Introduction
Over the Arctic, the surface temperature has increased twice as much as the
global average rate in the past 100 years
(Bernstein et al., 2007; Chae et al., 2015;
Najafi et al., 2015), indicated by the decline of sea ice cover. The change
of the Arctic climate (e.g., temperature) along with the decline of sea ice
cover is expected to affect the global climate
(Vihma, 2014). The lower troposphere is one of the most
critical components of the Arctic climate system, which has been intensely
investigated by various observations (e.g., in situ balloon sounding;
ground-based, airborne, and satellite remote sensing). However, the lack of
continuous high-vertical-resolution measurement in the Arctic lower
troposphere impedes our understanding of the complex physical processes that
controls the air–sea-ice interaction, which is the key to improving Arctic
weather forecasting and climate prediction.
Traditional radiosonde balloon soundings have long been the most reliable
for sensing the atmospheric properties (e.g., temperature, pressure, and
humidity) with high vertical resolution
(Pelliccia et al., 2011) and
widely used to calibrate and validate the satellite-borne retrievals
(John and Buehler, 2005; Kuo et al.,
2005). Over the Arctic, most of the radiosondes are only sparsely available
over the land near the Arctic Circle, with only a few over the ocean from a handful
of field campaigns. The ground-based and airborne remote sensing also suffers
a limitation in spatial and temporal coverage over the Arctic. The satellite
remote sensing offers the opportunity for uniform observation over the
Arctic. Passive infrared sounders – such as AIRS (on Aqua), IASI (on
MetOp A and B), and CrIS (on Suomi NPP) – provide nearly daily and global vertical
atmospheric profiles of temperature and moisture with ∼ 1 to 2 km vertical resolution. However they cannot profile beneath the clouds that
cover the Arctic Ocean up to 90–95 % of the time in summer months and around
50 % in winter (Zygmuntowska et al., 2012). Microwave sounders, such as the AMSU-A (on Aqua) and ATMS (on Suomi NPP), could
sense below the clouds but also have coarse vertical resolution
(∼ 1–2 km) in the lower troposphere and encounter large
uncertainty over land due to limited knowledge of the land emissivity
(Deng et al., 2009; Weng et al., 2012).
Since the advent of the Global Positioning System (GPS) radio occultation
(RO) technique in the early 1990s, the RO soundings have demonstrated a
high-quality observation with sub-kelvin temperature precision
(Kursinski et al., 1997;
Melbourne, 2004; Healy et al., 2005). Since the launch of the six-satellite
Constellation Observing System for Meteorology, Ionosphere, and Climate
(COSMIC), GPS RO has provided near-real-time, high-vertical-resolution,
uniformly distributed global soundings of atmospheric bending angle and
refractivity
(Anthes et al., 2008) from the stratosphere down to near the surface in all weather
conditions. GPS RO bending angle and refractivity measurements have been
operationally assimilated into global weather forecasting models and
demonstrate significant positive impact especially over the upper
troposphere and above the open ocean (Healy et al.,
2005; Cucurull et al., 2008). GPS RO measurements are also considered for
global climate benchmark monitoring
(Ho et al., 2009; Steiner et al., 2013)
for their self-calibration and long-term stability (Steiner et al.,
2013). Moreover, GPS RO has demonstrated its capability to observe the lower
troposphere (Sokolovskiy et
al., 2006a, 2010) and the planetary boundary layer (PBL; von
Engeln et al., 2005; Sokolovskiy et al., 2007; Ao et al., 2012; Xie et al.,
2012; Ho et al., 2015). The GPS RO measurement could fill the gap in
observing the lower troposphere over the Arctic (e.g., Ganeshan and Wu,
2015; Chang et al., 2017). Nevertheless, the uncertainty of RO sounding
increases in the lower troposphere especially within the PBL, which remains
largely uncharacterized over the remote Arctic Ocean.
One issue of GPS RO sounding for the lower troposphere lies in that not all the
RO profiles penetrate down to the surface
(Ao et al., 2012;
Xie et al., 2012). The limited-penetration issue could be resulting from the
early termination of RO sounding due to the topographic blocking of RO
signals grazing the Earth's surface. In addition, the corruption of RO
signal due to the increasing receiver tracking error in the lower
troposphere could also lead to early termination of the sounding profiles
before reaching the surface. To measure the very shallow surface inversion
that is often observed over the Arctic Ocean
(Tjernström
et al., 2014) requires very deep penetration of RO sounding. The
implementation of the open-loop tracking technique has significantly improved
the fraction of soundings reaching within 1 km of the Earth's surface
(Ao et al., 2009). However, a
significant fraction of the RO soundings still cannot reach the lowest 500 m
above the surface, which could be especially problematic for sensing the
Arctic shallow PBL and warrants further studies.
Beyond the penetration issue, the structural uncertainty and parametric
uncertainty affecting RO sounding quality should also be investigated. The
structural uncertainty arises from different approaches of constructing the
dataset from the same raw data, whereas the parametric uncertainty is the
uncertainty for the chosen approach in the presence of a finite sample of
data (Thorne et al., 2005). Note that the NASA Jet
Propulsion Laboratory (JPL) and the University Corporation for Atmospheric
Research (UCAR) are two major independent COSMIC RO retrievals centers. The
two centers use different inversion algorithms to derive the RO parameters
from the same raw COSMIC RO measurements. The statistical comparisons
between JPL and UCAR retrievals will shed some lights on the structural
uncertainty of the COSMIC RO data.
Ho et al. (2009) and
Steiner et al. (2013) investigated the structure uncertainty of RO
soundings from the upper troposphere to the lower stratosphere (∼ 8 to 25 km), among different data centers (including JPL, UCAR, and several
European centers), and revealed very small uncertainty (e.g., less than 0.06 K in temperature). In this study, the focus will be on assessing the
structural uncertainty in the lower troposphere over the Arctic. In
addition, the parametric uncertainty of COSMIC RO soundings will also be
evaluated by comparing with independent radiosonde observations as well as
the global reanalysis. A better understanding of the penetration issue along
with the quantification of both structural and parametric uncertainties will
help improve RO retrievals and further RO science applications.
This paper will focus on COSMIC RO soundings in the lower troposphere over
the Arctic (65–90∘ N). Section 2 describes the
COSMIC, radiosonde, and global reanalysis data used for this study. Section 3
details the definition of RO penetration depth and the RO retrieval
difference between JPL and UCAR data centers. Section 4 presents seasonal
variation of penetration depth over the Arctic, the RO structure and
parametric uncertainties derived from the inter-center RO retrieval
comparison between JPL and UCAR, and the comparison of RO retrievals with
radiosonde and global reanalysis. Section 5 contains the
summary and conclusions.
The properties of the COSMIC RO, radiosonde, and
ERA-Interim.
Data type
Date
Parameters
Region
Vertical
Horizontal
resolution
resolution
COSMIC RO
UCAR
2007–2010
Refractivity, temperature
65–90∘ N
∼ 200 m
∼ 200 km
& humidity
JPL
2007–2010
Radiosonde
ASCOSa
03/08–
Pressure, temperature
∼ 78–87∘ N
∼ 5–100 m
07/09 2008
& humidity
ERA-Interimb
2007–2008
Pressure, temperature
65–90∘ N
60 layers (with 28 layers
0.75∘ latitude ×
& humidity
below ∼ 10km)
0.75∘ longitude
a ASCOS: Arctic Summer Cloud Ocean Study field campaign.
b ERA-Interim: European Centre for Medium-Range Weather Forecasts
Reanalysis Interim.
Data
This study analyzed Level 2 (e.g., refractivity, temperature, and humidity)
COSMIC RO data over the Arctic (65–90∘ N) from two
major RO data processing centers in the United States: JPL
(http://genesis.jpl.nasa.gov/genesis/) and UCAR
(www.cosmic.ucar.edu/cdaac/). In addition, radiosonde soundings from the
Arctic Summer Cloud Ocean Study (ASCOS) and the European Centre for
Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERA-Interim, ERA-I) data
over the Arctic are also used. The properties of the COSMIC RO, radiosonde,
and ERA-Interim are listed in Table 1.
COSMIC GPS RO
Three years of COSMIC RO soundings from both JPL and UCAR over the Arctic
(65–90∘ N) were analyzed, with a special emphasis on
the troposphere (e.g., below ∼ 10 km). The post-processed
version of the JPL retrievals and the UCAR retrievals from 2008 to 2010 were
used, where a consistent retrieval algorithm has been implemented throughout
the data processing period. The COSMIC RO data are grouped into four
seasons: winter – DJF (December–January–February); spring – MAM
(March–April–May); summer – JJA (June–July–August); and fall – SON
(September–October–November). Note that the month of December in the winter season
is from the previous year (e.g., DJF 2008 denoted as December 2007, January 2008, and
February 2008). The JPL retrieval algorithm has been detailed in
Hajj et al. (2002) and Ao et al. (2012), whereas the
UCAR retrieval algorithm can be found in Kuo et al. (2004). Different calibration (e.g., orbit and clock calibration; Wickert,
2002; Schreiner et al., 2009), retrieval algorithm, and
quality control procedure (e.g., Ho et
al., 2009) lead to some differences in the total throughput of COSMIC RO
soundings from the two data centers. JPL retrievals generally yield slightly
fewer soundings than UCAR retrievals. A total of 112 156 and 129 538 RO
profiles are retrieved from JPL and UCAR, respectively. Additional
discussions on the processing differences can also be found in Ho et al. (2009, 2012) and Steiner et al. (2013).
Radiosonde and global reanalysis
High-resolution radiosonde soundings from the ASCOS field campaign (red
dots) throughout the cruise in the Atlantic sector of the Arctic from 3
August to 7 September 2008
(Tjernström
et al., 2014) and the COSMIC RO (e.g., UCAR) soundings distribution (blue
diamond) during the same time period over 75–90∘ N
are shown in Fig. 1. Detailed description of ASCOS including the
instrumentation and measurements can be found in
Tjernström
et al. (2014). Radiosondes were launched from the helipad of the icebreaker
four times a day at approximately 00:00, 06:00, 12:00, and 18:00 Coordinated
Universal Time (UTC). A total of 145 soundings were collected throughout the
entire period of the cruise. Only one sounding was discarded due to
missing latitude and longitude information. Each profile was interpolated
onto uniform vertical levels with intervals increasing from 5 m in the
lowest 1 km of the troposphere to 100 m in the stratosphere
(Birch et al., 2012). In addition, the ERA-I reanalysis profiles over the Arctic were also
analyzed. The ERA-I applied a T255 grid scheme with 0.75∘ horizontal
grids (∼ 83 km near the Equator or ∼ 14 km near
latitude ∼ 80∘ N), and 60 vertical layers
(Dee et al., 2011).
Radiosonde soundings track (red dot) during the ASCOS field campaign
from 3 August to 7 September 2008, and the COSMIC RO (e.g., UCAR) soundings
distribution (blue diamond) during the same time period over 75–90∘ N.
Methodology
In this section, COSMIC RO sounding penetration depth, the RO retrieval
algorithm difference between JPL and UCAR centers, as well as the
equal-area-gridding technique used for this study will be discussed.
Penetration depth of COSMIC RO soundings
One major limitation of using GPS RO sounding to study the lower troposphere is
that not all the RO profiles reach the surface. Here we define the
“penetration depth” as the minimum height of each individual RO sounding
above the local surface. Note that the GPS height is the height above the
mean sea level (MSL), which is referred to as the height above the geoid, i.e.,
the equipotential gravity surface height from a standard gravity model such
as the Earth Gravity Model 1996 (EGM96). The high-resolution digital terrain
elevation data (0.16∘ grid) used in this paper are also in
reference to above the geoid. Thereafter, the penetration depth in this paper
will be the GPS height above the local surface after subtracting the terrain
elevation.
In the RO retrieval, the penetration depth of an individual occultation
sounding is affected by the quality of the GPS RO signal at the receiver and
the quality control criteria used. Several factors could result in the
degradation of RO signal that leads to earlier termination of RO profiles
before the sounding reaches the local surface, i.e., a positive penetration
depth. For example, the topography along the GPS and the RO receiver line of
sight could block the RO signals and lead to the early termination of the RO
sounding. In addition, the RO signal could be degraded due to receiver
signal tracking issues attributed to the presence of large vertical moisture
variation in the lower troposphere
(Ao et al., 2012).
In the JPL retrieval system, the ending of an RO sounding (i.e., the
penetration depth) is determined as the transition point where the RO signal
quickly degrades into an unusable noisy regime by fitting a step function to
the transformed signal amplitude after the canonical transform inversion
algorithm (Ao et al., 2012). A
similar approach is applied in the UCAR retrieval as well but using a
different transformed signal through the full-spectrum inversion (FSI) algorithm
(Jensen et al., 2003; Kuo et al.,
2004). With the implementation of open-loop tracking in the COSMIC mission,
the receiver tracking errors in the lower troposphere have been
significantly reduced
(Ao et al., 2003; Sokolovskiy et al., 2006a, b). However, the non-uniform RO
penetration depth across the globe remains and requires further
investigation (Ao
et al., 2012; Xie et al., 2012). It is important to note that the vertical
resolution of the Level 2 RO refractivity retrievals in the lower
troposphere is limited to be ∼ 200 m due to the vertical
smoothing of the retrieval profiles. Therefore, the penetration depth at or
below 100 m is essentially as good as reaching the surface.
COSMIC RO retrieval algorithm difference between JPL and UCAR data
centers
The detailed description of the GPS RO technique has been covered
extensively in several papers
(Kursinski
et al., 1997; Hajj et al., 2002; Anthes et al., 2008). Here we only
summarize the key concepts of the retrieval processes. GPS RO senses the
atmosphere by tracking the GPS radio signals that traverse the atmosphere as
a moving receiver sets (or rises) behind the horizon relative to the
transmitting satellite. The radio wave is refracted, and its travel time is
delayed due to the variations of refractivity of the atmosphere. GPS RO
precisely measures phase and amplitude of GPS signals that traverse the
Earth's atmosphere. After the phases are calibrated by removing the GPS and
LEO clock errors, a time series of excess phase at both GPS frequencies
(e.g., L1 and L2) is derived. Then under the assumption of a local
spherically symmetric atmosphere, the vertical profile of the bending angle
(α) and the refractivity index (n) can be derived. In a neutral
atmosphere, the refractivity (N=(n-1)×106) measured by GPS RO is
related to pressure (P in mbar), temperature (T in K), and water vapor partial
pressure (Pw in mbar) as the following Eq. (1)
(Smith and Weintraub, 1953):
N=77.6PT+3.73×105PwT2.
Based on the RO refractivity, the temperature and humidity profiles can be
derived with certain external information. In the upper troposphere and
above, the second term (the so-called “wet term”) on the right-hand side
of the refractivity Eq. (1) can be neglected. The so-called dry temperature
(Tdry) can be derived as follows:
Tdry=77.6PdryN.
As the saturation vapor pressure decreases rapidly with decreasing
temperature according to the Clausius–Clapeyron equation, the water vapor
pressure Pw can be neglected in the upper troposphere, where temperature is low
(e.g., T < 250 K; Kursinski et al.,
2000; Hajj et al., 2002; Melbourne, 2004). Given the pressure (or more
strictly, the dry pressure, Pdry, derived from the hydrostatic balance)
and the RO refractivity profiles, the temperature can be retrieved. Accurate
temperature profiles can be derived throughout the stratosphere down to
the mid-troposphere or even lower altitudes depending on latitudes, where the
water vapor is negligible.
In the middle or lower troposphere where the moisture is not negligible, the
derivation of temperature and humidity becomes an underdetermined problem,
which is generally referred to as the dry–wet ambiguity problem. To solve this
issue, the JPL and UCAR data centers use different approaches.
In the JPL retrieval (e.g., Hajj et al., 2002), the moisture
retrieval only starts when the dry temperature is over 250 K, i.e., when the
moisture contribution to the refractivity becomes non-negligible
(Kursinski et al., 1997). In the temperate and
tropical regions where the water vapor is abundant with large uncertainty,
the temperature profile from numerical weather prediction (NWP) model
analysis usually is relatively better known and thus can be used to aid the
water vapor retrieval. In practice, the nearest 6-hourly ECMWF global
analysis temperature profiles are interpolated into each RO
sounding location as a priori. Given the RO refractivity profile
(Tdry > 250 K) and the a priori ECMWF temperature profile,
the RO water vapor profile can be derived. On the other hand,
given the RO refractivity and the a priori ECMWF water vapor profile, the RO temperature
profile can also be derived. While the approach is relatively simple, the RO
water vapor (or temperature) will contain both measurement uncertainty in RO
refractivity and the uncertainty in the a priori ECMWF temperature (or water
vapor).
The UCAR data center applied the optimal estimation of the water vapor,
temperature, and pressure through a variational method
(Kuo et al., 2004). The variational method combines the
occultation measurements (e.g., refractivity) with the a priori (or
background) atmospheric condition in a statistically optimal way
(Zou et al., 1995;
Healy and Eyre, 2000). For example, the optimal solution to the state
vectors (e.g., T, Pw, and P) can be found by adjusting the state vector elements
in a way that is consistent with the estimated background errors, to produce
simulated measurement values that fit the observations to within their
expected observational errors (Healy and Eyre,
2000).
Both JPL and UCAR temperature and humidity retrievals require the a priori
information from models and thus are not independent measurements. Instead,
measurements (e.g., refractivity, dry temperature) are model-independent
observations. Note that the errors of the geophysical parameters derived from the
1-D-variational method (UCAR) could become more challenging to interpret
because the errors are a combination of the a priori model background errors
with the RO measurement errors.
The equal-area-grid mapping method
Note that a fixed latitude–longitude grid (2.5∘ × 2.5∘)
near the Equator has an area of ∼ 78 400 km2, which is
equivalent to a square cell with sides of ∼ 280 km. However,
the length of the fixed grid longitude is significantly reduced at higher
latitude, especially near the polar region. To accommodate such a
significant reduction of grid area at higher latitude resulting from the
fixed latitude–longitude grid, the equal-area-grid mapping method is
applied. A fixed 2.5∘ latitude interval (∼ 280 km)
is chosen, and each latitude band is evenly divided to have each grid area
close to 78 400 km2. Therefore, each grid will have a roughly equal
area across the polar region with a fixed 2.5∘ latitude interval
and a variable longitude interval (increasing at higher latitude or toward
the poles). The mapping technique will result in only 3 grids within
87.5–90∘ N latitude bands; 9 grids within 85–87.5∘ N; and, increasing further south,
the maximum of 58 grids within 65–67.5∘ N (Rossow and Schiffer, 1991).
Seasonal median penetration depth of COSMIC RO soundings
for JPL (a1–a4) and UCAR (b1–b4), and the corresponding
number of soundings in each season for JPL
(c1–c4) and UCAR
(d1–d4) over the Arctic
(65–90∘ N) from 2008 to 2010. Note the equal-area
mapping uses the equivalent square cell with sides of ∼ 280 km.
Results and discussions
In this section, spatial and temporal variation of the COSMIC RO sounding
penetration depth, the structural uncertainty and the parametric uncertainty
of the RO retrieval will be discussed.
Spatial and temporal variation of RO penetration depth
COSMIC RO refractivity profiles over the Arctic (65–90∘ N) from both JPL and UCAR are analyzed. The 3-year
(2008–2010) median seasonal variations of the penetration depth for both
centers and their associated RO profile sample numbers are shown in Fig. 2.
Both centers show rather homogeneous sampling with ∼ 60–120
soundings in each equal-area grid (equivalent to the area of a square cell
of 280 km × 280 km). JPL shows slightly less sounding at each grid than
UCAR, which is consistent with the overall smaller sample size.
A very similar geographical pattern of RO penetration depth is seen for both
centers in all seasons, with rather uniform deep penetration
(∼ 0–300 m) over the Arctic Ocean and generally poorer
penetration over land and islands. The distinctly poorer penetration (i.e.,
larger penetration depth) in summer than other seasons indicates
the degradation of RO retrieval due to the increase of the lower-tropospheric
water vapor during the warmer summer season. In addition, central Greenland shows deep
penetration (100–300 m) likely due to the relatively flat terrain. Over the
land surrounding the Arctic Ocean, the penetration depth varies from 100 to 900 m
with slightly poorer penetration in JPL data, over the northwestern edge
of Greenland. It is worth noting that ∼ 100 m penetration
depth is essentially as good as reaching the surface as both JPL and UCAR
centers apply ∼ 200 m vertical smoothing on the RO
bending/refractivity retrieval.
Seasonal-mean percentage of COSMIC RO profiles penetrating
into the troposphere above the terrain over the Arctic (65–90∘ N). Darker and lighter blue describe UCAR profiles over ocean
only and over both ocean and land, respectively. Red and orange describe JPL
profiles over ocean only and over both ocean and land, respectively. The
inlet plots show the lowest 1 km, with the two dashed lines marking the
heights of 0.3 and 0.5 km.
Figure 3 further illustrates the seasonal-mean percentage of RO profiles
reaching different altitude (binned into 100 m vertical intervals) above the
surface, with land and ocean separated. For both centers, a high percentage of
COSMIC RO soundings (∼ 85–90 %) penetrates below 1 km in all
seasons. But a sharp decrease by ∼ 8–20 % is seen in RO
profiles reaching 0.5 km above the surface. Above 1 km, no significant
difference in RO penetration is seen between the land and the ocean.
However, a slight decrease in the percentage of RO profiles reaching below 1 km over land could be due to the topography blocking effect.
The UCAR retrieval shows generally a higher percentage of profiles
(∼ 92–95 %) penetrating down to 1 km above the surface than
the JPL (∼ 88 %) in all seasons except the summer, when
the percentage drops to ∼ 84 % for both centers. UCAR also
shows a generally higher percentage of RO soundings extending to the height
levels within 0.5 to 1 km, except the summer season, when JPL shows a slightly
higher percentage of profiles extending to all levels below 1 km. In
addition, JPL also shows a slightly higher percentage of RO profiles extending
below 0.3 km than UCAR in all seasons, with a maximum of ∼ 5 % difference in summer.
The much higher percentage of deeply penetrating RO profiles below 0.5 km over
the very dry Arctic Ocean (65–80 %) than the moist tropics of 20–30 %
(Ao et al., 2012;
Xie et al., 2012) strongly indicates that the increase of lower-tropospheric
moisture could result in larger receiver tracking errors and the early
termination of the RO profile. The poorer penetration in the warmer and
moister summer Arctic further confirms the moisture impact on the RO
penetration issue.
Inter-center comparisons between JPL and UCAR retrievals
To quantify the structural uncertainty of the COSMIC RO soundings over the
Arctic (65–90∘ N), the RO retrieval difference between JPL and
UCAR data centers is analyzed. The RO soundings in both winter (DJF) and
summer (JJA) seasons of 2008 are investigated. Although both data centers
start from the same Level 1 raw COSMIC measurements, their retrieval
algorithms are slightly different in terms of calibration, retrieval, and
quality control processes (Ho et al., 2012). A total of 4782 pairs of common
soundings in winter (DJF) and 8375 pairs in summer (JJA) of 2008 have been
identified.
Statistical comparison between JPL and UCAR retrievals from COSMIC
RO.
Seasonal mean difference
Altitude range
DJF 2008
JJA 2008
(4782 pairs)
(8375 pairs)
(km)
μ (σ)*
μ (σ)
NJPL-NUCAR/NUCAR (%)
0–3
0.04 (0.19)
0.02 (0.44)
0–5
0.05 (0.18)
0.04 (0.38)
0–10
0.06 (0.19)
0.07 (0.33)
TJPL-TUCAR (K)
0–3
0.54 (0.45)
0–5
0.51 (0.47)
0.72 (0.60)
0–10
0.38 (0.40)
0.61 (0.62)
qJPL-qUCAR (g kg-1)
0–3
0.03 (0.33)
-0.05 (0.50)
0–5
0.03 (0.34)
-0.002 (0.41)
* Note that μ and σ represent mean and standard deviation,
respectively.
Statistical comparisons of COSMIC RO between JPL and UCAR
retrievals are shown in fractional refractivity (a, b), temperature (c, d), and humidity (e, f) over the Arctic (65–90∘ N) in
winter (DJF, a, c, e) and summer (JJA, b, d, f) of 2008. The mean difference
(μ, red), the mean plus/minus (±) 1 standard deviation
(σ, green), and standard error (δ, black horizontal bar) for
all three parameters are also shown. The number of common sounding pairs
varies with heights is shown (blue), with the scale marked on the top of each
panel.
All RO sounding refractivity, temperature, and specific humidity (e.g., N,
T, q) from both centers are interpolated into 100 m vertical intervals.
The ensemble mean, standard deviation and standard error of the inter-center
differences, and the sampling number are shown in Fig. 4. The statistics at
three selected altitude ranges (e.g., from the surface up to 3, 5, and 10 km) are summarized in Table 2. It is also worth noting that
the significantly decreasing number of common refractivity soundings in the
lowest 3 km is mainly due to the limited RO sounding penetration depth as
discussed in Sect. 4.1. The statistics for the lowest 200 m were discarded
due to the significant drop of the number of RO observations near the
surface, limited by the ∼ 200 m vertical resolution of RO
retrievals. For the RO temperature and specific humidity profiles, the
variation of common sounding numbers at different altitudes is mainly
limited by the availability of JPL retrievals. For example, UCAR retrieves
both T and q at all altitude where refractivity retrievals are available.
However, JPL only retrieves tropospheric temperature at altitudes cooler
than a threshold of about 250 K (see Sect. 3.2), leading to a significant
reduction in the number of JPL temperature retrievals in the lower
troposphere, especially in the warmer summer season (Fig. 4d). To account
for the number of pairs that change with the altitude, the standard error is also
computed (i.e., the standard deviation divided by the square root of the
sample size), which is an estimation of the difference of the sample mean
from the population mean affected by the sample size.
In winter, small but persistent biases are seen in the JPL retrieval as compared
to the UCAR retrieval below 3 km in refractivity (∼ 0.04 %),
temperature (0.54 K), and specific humidity (0.03 g kg-1), with a standard
deviation of 0.19 %, 0.45 K, and 0.33 g kg-1, respectively. In summer, the
difference for refractivity and specific humidity slightly changes, with a
mean difference of 0.02 % and -0.05 g kg-1, and a corresponding standard
deviation of 0.44 % and 0.50 g kg-1, respectively.
The mean fractional refractivity difference between JPL and UCAR is
∼ 0.06 in winter and 0.07 % in summer below 10 km, which
is consistent in magnitude with the inter-center comparison of CHAMP RO data
within 8–12 km over 60–90∘ N
(Ho et al., 2009). No significant
difference is seen between the winter and the summer. The mean temperature
difference is 0.38 in winter and 0.61 K in summer below 10 km, with a
corresponding standard deviation of 0.40 and 0.62 K, respectively. The
mean specific humidity difference is 0.03 in winter and 0.002 g kg-1 in
summer below 5 km, with a corresponding standard deviation of 0.34 and
0.41 g kg-1, respectively. The standard errors are generally small, except
when there is a sharp decrease in sample size resulting from fewer JPL
temperature retrievals below 5 km in summer (Fig. 4d) and moisture retrieval
above ∼ 3 km in winter (Fig. 4e). The generally small standard
error indicates reliable statistics given the large sample size at most
altitude levels.
Statistical comparisons between COSMIC RO (JPL/UCAR) and the
near-coincident radiosonde (RDS).
Mean errors
Altitude range
JPL–RDS
UCAR–RDS
ERA-I–RDS
(65 pairs)
(68 pairs)
(144 pairs)
(km)
μ (σ)
μ (σ)
μ (σ)
ΔN/NRDS (%)
0–3
-0.43 (1.73)
-0.50 (1.58)
-0.12 (1.01)
0–5
-0.29 (1.46)
-0.36 (1.34)
-0.21 (0.94)
0–10
-0.10 (1.12)
-0.20 (0.99)
-0.21 (0.67)
ΔT (K)
0–3
1.05 (2.32)
-0.17 (1.22)
0–5
0.74 (2.15)
-0.05 (1.07)
0–10
0.62 (1.94)
0.53 (2.06)
0.12 (0.96)
Δq (g kg-1)
0-3
-0.16 (0.67)
-0.08 (0.59)
-0.003 (0.39)
0–5
-0.07 (0.55)
-0.08 (0.49)
-0.02 (0.34)
0–10
-0.03 (0.29)
0.004 (0.20)
Difference between ASCOS radiosonde soundings and the
near-coincident COSMIC RO from JPL (left), UCAR (middle), and ERA-I (right)
in terms of fractional refractivity (a1–a3),
temperature (b1–b3),
and specific humidity (c1–c3). The median
difference (μ, red), the median difference plus/minus (±) median
absolute deviation (σ, green), and median standard error (δ,
gray horizontal bar) for all three parameters are also shown. The number of
near-coincident pairs as a function of height is shown in blue, with the scale
marked on the top of each panel.
Comparisons of the radiosonde with the RO retrievals and the ERA-I
profiles
To quantify the parametric uncertainty of the COSMIC RO soundings over the
Arctic (65–90∘ N), both JPL and UCAR retrievals are
collocated with a total of 144 radiosonde soundings collected from the ASCOS
summer field campaign in 2008. A total of 65 JPL profiles and 68 UCAR
profiles were found to be collocated with the radiosondes within 3 h and
300 km. The ensemble mean difference, standard deviation, and standard
errors are shown in Fig. 5. The statistics of the comparisons at three
selected altitude ranges (e.g., 3, 5, and 10 km from the surface) are
listed in Table 3.
Compared with the radiosonde from the surface up to 10 km, the COSMIC RO
refractivity shows an overall median bias of 0.10 % in JPL retrievals (Fig. 5a1), -0.20 % median bias in UCAR retrievals (Fig. 5a2), and
-0.21 % overall median bias in ERA-I refractivity retrievals (Fig. 5a3). However, the overall JPL median bias (0.10 %) is comprised of
both positive bias (e.g., ∼ 0.2 % in 6–8 km) and negative
bias (e.g., ∼ -0.5 % below 4 km). Most of the negative bias
is coming from the lower troposphere in JPL and UCAR. For example, JPL
refractivity retrievals show a negative bias below ∼ 2 km,
which increases to a maximum of about -1 % in the lowest 1 km above the
surface. Similarly, UCAR refractivity retrievals also show a negative bias,
albeit starting from ∼ 5 km down to the surface, with a smaller
maximum bias of about -0.5 %. On the other hand, the ERA-I shows a generally
negative bias above ∼ 1 km but a small positive mean bias below
∼ 0.5 km.
Figure 5b1 shows a warm bias of 0.62 K in JPL temperature retrievals
from 10 km down to ∼ 4.5 km, where JPL stops retrieving
temperature. A sharp increase in standard error in JPL temperature below 5 km is due to the sharp drop in the available JPL temperature retrieval. On
the other hand, UCAR temperature shows a smaller overall median bias of 0.53 K below 10 km, with an increasing bias below 2 km up to near 2 K
(Fig. 5b2). ERA-I temperature shows a smallest overall median bias of 0.12 K
below 10 km (Fig. 5b3), with a warm bias (∼ 1K) below 1 km
and small cold bias above, which are consistent with
Wesslén et al. (2014).
For specific humidity, the JPL retrievals exhibit negative biases from
the surface up to ∼ 2 km and transition to positive biases above,
with a median bias of about 0.07 g kg-1 from the surface up to 5 km (Fig. 5c1). The UCAR specific humidity has a negative overall bias
of -0.03 g kg-1, mostly coming from below ∼ 5 km (Fig. 5c2). The
ERA-I specific humidity has an overall bias of 0.004 g kg-1, mostly coming
from below ∼ 0.6 km (Fig. 5c3). It is also worth noting
that the increasing biases and variations of RO refractivity, temperature,
and specific humidity retrievals below 5 km strongly indicate the impact of
the increasing availability and variation of water vapor in the Arctic
summer on the RO retrievals.
Overall, JPL and UCAR refractivity retrievals are consistent with
temperature and humidity retrievals (e.g., negative refractivity bias
corresponding to positive bias of temperature and negative bias of the
specific humidity), except at certain altitudes, where the JPL and radiosonde
comparison shows positive fractional refractivity bias corresponding to
positive temperature bias (e.g., 6–8 km), which may be affected by pressure.
In the JPL retrieval, the refractivity errors are directly mapped into both
temperature and humidity errors. In the UCAR 1-D-variational retrieval, the
refractivity errors can be mapped to both/either temperature and/or humidity
errors. In addition, the errors in the a priori information from ECMWF model
analysis used for both JPL and UCAR will also affect the RO temperature and
humidity retrieval. It is worth noting that the errors of the geophysical
parameters derived from the 1-D-variational method in the UCAR retrieval become
more challenging to interpret as they include errors from both the model
background and the RO measurement. For instance, the errors of UCAR specific
humidity are very similar to the ERA-I but not consistent with the
refractivity errors, which indicate the model a priori humidity might
dominate the 1-D-variational UCAR humidity retrieval.
It is important to point out that the sharp drop in the number of
near-coincidence profiles below ∼ 1 km (Fig. 5) is primarily
due to the limited number of RO soundings penetrating deep into the PBL,
especially the bottom ∼ 300 m above the surface.
Comparisons between the RO retrievals and the ERA-I profiles
To further quantify the parametric uncertainty, the COSMIC RO soundings from
JPL and UCAR are also compared with the near-coincident ERA-I reanalysis
profiles. Since the ERA-I assimilated COSMIC RO bending angles retrieved by
the UCAR data center, they are not fully independent datasets. However, in the
data assimilation, large RO measurement errors at lower altitudes (e.g.,
20 % in bending angles errors near the surface) are normally applied,
along with a limited number of available RO soundings; the impact of RO
sounding on ERA-I in the lower troposphere remains limited
(Poli et al., 2010).
Statistical comparisons between COSMIC RO (JPL/UCAR) and the
near-coincident ERA-Interim.
Mean difference
Altitude range
UCAR–ERA-I DJF
UCAR–ERA-I JJA
JPL–ERA-I DJF
JPL–ERA-I JJA
(4782 pairs)
(8375 pairs)
(4782 pairs)
(8375 pairs)
(km)
μ (σ)
μ (σ)
μ (σ)
μ (σ)
ΔN/N (%)
0–3
0.07 (0.78)
-0.26 (1.63)
0.12 (0.78)
-0.25 (1.63)
0–5
0.08 (0.70)
-0.11 (1.45)
0.13 (0.70)
-0.07 (1.45)
0–10
0.07 (0.62)
-0.05 (1.04)
0.13 (0.62)
0.12 (1.04)
ΔT (K)
0–3
-0.12 (1.83)
0.58 (2.25)
0.14 (1.83)
0–5
-0.13 (1.63)
0.31 (2.00)
0.20 (1.63)
0–10
-0.14 (1.36)
0.11 (1.65)
0.15 (1.36)
0.25 (1.65)
Δq (g kg-1)
0–3
-0.02 (0.17)
-0.08 (0.63)
-0.002 (0.17)
-0.12 (0.63)
0–5
-0.01 (0.13)
-0.06 (0.51)
-0.001 (0.13)
-0.05 (0.51)
0–10
-0.01 (0.08)
-0.03 (0.29)
-0.001 (0.08)
-0.02 (0.29)
Fractional refractivity difference between COSMIC RO
(JPL – top; UCAR – bottom) and the near-coincident ERA-I reanalysis over the
Arctic (65–90∘ N) during winter (DJF, a, c) and
summer (JJA, b, d) of 2008. The mean difference (red), mean plus/minus
1 standard deviation (black), and number of RO profiles that penetrated
down to a given altitude (blue) are shown.
The total numbers of common COSMIC RO soundings from both JPL and UCAR are
4782 pairs in winter and 8375 pairs in summer of 2008 over the Arctic
(65–90∘ N). The comparison between
COSMIC RO from the two centers and their near-coincident ERA-I profiles are
presented in terms of fractional refractivity (Fig. 6), temperature (Fig. 7),
and specific humidity (Fig. 8) differences. All profiles are interpolated
into 100 m vertical intervals before the difference statistical calculation.
Again, the statistics for the lowest 200m were discarded due to the
significant drop of the number of RO observations near the surface. The
statistical differences between COSMIC RO and ERA-I at three selected
altitude ranges (e.g., 3, 5, and 10 km from the surface) are detailed
in Table 4.
Same as Fig. 6 but for temperature difference (K) between
COSMIC RO and ERA-I.
Same as Fig. 6 but for specific humidity difference
(g kg-1) between COSMIC RO and ERA-I.
In Fig. 6, the fractional refractivity difference between RO (JPL and
UCAR) profiles and the near-coincident ERA-I profiles shows small biases in
winter. In the lowest 3 km, JPL refractivity exhibits a small positive bias
of ∼ 0.12 %, whereas UCAR has a smaller positive bias of
∼ 0.07 %. In summer, an increase in positive bias above
∼ 4 km and negative bias below ∼ 2 km are seen.
The maximum negative bias reaches about -0.7 % (-0.6 %) in the JPL (UCAR)
retrieval near 0.5 km. An average negative refractivity bias of about
-0.3 % below 3 km is seen in both centers.
Three COSMIC RO soundings along with the near-coincident
ASCOS radiosonde in summer (a, b) and ERA-I profile in winter (c). (Left)
the COSMIC L1 SNR (blue) and excess Doppler (red) from JPL retrievals with
the estimated tangent height shown on the right y axis, and the two
horizontal thin lines indicating the surface and 5 km altitude; (center) JPL
RO refractivity (blue) and the near-coincident radiosonde and ERA-I profiles
(black), along with the fractional refractivity difference (red); (right)
radiosonde and ERA-I profiles of temperature (black), specific humidity
(blue), and 1/10 of relative humidity (green).
Similarly, Fig. 7 shows the temperature difference between COSMIC RO and
the near-coincident ERA-I profiles. Below 3 km, in winter, JPL shows a
positive bias of 0.14 K, whereas UCAR has a negative bias of -0.12 K. In
summer, UCAR has a warm bias of 0.58 K, whereas JPL does not have retrievals
below 3 km. Below 10 km, temperature difference in winter (Fig. 7a, c)
exhibits much smaller variation than that in summer. For instance,
relatively large positive biases are seen in temperature retrievals from UCAR
below 2 km (Fig. 7d) and from JPL within 6–10 km (Fig. 7b) as compared with
the ERA-I profiles.
Moreover, Fig. 8 shows the difference between the specific humidity
retrievals from COSMIC RO and the near-coincident ERA-I. Within 3 km, JPL
and UCAR have a negligible bias of -0.002 and -0.02 g kg-1, respectively,
in winter. In summer, much larger dry biases of about -0.12 in JPL and
-0.08 g kg-1 in UCAR retrievals are detected. Overall, the RO specific
humidity exhibits negative biases below 2 km, likely due to the abundant
water vapor near the ocean surface.
In summary, the COSMIC RO (JPL or UCAR) refractivity difference from the
near-coincident ERA-I retrievals is consistent with the temperature and
humidity retrieval differences (e.g., negative refractivity bias
corresponding to positive bias in temperature and negative bias in the
specific humidity). However, positive fractional refractivity bias
corresponds to positive temperature bias in JPL retrievals. Most of the mean
biases have negligible standard errors due to large sample sizes.
Case study of RO signal dynamics
To further study the impact of moisture on the RO soundings, the L1 signal-to-noise ratio (SNR) and
excess Doppler for two typical JPL COSMIC RO soundings from summer (a, b)
and one from winter (c) were presented along with the near-coincident ASCOS
radiosonde (in summer) and the ERA-I profiles (in winter; Fig. 9). The two
summer cases show nearly double the moisture with specific humidity
(∼ 2 g kg-1) near the surface than that in the winter (less than
∼ 1 g kg-1). In the winter case, very smooth excess Doppler
along with a relatively quiet SNR is shown. A sharp drop in SNR to the
noise background is clearly seen around 39 s (Fig. 9c1), when
tangent point descends to the smooth Arctic Ocean surface. In contrast,
much larger variations in both the SNR and excess Doppler are seen in the
two summer cases, especially below 5 km. The transition of the SNR to noise
background near the surface is smeared due to more lower-troposphere moisture
variations, which likely introduce multipath and SNR variations. Even though
the lower-troposphere moisture in Arctic summer is still rather low (less
than 2 g kg-1 near the surface) as compared to the low latitudes, a surprisingly
large difference in refractivity (-7 % near surface) is seen (Fig. 9a2). The systematic negative RO N bias (-1 %) in summer season
(e.g., Figs. 5 and 6) could be directly attributed to the lower-troposphere
moisture variations. However, the impact of moisture and its variations on
the RO signal dynamics and so the RO calibration and retrieval processes
warrant a more comprehensive investigation.
Summary and conclusions
In summary, over the Arctic (65–90∘ N), 3-year
(2008–2010) COSMIC RO soundings show uniform spatial sampling with average
penetration depth (the minimum profile height) within 300 m above the ocean
surface. The fraction of the deeply penetrating COSMIC soundings (within 300 m) is over 70 % in all non-summer seasons but reduces to only 50–60 % in
the summer. The increase of the near-surface moisture and its variation in
summer, even though relatively small compared to the tropics, can lead to
significant GPS RO SNR and excess Doppler variations, which could complicate
the GPS RO signal tracking and lead to early sounding termination before
reaching the surface.
Both structural uncertainty and parametric uncertainty of COSMIC RO
soundings have been quantified. The structural uncertainty of RO is
estimated by comparing the retrieved refractivity, temperature, and specific
humidity from JPL and UCAR processing centers, which process the same raw
COSMIC GPS data. The comparisons using 1-year COSMIC data in 2008 show the
inter-center RO retrieval difference (i.e., structural uncertainty) within
∼ 0.07 % in refractivity, ∼ 0.72 K in
temperature, and ∼ 0.05 g kg-1 in specific humidity below 10 km.
The parametric uncertainty is quantified by comparing RO with the
near-coincident radiosonde and the ERA-I reanalysis. COSMIC RO shows
slightly larger difference from the near-coincident radiosondes than the
ERA-I, which assimilated UCAR COSMIC RO retrievals. A systematic negative
bias up to ∼ 1 % in refractivity below 2 km is only observed
during the summer, which further confirms the impact of the lower-tropospheric summer moisture on RO retrievals. The parametric uncertainty of
the COSMIC RO refractivity sounding in the summer season is about 2 orders of
magnitude larger than the structural uncertainty, implying highly
consistent, precise COSMIC RO observations in the troposphere. It is
reasonable to expect the parametric uncertainty in the winter season to
be even smaller due to much less impact of moisture on the RO retrievals.
In conclusion, GPS RO provides high-quality measurement (especially in
refractivity) in the lower troposphere over the Arctic. The high-precision
COSMIC RO measurements with uniform spatial and temporal sampling provide
a promising opportunity for studying the lower-tropospheric dynamic process,
especially the PBL study. However, the early termination of RO sounding
before reaching the surface and the systematic RO refractivity bias inside
the PBL in summer limit the RO sounding capability inside the PBL and
impede its application for the physical process study involving the
interaction of ocean, atmosphere, and sea ice. Preliminary study shows the
impact of moisture on the RO signal dynamics. Further study is needed to
improve the RO sounding quality and to enhance the scientific application of RO
observations in the lower troposphere.