AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-2241-2016A microwave satellite water vapour column retrieval for polar winter conditionsPerroChristopherLesinsGlenDuckThomas J.CadedduMariaDalhousie University, Halifax, Nova Scotia, CanadaArgonne National Laboratory, Argonne, IL, USAChristopher Perro (christopher.perro@dal.ca)20May2016952241225210June201524September201530April20164May2016This 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/9/2241/2016/amt-9-2241-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/2241/2016/amt-9-2241-2016.pdf
A new microwave satellite water vapour retrieval for the polar winter
atmosphere is presented. The retrieval builds on the work of
and , employing auxiliary
information for atmospheric conditions and numerical optimization. It was
tested using simulated and actual measurements from the Microwave Humidity
Sounder (MHS) satellite instruments. Ground truth was provided by the G-band
vapour radiometer (GVR) at Barrow, Alaska. For water vapour columns less than
6kgm-2, comparisons between the retrieval and GVR result in a root
mean square (RMS) deviation of 0.39kgm-2 and a systematic
bias of 0.08kgm-2. These results are compared with RMS
deviations and biases at Barrow for the retrieval of ,
the AIRS and MIRS satellite data products, and the ERA-Interim, NCEP, JRA-55,
and ASR reanalyses. When applied to MHS measurements, the new retrieval
produces a smaller RMS deviation and bias than for the earlier retrieval and
satellite data products. The RMS deviations for the new retrieval were
comparable to those for the ERA-Interim, JRA-55, and ASR reanalyses; however,
the MHS retrievals have much finer horizontal resolution (15km at
nadir) and reveal more structure. The new retrieval can be used to obtain
pan-Arctic maps of water vapour columns of unprecedented quality. It may also
be applied to measurements from the Special Sensor Microwave/Temperature 2
(SSM/T2), Advanced Microwave Sounding Unit B (AMSU-B), Special Sensor
Microwave Imager/Sounder (SSMIS), Advanced Technology Microwave Sounder
(ATMS), and Chinese MicroWave Humidity Sounder (MWHS) instruments.
Introduction
The polar winter troposphere is very dry, with water vapour columns typically
near 3kgm-2. Climate change is expected
to increase absolute humidity and alter the polar radiative balance
with consequences for sea ice and global climate.
Accurately monitoring polar humidity variations is necessary, but is difficult
to do because of the small water vapour concentrations and the few
ground-based stations from which observations can be made. Infrared and
visible satellite measurements have better spatial coverage but are
challenged by scattering and absorption from clouds and the lack of solar
radiation during polar winter.
AMSU-B and MHS instrument specifications including frequencies,
noise equivalent differential temperature, and nadir polarization
orientations . Entries like 183.31±1GHz
imply that two frequency bands at 182.31 and 184.31GHz are
combined. Vertical and horizontal polarization refers to cross-track and
along-track polarization respectively.
Microwave satellite measurements overcome many of the difficulties.
Microwaves have a strong water vapour absorption line at 183 GHz that
is useful for dry conditions, with emissions that can be observed during any
part of the diurnal cycle. Microwaves are less affected by scattering and
absorption from clouds, allowing for water vapour measurements in most
weather conditions . Microwave instruments aboard a series
of polar-orbiting satellites since 1991 (F11 to 19, NOAA-15 to 19,
MetOP-A and B, FY3-A to C, and NPP) already provide a substantial data set
for water vapour studies. Planned missions include JPSS-1 and 2, MetOP-C,
MetOP-SG, and DMSP-S20.
This paper introduces a modified technique for retrieving water vapour
columns from microwave satellite measurements in polar winter conditions that
are characterized by low optical depths. The retrieval uses the microwave
signal formulation given by ; hereafter M98. M98's
retrieval technique involves several approximations that were somewhat
relaxed in a variation by ; hereafter MH08. Our
retrieval (hereafter referred to as PLDC16) employs fewer approximations but
requires auxiliary data for the atmospheric conditions. The results are more
accurate, but come at the cost of increased computational complexity.
MH08 and PLDC16 are tested against simulated signals in order to determine
the impacts of different sources of error. Their performance is also assessed
using Microwave Humidity Sounder (MHS) measurements from MetOP-A and NOAA-18
in comparison with surface based G-band vapour radiometer (GVR) measurements
at Barrow, Alaska (71.3∘N, 156.8∘W). MHS
measurements were chosen because they provide the longest period of overlap
with the GVR, with continuous water vapour column measurements since 2005
. The GVR measures brightness temperatures at four
double-sideband frequencies near the 183GHz water vapour
absorption line. The water vapour column is estimated to have 5 % error
for values between 2 and 7 kgm-2.
Comparisons for water vapour columns less than 8 kgm-2 between
the GVR and Vaisala radiosondes launched from the ARM Climate Research
Facility in Barrow, Alaska have an RMS deviation of 0.23 kgm-2.
The continuous measurements, relatively low uncertainties, and availability
of complementary measurements (most notably a micro pulse lidar) make the GVR
an ideal instrument against which to test satellite retrievals.
Similar to the GVR, MHS measures microwave radiances at five frequencies near
the 183GHz water vapour absorption line. MHS is the successor to
AMSU-B, the target instrument for MH08's analysis. The specifications for
both instruments are summarized in Table . The instruments
have slightly different frequencies and there is decreased noise for MHS.
The structure of this paper is as follows. Section
introduces M98's microwave signal formulation and the three techniques (M98,
MH08, and PLDC16) for retrieving water vapour columns.
Section describes how different water vapour
column regimes are treated. The application of PLDC16 and MH08 to simulated
signals is examined in Sect. .
Section follows by comparing the PLDC16 MHS retrieval
with the GVR, other satellite data products (AIRS and MIRS) and atmospheric
reanalysis data sets (ERA-Interim, NCEP, ASR, and JRA-55). The results are
discussed in Sect. .
Satellite microwave signal formulation and retrieval techniques
The brightness temperature Ti measured at frequency νi by channel i
of a satellite-borne microwave instrument is parameterized by
Ti=mp(νi)Ts-(To-Tc)1-εie-2τisecθ,
where Ts is the skin temperature, To is the
surface air temperature, Tc is the cosmic background
temperature, εi is the surface emissivity,
τi≡τi(0,∞) is the total optical depth, θ is the
zenith viewing angle of the satellite, and mp is a factor incorporating
the vertical structure of the atmosphere.
Equation () is a combined form of the
upwelling and downwelling brightness temperature equations that includes
a contribution from cosmic microwave background radiation. Microwave
contributions are assumed to be identical in both polarizations, and the
surface is assumed to be a perfect specular reflector.
The common idea of M98 and subsequent retrieval schemes is to combine
brightness temperatures T1, T2, and T3 from three channels with
τ1<τ2<τ3 to obtain
ΔT12-b12ΔT23-b23=r1r2e-2τ1secθ-(r2/r1)e-2τ2secθe-2τ2secθ-(r3/r2)e-2τ3secθ,
where ΔT12=T1-T2 and ΔT23=T2-T3 are brightness
temperature differences, and r1, r2, and r3 are surface reflectances
with ri=1-εi. The factors b12 and b23 are bias
coefficients given by
bij=∫0∞e-τj(z,∞)secθ-e-τi(z,∞)secθdT(z)dzdz+To-Ts(εje-τjsecθ-εie-τisecθ)+rje-2τjsecθ∫0∞1-eτj(z,∞)secθdT(z)dzdz-rie-2τisecθ∫0∞1-eτi(z,∞)secθdT(z)dzdz,
where τi(z,∞) is the optical depth above altitude z.
The three retrieval techniques (M98, MH08, and PLDC16) used to solve for the
water vapour column are described next. The retrieval techniques are subject
to water vapour column regimes with different frequencies and reflectance
choices, and these are discussed in Sect. .
M98
The M98 retrieval simplifies the formulation of
Eqs. () and
(). It is assumed that the frequencies for each
measurement are similar enough that r1=r2=r3, and all but the first term
in Eq. () is neglected. It is also assumed that
water vapour is the only significant absorber in the frequency range of
interest and that the total optical depth depends linearly on the water
vapour column. This allows a series expansion of
Eq. () to yield
Wsecθ=C0+C1logΔT12-b12ΔT23-b23,
where W is the water vapour column, and C0 and C1 are coefficients
that combine integrated mass absorption coefficients. Notice that the
dependence on surface reflectance is eliminated.
M98 assumed constant coefficients b12, b23, C0, and C1, and
determined them using the 1-D radiative transfer model Microwave Model (MWMOD
) with radiosonde profile inputs. A separate
calibration is required for each frequency triplet ν1, ν2, ν3.
MH08
MH08 proposed a variation of the M98 retrieval for cases with water vapour
columns greater than 8kgm-2. Instead of assuming all surface
reflectances to be the same, they allow for the possibility that r1
differs from r2=r3. Following M98, a series expansion of
Eq. () results in
Wsecθ=C0+C1log[r2r1ΔT12-b12ΔT23-b23+C-C].
MH08 found C to be constant for the range of water vapour columns under
consideration. The coefficients b12, b23, C0, and C1 were
determined using the same approach as in M98 except with a viewing angle
dependency. A separate calibration is required for each frequency triplet
ν1, ν2, ν3. Aircraft measurements of sea ice emissivity were
used to establish a constant value for r2/r1.
PLDC16
Our approach is to employ Eqs. () and
(), but with fewer assumptions. Unlike M98 and
MH08, auxiliary information for the atmospheric conditions is required. This
information may be obtained from atmospheric reanalyses or other sources.
As a practical matter, the second term in Eq. () is
ignored. It is proportional to the difference between the skin and surface
air temperatures, and comparisons between atmospheric reanalysis products for
this factor show considerable disagreement. We also take r=ri=rj in the
third and fourth terms of Eq. (), leaving
bij≈∫0∞e-τj(z,∞)secθ-e-τi(z,∞)secθdT(z)dzdz+r[e-2τjsecθ∫0∞1-eτj(z,∞)secθdT(z)dzdz-e-2τisecθ∫0∞1-eτi(z,∞)secθdT(z)dzdz].
A constant value for r is assumed, and auxiliary information is used to
determine dT(z)/dz. The sensitivity of our retrieval to
these approximations is discussed in Sect. .
Next, suppose that the true optical depth profile τi(z,∞) is
related to a trial optical depth profile τi,n(z,∞) by
τi(z,∞)=xnτi,n,
where xn is a scaling factor and n={0,1,2,3,…} is the
trial number. The trial optical depth profile is given by
τi,n(z,∞)=∫z∞ki(p(z),T(z))wn(z)dz+τio(z,∞),
where p(z) and T(z) are pressure and temperature profiles, respectively,
wn is the trial water vapour mass density profile, ki is the mass
absorption coefficient, and τio(z,∞) is the optical
depth profile for other constituents (most notably O2 for the
89GHz channel). We determine τi,n(z,∞) for each trial
using the RTTOV 1-D radiative transfer model .
Pressure and temperature profiles are taken from the auxiliary information.
The calculation begins with a trial water vapour profile w0(z) taken from
the auxiliary estimate. The scaling factor xn is the only unknown
variable. It is determined for each trial by solving
Eqs. (),
() and () with a numerical
nonlinear optimizer. Trial water vapour profiles for iterations n>0 are
determined using
wn+1(z)=xnwn(z).
Iterating gradually re-balances the contributions in
Eq. () between water vapour and other atmospheric
constituents.
Having obtained a scaling factor, the water vapour column for iteration n+1
is given by
Wn+1=xn∫0∞wn(z)dz.
Note that the final result depends on the shape of the auxiliary water vapour
profile but not on its column amount.
PLDC16 retrieval flow chart. The retrieval starts with auxiliary
temperature, pressure, and water vapour profiles as input to a 1-D radiative
transfer model. Optical depth profiles are produced for each channel. These
are used together with satellite brightness temperatures and
Eqs. (),
() and () to retrieve the
scaling factor xn for each trial n. xn is used to scale the trial
water vapour profile through Eq. () and produce a water
vapour column using Eq. (). If the change ΔW in the water
vapour column between trials is greater than the threshold then the scaled
water vapour column is used in the next trial. The process is repeated until
convergence is achieved.
Unlike M98 or MH08 there is no need to perform a separate frequency
calibration. We stop iterating when the change in the water vapour column is
less than 0.1% between iterations. The number of iterations
varies for each measurement, and a maximum of 20 iterations is applied.
Figure summarizes the PLDC16 retrieval process.
Although it is not inherently required by the formulation above, for the
remainder of this paper we shall assume that the influence of liquid clouds
and ice crystals on the retrieval is negligible. The mass absorption
coefficient for liquid water in particular is almost constant across the
frequencies of interest . The impact of this assumption is
explored in Sect. .
MHS frequencies for the low, mid, and extended regimes for the
retrievals of water vapour column with typical water vapour column (W)
ranges. The frequencies ν1, ν2 and ν3 in each regime are
ordered so that τ1<τ2<τ3.
RegimeMHS frequencies (GHz) W rangeν1ν2ν3(kgm-2)Low190.311183.311±3183.311±10–2.5Mid157190.311183.311±31.5–9Extended89157190.3118–15Regime selection
Three sets of frequencies are used for MHS retrievals, giving rise to the
“low”, “mid”, and “extended” regimes, as summarized in
Table . These correspond to measurements at highly,
moderately, and weakly absorbed frequencies. M98 applies to only the low and
mid regimes while MH08 and PLDC16 apply to all three. The retrievals use
different criteria for choosing between regimes.
Brightness temperatures typically increase with increasing water vapour
column, but decrease for larger columns as the weighting function peaks at
higher (and therefore colder) altitudes. M98 therefore switches from the low
to mid regime when ΔT12>0 or ΔT23>0. MH08 switches to a
higher regime if ΔT12-b12>0 or ΔT23-b23>0.
A difficulty with the above approach is that brightness temperatures are
strongly affected by temperature profile structure, and in particular by
surface temperature inversions that are ubiquitous during polar winter (e.g.
). This causes regime selection artifacts,
as will be seen in Sect. .
We take a different approach. The slant water vapour column is determined
from auxiliary information, with the slant given by the instrument's viewing
angle. The low regime is used for slant water vapour columns between 0 and
2.5kgm-2, the mid regime is used from 1.5 to
9kgm-2, and the extended regime is used above
8kgm-2. The boundaries of the regimes were chosen by
comparing multiple GVR and PLDC16 water vapour columns. When a regime becomes
too moist for its strongest absorbing frequency, the retrieval shows
a decrease in sensitivity with increasing water vapour. By comparing the RMS
deviation and bias for adjacent regimes the optimal regime for a particular
range of water vapour column was chosen. Weighted averages are used where
regimes overlap in order to smooth the transition. Measurements near the
lower boundary of a regime sometimes do not have a solution, and in this case
the nearest regime in terms of the slant water vapour column is used.
MH08 retrievals in the low and mid regimes assume r1=r2=r3, and as such
it is equivalent to M98 in those regimes. For the extended regime, the
reflectance r1 is taken to be different from r2=r3 because of the
separation in frequencies. MH08 found a ratio r2/r1=1.22 from the
Surface Emissivities in Polar Regions Polar Experiment (SEPOR/POLEX) aircraft
campaign measurements. It is important to note that this value is fixed in
their retrieval because it is used in the determination of the constants
C0 and C1 in Eq. ().
For the PLDC16 retrieval, we assume r1=r2=r3 in the low regime, r1
different from r2=r3 in the mid regime, and all three reflectances
different in the extended regime. Because there are no pre-determined
coefficients in our retrieval, we are able to set the reflectance ratios as
required. Different assumptions were made for the simulations and measurement
retrievals, as will be explained.
Retrieval performance with simulated measurements
To test the retrieval techniques, we used the RTTOV 1-D radiative transfer
model to simulate brightness temperature measurements, employing operational
radiosonde profiles from Barrow, Alaska as inputs. A total of 1490 profiles
between December and March for 2008 to 2014 were used. The maximum water
vapour column allowed was 15kgm-2.
All simulations assumed nadir satellite measurements, and the surface air and
skin temperatures were taken to be equal. The surface reflectance was set to
0.2 for all frequencies in both the simulations and retrievals. Simulations
at different viewing angles show insignificant differences in the retrieval
of the water vapour column.
RTTOV was used to provide cloud-free brightness temperatures for both the MHS
and AMSU-B instruments. We used AMSU-B simulations for MH08's retrieval given
that their retrieval coefficients are calibrated for that instrument. MHS
simulations were used for our retrieval.
The retrieval techniques were tested against three different cases, with
results given in Sects. –:
simulated signals with no detector noise and perfect auxiliary information;
simulated signals with detector noise and perfect auxiliary information;
simulated signals with detector noise and climatological auxiliary information.
In each case we compare the retrieved water vapour columns against the input
columns. The simulations are also used in Sect. to
evaluate the impacts of our assumptions, and in
Sect. to evaluate the possibility of applying the
MH08 retrieval to MHS measurements. All three cases assume perfect knowledge
of the surface reflectance.
Comparisons of mid regime retrievals (excluding overlap) from
simulated signals against the input water vapour columns for (a) PLDC16
and (b) MH08. The simulated signals are noiseless and perfect
auxiliary information is provided. The black line represents a perfect
retrieval.
Case 1
The intrinsic accuracy of each retrieval is tested by using noiseless
simulated signals and perfect auxiliary information.
Figure compares mid regime retrievals (2.5 to
8kgm-2, excluding overlap) to simulated water vapour columns.
RMS deviation and bias values are given in Table . The
PLDC16 retrieval has negligible RMS deviations and biases. This is expected
given the ideal conditions for the test, with non-zero values arising from
the small disagreements between RTTOV and our radiative transfer
parameterization. The greater scatter and bias values for MH08 are due to the
inherent error in that retrieval's constant coefficients. The reduction of
standard error by PLDC16 over MH08 is due entirely to the calculation of bias
coefficients. Iterations have an insignificant effect on the retrieval.
Root mean square deviation (RMSD) and bias (kgm-2) for
PLDC16 and MH08 retrievals from simulated signals for the low, mid and
extended regimes (excluding overlap). Results from three cases are provided.
Case 1 uses noiseless simulated brightness temperatures with perfect
auxiliary information. Case 2 uses simulated brightness temperatures with
Gaussian noise and perfect auxiliary information. Case 3 uses simulated
brightness temperatures with Gaussian noise and a climatological auxiliary
profile. Case 3 does not include a column for combined measurements because
regime selection requires better auxiliary information.
Figure shows results for the three combined
regimes. The MH08 retrieval shows significant bias at the boundary between
the low and mid regimes (2.5–3kgm-2). It can also be seen
that the mid regime extends up to approximately 10kgm-2,
which is where the extended regime should be used.
Table summarizes the low, mid, and extended regime
results for both retrievals. Similar to the mid regime, the standard
deviation for the PLDC16 low and extended regimes is significantly less than
for MH08. There is a positive bias in the extended regime of the PLDC16
retrieval, and this is due again to the small disagreement between RTTOV and
our parameterized radiative transfer.
Comparisons of combined regime retrievals from simulated signals
against the input water vapour columns for (a) PLDC16 and
(b) MH08. The simulated signals are noiseless and perfect auxiliary
information is provided. The black line represents a perfect retrieval.
Case 2
Gaussian-distributed noise with a standard deviation of 0.5K was
added to the simulated brightness temperatures for this second case. The
value was chosen to be consistent with the noise equivalent differential
temperature for the MHS instruments (see Table ). Perfect
auxiliary information was provided to the retrievals.
Figure compares the PLDC16 and MH08 mid regime
retrievals to the input water vapour column. The RMS deviations are increased
compared to case 1, but more so for PLDC16 (see
Table ). Nevertheless, the RMS deviation for MH08 is
78% greater than for PLDC16. The reduction of standard error by
PLDC16 over MH08 is due primarily to the calculation of bias coefficients. In
the extended regime, however, iterations account for 24% of the
overall correction.
Table summarizes the results for the low, mid, and
extended regimes. In each case the PLDC16 retrieval has a smaller standard
deviation and bias. PLDC16's RMS deviation is significantly lower for
combined regimes, although this is partly due to the improved regime
selection of PLDC16. The results indicate that the PLDC16 retrieval is more
accurate if there is perfect auxiliary information.
Case 3
In the third case climatological auxiliary information is used, which
represents severely degraded knowledge of the atmospheric conditions. The
climatological water vapour and temperature profiles were obtained by
averaging the profiles from all 1490 measurements considered in this study.
The noise and MH08 retrievals are the same as for Case 2.
Comparisons of mid regime retrievals (excluding overlap) from
simulated signals against the input water vapour columns for (a)
PLDC16 and (b) MH08. The simulated brightness temperatures include
Gaussian noise with a 0.5K standard deviation, and perfect
auxiliary information is provided. The black line represents a perfect
retrieval.
Comparison of the mid regime retrieval (excluding overlap) from
simulated signals against the input water vapour columns for PLDC16. The
simulated brightness temperatures include Gaussian noise with
a 0.5K standard deviation, and climatological auxiliary
information is used. The black line represents a perfect retrieval.
Figure compares the PLDC16 retrieval to the input water
vapour column for the mid regime. The RMS deviation is
0.21kgm-2 larger than for Case 2, and
0.03kgm-2 larger than for MH08. The low regime results (not
shown) are nearly the same. For the extended regime (not shown), PLDC16
performs slightly better in terms of RMS deviation, but has significantly
larger bias. The results show that when the auxiliary information is severely
degraded, the PLDC16 retrieval can be expected to perform comparably to MH08
for the low and mid regimes.
Discussion
Three test cases were given to theoretically evaluate the PLDC16 and MH08
retrievals. Case 1 tests their intrinsic accuracy for noiseless brightness
temperatures and perfect auxiliary information. Both retrievals performed as
expected, with the PLDC16 retrieval faithfully reproducing the model water
vapour data. Case 2 included randomized noise as found in the MHS
instruments. Given perfect auxiliary information, the PLDC16 retrieval more
accurately reproduced the model water vapour. Case 3 employed climatological
auxiliary information, which represents a worst-case scenario for PLDC16. The
test yielded comparable errors for the two retrievals for the low and mid
regimes.
We expect that reanalysis data will always be available to provide auxiliary
information. As such, the most realistic retrieval comparison is given by
Case 2. Notwithstanding, there are uncertainties in reanalyses
, spatiotemporal variations in water vapour distribution
, and systematic uncertainties which are
difficult to treat quantitatively in simulations. Results from testing in
real-world conditions are given in Sect. .
Assessment of PLDC16 assumptions
Simulations may also be used to assess the impact of two approximations made
in the development of the PLDC16 retrieval.
The second term of Eq. (), which contains
the difference between the surface air and skin temperatures, was ignored.
A constant value for r=ri=rj must be assumed in
Eq. () and may be in error.
Case 1 simulations were performed so that we could completely isolate the
effects of each item.
To evaluate the impact of (i), we ran simulations with To-Ts=±5K and ±2K. Note that although
atmospheric reanalyses often disagree on To-Ts,
values up to 2K are typical for multi-year Arctic sea ice
. As such, the ±5K test represents an
extreme case.
Root mean square deviation (RMSD) and bias (kgm-2) for
MH08 retrievals from simulated signals for the low, mid and extended regimes
(excluding overlap). The AMSU-B results are the same as in Case 1 from
Table .
We found that inclusion of To-Ts in the
simulations caused a bias in the retrieved water vapour columns. The bias was
positive for To-Ts>0 and negative for
To-Ts<0. The bias varied for each regime in the
retrieval. The low regime bias for To-Ts=±5K ranged from 3 to 5% with increasing water vapour
column. Similarly the mid regime bias ranged from 3 to 7% and the
extended regime bias ranged from 3 to 4%. For the more typical
case with To-Ts=2K, we found a bias in
all regimes of less than 3%.
To assess the impact of (ii), we performed separate simulations using surface
reflectance values of 0.05 and 0.35, which represent extremes in the Arctic
, for all channels. Assuming r=0.12 in
Eq. () provides the best retrieval. We found
that a maximum random error of less than 3 % in the water column was
introduced. The error is largest for the low-humidity end of each regime.
Evaluation of MH08 as applied to MHS measurements
The MH08 retrieval was designed for application to AMSU-B measurements.
Section , however, applies the MH08 retrieval to MHS
measurements instead. The error due to the application of MH08 to MHS can be
assessed using the simulations from Case 1.
Table shows the results when MH08 is applied to simulated
MHS and AMSU-B brightness temperatures for each regime. In the low regime
both the RMS deviation and bias are small. The mid regime's bias effectively
changes sign and the RMS deviation increases by 6%. For the
extended regime the RMS deviation increases by 12%, whereas the
absolute bias increases by 193%. We conclude that the MH08
retrieval can be reasonably applied to MHS measurements for the low and mid
regimes.
The simulations do not account for the difference in polarization measured by
the two instruments. This has an unknown effect on the retrieved columns.
(a) PLDC16 retrieval of water vapour column from MHS brightness
temperatures compared to GVR retrievals at Barrow, Alaska. (b) The
corresponding root mean square deviations (RMSDs).
Measurements
This section examines PLDC16 water vapour columns retrieved from MHS
overpasses of Barrow, Alaska. The retrievals are compared with simultaneous
GVR measurements and a variety of other data sets. Swath data are used to
illustrate the spatial distribution of retrieved water vapour columns.
Water vapour column root mean square deviations (RMSDs) for various
data sets against GVR measurements for columns less than
6kgm-2. Values in brackets give the deviations and biases as
a fraction of the mean column amount.
Data set/retrieval Nadir resolutionSamplesRMSDBias (kgm-2)(km)(kgm-2)Reanalyses NCEP28026930.79 (29.9%)-0.04 (-1.6%)JRA-5514026940.39 (14.8%)-0.49 (-18.7%)ASR (≤2012)3040470.40 (15.6%)-0.18 (-6.9%)ERA-Interim8026940.42 (15.8%)-0.11 (-4.3%)Satellite AIRScombined45107741.03 (38.9%)-0.34 (-12.8%)infrared1.10 (41.6%)-0.22 (-8.3%)microwave1.05 (39.6%)0.11 (4.1%)MIRS (MHS DJFM 2013/4)1510020.69 (22.0%)-0.18 (-5.7%)MH08 (AMSU-B ≤2009)1542770.95 (39.2%)0.20 (8.1%)MH08 (MHS)1597390.71 (27.2%)0.23 (8.6%)PLDC16 (MHS)1597410.39 (14.9%)0.08 (3.2%)Assessment of water vapour column using GVR
A total of 11 333 MHS measurements from MetOP-A and NOAA-18 within
50km of Barrow, Alaska were obtained for the same time period as
in Sect. . We retrieved water vapour columns from these
data using PLDC16 with the ERA-Interim reanalysis providing auxiliary
information. ERA-Interim data have an 80 km resolution in latitude and
are provided four times per day.
For the reflectance ratio in the mid regime we chose r1/r2=1.12 from
SEPOR/POLEX data which is representative of ice and open water
. For the extended regime, we chose r1/r2=1.19 for
a mixture of coastal ice and snow-covered land using MACSI aircraft campaign
data . The second ratio was chosen to be r2/r3=1.12
as these are the same frequencies as r1/r2 from the mid regime.
Figure shows the results of the PLDC16 retrieval
compared to coincident GVR measurements in terms of water vapour column. The
GVR obtains four measurements per minute , and these are
averaged over 3 min to reduce noise.
For the full data set the RMS deviation is 0.72kgm-2 and the
bias is 0.02kgm-2. Note, however, that the error is larger at
water vapour columns greater than 6kgm-2. The RMS deviation
and bias for GVR-measured columns less than 6kgm-2 are
reduced to 0.39 and 0.08kgm-2, respectively. During the dry
Arctic winter the water vapour column is typically less than
6kgm-2.
Table provides a statistical comparison of various
water vapour data sets with the GVR, all for GVR-measured columns less than
6kgm-2. Reanalysis data sets include the European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA-Interim product ,
the National Centers for Environmental Prediction (NCEP;
) product, the Arctic System Reanalysis (ASR;
), and the Japanese 55 year Reanalysis
(JRA-55; ). Satellite products included were the
Atmospheric Infrared Sounder (AIRS) ), Microwave
Integrated Retrieval System (MIRS) , MH08 retrieval,
and PLDC16 retrieval. AIRS satellite data included three different products:
infrared measurements, microwave measurements (using AMSU-A), and combined
(infrared and microwave) measurements. MIRS is a data product that uses a one
dimensional variational inversion scheme (1D-VAR) in conjunction with
satellite measurements from MHS and AMSU sensors to determine atmospheric
quantities such as water vapour column. MH08 was applied to both the MHS and
AMSU-B instruments and PLDC16 was applied to MHS.
Table shows that the RMS deviation and bias for
PLDC16 MHS retrievals is smaller than for the other satellite data products.
The MH08 retrievals from MHS measurements also have smaller RMS deviations
than most of the other satellite data products. The comparison between the
PLDC16 and MH08 results is consistent with our conclusions from
Sect. .
The PLDC16 retrieval has similar RMS deviations to the ASR, ERA-Interim, and
JRA-55 reanalyses; NCEP, on the other hand, has RMS deviations that are twice
as large. The JRA-55 bias is significantly larger than every other reanalysis
and satellite product in this comparison. The biases are negative for each of
the reanalyses ranging from -0.04 to -0.49kgm-2. The
excellent performance of the reanalyses is not surprising given that they
incorporate data from radiosonde launches at Barrow. It is unclear how the
measurements and analyses compare away from the radiosonde anchor points, and
this is the subject of ongoing study.
Spatial distributions of water vapour column
As an example of how PLDC16 can be applied to swath data,
Fig. a shows the retrieval for the NOAA-18 MHS
measurement from 31 January 2008. The area chosen is centred over the
Chukchi Sea north of Alaska. The ERA-Interim reanalysis was used to provide
auxiliary information, and the reflectance ratios from
Sect. were used for simplicity. A detailed
analysis of the Arctic-wide, reflectance-dependent PLDC16 retrieval is left
for future work.
The plot shows individual footprints which vary in size due to the MHS's
viewing angle. For comparison, Fig. b shows the
equivalent ASR water vapour column for the same period. The ASR resolution is
30km in latitude. The comparison reveals PLDC16 applied to MHS
data has the finer intrinsic resolution. The ASR reanalysis tends to smooth
out fine details in the water vapour column.
The spatial distribution of water vapour column centred over the
Chukchi Sea north of Barrow, Alaska: (a) the PLDC16 retrieval from
NOAA-18 MHS brightness temperature measurements on 31 January 2008 at 23:09 UTC;
and (b) the Arctic System Reanalysis (ASR) product for
1 February 2008 at 00:00 UTC.
Uncertainties
The PLDC16 errors in the measurements of Sect. were
greater than were obtained for the simulations in
Sect. . This is not unexpected. Sources of error that
exist in measurements that are not simulated include:
differences in the scene viewed by GVR and MHS;
uncertainties in the reflectance ratio terms in the mid and extended regimes;
uncertainties in the auxiliary temperature profile ;
optically thick ice crystal and liquid water clouds;
removal of the second term in Eq. ();
changes with time in MHS noise;
polarization in the MHS measurements for different frequencies;
uncertainties in the GVR measurements;
the assumption of a purely specular reflecting surface.
Water vapour column root mean square deviations (RMSDs) for PLDC16
retrievals using different auxiliary data sets against GVR measurements for
columns less than 6kgm-2.
The error in (i) arises from the GVR being a stationary zenith-pointing
instrument, while the satellite-borne MHS has varying downward-pointing
viewing angles. The criteria for an overpass match in
Sect. allows the centre of the MHS footprint to be up
to 50km from Barrow, Alaska. Any geophysical variation in the
water vapour field can be expected to result in differences between the two
measurements. The viewing geometry error can potentially be larger than the
random error from either instrument. estimated the error
to vary from 0.66 to 1.05kgm-2 for the AMSU-B's largest
footprints. The impact of elevation differences at Barrow for the various
data products were tested using the Case 3 simulations. The terrain around
Barrow ranges from heights of 7 to 20m with small amounts of
vegetation. Our calculations indicate differences in water vapour columns of
less than 1 % owing to elevation variations.
The error in (ii) depends on the regime and frequencies selected. SEPOR/POLEX
data show a high correlation for the 157 and 183GHz surface
emissivity measurements over different sea ice types. The high correlation
corresponds to a small range of 0.96 to 1.13 for the reflectance ratio
(r1/r2 for mid, r2/r3 for extended) over different types of sea ice
and water surfaces. The 89 and 157GHz surface emissivity
measurements have very little correlation and produce a large range of 0.56
to 1.26 for the reflectance ratio (r1/r2 for extended regime). The
range of r1/r2 for the mid regime term translates to a variation in the
water vapour column of 25%. Similarly, in the extended regime,
the range of r2/r3 results in a variation of 2%, and the
large range of r1/r2 yields a variation of 143%.
For (iii), Table provides RMS deviations
from GVR measurements for the PLDC16 retrieval using different reanalyses for
the auxiliary information. Only GVR water vapour columns of less than
6kgm-2 were considered. The results from
Table show the RMS deviation varies only
slightly depending on the data set used to provide auxiliary information. The
ERA-Interim auxiliary information provides the smallest RMS deviation while
the NCEP auxiliary information gives the largest. Even the ERA-Interim
monthly mean profile provides a good retrieval, indicating that the monthly
mean provides a reasonable representation of the profile shape. Note,
however, that the daily ERA-Interim reanalysis was still used for the regime
selection.
For (iv), MHS measurements at Barrow were separated into cases with liquid
water clouds, ice clouds, and clear skies by using micro pulse lidar (MPL)
backscatter and depolarization data. The PLDC16 retrieval was applied to each
set of measurements and then compared to radiosonde measurements that came
within 1 h of the MHS measurements. Radiosonde measurements were used
because the GVR and MHS might observe similar effects given that they are
both microwave instruments. The liquid water and ice cloud cases had
increases in the RMS deviation of 0.06 and 0.05kgm-2,
respectively, when compared to the clear sky cases. The bias did not change
significantly between the three cases. This indicates that clouds do not
present a large source of error in the retrieval.
For (v), the removal of the second term in the retrieval equation typically
translates to a change of 3% in water vapour column (as discussed
in Sect. ). The error of the GVR measurements in (viii)
is ±5%. Other sources of error are difficult to quantify.
Conclusions
A new retrieval based on the microwave formulation developed by
was introduced. Simulations show that the new technique
reduces errors compared to earlier approaches when good auxiliary information
for the atmospheric conditions is used. In a comparison with ground-truth
measurements, the new PLDC16 retrieval provides more accurate water vapour
columns than other satellite measurements.
Maps of water vapour can be created that reveal fine structure that
reanalyses do not discern. Pan-Arctic water vapour charts can be created
twice per day using the combination of overpasses from NOAA-18 and
MetOP-A alone. Temporal resolution may be further improved by including
additional instruments. Given historical satellite data sets and planned
launches, microwave water vapour measurements may provide new insights into
changing Arctic conditions. Complications arising from varying microwave
surface emissivity were not treated in this paper, which only examines the
retrieval at a single location. A follow-on paper that applies the retrieval
in a pan-Arctic context will explore this important topic.
Acknowledgements
The Microwave Surface and Precipitation Products System (MSPPS) provided the
MHS brightness temperatures from NOAA and MetOP series satellites operated by
the National Oceanic and Atmospheric Administration (NOAA) and the European
Organisation for the Exploitation of Meteorological Satellites (EUMETSAT),
respectively. The Satellite Application Facility for Numerical Weather
Prediction (NWP SAF) provided the RTTOV radiative transfer model. The
Atmospheric Radiation Measurement (ARM) program supported the GVR. The
Goddard Earth Sciences Data and Information Services Center (GES DISC)
provided the AIRS data set. ECMWF provided ERA-Interim data set. The Japan
Meteorological Agency (JMA) provided the JRA-55 data set. NOAA/OAR/ESRL PSD
provided the NCEP reanalysis data. The Polar Meteorology Group from Ohio
State University provided the ASR data set.
Edited by: T. von Clarmann
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