWV observed at CJC was 1.20 mm), the MILO-computed TB was typically
larger than the WANG-computed values at all GVRP frequencies (Fig. 5, green
spectra), which implies that the MILO-corrected radiosondes are moister over
the entire profile. However, for the driest 30 % of the CJC radiosondes
(i.e., PWV < 0.37 mm), the TB values computed using the
WANG-corrected profiles are larger than the MILO-computed radiance for
frequencies below 182 GHz (Fig. 5, orange spectra). This suggests that the
WANG-corrected radiosondes are moister than the MILO-corrected data,
especially in the lowest several kilometers of the atmosphere. Most
importantly, this analysis suggests that the significant differences in how
the two correction algorithms behave at different PWV amounts can be used
with GVRP spectral observations to evaluate both algorithms.
Downwelling brightness temperature differences between MonoRTM
calculations using the WANG- and MILO-corrected RH profile as input. Data are
sorted by the moistest 30 % and driest 30 % of all profiles in the CJC
data set (green and orange, respectively). The thick black lines are the
mean spectral residual for the two subsets of data.
Median MonoRTM minus GVRP spectral residuals, where the MonoRTM
was driven by WANG- and MILO-corrected radiosondes (red/green and
blue/brown, respectively) and uncorrected radiosondes (gray lines). These
median residuals were computed for the moistest and driest 30 % of the CJC
radiosondes, as shown in Fig. 4.
A summary of the median Tb biases between the MonoRTM-derived
Tb and GVRP TB measurements using original radiosonde RH data and
WANG/MILO-corrected radiosonde RH data. Data are represented as a median bias
with ± 1 standard deviation.
The median observed minus computed brightness temperature spectra for the
WANG- and MILO-corrected radiosondes are shown in Fig. 6; these data are
also divided into the 30 % moistest and 30 % driest profiles, each of
which has 26 cases. Table 2 summarizes the median biases for the 30 %
moistest profiles and 30 % driest profiles with standard deviations. For
the median of the driest cases, the MonoRTM-derived TB calculations for
both correction algorithms are approximately 1–4 K warmer than the GVRP
observations for frequencies between 170 and 178 GHz, increasing to over
13 K warmer than the GVRP at the center of the water vapor absorption line at
183.3 GHz. This suggests that both correction algorithms actually worsen the
MonoRTM-derived TB measurements (compared to TB measurements
derived from the original RH data) in the most extreme of dry cases seen in
the CJC data set. Interestingly, the MonoRTM calculations that used the
original uncorrected radiosondes provide a much better agreement with the
GVRP observations for these very dry cases. Furthermore, the application of
the two correction algorithms increases the scatter between the GVRP and
MonoRTM-computed TB at 183.0 and 183.31 GHz relative to the original
uncorrected radiosonde (Table 2), suggesting that neither algorithm adds
skill at the very low PWV amounts seen in this category of cases. Given the
extremely low RH values of ∼ 10 % characteristic of the CJC
site (Fig. 7), the precision of the RH measurement itself (0.5 %)
propagates an additional error as high as 0.5 % in the resultant WANG/MILO
corrections at the CJC site (result not shown). This adds an additional
residual error to the otherwise bias-corrected MonoRTM-computed TB
values.
Median (uncorrected) RH profiles for four arm sites. RH is grouped
in 25 hPa bins (starting at 1000 hPa), and the median is computed from that
bin. There are 142 soundings for the CJC site, 2500 soundings across the
annual cycle for the SGP and TWP (Nauru) sites, and 1712 soundings for the
NSA site.
A much different story, however, is seen in the 30 % moistest profiles.
The mean TB bias between the GVRP observations and the MonoRTM
calculations using both the WANG and MILO-corrected input data from this
moist subset is much smaller than for the 30 % driest profiles. The
WANG/MILO MonoRTM calculations also yield slightly moist-biased results
compared to the original RH MonoRTM calculations, which are dry biased (Fig. 6). The good agreement between the observed and computed spectra for
frequencies less than 177 GHz suggests that both algorithms have the PWV
correct, as these channels have relatively constant weighting functions with
height. At 183.0 and 183.31 GHz, the MonoRTM-derived TB calculations
for the WANG calculation are warm biased by 0.42 and 0.33 K, respectively,
whereas the TB calculations using the MILO-corrected radiosondes are
warm biased by approximately 1.8 K. While these results seem to indicate
that WANG-corrected radiosondes are in better agreement with the GVRP
observations, this result is not statistically significant. Interestingly,
the scatter in the GVRP minus MonoRTM residuals at these two frequencies is
very similar between the calculations that used the original RH profile and
either of the two corrected RH profiles (Table 2). The moist 30 % cases in
this analysis, when compared to other distinct climatological locations
(Fig. 8), are considerably drier when compared to a tropical location (e.g.,
the ARM TWP Nauru site).
Distributions of upper-tropospheric integrated water vapor (IWV)
from 530 to 200 hPa for four ARM sites, each with distinct climates. The
mean surface pressure at the CJC site is 530 hPa, while 200 hPa is the
approximate height of the tropopause.
As a consistency check for the TB residuals (computed as observed minus
computed) derived from original, WANG- and MILO-corrected RH data, a
one-sided Student t test is performed on the 30 % partitioned moist and
dry cases for all 15 MonoRTM frequencies (results not shown here). For the
moistest and driest 30 % of cases, WANG- and MILO-corrected RHs are
statistically significant (at the p=0.05 level) from the original RH
data. A one-sided Student t test between WANG and MILO for the moistest or
driest 30 % of cases, however, reveals no statistical significance at any
frequency. Despite the noted difference in biases from Fig. 6, we cannot
reasonably conclude that one correction algorithm is better than the other.
Hence, a second experiment is needed to further deduce differences between
the WANG and MILO corrections.
Upwelling experiment
The downwelling radiance closure experiment demonstrated that both WANG- and
MILO-corrected RH data are improved over the original RH data only for the
moister cases at CJC. However, while the CJC site is representative of a
mid-tropospheric site in terms of altitude and pressure, its very dry
climate resulted in water vapor amounts (as indicated by the integrated water vapor (IWV) histograms
in Fig. 8) that are significantly drier than those found at other ARM sites.
Thus, downwelling radiance closure studies at the other sites would prove
difficult because lower-tropospheric water vapor is much higher, meaning the
downwelling radiance would have little sensitivity to change in
upper-tropospheric humidity. The one-sided Student t test results further
suggest little variation between the correction algorithms despite the fact
they correct differently in the upper troposphere.
However, upwelling spectral infrared radiance observations are very
sensitive to the vertical distribution of water vapor. The SGP site
experiences a wide range of weather phenomena throughout the year, which
results in a wide range of upper-tropospheric IWV throughout the year (Fig. 8 – green line). During the cold season, upper-tropospheric IWV at the ARM
SGP site is representative of that measured at the ARM's NSA (Barrow) site
(Fig. 8 – blue line), whereas during the warm season at the ARM SGP site
the upper-tropospheric IWV is representative of a tropical location (e.g.,
the ARM's TWP sites; see Fig. 8 – orange line). For this reason,
radiosonde data from the SGP site are chosen for the upwelling radiance
closure exercise.
We used the infrared radiance observations made by the Atmospheric Infrared
Sounder (AIRS; Aumann and Pagano, 1994). Launched into a sun-synchronous
polar orbit on 4 May 2002 aboard NASA's Aqua satellite (Parkinson, 2003),
this instrument has provided extensive insight into a host of weather and
climate-related phenomena (e.g., Chahine et al., 2006;
Shu and Wu, 2009; Shimada and Minobe, 2011). The high spectral resolution of
the AIRS, with 2378 channels, provides a wealth of information for our
study. Its data have been extensively compared with data from infrared
spectrometers flown on aircraft (e.g., Tobin et al., 2006), demonstrating
excellent calibration accuracy and stability. One caveat to using the AIRS,
like any sun-synchronous polar-orbiting satellite, is the temporal
resolution of the data: although approximately 12.5 years of AIRS data are
available, surface locations near the poles will have more measurements than
surface locations in the midlatitudes or near the equator. The ARM SGP site
launches radiosondes around 18:00 UTC every day, which is about 2 to 3 h before the AIRS overpass time (i.e., around 20:00 to 21:00 UTC). For this
experiment, AIRS TB and radiosonde data from a 5-year period from
January 2005 through December 2009 were used.
Upwelling infrared radiation is highly sensitive to changes in water vapor,
so we needed to ascertain if the PWV changed appreciably between the sonde
launch and AIRS overpass. Clouds must also be filtered from the data set,
because measured upwelling radiation is very sensitive to changes in cloud
properties. The development or advection of clouds at the time of the
radiosonde launch or AIRS overpass can obscure the atmosphere below the
cloud-top height. To minimize these impacts, we included data only:
where the AIRS overpass occurred within 135 min of the radiosonde
launch
during cloud-free scenes, as discerned by the AIRS and radiosonde
observations (methodology explained in the following paragraphs)
when the MWR PWV did not change by more than 5 % between the time of the
radiosonde launch and AIRS overpass.
In short, only data during completely cloud-free
conditions are examined. This is
especially necessary because both the WANG and MILO correction algorithms
are intended for use mainly in clear-sky conditions.
The 5 % threshold was determined through a sensitivity study: for two
standard atmospheres (summer and winter), we perturbed the column water
vapor across a range of values for a fixed temperature profile typical for
that season (results not shown here) and used the LBLRTM (Clough et al.,
2005; see next paragraph for description) to evaluate changes in the peaks
of the weighting function height computed for each of 467 total frequencies
(subset from the 2378 AIRS channels) from each profile. The vertical
resolution of the model for altitudes lower than 16 km was set to 100 m. For a change in PWV of 5 %, approximately 16 % (summer) and
14 % (winter) of the weighting function peak heights changed by more than
100 m. It should also be noted that 11 % of the total peaks (for each
season) changed by less than 200 m (meaning than 5 % (3 %) of the
summer (winter) weighting function peak heights changed by 200 m or
more). Considering we use 1 km altitude bins in the main analysis, and the
vertical resolution of the model is an order of magnitude smaller than this
bin size, we feel this threshold is more than reasonable.
Additional screenings were implemented to account for the effects of cloud
cover during this time threshold. The AIRS provides radiance measurements in
a “footprint”, which is a 3 × 3 set of pixels. Data were chosen such that
the center pixel was the measurement closest to the SGP site. At 938 cm-1 the atmosphere is transparent to nearly all gases except for water
vapor, thereby making this channel very sensitive to surface temperature in
clear conditions. The standard deviation of the TB values obtained from
the 938 cm-1 channel radiances (TB,938 hereafter) was computed for
all nine pixels and thresholds were determined based on all available
footprints (Table 3). To account for seasonal variability in the TB,938
measurements, thresholds are determined on a monthly basis: TB,938
measurements in all pixels (for a clear-sky scene) result in a small
standard deviation (generally less than 2 K).
A summary of the monthly brightness temperature thresholds used to
screen cloudy-sky scenes from the AIRS data.
For comparison sake, previous AIRS validation studies at this channel over
the ocean (e.g., Hagan and Minnett, 2003) demonstrated that the AIRS
radiometric uncertainty is approximately 1 %, which is about 0.5 at
300 K for 938 cm-1. Tobin et al. (2006) later demonstrated that the root
mean square error of brightness temperature and water vapor measurements
over the ocean approached the theoretical expectations of clear-sky
conditions. Even in clear-sky data, some variability in TB,938
measurements occurs as a result of local differences in surface temperature
across the swath of the footprint. To account for these deviations in
surface temperature while keeping the error to within ∼ 6 %
or ∼ 3 K, we defined a clear-sky threshold equal to twice the
25th percentile of the TB,938 standard deviation for that month
(Table 3). The factor of 2 ensures that enough cases make it into the
analysis while staying under 3 K for any season, which accounts for the
prescribed natural variability in TB,938. High TB standard
deviations are primarily a signature of partly or mostly cloudy skies, since
cloud tops are almost always colder than the surface.
Stratiform cloud decks are also accounted for: low TB,938 standard
deviations but lower than average TB,938 values (relative to the mean
for that month) signify a cloud deck and therefore are also screened from
the data. Subvisible cirrus clouds, which affect the radiance budget but are
too optically thin to be easily identified in the AIRS observations, were
identified using the radiosonde RH data. Any original RH profile that has an
RHICE measurement greater than 90 % anywhere in the column is
removed. Using all of the above criteria to account for cloud coverage and
environmental homogeneity, 96 cases pass these screenings.
The line-by-line radiative transfer model LBLRTM (Alvarado et al., 2013;
Clough et al., 2005; Turner et al., 2004), which shares the physical basis as
the MonoRTM used in the downwelling experiment, is used to compute upwelling
infrared radiance from the original and corrected RH data. The LBLRTM
computes very high-resolution radiance data; in order to match the 2378 AIRS
channels, the monochromatic LBLRTM output is convolved with the AIRS
instrument spectral response function for each of the 2378 AIRS channels.
The atmosphere is generally opaque in the spectral region between
approximately 1300 and 2000 cm-1 at the SGP site due to absorption by
water vapor. Our analysis focused on the radiative closure in this spectral
region, using only AIRS channels where the transmission of the atmosphere
was 0. By restricting our analysis to this set of channels, uncertainties
associated with the emission of the earth's surface were avoided.
For each radiosonde/AIRS overpass pair, the upwelling TB was computed
using the LBLRTM along the viewing angle of the AIRS instrument, and the
observed minus computed TB differences were assigned to different
altitudes. We attributed the TB(λ) difference to the altitude
where the weighting function for that wavelength (λ) had its
maximum value. The weighting functions as a function of height W(z) were
computed as
Wz=βze-τ(z),
where β(z) is the gaseous absorption coefficient and τ(z) is the
cumulative optical depth from the AIRS sensor to height z computed
as
τz=∫z∞βz′dz′,
and the wavelength dependence is inferred. In the 1300–2000 cm-1
spectral region, water vapor is the primary gaseous absorber. Weighting
functions “peak” at various heights depending on the respective channel's
sensitivity to water vapor and the shape of the water vapor profile. For
midlatitude atmospheres, weighting functions for the different spectral
channels generally peak between 5 and 12 km depending on the water vapor
profile (which determines the optical depth profile) and the temperature
profile. AIRS channels where the weighting function peaks above 2 km and
below the tropopause are considered valid for this study. If a peak fell
within a 1 km altitude range (e.g., 5–6, 6–7 km), the observed minus
computed TB residual for that channel was binned in this height range.
Similar to the downwelling experiment, mean residuals are computed according
to the 30 % moistest and 30 % driest cases, which corresponded to IWV
thresholds (for all radiosondes having valid measurements between 525 and
200 hPa) of above 0.96 mm and below 0.37 mm, respectively.
Median brightness temperature biases between the AIRS and un/corrected RH
data (Fig. 9) reveal an average correction for any given layer of
approximately 0.2 to 0.4 K, depending on the correction. Below 5 km, TB
computations using WANG-corrected RH are less biased than TB
computations using MILO-corrected RH (a result consistent with Fig. 2).
Above 5 km, MILO-corrected RH results in model-computed TB that is less
biased than WANG, but both WANG- and MILO-corrected RHs result in TB
computations that are statistically significant from TB model
computations using original RH as input (for all altitude levels). When
comparing WANG- and MILO-corrected TB residuals against one another,
the corrections become statistically significant (at p=0.05) from one
another above the 5–6 km height bin. Also, MILO-corrected TB residuals
are less biased than WANG-corrected TB residuals except at the 12–13 km
height bin. We reasonably conclude that MILO-corrected RH for all cases
performs better than WANG-corrected RH; however, we feel it is necessary to
partition the cases by upper-tropospheric IWV in order to further deduce
differences between the WANG and MILO RH correction algorithms.
The median LBLRTM minus AIRS brightness temperature difference
(residual) as a function of height (for all data), where the residual in a
spectral channel was assigned to a particular height (in 1 km intervals)
based upon where the weighting function for that channel peaked with
altitude (using the original RH profile). Error bars represent the
25th/75th percentile of brightness temperature residuals.
A summary of the brightness temperature biases between the AIRS and
the LBLRTM derived data over the SGP site using un/corrected RH data as
input as a function of height, where the height for each spectral residual
was determined as the height where the weighting function for that profile
peaks. The driest 30 % and moistest 30 % of the data correspond to
upper-tropospheric IWV thresholds of less than 0.37 mm and greater than 0.96 mm, respectively.
When evaluating the driest 30 % of data and moistest 30 % of data in
Fig. 9, brightness temperature biases between the AIRS and un/corrected RH
data (Fig. 10) are corrected, on average, by 0.2 to 0.5 K for the driest
cases and 0.3 to 0.4 K for the moistest cases, depending on the correction
algorithm that was used. Table 4 summarizes the median biases for the driest
and moistest cases with standard deviations. Aside from the 12–13 km layer
for WANG and the 6–7, 10–13 km height bins for MILO, the correction
algorithms remain slightly dry biased. This result is consistent with the
findings in Fig. 2: since MILO generally adds more WV in the middle and
upper troposphere, it follows that MILO corrects more than WANG in these
driest cases (though no more than about 0.2 K) and appears to be better. The
moist cases, however, result in TB residuals closer to the
observed AIRS TB, with MILO-corrected TB residuals being less
biased than WANG-corrected TB residuals at every height bin except the
12–13 km height bin. Again, these results are consistent with Fig. 2: MILO
corrects more than WANG (as much as 0.10 to 0.15 K more), which is only
possible in the presence of increased WV in the middle and upper
troposphere. It should be noted that many more observations (i.e., usable
channels resulting from the weighting function analysis) are available for
the moist case category (especially above the 5–6 km height bin). In the
drier profiles, the opacity of the atmosphere due to water vapor absorption
decreases and thus more AIRS channels are eliminated from the analysis
because the channel is sensitive to surface emission, thereby making fewer
measurements available. The number of measurements (i.e., number of
brightness temperature measurements between 1300 and 2000 cm-1 from the
partitioned cases) per height bin for the driest 30 % and moistest 30 %
of data is also given in Table 4.
Same as in Fig. 9, but where the residuals are for the moistest
30 % and driest 30 % of the water vapor profiles. The median values
shown in this plot, along with the standard deviations, are given in greater
detail in Table 4.
For both WANG and MILO, Table 4 shows that both corrections have a slightly
decreased standard deviations compared to the original measurements at
nearly every height bin. MILO, in most cases, has a slightly lower standard
deviations compared to WANG.
We also computed statistical significance among the TB residuals for
original, WANG- and MILO-corrected TB data (for the 30 % moistest and
driest cases). Again, both the WANG- and MILO-corrected TB are
significantly different from the TB derived from the original RH data
for all altitudes. When coupled with the fact that TB residuals among the
correction algorithms are much less biased compared to TB residuals
using original RH data, we can conclude that WANG- or MILO-corrected RH is
much improved over the original RH measurements. For the driest 30 % of
cases, the WANG and MILO corrections are statistically significant from each
other (at the p=0.05 level) at and above the 9–10 km bin. For the
moistest 30 % of cases, WANG- and MILO-corrected TB become
statistically significant from one another at and above the 7–8 km bin. In
both cases, MILO is less biased than WANG above the stated altitude bins
(except the 12–13 km bin); therefore we can also conclude that
MILO-corrected RH is better representative of upper-tropospheric RH compared
to WANG-corrected RH.
For both the upwelling and downwelling experiments, the dry thresholds are
the same (0.37 mm), however, the TB residuals computed for the
upwelling experiment from each correction algorithm reduced the bias, which
was not the case for the driest 30 % of results from the downwelling
experiment. At this time, we cannot conclude why results for the respective
subsets of data differ. The moist threshold is higher for the upwelling
experiment compared to the downwelling experiment (0.96 vs. 0.57 mm) –
likely because water vapor can more easily reach the upper troposphere due
to phenomena such as deep convection at the SGP, while at CJC there are a
range of processes at work keeping the troposphere relatively dry (Rutllant
Costa, 1977). Figures 7 and 8 corroborate this idea as well considering the
CJC observes lower RH and IWV, respectively, compared to the SGP site. With
the exception of the 12–13 km bin, TB residuals (Fig. 10) computed from
MILO-corrected RH are less biased than TB residuals computed from
WANG-corrected RH but remain slightly dry biased. Despite the limitations
present in the upwelling experiment, but given the statistical significance
between MILO- and WANG-corrected RH, the results from this experiment
suggest that MILO-corrected RH is better representative of clear-sky RH
compared to WANG-corrected RH in the upper troposphere, and both corrections
represent improvements compared to uncorrected sondes.
Conclusion
Both the WANG and MILO corrections significantly improve the original
Vaisala RS92 RH data, as demonstrated in an analysis of PWV at multiple
sites, yielding approximately the same improvement in PWV relative to the
MWR-retrieved value. However, the two algorithms differ in their corrections
as a function of height due to their different methodologies.
Given this difference, radiative closure experiments were performed to
determine whether one of the two corrections was better than the other. Comparing
radiative transfer calculations that use the WANG- and MILO-corrected
radiosondes, an analysis of downwelling measurements at the 183.00 and
183.31 GHz channels of the CJC GVRP indicated that the WANG median TB
calculation was not statistically different compared to the MILO median
TB calculation for the moist cases that are more typical of upper
troposphere in midlatitude atmospheres. Also, both corrections
significantly improved the TB bias for the moist cases: the original
median TB calculation was ∼ 10 K too warm (implying the
original sonde was too dry) at 183.00 and 183.31 K. However, radiosondes in
the very dry category, corresponding to upper-tropospheric conditions not
typically found in midlatitude or tropical locations, were made
significantly too moist by both corrections, yielding much poorer agreement
with the GVRP than the original uncorrected radiosonde profile. We find
WANG- and MILO-corrected RH to be statistically better than the original RH
for the moist cases; however, WANG- and MILO-corrected RHs are not
statistically different when tested against one another.
The upwelling experiment using AIRS measurements revealed additional
differences between WANG and MILO, likely owing to the fact the SGP site has
a great seasonal dependence on upper-tropospheric IWV. The driest cases show
that WANG is slightly less biased than MILO below 5 km, which is likely due to
the fact that WANG corrects more than MILO in the lower troposphere.
Otherwise, MILO is less biased than WANG in nearly every other scenario, as
indicated by the partitioning of radiances by height using weighting
functions. Both the WANG and MILO corrections result in TB computations
that are statistically significant from TB computations derived from
original RH – a result consistent with the results found in the downwelling
experiment. We find, however, that MILO is statistically different from WANG
above 8 km in the moistest 30 % of cases and above 10 km in the driest
30 % of cases. We conclude that MILO offers a more realistic
representation of upper-tropospheric RH compared to WANG because of the
lower TB bias at nearly all altitudes coupled with the statistical
significance between MILO and WANG.
The outcome of the upwelling radiance closure experiment suggests that the
correction factor “cf” used to scale the temperature correction in WANG
may be too low. However, the intent of this correction factor is to account
for both clear and cloudy conditions and despite the fact WANG offers a much
better agreement than the original RH measurements, our results indicate
that WANG seemingly under-corrects for solar radiative dry bias. This also
likely explains (from the upwelling experiment) why WANG is statistically
different from MILO in the upper troposphere. Given the ease of use of the
WANG correction, we suggest that the “cf” be computed separately for clear
and cloudy skies. This change, however, may be complicated by the fact that
cloud extinction varies significantly between high ice clouds and
low-altitude liquid clouds, and considering the large variability in the
microphysical properties between these two types of clouds, adjusting the
“cf” would at minimum need to be a function of altitude and water phase.
If this adjustment could be made, the WANG correction would become more
robust and would be applicable to an increased number of applications.
Regardless, our results demonstrate the utility of both correction
algorithms across a wide range of climatic regimes, where MILO is especially
effective in the upper troposphere for clear-sky conditions.
Acknowledgements
The radiosonde, MWR, and GVRP data were obtained from the Atmospheric
Radiation Measurement (ARM) Program sponsored by the US Department of
Energy, Office of Science, Office of Biological and Environmental Research,
Climate and Environmental Sciences Division. We would also like to thank the
Dave Tobin for providing the AIRS footprint data needed to perform the
upwelling experiment. Comments from Larry Miloshevich, Isaac Moradi, and one
anonymous reviewer helped to improve the clarity of this manuscript. This
work was supported by the US Department of Energy's Atmospheric System
Research (ASR) program with grant DE-SC0008830.
Edited by: I. Moradi
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