Radio occultation (RO) and radiosonde (RS) comparisons provide a means of
analyzing errors associated with both observational systems. Since RO and RS
observations are not taken at the exact same time or location, temporal and
spatial sampling errors resulting from atmospheric variability can be
significant and inhibit error analysis of the observational systems. In
addition, the vertical resolutions of RO and RS profiles vary and vertical
representativeness errors may also affect the comparison. In RO–RS
comparisons, RO observations are co-located with RS profiles within a fixed
time window and distance, i.e. within 3–6 h and circles of radii
ranging between 100 and 500 km. In this study, we first show that
vertical filtering of RO and RS profiles to a common vertical resolution
reduces representativeness errors. We then test two methods of reducing
horizontal sampling errors during RO–RS comparisons: restricting co-location
pairs to within ellipses oriented along the direction of wind flow rather
than circles and applying a spatial–temporal sampling correction based on
model data. Using data from 2011 to 2014, we compare RO and RS differences
at four GCOS Reference Upper-Air Network (GRUAN) RS stations in different
climatic locations, in which co-location pairs were constrained to a large
circle (∼ 666 km radius), small circle
(∼ 300 km radius), and ellipse parallel to the wind
direction (∼ 666 km semi-major axis,
∼ 133 km semi-minor axis). We also apply a spatial–temporal
sampling correction using European Centre for Medium-Range Weather Forecasts
Interim Reanalysis (ERA-Interim) gridded data. Restricting co-locations to
within the ellipse reduces root mean square (RMS) refractivity, temperature,
and water vapor pressure differences relative to RMS differences within the
large circle and produces differences that are comparable to or less than
the RMS differences within circles of similar area. Applying the sampling
correction shows the most significant reduction in RMS differences, such that
RMS differences are nearly identical to the sampling correction regardless
of the geometric constraints. We conclude that implementing the
spatial–temporal sampling correction using a reliable model will most
effectively reduce sampling errors during RO–RS comparisons; however, if a
reliable model is not available, restricting spatial comparisons to within an
ellipse parallel to the wind flow will reduce sampling errors caused by
horizontal atmospheric variability.
Introduction
Radio occultation (RO), a relatively new method of atmospheric measurement,
has established itself as an important atmospheric observational system. By
measuring the phase delay of radio waves sent from Global Positioning System (GPS) satellites traversing quasi-horizontally through Earth's atmosphere to
low-Earth orbiting satellites, RO obtains accurate and precise vertical
profiles of bending angles . Refractivity is obtained by
inverting bending angle profiles using the Abel transform. Refractivity is a
function of temperature and water vapor pressure; therefore, with auxiliary
information (observations or model) of one, the other can be retrieved.
Either bending angles or refractivity may be assimilated into numerical
weather prediction models .
Since the proof-of-concept GPS/MET mission in 1995 , RO
profiles of refractivity, temperature, and water vapor have been compared to
radiosondes (RS) to assess the quality of RO retrievals and the performance
of RS. RS are considered a standard for comparison due their long
history of in situ measurements, and several studies have used RS as a
reference for RO retrieval analysis . Conversely, due to RO's properties
of high accuracy and precision, high vertical resolution, and global
coverage, RO has been used to evaluate the performance of various RS. For example, demonstrated that RO observations are
of high enough accuracy and resolution to differentiate between RS and assess their performance, particularly instrument biases due
to geographic region, radiation errors, day and night biases, etc.
One of the main difficulties associated with RO–RS comparisons comes from
temporal and spatial differences between nearby RS and RO soundings. Since
both measurements are not taken at the exact same time or location, temporal
and spatial errors (sampling errors) can be a significant part of the
computed RO–RS differences. To reduce the effects of sampling errors, the
majority of previous studies have restricted co-located RO observations to
within a fixed time range and distance, typically within 3–6 h of
the RS launch and within circles of radii ranging from
100 to 500 km centered at the RS launch site. Alternatively,
used smaller radii circles (3–36 km) and fitted
a second-order polynomial to the root mean square (RMS) differences in order
to filter out atmospheric variability. Weather-scale atmospheric variability
within these circles and time ranges is the major cause of these sampling
errors .
compared RS with infrared (IR) soundings and noted that
sampling errors generally dominate the total error associated with the
comparison. compared RS with satellite retrieved temperature,
also discussing the impacts of sampling errors. applied a
bilinear best fit plane to microwave radiometer observations to reduce
sampling errors during comparisons with ground-based GPS observations.
and both propose a statistical modeling
approach to reduce sampling errors associated with RS intercomparisons and
balloon drift analysis. In this study, we focus on RO–RS comparisons, and
attempt to reduce the sampling errors that occur during these comparisons.
We apply two methods to reduce sampling errors caused by atmospheric
variability in RO–RS comparisons. First, we restrict co-location pairs to
within ellipses oriented along the direction of wind flow rather than
circles. Temperature and water vapor gradients in the free atmosphere tend to
be perpendicular to wind flow, resulting in refractivity (a function of both
temperature and water vapor pressure) gradients to also approximately lie
perpendicular to wind flow. Therefore, we hypothesize that the spatial
variability of refractivity within ellipses of semi-major axis a oriented
along the wind direction will be reduced compared to the variability within
circles of radius a. The second method consists of applying a
spatial–temporal sampling correction to the RO–RS co-location pairs using
the “double-difference” method . In the
double-difference method, each data set is compared to an intermediate
reference data set; in our study we use the European Centre for Medium-Range
Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) model data and
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim,
last access: 23 March 2018.
By subtracting the reference model data at the corresponding RO and RS
locations in space and time from the RO and RS observations, the
spatial–temporal sampling errors are largely eliminated, leaving mainly the
RO and RS observational errors. In addition to the sampling error, the
different vertical resolutions of the RO and RS profiles can lend to
representativeness errors when compared. To reduce these errors, we filter
the profiles to a common vertical resolution before the comparisons.
Although this paper considers RO–RS comparisons, the vertical filtering and
methods of reducing sampling errors can be applied to comparisons of other
data pairs, such as any two sounding systems or observations and models.
However, the amount of filtering and the geometric constraints on the
co-location pairs may have to be adjusted for different comparisons. For
example, comparisons of RO or RS soundings with IR or microwave soundings, which have much different vertical resolutions, would require
a greater filtering of the RO or RS profiles to make them comparable to the
lower-resolution profiles.
The first section describes the data sets, filtering methods, and methodology
implemented in this study. Next, we discuss aspects of the ellipse
co-location method conducted using the Lindenberg RS station. The following
section describes the results of co-locations using both the ellipse and
sampling correction methods at four different RS stations. In the final
section we summarize the results and discuss further impacts and
implications, followed by an appendix which includes mean and standard
deviation differences and further discussion of the spatial–temporal sampling
correction.
MethodsRO and RS data sets
All RO profiles are provided by the COSMIC Data Analysis and Archive Center
(CDAAC), which can be found at http://cdaac-www.cosmic.ucar.edu
(last access: 25 April 2018). RO
refractivity, temperature, and water vapor pressure profiles are taken from
the wetPrf files provided on a uniform 100 m mean sea level height
grid. All available processed, high-quality profiles from the COSMIC-1, GRACE
1 and 2, and Metop-A and B missions during the time periods of comparisons are
used. Summaries of these missions can be found at the CDAAC website above.
The temperature and water vapor pressure profiles are computed using a 1D-VAR
method for moisture retrievals. Details on the 1D-VAR wet temperature and
water vapor retrievals can be found at
http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/1dvar.pdf
(last access: 15 March 2018).
Map of the GRUAN RS network. Labeled locations with red dots are RS
stations used in this study, and black dots mark the other GRUAN network RS
stations. For more information about these and other GRUAN stations, see
https://www.gruan.org
(last access: 25 April 2018).
All RS profiles are provided by the Global Climate Observing System (GCOS)
Reference Upper-Air Network GRUAN; seefor more information on the
GRUAN project and downloaded from the National Oceanic and
Atmospheric Administration (NOAA) National Climatic Data Center. GRUAN RS
were chosen for their reference-quality observations and well-documented
uncertainties , allowing for a better
analysis of the reduction of sampling errors associated with each co-location
method.
We chose four stations in different climates for this study for the time periods of 2014, 2013, 2012, and
2011–2013, respectively: Lindenberg,
Germany (LIN); Ny-Ålesund, Norway (NYA); Tateno, Japan (TAT); and Nauru,
Nauru (NAU). (Nauru is the only station in which the full
period of activation was used; this is due to the low number of RS launches
during 2011 through late August 2013.) Figure is a map of the
GRUAN RS stations with the locations of the four stations used in this study
labeled and marked in red. All four stations use the Vaisala RS92 radiosonde
instruments for details on GRUAN RS processing see.
For refractivity comparisons, RS refractivity is computed under the
assumption of a neutral atmosphere :
N=77.6pT+3.73×105eT2,
where N is refractivity (N-units), p is dry air pressure
(hPa), T is temperature (Kelvin), and e is water vapor
pressure (hPa).
We generate two data sets: an unfiltered data set which contains all original
RO and RS profiles and a filtered data set containing the vertically
filtered versions of the original RO and RS profiles.
Filtering
Representativeness errors result from two different aspects of the RO and RS
observations. Firstly, GRUAN RS have a temporal resolution of 1 s, with
vertical resolution of 5–10 m on average ,
which is much finer than the 100 m vertical resolution of the RO
wetPrf profiles. Secondly, RS observations are a series of point measurements,
whereas RO observations are weighted averages of a cylindrical volume of
atmosphere with horizontal scales of 150–300 km. Vertical filtering of both RO and RS
profiles should decrease the representativeness errors caused by differences
in observation type and vertical resolutions , resulting in
a more meaningful comparison between profiles. Removal of structures with
very small vertical scales also has the effect of reducing representativeness
errors associated with different horizontal scales (footprints) of the
observations . Structures with very small vertical scales in
the RS profile are likely associated with horizontal scales too small to be
resolved by the RO observations. Though the majority of previous RO–RS
comparison studies do not filter the RO or RS profiles before comparison,
filtered the profiles to remove structures with vertical scales
less than 1 km before comparison with model analyses in an effort to
minimize vertical representativeness errors.
To remove small-scale, unrepresentative structures in both the RO and RS
profiles, we applied the Savitzky–Golay low-pass filter .
We first linearly interpolated profile variables (refractivity, temperature,
and water vapor pressure) to a 10 hPa (∼ 100 m)
uniform vertical grid, then filtered the full profile using a fixed
40 hPa (∼ 400 m) filter window and quadratic fitting
polynomial. We tested various combinations of filter windows and number of
passes to determine the effects of filtering on the RS and RO profiles (not
shown). The number of passes of the same filter result in minor smoothing
effects relative to different filter windows, and filter windows larger than
40 hPa cause too much smoothing of the RS profile. RO profiles, due
to their lower resolution relative to the RS profiles, showed little change
under different filter windows. Therefore, 40 hPa was chosen as a
sufficient window to remove small-scale features in the RS profile while
preserving the overall structures of both the RS and RO profiles. To
accommodate for the differences in vertical resolution, RO profiles underwent
a single pass of the 40 hPa filter and RS profiles underwent three
passes of the 40 hPa filter.
RO–RS co-location criteria and statistics
500 hPa ERA-Interim refractivity field (color contours)
centered at Tateno for 11 and 12 February 2012 at 00:00 UTC. The three
spatial geometries (large circle, small circle, ellipse) are shown in black
centered at Tateno with wind vectors (black) denoting wind direction and
magnitude. Generally, refractivity isopleths lie parallel to the wind flow,
even as wind direction changes, resulting in minimal refractivity variability
within the ellipse relative to both circles.
We co-located RO and RS observations using the ellipse method every
10 hPa between 1000 and 10 hPa at each RS location. We
included RO occultations taken within 3 h of the RS launch at
Ny-Ålesund, Tateno, and Nauru and within 1 h at Lindenberg due to
the high number of RS launches (at least four times daily). We considered
three different geometric constraints to co-locate RO profiles: (1) large
circle with 6∘ latitude radius (∼ 666 km); (2) small circle with 2.6∘ latitude radius
(∼ 300 km); and (3) ellipse parallel to the wind direction,
6∘ latitude semi-major axis and 1.2∘ latitude
(∼ 133 km) semi-minor axis, each centered at the RS
location. (The ellipse and small circle are circumscribed by the large
circle, and the small circle was chosen such that the area within the small
circle and ellipse are approximately the same; see Fig. .) The X
and Y coordinates of the points on the circles and ellipses are constructed
using the following parameterization (a is semi-major axis, b is semi-minor
axis; for circles, a=b= radius, where both a and b are in kilometers):
X(s)=acos(s)cos(θ)-bsin(s)sin(θ),Y(s)=acos(s)sin(θ)+bsin(s)cos(θ),
where s is a parameter that varies between 0 and 2π and is stepped in
increments of 0.01, yielding a series of 629 points that approximate the
circle or ellipse, and θ is the wind direction in radians (converted
from meteorological to polar coordinates). The X and Y coordinates of the
circle or ellipse are first computed at the Equator, where 1∘
of latitude and longitude equals 111 km, and then the X and Y
coordinates are adjusted to the latitude and longitude of the RS station
according to the following:
Lat(Y,s)=Y(s)111+lat(RS),Long(X,s)=X(s)111cos(lat(Y,s))+long(RS).
The ellipses change orientation such that the semi-major axis is parallel to
the wind direction at each pressure level and time per RS. The circles remain
fixed and unaffected by the change in wind direction, pressure level, or
time.
Figure illustrates the three geometries centered at Tateno at
500 hPa for 2 days in February 2012, noting that the ellipse
adjusts its orientation as wind direction changes with time and at a pressure
level. Preliminary testing of the ellipse co-location method with ERA-Interim
model refractivity fields confirmed that refractivity isopleths tend to
follow wind flow (as illustrated in Fig. ); thus, orientation of
the ellipse along the direction of the wind flow should increase atmospheric
homogeneity relative to the large circle.
For each RS at a given time and pressure level, RO profiles are co-located
with the RS profiles under the time and geometric constraints discussed
above. There can be (and often are) multiple co-location pairs with the same RS
at a given time and pressure level. We computed differences for each
co-location pair:
XRO-RS=XRO-XRS,
where X refers to refractivity, temperature, or water vapor pressure. We
computed the RMS of the differences for each pressure level over the full
time period and used the RMS to quantify the reduction in sampling errors. In
certain cases we also computed the percent difference between two quantities
Xi and Xj:
%diff=100×Xi-XjXj.
Spatial–temporal sampling correction algorithm
Schematic of the sampling correction applied to RO and RS
co-location.
Applying a spatial–temporal sampling correction to the RO–RS differences is
an alternate method of reducing sampling errors in the presence of an
auxiliary data set. This method has been applied in previous studies and is
not restricted to RO–RS comparisons. used this approach
to homogenize RS records. used ECMWF forecasts for
double-differencing to reduce the sampling differences between Atmospheric
Infrared Sounder (AIRS) and RS co-located pairs.
calculated an observed–background (O–B) double difference to
estimate the mean RS temperature bias using co-located RO profiles and Met
Office model background fields. These studies use a double-difference
correction, but do not verify or discuss how the correction reduces sampling
errors.
Here, we apply a spatial–temporal sampling correction double difference
computed with model data and assess its effects on reducing sampling errors.
We use ERA-Interim data to subtract the model background from both the RO and
RS observations, removing spatial and temporal sampling differences and
isolating the observational errors associated with the RO and RS pair. As
shown in Fig. , we use the ERA-Interim profiles interpolated in
time and space to the RO time and location (eraPrf files provided by
CDAAC) and the ERA-Interim grid point nearest the RS location at the RS
launch time to compute the sampling correction. Due to the coarser vertical
resolution of the ERA-Interim grid relative to the RS and RO vertical
resolutions, comparisons with the sampling correction are conducted on a
common 50 hPa uniform pressure grid from 1000 to 100 hPa to
avoid further vertical interpolation.
The spatial–temporal sampling corrected differences (Xsc) for
the co-location pairs are computed as follows for the comparison variables
(refractivity, temperature, and water vapor pressure):
Xsc=(XRO-XERA(RO))-(XRS-XERA(RS))=(XRO-XRS)-(XERA(RO)-XERA(RS)),
where XERA(RO) is the ERA-Interim model value interpolated in
time and space to the RO location, XERA(RS) is the ERA-Interim
model grid point value closet to the RS at the RS launch time, and their
difference, XERA(RO)-XERA(RS), is referred to as
the model correction term. We computed the RMS differences per pressure level
on the 50 hPa grid.
The spatial–temporal corrected difference (Xsc) between the two
data sets (Eq. 6a and b) can be interpreted in two
ways. When the two data sets are close to each other in time and space, one
interpretation is that the RO–RS differences are corrected for local spatial
and temporal variability by subtracting these sampling differences, which are
estimated by another data set (in this case ERA-Interim) from the measured
differences (Eq. 6b). This is the interpretation used here and
by . However, as the equivalent Eq. (6a) shows, the
corrected difference is also the difference of the departures of the two data
sets from a common, reference data set, or the “double-differencing” method
. In this interpretation, the two data sets are
not necessarily required to be close in space or time; the method is valid as
long as the biases of the reference data set do not vary over the spatial and
temporal scales of the comparison. We tested the sensitivity of the sampling
correction method to the spatial and temporal scale of the comparisons in
Appendix for RO–RS pairs over a much larger spatial scale
(within circles of radius 15∘ latitude) and longer time
window (24 h) at Lindenberg. We find that the RMS, mean, and standard
deviations of the comparisons are insensitive over this range of spatial and
temporal scales, allowing for a large increase in the number of co-located
pairs of data.
Results and discussion
Preliminary proof-of-concept testing of the ellipse method using only
ERA-Interim data demonstrated a significant reduction in RMS refractivity
differences within the ellipse relative to both the large circle and circles
of similar area to the ellipse (not shown here). In the following two
sections, aspects of the ellipse co-location are analyzed at the Lindenberg
station. The final section presents the results of both the ellipse
co-location and sampling correction at all four RS stations.
Filtered vs. unfiltered
Filtered vs. unfiltered RMS percent differences* for
refractivity, temperature, and water vapor pressure at Lindenberg, 2014. RMS
differences are computed using all co-located pairs within 100 hPa
pressure layers for the filtered and unfiltered data sets, and then the percent
differences between the two are computed and reported here.
* Percent differences computed as
100 × (RMSfilt –
RMSunfilt)/RMSunfilt.
Filtering both the RO and RS profiles has a small, positive impact on
reducing RMS differences in refractivity, temperature, and water vapor
pressure. Compared to RMS differences computed using the unfiltered profiles
at Lindenberg, filtering both the RO and RS profiles before co-location
reduces RMS differences by about 1 % on average, up to almost 8 % in some
instances (see Table ). Within the large circle, filtering has
less of an impact on reducing vertical representativeness percent errors
since sampling errors tend to dominate (mostly due to large spatial
differences). The RO–RS percent differences in the small circle and ellipse
are more affected by vertical representativeness errors since the sampling
errors are relatively small. Based on these results, we filtered all RO and
RS profiles before computing their differences to reduce representativeness
errors.
Effects of wind speed on ellipse co-location
RMS refractivity differences at Lindenberg 2014 for two sets of
RO–RS pairs separated by reported RS wind speeds: pairs with wind speeds
less than 5 ms-1(a, b) and pairs with wind speeds greater than
or equal to 5 ms-1(c, d). Geometric constraints are shown in
the following colors: large circle (orange), small circle (grey), and ellipse
(magenta).
The relationship between the wind direction and horizontal variability (and
sampling error) of refractivity is expected to break down for light wind
speeds – indeed we found that when wind speeds are low, the effectiveness of
orienting the ellipse along the direction of wind flow is significantly
reduced. We separated co-located RO–RS pairs at Lindenberg into two groups
based on the reported wind speed of the RS at a given time and pressure
level: (1) wind speeds less than 5 ms-1 and (2) wind speeds
greater than or equal to 5 ms-1. Figure shows the
RMS differences in refractivity for each group.
As shown in Fig. , the clear distinction between RMS profiles for
the ellipse and two circles at wind speeds greater than 5 ms-1
essentially vanishes when wind speeds are less than 5 ms-1.
Similarities in RMS refractivity differences when wind speeds are less than
5 ms-1 are likely associated with greater atmospheric
homogeneity and weaker gradients in the region, resulting in similar
observations within the area containing both circles and the ellipse. When
wind speeds increase to larger than 5 ms-1, there is a greater
separation between the circles and ellipse with respect to the RMS
difference. Under moderate to high wind speed conditions, the ellipse reduces
RMS refractivity differences relative to both the large and small circle,
particularly below about 700 hPa. In the upper troposphere, both the ellipse
and the smaller circle give similar results and smaller RMS differences than
the large circle (Fig. ). In the case of Lindenberg, since the
majority of wind speeds are greater than 5 ms-1 (as noted by the
differences in co-location counts under the two wind constraints in
Fig. ), low wind speeds do not have a significant effect on the
overall RMS differences when co-location pairs are not separated by wind
speed (see Fig. ).
Ellipse co-location and sampling correction applied at four RS stations
RMS differences for RO–RS co-locations at Lindenberg, 2014. RMS
differences with ellipse method only (solid; large circle: orange; small
circle: grey; ellipse: magenta) and RMS differences with ellipse method and
sampling correction (dashed, abbreviated as SC) are shown for all three
variables: (b) refractivity, (c) temperature, and (d) water vapor pressure.
The number of pairs within each ellipse (solid) and ellipse plus sampling
correction (dashed) is shown in panel (a).
Same as Fig. for two different locations:
Ny-Ålesund, 2013 (a–d) and Tateno, 2012 (e–h).
We carried out two RO–RS comparisons at four different RS locations
(Lindenberg, Ny-Ålesund, Tateno, and Nauru): first, we compared pairs with
RO observations co-located within the large circle, small circle, and ellipse
centered at the RS station, and second, we applied the sampling correction to
the RO–RS pairs within the ellipse and two circles. We then computed RMS
differences in refractivity, temperature, and water vapor pressure.
Figure illustrates the RMS differences for refractivity,
temperature, and water vapor pressure with and without the sampling
correction at Lindenberg. Considering the ellipse method only (solid lines),
the ellipse reduces RMS differences relative to the large circle for all
three variables at all pressure levels, having the most significant reduction
in the temperature RMS differences. Generally, the small circle and ellipse
have similar RMS differences, but there are pressure layers in which the
ellipse reduces the RMS relative to the small circle. When the sampling
correction is applied to the circles and ellipse (dashed lines), the RMS
differences are significantly reduced and converge with minimal differences
between the ellipse and circles. The RMS differences at Lindenberg are mostly
affected by spatial sampling errors; temporal sampling errors are minimized
by the reduced time window of 1 h due to frequent (four times daily)
RS launches at this station.
The results at Ny-Ålesund and Tateno are very similar to those at
Lindenberg, with some minor differences in the lower troposphere
(Fig. ). Again, using pairs in the ellipse only (solid, magenta)
results in significantly reduced RMS differences relative to the large circle
(solid, orange) and equal to or smaller RMS differences compared to the small
circle (solid, grey). Including the sampling correction shows the largest
reduction in RMS differences such that the differences from all three
geometric types converge to similar values. At both Ny-Ålesund and
Tateno, the reduction of RMS differences in the ellipse compared to the large
circle becomes less in the lower troposphere (below 800 hPa), where
the frequency of wind speeds less than 5 ms-1 increases and the
relationship between wind direction and RO–RS differences breaks down. For
example, at Ny-Ålesund and Tateno the percentages of RO–RS pairs for
which the wind speed is less than 5 ms-1 below 800 hPa
are 36.1 and 32.7 %, respectively. The small sample size in the ellipse
compared to the large circle (Fig. a, e) may also play a role,
allowing a few outliers to dominate the statistics.
Same as Fig. for Nauru, 2011–2013.
The results at the tropical location of Nauru (Fig. ) generally
confirm the findings at the other three RS locations. The ellipse alone
(solid, magenta) decreases RMS differences relative to the large circle
(solid, orange) and produces RMS differences that are comparable to the
differences of the small circle (solid, grey). There is less distinction,
however, between the ellipse method only (solid) and sampling correction
(dashed) RMS differences, particularly for temperature. All six geometric and
sampling correction combinations have overlapping RMS temperature differences
and show little separation between co-location methods, unlike the results at
Lindenberg, Ny-Ålesund, and Tateno. This is most likely caused by the
relative horizontal homogeneity in temperature at Nauru, which is located in
the deep tropics. Thus, there is little to no distinction between RMS
temperature differences using various geometrical constraints or sampling
correction.
Refractivity and water vapor pressure RMS differences in the lower
troposphere (1000–700 hPa) at Nauru are the largest of the four
stations, caused primarily by atmospheric conditions at its location in the
deep tropics. RO is known to have negative refractivity biases in the lower
troposphere, particularly in moist tropical regions where large water vapor
and associated refractivity gradients often result in super-refraction
. Overall, this
results in larger RMS refractivity differences at Nauru. Since
super-refraction is an error characteristic of RO retrievals and not related
to horizontal sampling errors, the sampling correction has no impact on the
RO–RS refractivity differences.
Conclusions
We have shown that vertical filtering of the RO and RS profiles before
comparison reduces representativeness errors associated with different
vertical resolutions and observation types by a small amount (typically a few
percent). Using these filtered profiles, we tested two methods to reduce
spatial and temporal sampling errors during RO–RS comparisons: (1) restricting RO and RS pairs to within ellipses oriented along the direction
of wind flow and (2) applying a spatial–temporal sampling correction using
model data to remove differences caused by horizontal atmospheric gradients
and time differences in the observations. When wind speeds exceed about
5 ms-1, co-locations within the ellipse parallel to the wind
flow reduce RMS differences in refractivity, temperature, and water vapor
pressure relative to co-locations within the large circle and either reduce
or result in RMS differences that are approximately equal to the differences
within the smaller circle. The effectiveness of co-locating RO–RS pairs
within the ellipse is reduced for wind speeds less than 5 ms-1.
Applying the spatial–temporal sampling correction using ERA-Interim model
data showed the most significant reduction in RMS differences, more so than
applying the ellipse constraint alone. The sampling correction reduced RMS
differences in refractivity, temperature, and water vapor pressure by an
average of 55 %. The reductions of sampling errors within both large and
small circles and the ellipse tend to converge with the sampling correction
applied, rendering the differences in geometric constraints of the circles
and ellipse negligible. An exception to this reduction in RMS occurs at Nauru
in the lower troposphere, where super-refraction associated with the
atmospheric conditions of the deep tropics tends to dominate the RMS
differences.
In order to reduce sampling errors for future RO–RS co-location comparisons,
our results suggest that applying the sampling correction under more lenient
co-location criteria would be most effective. By using a large distance
constraint, the sample size will be sufficiently large and applying the
sampling correction eliminates most sampling errors, even for the large
distance restriction (greater than 600 km). However, if a reliable
model is unavailable, restricting co-locations within ellipses oriented along
the direction of wind flow will help to reduce sampling errors caused by
atmospheric variability. Both of these methods are effective in reducing
sampling errors caused by spatial and temporal differences during comparisons
and should provide a more accurate error analysis of RO and RS observations.
Code and data availability
The code used in this study will be made available upon request.
Mean and standard deviation of differences
In this section, we compute the mean and standard deviation (SD) of the
RO–RS differences. The mean difference is defined as
M(RO-RS)=1N∑i=1N(RO-RS)i.
The variance about the mean is given by
VARM(RO-RS)=1N∑i=1N(RO-RS)i-M(RO-RS)2,
and the standard deviation of the difference about the mean is the
square root of the variance, SDM = VARM1/2. The
SDM can be compared to the RMS RO–RS difference, which is
defined by
RMS(RO-RS)=MS(RO-RS)1/2,
where the mean square difference (MS) is defined as
MS(RO-RS)=1N∑i=1N(RO-RS)i2.
It can be shown by expanding Eq. () that
VARM(RO-RS)=MS(RO-RS)-M(RO-RS)2.
Thus, the SDM(RO-RS) is always less than or
equal to the RMS(RO-RS), and equal if and only if the
mean RO–RS difference is zero.
Figures – show the mean and
SD of the RO–RS differences for refractivity, temperature, and water vapor
pressure, respectively, for all six co-location methods. For each variable,
the mean difference profiles are all similar and show no systematic
differences, which indicates that the mean differences are not sensitive to
the co-location method and are relatively unaffected by sampling errors. This
is because the distribution of the RO observations with respect to the RS
observations is approximately random, and so the sampling (and
representativeness) errors tend to cancel for a large enough sample size.
However, the mean differences do illustrate differences in bias errors
between the two data sets. The bias errors in the GRUAN RS are expected to be
small, so the differences in the mean RO–RS differences are likely due to
the RO biases, notably the negative refractivity bias caused by
super-refraction in the lower troposphere under moist conditions.
RO–RS refractivity mean (thick) and SD (thin) difference profiles
for the six co-location methods at Lindenberg (a), Ny-Ålesund (b),
Tateno (c), and Nauru (d). The number of co-located RO–RS pairs are given on
the left side of each panel (without the sampling correction: solid; with the
sampling correction: dashed). The color schemes are the same as
Figs. – and are given in the legend in (a)
where SC stands for sampling correction.
In Fig. we see that the differences in mean
refractivity are small above 700 hPa for all four stations. Below
700 hPa, there is a negative RO refractivity bias, which is most
pronounced at the tropical Nauru station where super-refraction is most
common. The bias is minimal at the most northern station, Ny-Ålesund,
which is expected to have the fewest cases of super-refraction.
Same as Fig. for temperature.
The biases for temperature differences (Fig. ) are less
than 1 K from 1000 to 100 hPa for Lindenberg, Ny-Ålesund,
and Tateno and show a negative bias at Nauru, reaching about 2 K at
600 hPa. This is probably not caused by super-refraction in the RO
observations because the negative bias is above the level where most
super-refraction occurs and the bias becomes smaller below the
600 hPa level, in contrast to the bias in refractivity, which
increases sharply with decreasing height in the lower troposphere. Instead,
the bias in the temperature is probably related to the 1D-VAR retrieval of
temperature in the RO observation, which uses ERA-Interim temperatures as the
first guess.
Same as Fig. for water vapor pressure.
Finally, the differences in the mean RO–RS water vapor pressure reflect the
negative refractivity bias due to super-refraction at Lindenberg, Tateno, and
especially Nauru, with little bias at the colder, drier Ny-Ålesund
station (Fig. ).
The SD profiles in Figs. –
are similar to the RMS profiles associated with the different co-location
methods in Figs. –. This is expected because the
SD and RMS values are close when mean RO–RS differences are near zero, as
shown by Eq. (). The largest differences between the SD
and RMS profiles are in the lower troposphere, where the mean differences are
the largest.
Sensitivity of spatial–temporal correction method to spatial and temporal scale
In Sect. , we noted that when
using the double-differencing correction method to reduce spatial and
temporal sampling errors, it was not necessary that the RO profiles be close
in space and time to the RS profiles, as long as the biases of the reference
data set (in our case ERA-Interim) remain constant in space and time
throughout the comparison. To illustrate this property of the
double-differencing method, we co-located RO profiles within a much larger
circle (15∘ latitude radius, ∼ 1665 km) and a time
window of 24 h at Lindenberg between January and March 2014 and
compared the RMS, mean, and SD of these RO–RS differences to those computed
under the 6 and 2.6∘ radius circles with a
1 h time window.
RMS (b–d), mean (thick; e–g), and SD (thin; e–g) RO–RS profiles
for refractivity (b, e), temperature (c, f), and water vapor pressure (d, g)
under three different co-location restraints with (dashed) and without
(solid) the sampling correction (SC) applied: circles of radius
6∘ latitude (orange) and 2.6∘ latitude (grey)
for time windows of 1 h, and a circle of radius 15∘
latitude for a 24 h time window (black). Comparisons are conducted at
Lindenberg for January–March 2014. The number of co-located pairs for
each co-location restraint (with SC: dashed; without SC: solid) are given in panel (a), where the x axis is a logarithm scale.
Figure illustrates the RMS (b–d), mean, and SD (e–g)
profiles for the RO–RS differences in refractivity, temperature, and water
vapor pressure under the three different co-location restraints with and
without the sampling correction applied. Across all three variables, the RMS,
mean, and SD profiles with the sampling correction remain nearly the same
regardless of the spatial or temporal differences applied to the RO–RS
co-location. This verifies that the double-differencing method is insensitive
to spatial and temporal separations of the RO and RS observations for this
example when using ERA-Interim as a reference data set. The similarity in the
RMS, mean, and SD profiles with the sampling correction also indicates that
any existing bias in the ERA-Interim reference data set is nearly constant
over these spatial and temporal scales.
These results show that using the double-differencing method to reduce
spatial and temporal sampling errors in RO–RS comparisons allows for many
more RO–RS pairs to be included in the comparison (more than 35 000 for the
15∘ circle with 24 h time window compared to approximately
250 for the 6∘ circle and 1 h time window), as
illustrated Fig. a, provided that the bias of the reference
data set does not vary significantly over the spatial and temporal scales of
the comparison. Increasing the size of the spatial and temporal scales of the
comparison is thus a tradeoff between reducing the random error effects by
increasing sampling size and possibly increasing the errors by allowing a
greater effect of varying biases in the reference data set.
Author contributions
All co-authors contributed to developing the ideas and methodologies of this project.
SG conducted the majority of the coding, data retrieval, computations, and analysis, with TR contributing to coding and
data retrieval. SG prepared the manuscript with contributions from both co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Eric DeWeaver (NSF) and Jack Kaye (NASA) for their
support of this research through NSF-NASA grant AGS-1522830. Sergey Sokolovskiy provided many useful comments and suggestions during this study.
The first author was supported in part by the Significant Opportunities in
Atmospheric Research and Science (SOARS) program, NSF grant AGS-1641177, and
by the Constellation Observing System for Meteorology, Ionosphere, and
Climate (COSMIC) program at UCAR, which is sponsored by the National Space
Office in Taiwan, NSF, NASA, NOAA, and the U.S. Air Force. Thanks to CDAAC
for the provided RO data sets, NOAA NCDC and GRUAN for the provided RS data
sets, and the ECMWF for the provided ERA-Interim data sets. The authors thank
Keith Maull for his suggestions during this study. The authors would like
to thank the three anonymous reviewers for their comments and constructive
suggestions which improved this paper.
Edited by: Marcos Portabella
Reviewed by: three anonymous referees
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