AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-6617-2018Radiometric correction of observations from microwave humidity soundersRadiometric correction of microwave observationsMoradiIsaacimoradi@umd.eduBeauchampJamesFerraroRalphESSIC, University of Maryland, College Park, Maryland, USASTAR, NOAA, College Park, Maryland, USANASA Global Modelling and Assimilation Office, Greenbelt, Maryland, USAIsaac Moradi (imoradi@umd.edu)17December201811126617662626July201827August201812November201826November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/6617/2018/amt-11-6617-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/6617/2018/amt-11-6617-2018.pdf
The Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity
Sounder (MHS) are total power microwave radiometers operating at frequencies
near the water vapor absorption line at 183 GHz. The measurements of these
instruments are crucial for deriving a variety of climate and hydrological
products such as water vapor, precipitation, and ice cloud parameters.
However, these measurements are subject to several errors that can be
classified into radiometric and geometric errors. The aim of this study is to
quantify and correct the radiometric errors in these observations through
intercalibration. Since the bias in the calibration of microwave instruments
changes with scene temperature, a two-point intercalibration correction
scheme was developed based on averages of measurements over the tropical
oceans and nighttime polar regions. The intercalibration coefficients were
calculated on a monthly basis using measurements averaged over each specified
region and each orbit, then interpolated to estimate the daily coefficients.
Since AMSU-B and MHS channels operate at different frequencies and
polarizations, the measurements from the two instruments were not
intercalibrated. Because of the negligible diurnal cycle of both temperature
and humidity fields over the tropical oceans, the satellites with the most
stable time series of brightness temperatures over the tropical oceans
(NOAA-17 for AMSU-B and NOAA-18 for MHS) were
selected as the reference
satellites and other similar instruments were intercalibrated with respect to
the reference instrument. The results show that channels 1, 3, 4, and 5 of
AMSU-B on board NOAA-16 and channels 1 and 4 of AMSU-B on board NOAA-15 show
a large drift over the period of operation. The MHS measurements from
instruments on board NOAA-18, NOAA-19, and MetOp-A are generally consistent
with each other. Because of the lack of reference measurements, radiometric
correction of microwave instruments remain a challenge, as the
intercalibration of these instruments largely depends on the stability of the
reference instrument.
Introduction
Measurements from microwave instruments on board space-borne platforms
operating near the water vapor absorption line at 183 GHz are one of
the main sources of observations for tropospheric water vapor, total
precipitable water vapor, and cloud ice water path . These
data are also increasingly assimilated into NWP models for the purpose of
improving weather forecasting or atmospheric reanalyses
. AMSU-B and MHS are two of the main microwave
humidity sounders that have been flying on NOAA and MetOp satellites since
1998. However, the measurements of these instruments are subject to several
errors that can be classified into radiometric and geometric. Geometric
errors are related to a shift in the Earth location of measurements and are
introduced by sources such as timing error, instrument mounting errors, and
errors in instrument modeling and geolocation algorithms
. investigated
the geolocation errors in these instruments using the difference between
ascending and descending observations along the coastlines and reported
several errors including more than 1 degree antenna pointing error in
AMSU-A on board NOAA-15, about 1 degree pointing error in AMSU-A2 on board
NOAA-18, as well as a timing error up to 500 ms in NOAA-17.
generally reported relatively good accuracy
for the geolocation of AMSU-B and MHS instruments. However, the radiometric
errors in these instruments have not yet been fully investigated or corrected
due to the lack of reference measurements.
Once the satellites are launched, it is very difficult to determine the cause
of the radiometric errors, but some of the factors that may contribute to
these errors include errors in the hot and cold calibration targets, antenna
emissivity, radio frequency interference (RFI), antenna pattern correction,
and non-linearity in the calibration
.
The radiometric accuracy of microwave measurements cannot be easily evaluated
because of the lack of reference measurements. One main feature of
radiometric errors is that the errors are normally scene dependent and change
with the scene brightness temperatures (Tb) and polarization. Over the years
some alternative methods have been developed to determine the relative
accuracy of microwave measurements, including validation using measurements
from similar instruments on board airborne platforms
e.g.,, comparison with simulations
conducted using a radiative transfer model and atmospheric profiles
, and intercomparison with respect to similar
instruments on board space-borne platforms
.
Although comparing observed and simulated brightness temperatures can to some extent reveal errors
in microwave satellite measurements, the application is very limited due to
the biases in NWP fields and radiosonde sensor biases, as well as errors in
the RT models and inputs provided to the RT models such as surface
emissivity. One of the methods that has been extensively used to validate the
radiometric accuracy of microwave measurements is intercalibration or
intercomparison of data from similar instruments operating on different
platforms. In this case, one of the instruments that is more stable in time
is chosen as the reference instrument and all other similar instruments are
intercalibrated with respect to the reference instrument. Although
intercalibration cannot be used for absolute validation of microwave
measurements, once the reference instrument is determined, other instruments
can be relatively validated with respect to the reference instrument.
Assuming that data from the reference instrument are stable and valid over
time, the intercalibration can serve as a reliable method to develop
homogenized data records from microwave measurements.
investigated the radiometric difference
between microwave radiometers in the Global Precipitation Measurement Mission
(GPM) constellation and reported about 2–3 K difference between most
instruments and GPM Microwave Imager (GMI). However, they reported a
7–11 K difference between GPM GMI and some of the SSMI channels on
board DMSP F19. used global simultaneous
nadir observations (SNOs) to intercalibrate microwave humidity sounders (MHS
and AMSU-B). Global SNOs normally become available due to orbital drift when
the equatorial crossing times of the polar-orbiting satellites become close.
Based on time–distance matchups, they suggested a collocation criteria of
5 km and 300 s for intercalibrating microwave sounders and reported the
instrument noise as the major factor affecting the inter-satellite
differences. However, it should be noted that global SNOs are only available
for a limited time frame and cannot be used to intercalibrate time series of
satellite measurements, as the inter-satellite differences are expected to
vary with time as shown in this paper. used
several techniques, including polar SNOs and differences against radiances
simulated using a RT model and reanalysis fields, for developing a
fundamental climate data records from the Special Sensor Microwave Imager
(SSM/I) radiances. They reported a good agreement between different
techniques with a bias of 0.5 K at the cold end and slightly larger bias at
the warm end. They reported a smaller intercalibration difference for recent
SSM/I instruments (F14 and F15 compared to F13) than for the older
instruments (F08, F10, and F11 compared to F13).
used double difference between brightness
temperatures simulated using a RT model and NWP fields and measurements from
several MW and IR instruments and concluded that the biases due to NWP models
or RT calculations are canceled out by double differences. However, it should
be noted that a bias in NWP fields with a diurnal cycle will not be canceled
out by double difference techniques as different satellites pass the same
regions at different times of the day. used
global ocean mean differences along with SNOs to intercalibrate radiances of
AMSU-A instruments on board NOAA-15 to NOAA-18 and MetOp-A. They reported
five different sources of bias for inter-satellite difference including
instrument temperature variability due to solar heating, inaccuracy in the
calibration non-linearity, and channel frequency shift.
used simulated radiances from synoptic
radiosondes and NWP models to investigate the calibration of SSMI/S lower
atmospheric sounding channels. They reported two major sources of bias,
including the emissivity of primary reflector and uncompensated solar heating
for the hot load of calibration. used the
Simplified General Perturbation No. 4 (SGP4) to predict SNOs among
polar-orbiting satellites. SNO is the most common technique used to
investigate the inter-satellite differences when the two satellites pass over
the same region at the same time. A 30-year-long fundamental climate data
record from the High-resolution Infrared Sounder channel 12 clear-sky radiances was produced by
. reported scan-dependent
biases, causing major differences among the instruments.
The purpose of this research was to quantify and correct the radiometric
errors in AMSU-B and MHS observations through intercalibration in order to
develop a homogenized data record that can be used for retrieving geophysical
variables such as rain rate and tropospheric humidity as well as NWP
reanalysis. The rest of this paper is organized as follows: Sect. 2
introduces the instruments, Sect. 3 describes the methodology, Sect. 4
reports the results, and Sect. 5 sums up the study.
Satellite instruments
AMSU-B and MHS are total power microwave radiometers with five channels
operating at frequencies ranging from 89 to 190 GHz. AMSU-B was on board
NOAA-15 to NOAA-17, but for NOAA-18 and MetOp-A, AMSU-B was
replaced by MHS. The primary goal of these instruments was to measure the
atmospheric water vapor profiles, but the measurements, especially from
89 GHz, can also provide information on surface temperature and emissivity (in
conjunction with AMSU-A channels) and detect clouds and precipitation. Both
instruments have five channels, three of which are centered around the water
vapor absorption line at 183 GHz. AMSU-B channels 1–5 operate at 89.0, 150.0,
183.3 ± 1.0, 183.3 ± 3.0, and 183.3 ± 7.0 GHz, and MHS channels 1–5 operate
at 89.0, 157.0, 183.3 ± 1.0, 183.3 ± 3.0, and 190.3 GHz.
The combination of these channels can be used to derive a wide range of
atmospheric and hydrological parameters.
AMSU-B channels are all vertically polarized at nadir
, but MHS channels 3 and 4 are horizontally
and the rest are vertically polarized at nadir . The beam
width of AMSU-B is 1.1∘ but that of MHS is 10/9∘. Both
instruments are continuous scanners meaning that the integration is performed
while the scanner is moving; therefore the effective field of view (FOV) is
larger than instantaneous FOV. The instruments take 8/3 s to complete
one full scan which includes Earth measurements, as well as scanning hot and
cold loads. Spatial resolution at nadir is nominally 16 km and the antenna
provides a cross-track scan, scanning ±48.95∘ from nadir
with a total of 90 Earth FOVs per scan line.
We used level-1b satellite radiances in this study. The calibration
coefficients are included in level-1b data but the coefficients have not been
applied to the measurements (counts). In addition to the routine calibration
performed by NOAA, which includes converting satellite measurements from
counts to radiances or brightness temperatures using a linear calibration
equation, we also applied several post-calibration corrections including RFI
and Antenna Pattern Correction (APC). This information is not provided in
level-1b data. The RFI corrections are provided in NOAA KLM Users' Guide
. The antenna pattern correction for AMSU-B on board NOAA-15
to NOAA-17 is discussed in and the MHS
antenna pattern correction was extracted from the ATOVS and AVHRR
Pre-processing Package (AAPP) available at
https://www.nwpsaf.eu/site/software/aapp (last access: 1 June 2018).
Intercalibration method
The most common method for the intercalibration of satellite measurements is
to directly compare coincident observations of similar channels on the
reference and target instruments. In addition to being measured at the same
time and location, these coincident observations should also be measured
using the same geometry, especially in terms of the Earth incidence angle.
These coincident observations are often limited to (near) nadir field of
views and are known as simultaneous nadir observations (SNOs). In the case of
intercalibrating instruments on board polar-orbiting satellites such as NOAA
and MetOp, the SNOs normally occur near the polar region. The differences
between reference and target satellites are normally scene dependent;
therefore the coincident observations are required to cover a wide range of
brightness temperatures. The biggest limitation for finding global SNOs is
that polar-orbiting satellites overpass the same location at different local
times. The coincident time requirement for SNOs is because of the diurnal
cycle of environmental variables such as temperature, water vapor, clouds, and
other parameters that affect the satellite radiances. The time requirement
can be neglected over regions where the diurnal cycle is negligible. There are
regions where the diurnal cycle is mainly introduced by random processes and
is canceled out after averaging. For example,
reported a negligible diurnal cycle for relative humidity in all layers of
the troposphere over the tropical oceans but a somewhat significant diurnal
cycle over the tropical lands. shows that
in the tropical region, the impact of a 1 h difference in overpass times on
the differences between collocated observations is less than 0.5 K.
During winter in polar regions, because of the lack of direct heating
from the sun, the diurnal cycle of temperature is mainly affected by the
advection of air from large-scale circulations
. Although this phenomenon can cause
significant change in the lower-level air temperatures, it does not have a
diurnal cycle .
Therefore, we employed area-averaged brightness temperatures from the
tropical oceans (tropical band expanding from 30∘ S to 30∘ N) as one
intercalibration point and also area-averaged brightness temperatures from
Antarctica (<75∘ S) and the Arctic (>75∘ N) as the second point of
calibration. There is a small diurnal cycle of temperature and humidity over
convective regions of the tropical band; therefore we used a cloud filter
which is based on the difference between brightness temperatures from
different channels to filter out cloud-contaminated observations; see Sect. . Besides, since in the tropical region the diurnal
cycles over land can be significant, we only used the area-averaged data over
ocean. Since the polar regions are covered by ice and
snow during winter and the surface cover does not change significantly over a short period, we
did not apply any surface filter to the polar region averages. Additionally,
convective clouds are not common for the polar regions during winter;
therefore applying the cloud filter does not have any impact on the results
but removes a lot of observations that are not necessarily cloudy. The
channels that we used for cloud filtering are significantly affected by the
surface in polar regions and therefore the difference between those channels
does not necessarily reflect the cloud contamination. The intercalibration
method can be summarized as follows:
calculate the area-averaged Tbs over clear-sky tropical oceans and polar nights separately for each instrument and each
orbit,
determine the reference instrument by analyzing the time series of tropical averages as the time series is expected to be stable over
time,
determine the linear relation between area-averaged Tbs for reference and target instruments in a monthly
basis,
interpolate the regression coefficients using cubic splines to daily values,
correct the observations from target instrument using the intercalibration
coefficients.
Results and discussionCloud filter
Clouds are expected to have a diurnal cycle, especially over the convective
regions of tropics; therefore it is required to eliminate convective regions
from the intercalibration process. Cloud-contaminated observations were
filtered using a channel difference as discussed in previous studies
e.g.,. The idea is that,
because of the lapse-rate in atmospheric temperature, the channels peaking
lower in the troposphere have higher brightness temperature than the channels
peaking higher. Therefore, in clear-sky conditions the Tbs of lower channels
are warmer than the Tbs of channels peaking higher in the atmosphere. In the
case of clouds, the relation is changed as the channels peaking lower are
normally more affected by clouds than the channels peaking higher in the
atmosphere. Therefore, the channel differences can be used as a filter to
remove cloud-contaminated observations.
It was found that, because of the dry atmosphere in the polar region, the
brightness temperatures from channels used to define the cloud filter become
sensitive to the surface and the difference between them is not necessarily a
function of the cloud optical thickness any more. Additionally, microwave
observations are sensitive to deep convective clouds, which are not normally
present in the polar region. Therefore, we only applied the cloud filter to
observations from the tropical region. Although any combination of the
differences between channels 3, 4, and 5 can be used for the cloud filter, we
used the difference between channels 3 and 4 as explained in
.
Figure shows a time series of the difference of the
differences (Δ) (known as double difference) between clear-sky and
all-sky AMSU-B measurements on board NOAA-15 and NOAA-17,
Δ=N15all-sky-N17all-sky-N15clear-N17clear. These double differences show the impact
of clouds on the inter-satellite differences. As shown, Channel 1 operating at
89 GHz is the most sensitive to cloud screening because its
Jacobians peak in lower troposphere near the surface, while other channels,
in the moist conditions of the tropical region, peak in the middle and upper
troposphere and are less sensitive to clouds.
Differences between all-sky N15 minus N17 and clear-sky N15 minus
N17 (channels 1–5 from a to e). The values
are differences between all-sky NOAA-15 and NOAA-17 measurements minus the
differences between the two satellites in clear-sky conditions. The blue
lines show the weekly-moving averages.
Diurnal cycle effect
The effect of land and ocean on the intercalibration, which is due to a
stronger diurnal cycle over land, especially for the near surface-peaking
channels, was investigated by separating land and ocean brightness
temperatures over the tropical region, then calculating the intercalibration
coefficients. Similarly to the impact of clouds, we employed double differences
to evaluate the impact of a larger diurnal cycle over land on the
inter-satellite differences. In this case, the double difference is calculated
as the difference of the differences between land and ocean brightness
temperatures of AMSU-B on board NOAA-15 vs. NOAA-17. If we indicate
reference (NOAA-17) and target (NOAA-15) instruments using r and t
indices and land and ocean using L and O, then the double difference
is calculated as (TbtL-TbrL)-(TbtO-TbrO).
Figure shows an example of double differences between
collocated brightness temperatures of NOAA-17 (reference satellite) and
NOAA-15 (target satellite) over land and ocean. As expected, the surface
channels are more sensitive to the diurnal cycle of Tb over land and a small
trend after 2005 is observed that can be explained by the orbital drift of
both satellites. The double difference is maximum around 2005 when the NOAA-15
ascending (descending) overpass was around 18:00 LT (06:00 LT) and NOAA-17
ascending (descending) overpass time was around 22:30 LT (10:30 LT).
Therefore, the intercalibration was limited to tropical oceans to avoid the
effect of a diurnal cycle. Since during polar winters, that region is normally
covered by ice and snow, we averaged all the data over polar regions and no
land–ocean mask was applied. All the experiments for this section were
conducted using clear-sky data.
Difference between N17 minus N15 over land and N17 minus N15 over
ocean in the tropical region. The blue lines show the weekly-moving averages.
(a)–(e) are channels 1–5.
Polarization difference
Although AMSU-B and MHS are two similar instruments, there are several
differences in terms of polarization and frequency of some of their channels.
Both instruments have single polarization at nadir. All AMSU-B channels and
channels 1, 2 and 5 of MHS are vertically polarized but channels 3 and 4 of
MHS are horizontally polarized at nadir . The vertical and
horizontal components of the polarized radiation are the same over ocean at
the nadir location, but the polarization changes as the antenna moves toward
the edge of the swath. Therefore, the inter-satellite differences at nadir
should not significantly depend on the channels' polarization, but as the
antenna rotates the polarization becomes mixed and introduces differences.
Other factors that may impact off-nadir differences include the scan-angle
dependent bias as well as change in the height of the weighting functions.
Figure shows the inter-satellite differences for NOAA-17
AMSU-B and NOAA-18 MHS vs. FOVs averaged over tropical oceans for the
entire period. The FOV numbers start from the left side of the scan (FOV1),
so that the nadir view is FOV45 and the most right view is FOV90. Note that
the NOAA-18 overpass time is around 13:00 LT but the NOAA-17 overpass time is around
22:00 LT. As shown in Fig. , the differences between the
two instruments significantly change with FOV, especially for Channel 1.
Figure shows the time series of the differences between
the two instruments. As shown in Fig. , the differences
exist for the entire period, and other than some small variations, do not vary
with time. Figure shows the difference between the two
instruments over tropical land. If the differences were due to different
overpass times, then the differences between the two instruments should be
larger over land. However, not only are the differences generally smaller over
land but also they do not depend on the FOV. Since the ocean is a polarizer
in MW frequencies but the land generally is not a polarizer, the difference
between Figs. and particularly
highlights the effect of polarization on the differences between the two
instruments over tropical oceans. Note that this exercise is not able to rule
out other factors that may affect the inter-satellite differences. One
possible explanation is that the weighting functions peak higher as the field
of view moves from nadir to the edge of the scan so that some of the FOVs
peak high enough in the atmosphere to become insensitive to the surface
conditions.
Effect of polarization on the difference between NOAA-17 AMSU-B and
NOAA-18 MHS observations averaged over tropical ocean for different FOVs.
Effect of polarization on the difference between NOAA-17 AMSU-B and
NOAA-18 MHS observations for different FOVs over tropical ocean. FOV numbers
are printed on the left side of each subplot. Channels 4 and 5 are not
included because they were very similar to Channel 3.
Effect of polarization on the difference between NOAA-17 AMSU-B and
NOAA-18 MHS observations for different FOVs over tropical land. FOV numbers
are printed on the left side of each subplot.
Reference instrument
As stated before, due to the lack of reference measurements, one of the
instruments which is stable over time is chosen as the reference instrument
and the other instruments (target instruments) are calibrated with respect to
it. Determining the reference instrument is likely to be the biggest
challenge in conducting intercalibration. All other instruments will be
corrected with respect to the reference instrument; therefore selecting a
biased instrument as the reference instrument means that the intercalibrated
measurements will suffer from an even larger bias than the original
measurements. Because of the lack of reference measurements, it is almost
impossible to select an instrument as a reference without any uncertainty.
One important feature of the intercalibrated measurements is that they are
expected to be representative of the climate; thus they may be used for
studies related to climate change and variability. As stated before, because
of negligible diurnal cycle over the tropical oceans, the orbital drift
should not introduce a significant trend in the observations. Thus
variability in the measurements averaged over the tropical oceans is expected
to be similar to that reported for geophysical variables affecting the
brightness temperatures. For instance, variability in the measurements of
surface sensitive channels is expected to be very close to the change in
surface temperature as the brightness temperatures for those channels are
mostly affected by the surface temperature and emissivity. Since the
emissivity is not expected to change with time, the variability in the
brightness temperatures is expected to follow the change in surface
temperature. Figure shows the averages over
tropical oceans for different satellites and all five AMSU-B/MHS channels. As
mentioned before, we decided not to intercalibrate AMSU-B with MHS
measurements; therefore we were required to select one satellite as the
reference for the AMSU-B instruments and one for the MHS instruments. NOAA-16
channels 3–5 show a large drift with time; therefore NOAA-16 was excluded.
NOAA-15 experienced some calibration issues, especially with regard to RFI;
thus we decided to use NOAA-17 AMSU-B as the reference instrument for the
AMSU-B instruments. There is a good consistency between NOAA-17 and NOAA-15
Channel 1, but a systematic difference between AMSU-B and MHS observations
for Channel 1. Additionally, there is a systematic difference between NOAA-17
Channel 4 and MHS observations for the same channel. Although NOAA-15 matches
MHS data during that time frame, that is basically caused by a positive and then a reverse trend in the
NOAA-15 observations. The MHS instruments are generally consistent with each
other, but we choose NOAA-18 for the reference satellite because the data are
available for a longer time period.
Analysis of the time series of observations averaged over the tropical
oceans for selecting the reference satellites. NOAA-19 and MetOp-A (MOA) are
intercalibrated with reference to MHS on board NOAA-18.
Determining the empirical calibration coefficients using a linear
relation between the measurements from the target and reference instruments.
(a)–(e) are channels 1–5 from left to right. The solid black line shows the regression line. Different
colors show the data for Antarctic (Ant), Arctic (Arc), and tropical (Trop)
regions.
Interpolating monthly coefficients using cubic splines (NOAA-15 vs.
NOAA17).
Intercalibration coefficients
The primary measurement of the microwave instruments is digital counts which
are converted through a two-point calibration into radiances or brightness
temperatures. The calibration equation is based on the relation between
digital counts and measured radiances for a radiometrically cold reference
(normally when the instrument measures the background space radiance) and a
hot (warm) reference (normally a blackbody on board the satellite). The
radiometric error can change with scene temperature if the error is not
stable from one load to the other one due to, for instance, non-linearity in
the calibration. Because of this scene dependency, it is required to evaluate
the inter-satellite differences for a wide range of brightness temperatures.
This is one of the main reasons that SNOs are not sufficient for the
intercalibration of microwave instruments, as SNOs normally occur at high
latitudes and only cover a small range of Tbs. In this study, we utilized the
averages of brightness temperatures over the tropical region at one end of
the measurements and the polar averages at the other end. Note that either of these
can form the lowest or highest values depending on the channel as well as the
surface type. As stated earlier, we only used the brightness temperatures
over ocean to calculate the tropical averages.
Intercalibrated time series of AMSU-B and MHS observations. NOAA-19
and MetOp-A (MOA) are intercalibrated with reference to MHS on board NOAA-18.
Figure shows an example of the relation between Tbs
from reference and target instruments. All the coefficients are derived using
a linear relation as we did not have any evidence of the non-linearity
between the differences in target and reference instruments. The calibration
coefficients were calculated as Tbt=a⋅Tbr+b, where Tbt and
Tbr refer to the measurements from the target and
reference instruments. The intercalibration coefficients were calculated on a
monthly basis, then were interpolated to daily values using cubic-spline
functions. This helps to reduce the noise in the coefficients. Therefore, the
intercalibration process can be explained as follows: (1) data are averaged
over the clear-sky tropical oceans and polar nights,
(2) 1 month of data from both
regions are used to make the scatterplots between reference and target
satellites, (3) monthly intercalibration coefficients are calculated, then
interpolated to daily values, (4) the coefficients are applied to level-1b
data to calculate the intercalibrated brightness temperatures.
We did not find any advantage to use moving-window averages, i.e., collocate
1 month of data around the day of interest, then move the window to other
days. Figure shows an example of the monthly
intercalibration coefficients as well as interpolated values. We also found
that calculating the intercalibration coefficients on an annual basis is not
enough since there might be short-term changes in the data that cannot be
accounted for using annual coefficients. NOAA-15 was launched in 1998 but
NOAA-17 data are only available since 2002. Since extrapolation of the
coefficients is not recommended, we intercalibrated NOAA-15 data before 2002
using the coefficients calculated based on NOAA-15 and NOAA-17 2002 data. The
only problem with this method is that the pre-2002 trend in NOAA-15 data is
removed.
The results were evaluated using area-averaged values over the tropical
oceans. Figure shows the time series of
intercalibrated brightness temperatures. The time series for NOAA-17 and
NOAA-18 are only subtracted from their own mean values for the entire period.
Overall, the intercalibrated Tbs are consistent with each other within about
0.5 K. However, there are some periods where the differences are even larger
than 1.0 K. The difference between intercalibrated NOAA-15 and NOAA-17
observations is generally less than that for NOAA-16 and NOAA-17. For
instance, around 2009, NOAA-16 Channel 5 observations show a difference of up
to 2.0 K compared to NOAA-17 Channel 5 measurements. Given that the goal of
study was not to completely remove the differences between measurements from
different instruments but rather to remove possible biases in the
measurements, the consistency observed in Fig. is very satisfactory. In the 183 GHz
frequencies, a 1 degree kelvin change in brightness temperature is roughly
equal to a 7 %–10 % change in relative humidity
;
therefore it is expected that the derived humidity products have an error
less than 10 %.
Conclusions and summary
Satellite observations from AMSU-B and MHS are used to retrieve global
climate and hydrological products such as water vapor, precipitation, and ice
cloud parameters. However, these observations are prone to errors and
uncertainties that can be classified into radiometric and geometric errors.
In the current study, we quantified and corrected the radiometric errors in
these observations for the period of 2000–2010. The AMSU-B observations
suffer from several instrument failure after 2010. Work is currently
under progress to correct some of the AMSU-B observations for the period
2010–2015. A unique characteristic of the radiometric error is that it
changes with the scene temperature. A common technique that is used for the
radiometric correction is intercalibration of observations measured by
similar instruments. A key parameter in intercalibrating satellite
observations is to find coincident observations or observations for the same
location and same time. Since finding such coincident observations is
challenging, we used daily averages of brightness temperatures over regions
with negligible diurnal variations. In this study, we used monthly averages
of measurements over the tropical oceans and nighttime polar regions to
perform the intercalibration. In this two-point scheme, the intercalibration
coefficients are calculated using monthly averages, then interpolated to the
daily values using a cubic spline. We selected AMSU-B on board NOAA-17 as the
reference instrument for all AMSU-B instruments and MHS on board NOAA-18 as
the reference for all MHS instruments. We did not intercalibrate AMSU-B and
MHS because of some differences in the frequency and polarization among the
two instruments. Most AMSU-B channels on board NOAA-16 and channels 1 and 4 of
AMSU-B on board NOAA-15 showed a large drift with time and were corrected
with respect to NOAA-17 data. Measurements from MHS instruments were very
consistent. Selecting a reference instrument is the most
challenging part of the intercalibration because of the lack of reference
observations. Selecting a biased reference instrument means that all the
intercalibrated measurements will be biased. Another challenge is the
intercalibration of cloud-contaminated observations. Due to a larger diurnal
variation for the clouds over the tropical regions, we only used clear-sky
observations to perform the intercalibrations. Neither the simultaneous nadir
observations nor the technique used in this study can be used for the
intercalibration of cloud-contaminated measurements because of the dynamic
nature of the clouds.
The satellite observations used in the study are available
free of charge from the NOAA Comprehensive Large Array-data Stewardship
System (CLASS) at https://www.class.noaa.gov/ (last access: 1 September 2018). The reprocessed observations are available from the
NOAA Climate Data Record Program .
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was supported by NOAA grant no. NA09NES4400006 (Cooperative
Institute for Climate and Satellites – CICS) at the University of Maryland,
Earth System Science Interdisciplinary Center (ESSIC). The views, opinions,
and findings contained in this report are those of the authors and should not
be construed as an official National Oceanic and Atmospheric Administration
or U.S. Government position, policy, or decision. Edited by: Murray Hamilton
Reviewed by: two anonymous referees
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