AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-4927-2017Noise performance of microwave humidity sounders over their lifetimeHansImkeimke.hans@uni-hamburg.deBurgdorfMartinhttps://orcid.org/0000-0002-5854-4217JohnViju O.MittazJonathanBuehlerStefan A.https://orcid.org/0000-0001-6389-1160Meteorologisches Institut, Centrum für Erdsystem- und Nachhaltigkeitsforschung (CEN), Universität Hamburg, Bundesstrasse 55, 20146 Hamburg, GermanyEuropean Organisation for the Exploitation of Meteorological Satellites, Eumetsat Allee 1, 64295 Darmstadt, GermanyMet Office Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UKDepartment of Meteorology, University of Reading, Reading, RG6 6AL, UKNational Physical Laboratory, Teddington, TW11 0LW, UKImke Hans (imke.hans@uni-hamburg.de)18December201710124927494528July201724August201726October201712November2017This 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/10/4927/2017/amt-10-4927-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/4927/2017/amt-10-4927-2017.pdf
The microwave humidity sounders Special Sensor Microwave Water Vapor
Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave
Humidity Sounder (MHS) to date have been providing data records for 25 years.
So far, the data records lack uncertainty information essential for
constructing consistent long time data series. In this study, we assess the
quality of the recorded data with respect to the uncertainty caused by noise.
We calculate the noise on the raw calibration counts from the deep space
views (DSVs) of the instrument and the noise equivalent differential
temperature (NEΔT) as a measure for the radiometer sensitivity. For
this purpose, we use the Allan deviation that is not biased from an
underlying varying mean of the data and that has been suggested only recently
for application in atmospheric remote sensing. Moreover, we use the bias
function related to the Allan deviation to infer the underlying spectrum of
the noise. As examples, we investigate the noise spectrum in flight for some
instruments. For the assessment of the noise evolution in time, we provide a
descriptive and graphical overview of the calculated NEΔT over the
life span of each instrument and channel. This overview can serve as an
easily accessible information for users interested in the noise performance
of a specific instrument, channel and time. Within the time evolution of the
noise, we identify periods of instrumental degradation, which manifest
themselves in an increasing NEΔT, and periods of erratic behaviour,
which show sudden increases of NEΔT interrupting the overall smooth
evolution of the noise. From this assessment and subsequent exclusion of the
aforementioned periods, we present a chart showing available data records
with NEΔT< 1 K. Due to overlapping life spans of the
instruments, these reduced data records still cover without gaps the time
since 1994 and may therefore serve as a first step for constructing long time
series. Our method for count noise estimation, that has been used in this
study, will be used in the data processing to provide input values for the
uncertainty propagation in the generation of a new set of Fundamental Climate
Data Records (FCDRs) that are currently produced in the project “Fidelity and
Uncertainty in Climate data records from Earth Observation (FIDUCEO)”.
Introduction
In this study, we calculate and assess the noise evolution over the lifetime
of all individual instruments of the microwave sounders Special Sensor
Microwave Water Vapor Profiler (SSMT-2), Advanced Microwave Sounding Unit-B
(AMSU-B) and Microwave Humidity Sounder (MHS). So far, their data sets lack
comprehensive information on uncertainty caused by noise: From the pre-launch
measurements, one knows the specifications on the precision that the
instruments had to meet. These values of noise equivalent differential
temperature (NEΔT) are provided per instrument and channel in the NOAA
KLM User Guide and by their nature as
specifications do not comprise any information on time evolution of noise.
The ATOVS and AVHRR Pre-processing Package (AAPP) software used for the
processing of raw level 1b data to level 1c data containing brightness
temperatures, now provides with version 7.13 a measure of noise, namely a
cold and a warm NEΔT, referring to the cold and warm calibration
targets on board those microwave sounders. However, this information on noise
in the AAPP-processed data sets is not available for all instruments.
Graphical information on noise evolution is given on the NOAA-STAR-ICVS
web page , but this is also limited to a few periods and
instruments. Comprehensive information on uncertainty caused by noise is not
available for the end user interested in the measurements of the SSMT-2,
AMSU-B or MHS instruments.
To close this gap, we determine and evaluate the time series of the noise for
the SSMT-2 instruments on board the Defense Meteorological Satellite Program
(DMSP) satellites F11, F12, F14 and F15, for the AMSU-B instruments on the
satellites NOAA-15, NOAA-16 and NOAA-17 launched by the National Oceanic and
Atmospheric Administration (NOAA) and for the MHS instruments on the
satellites NOAA-18, NOAA-19 and the Metop-A and Metop-B satellites controlled by
EUMETSAT. In the assessment of the noise evolution, we identify periods of
low-quality data. To make this information easily accessible, we provide a
graphical and descriptive overview over the whole lifetime of the
instruments. From this overview, users can estimate the uncertainty due to
noise and can decide on the applicability of the data set for their
purposes. Our method and tool to estimate count noise will be used in the
evaluation of the uncertainty for the generation of new microwave sounder
Fundamental Climate Data Records (FCDRs). Those are currently developed in the
project “Fidelity and Uncertainty in Climate data records from Earth
Observation (FIDUCEO)” in the framework of which this study has been carried
out, and that aims to adopt a rigorous metrological (measurement science)
perspective to understanding the origins and quantifying various instrumental
issues that lead to random and systematic errors .
Apart from the new comprehensive time series of noise evolution, our results
also include the analysis of the spectrum of the noise in flight. This
analysis is based on the statistical tool of the Allan deviation and its
general form the M-sample deviation . We also
use the Allan deviation for the calculation of the noise itself, in contrast
to what has been done for the previously available noise estimates. The Allan
deviation, well known in other disciplines , has been suggested only recently by
for the estimation of noise in the measurements
of microwave sounders in flight.
The noise in flight can be estimated with various methods.
reports on methods used and suggested by
different agencies for the calculation of cold and warm NEΔT. The
various methods include the standard deviation and also the Allan deviation
as suggested by . The disadvantage of the standard
deviation is that it is sensitive to variations in the mean that naturally
occur in the measurements of these kind of polar orbiting instruments
. In this study, we follow the suggestion of Tian
et al. and use the Allan deviation for the estimation of noise. To clarify
the notion of noise first, the next section is dedicated to the
elaboration of a consistent noise terminology in the context of the microwave
sounders.
This article is further structured as follows. After establishing the noise
terminology used here, we explain our methods and data in detail. Later, our
results on the analysis of the noise spectra and the time evolution of noise
are presented. The discussion of these findings is followed by concluding
thoughts. In the Appendix we provide a detailed description of the time
series of the individual instruments.
Noise terminology
In theory, noise in the measurements of a radiometer such as the microwave
sounders considered here can be related to the process of measuring and it
can be calculated from instrumental quantities. This theory of noise in the
measurements of a radiometer is explained by whom we
follow here. The antenna delivers a power Pa to the receiver. In the
Rayleigh–Jeans limit, this power is usually related to a temperature Ta as
Pa=kTaB, with k being Boltzmann's constant and B the bandwidth of the
receiver. The precision with which the temperature Ta can be estimated by
a measurement is referred to as the radiometer sensitivity dT. It is
subject to any noise that may impact on the true signal and depends on the
temperature of the whole system. So, the total system noise power
Psys=Pa+Prec=kTsysB relates to the system temperature
Tsys=Ta+Trec, with Ta being the antenna temperature (which
includes the true signal) and Prec and Trec being the power and
temperature of the receiver including the influence of the transmission line
between antenna and receiver. Since the final measured output voltage is an
integrated value from a receiver of bandwidth B and an integration time of
t, the noise uncertainty to the radiometer sensitivity is
dTN=TsysBt.
However, one also has to consider fluctuations in the gain G on timescales
shorter than one calibration cycle. These are not calibrated out, but impact
on the recorded voltage and hence lead to fluctuations in the final
measurement result. These short-term gain fluctuations lead to a term
dTG=TsysdGG.
Since both contributions are independent, the radiometer sensitivity finally
reads
dT=dTN2+dTG2⇒dT=Tsys⋅1Bt+dGG2,
where Tsys is the sum of antenna temperature and combined
receiver–transmission line temperature. This radiometer sensitivity dT
describes the smallest temperature difference that the radiometer can
distinguish when looking at a target inducing an antenna temperature of
Ta. It is therefore an uncertainty estimate on the measurement of Ta.
For in-flight monitoring of the radiometer sensitivity, Eq. () is not well suited, since the receiver–transmission
line temperature is not well accessible. Therefore, one does not usually use
Eq. () to calculate the radiometer sensitivity,
but one uses some kind of statistical estimation of the fluctuations in the
measurements, e.g. in the counts that are the digitized output voltage. The
counts may stem from the instrument's views of the cold or warm calibration
target (deep space views, DSVs, and on-board calibration target, OBCT). This
statistical estimation may be the standard deviation or the Allan deviation.
This estimation of the fluctuations in the counts, referred to as
count noise, comprises every noise that has contaminated the true signal from
the antenna over the transmission line through amplifiers and mixers,
including digitization noise. The count noise is therefore subject to both
effects described in Eq. () for the total radiometer
sensitivity: noise from all electronic devices, that remains due to a finite
integration time, and short-term gain fluctuations (on timescales shorter
than one scan, i.e. one calibration cycle). This count noise estimate is in
units of counts, i.e. one cannot compare the values directly among different
sensors since the absolute count values are somewhat arbitrary. However, we
can transform the count noise into a NEΔT, which then represents an estimate for the total
radiometer sensitivity described by Eq. (). This
transformation includes the gain, i.e. NEΔT= noise-in-counts/gain.
The actual value of the gain taken for this estimate is the one
corresponding to the scan lines from which the count noise has been
calculated.
Hence, this transformation translates the fluctuations that we see in the
counts (count noise) into a temperature difference that is equivalent to the
noise by using the current gain. Any long-term changes in the gain will
therefore impact on the time evolution of NEΔT. Altogether, this
NEΔT includes the actual noise, i.e. short-term fluctuations of
whatever origin, and the long-term variations of the gain. This is in
contrast to the count noise estimate, which reflects the pure short-term
fluctuations.
The local equator crossing times (ascending branch) for the
satellites considered here. The graphic shows all times for which there is
any data available from the instrument on board the respective satellite
(regardless of quality issue). For the F11 to F15 satellites, we only look at
part of the time series shown here, due to inconsistencies across the
different sources of data (see text).
The basic instrumental characteristics of SSMT-2, AMSU-B and MHS.
The values for the NEΔT stem from the specifications for SSMT2, from
NOAA-15 for AMSUB and NOAA-18 for MHS.
Channel (inOrig.Centre frequency in GHzBandwidthPre-launchthis article)Channelin GHzNEΔT in K1491.655 ± 1.2501.50.625150.00 ± 1.251.50.6SSMT-232183.31 ± 1.000.50.841183.31 ± 3.001.00.653183.31 ± 7.001.50.611689.01.00.37217150.01.00.84AMSU-B318183.31 ± 1.00.51.06419183.31 ± 3.01.00.70520183.31 ± 7.02.00.601H189.02.80.222H2157.02.80.34MHS3H3183.31 ± 1.00.50.514H4183.31 ± 3.01.00.405H5190.312.20.46
This in-orbit analysis of noise can be carried out on both the counts of the
DSV and OBCT views, whereas the Earth counts are not suited due to their
natural variability when scanning over different scenes on Earth. The choice
of target influences the antenna temperature in Eq. ()
and consequently influences dT. Therefore, the dT calculated from the
OBCT is expected to be larger than from the DSV. If choosing the DSV counts,
one takes advantage of the fact that the brightness temperature of the DSV is
very low. Therefore, the contribution of that signal to the antenna
temperature is rather weak. The remaining contribution to the antenna
temperature and of course the receiver temperature are of instrumental
origin. Hence, analysing the DSV will give results (almost only) on the
instrument itself. Converting the DSV count noise to a temperature we obtain
the cold NEΔT, which corresponds to the radiometer sensitivity when
looking at very cold scenes. The second choice, taking the count noise on
OBCT, will lead to the warm NEΔT after translating to a temperature.
This warm NEΔT corresponds to the radiometer sensitivity when looking
at a target of approximately 280 to 300 K (temperature of the OBCT,
TBB).
The end users, however, will be interested in the (scene) NEΔT that they have
to expect for a certain Earth pixel in their data sets.
However, the NEΔT cannot be calculated directly from the Earth counts,
as explained above. But, it can be estimated from the cold and warm
NEΔT. As Eq. () expresses, the NEΔT or
radiometer sensitivity depends on the antenna temperature. Therefore, the
NEΔT when looking at an Earth scene of 240 K will be close to but
slightly smaller than the warm NEΔT. Knowing both the cold and the
warm NEΔT, users can calculate their scene NEΔT as
NEΔT=NEΔTcold+(Ta,scene-Ta,DSV)⋅mwithm=NEΔTwarm-NEΔTcoldTa,OBCT-Ta,DSVandTa,OBCT-Ta,DSV=TBB-2.725K.
This equation is obtained from combining Eq. () applied
for warm and cold NEΔT (i.e. using Ta,OBCT and
Ta,DSV as antenna temperatures in Tsys). Using the
resulting two equations for Eq. () with scene NEΔT
will yield the above Eq. ().
To obtain an estimate of the current scene NEΔT, users need the
estimate for the cold and warm NEΔT corresponding to the time window
where their current Earth pixel belongs. Moreover, they need the
corresponding temperature of the OBCT (measured temperature of the black
body, TBB) and the Ta,scene of their data set to finally
calculate scene NEΔT with Eq. ().
Data and methodsMicrowave sounder data
This study covers a time range of 22 years from 1994 to 2016 of microwave
data. We investigate the lifetime stability of the polar orbiting satellites
DMSP-F11, 12, 14, 15, the NOAA-15 to NOAA-19 and the Metop-A and Metop-B
satellites. All of them are placed in sun-synchronous orbits, but over the
lifetime of most of the satellites the orbits drift nonetheless and
therefore have a changing local equator crossing time (LECT;
). Only the Metop satellites have a constant
local ECT due to their controlled orbit. An overview of the ECT over the
missions of the satellites is shown in Fig. . The
drifting as well as the constant orbits of the Metop satellites are clearly
visible.
The microwave sensors on board those satellites are the Special Sensor
Microwave Water Vapor Profiler (SSMT-2), the Advanced Microwave Sounder Unit
– B (AMSU-B) and the Microwave Humidity Sounder (MHS) instruments. Table (data taken from the NOAA KLM User Guide of
and from Table 1 in ) shows
the characteristics of those sounders. The instruments are cross-track
scanners with a very similar calibration cycle: after four views on the OBCT,
90 Earth views are scanned before four views on the deep space are recorded.
This procedure is continuously repeated with a duration of 8/3 s.
Therefore, every 8/3 s, i.e. every scan, a new calibration of the
instrument with OBCT and DSV is carried out. In the following, we describe
the location of the channels for the different instruments. Note that the
five AMSU-B channels are counted including the AMSU-A channels; therefore
they are correctly named as channels 16–20. However, since their order is the
same as for MHS with channels 1–5, we keep the 1–5 naming for simplicity. For
AMSU-B and MHS, the water vapour sensitive channels are channels 3–5 with
frequencies 183 ± 1, 183 ± 3 and 183 ± 7 GHz (only
190 GHz for MHS) around the 183 GHz water vapour absorption line.
They provide information on the tropospheric humidity. Channel 1 is at about
89 GHz and channel 2 at 150 GHz (157 GHz for MHS); both
offer a deeper view through the atmosphere down to the surface. For SSMT-2,
the order of the channels is different. The original channel 1 is at
183 ± 3 GHz, channel 2 at 183 ± 1 and channel 3 at
183 ± 7 GHz. The original channels 4 and 5 are surface channels placed at 92
and 150 GHz, respectively. However, again for simplicity, we will use
the MHS naming of channels for SSMT-2 as well and refer to the water vapour
channels at 183 ± 1, 183 ± 3 and 183 ± 7 GHz as channels 3, 4 and
5. The surface channels at 92 and 150 GHz are labelled as channels 1
and 2. Note that the actual frequencies are not exactly the same for the
different instruments, even though we will refer to them as one channel, e.g.
the “89 GHz channels” encompass the 89 GHz channels of AMSU-B
and MHS, but also the 92 GHz channel of SSMT-2.
We use binary level 1b data sets downloaded from the NOAA CLASS
(Comprehensive Large Array-data Stewardship System) archive. For SSMT-2 there
are some inconsistencies regarding the time range of available data – on the
NOAA NCEI (National Centers for Environmental Information, formerly NGDC,
National Geophysical Data Center) data availability web page, there are longer
time frames indicated for which SSMT-2 data should exist (reaching back to
1992) than on the NOAA CLASS page. This larger data set of SSMT-2 data has
been reformatted to NetCdf by and covers
the range according to NCEI (shown in Fig. ). But this
is not the raw file providing all information that goes into the calibration
and that we aim to look at. For example, the NCEI data file does not contain
the temperature measured on the internal black body. Hence, to stay in line
with the investigation of AMSU-B and MHS data obtained from NOAA CLASS, we
only used the binary data for SSMT-2 that NOAA CLASS provides and that cover
the time range indicated on the NOAA CLASS web site . The data
record format for the binary level 1b data that we use here is documented for
AMSU-B and MHS in the NOAA KLM User Guide and on
the NOAA CLASS web site for SSMT-2 . From this raw data record
we read the counts for the black body views, i.e. the on-board calibration
target views (OBCT), the counts for the deep space views and the counts for
the temperature sensors (platinum resistance thermometers, PRTs) on the black
body. The latter ones are transformed to temperature in kelvin. We do not
take into account any quality flags that might be set in the data record but
only use raw unfiltered data in order to preserve the original recorded
behaviour. For each channel and all scan lines of every orbit we calculate
from those values the gain Gn(i) for scan line n and channel i as
Gn(i)=C‾OBCT(i)-C‾DSV(i)T‾PRT-2.725K,
where C‾OBCT and C‾DSV indicate
the counts from the OBCT and DSVs, respectively, both averaged
over the four views. T‾PRT denotes the average temperature
measured by all temperature sensors (two for SSMT-2, seven for AMSU-B and five for
MHS). The OBCT and DSV counts, as well as the gain, are our input values for
the noise estimation, which is described in the next section.
The bias functions B1(M) per channel for the orbits 505 of the
year 2005 and 4500 of the year 2007 for MHS on NOAA-18.
Noise estimation
The standard deviation can be used to estimate the noise on the counts as
explained in the above section on noise terminology. This estimation has been
used before , but the
standard deviation has a disadvantage in the context of noise monitoring of
instruments on polar orbiting satellites – since the standard deviation is
based on measuring the difference of the values from the sample's mean, the
standard deviation will only provide a sensible representation of the
precision of the sample if the sample truly has a constant mean. However, due
to the orbiting movement of the instrument around the Earth, all measured
quantities show orbital variations, i.e. they have a non-stationary mean over
one orbit. For such cases, the standard deviation is biased as it expects a
stationary mean and would measure the deviation of a single measured value
from the overall (erroneously stationary) mean over the full orbit. To reduce
this bias, one has to define sub-samples along the orbit, for which the real
mean is approximately stationary. Hence, the standard deviation becomes
highly dependent on these chosen sample sizes and is therefore less suited
for consistent in-orbit monitoring of different instruments.
The Allan deviation does not show this bias and is less dependent on choices
. Therefore, we use the
Allan deviation as a statistical tool to estimate the noise on the counts. The
Allan deviation, or its square, the Allan variance, is a special case of the
more general M-sample variance , which is defined as
σM2(M)=1M-1∑i=0M-1yi2-1M∑i=0M-1yi2,
where yi is a measured value from the sample and M denotes the number of
values of the sample that are used for the calculation. In other words, M
adjacent measurements yield one value σMj2. The associated total
M-sample variance for a total sample of N measurements is then calculated
as the average over all σMj2 with j∈1,2,…,N/(M-1)-1.
With M=2 in Eq. (), one obtains the Allan variance that
effectively uses two adjacent measurements of the data series:
σAllan2=σM2(2)=12(y1-y0)2.
The total Allan deviation for N measurements is then written as
σAllan,tot=<σAllan2>N=12(N-1)∑n=1N-1(yn+1-yn)2.
It is not biased due to longer-term trends such as orbital variations,
because the Allan deviation averages over a sample size N the deviation of
directly adjacent measurements. For a sample size of N values, one computes
N-1 Allan deviations and averages these. The question of an appropriate
value of N has been investigated in for the new
instrument Advanced Technology Microwave Sounder (ATMS). Since it has the
same scanning routine as the SSMT-2, AMSU-B and MHS, we follow the
suggestions of Tian et al. – the lower limit of N is set by the stability of
the Allan deviation with changing N; for small sample sizes of N< 300, the
Allan deviations fluctuates; from N=300 on, it takes a stable value.
Following this study, we therefore use a sample size of N=300 scan lines,
providing us with about eight total Allan deviations per orbit. As expected, by
comparing the standard deviation with the Allan deviation we found up to 40
times larger variations in the noise estimate over one orbit for the standard
deviation. Also, increasing the sample size for the Allan deviation does not
significantly change our results. This agrees with the results in
concerning the stabilization of the Allan
deviation above N=300 scan lines for those instruments.
For defining the number of N, one could also use a different approach. This
relates to the question of what a single measurement is and what adjacent
means in the context of the investigated instruments. As explained above, the
instruments have a scanning and calibration cycle of 8/3 s during which
they record the signal from four warm calibration target views, from 90 Earth
views and from four DSVs. Between the different targets they
record nothing. Having in mind this scanning and calibration cycle, there are
two approaches of noise estimation using the Allan deviation. On the one hand, one could use
the Allan deviation between the individual adjacent four calibration views – i.e. in each cycle one gets three Allan deviations. Over one orbit one
averages these 3⋅k Allan deviations (with k= number of scan lines in
the orbit) to get the final total Allan deviation. We will call this the
inter-pixel method. Opposed to that, one can act on the scale of scan lines,
as usually done for noise investigations so far . For this, one calculates the Allan
deviation between two adjacent scan lines for all four views separately and
then averages over the four obtained Allan deviations before applying the
average over N scan lines. We chose this inter-scan-line method with
N=300 in our study. The reason for this is that the results of our analysis of the
noise spectrum speak in favour of this inter-scan-line method for noise
estimation (see Sect. ): the inter-scan-line method
will give a better estimate of the uncertainty in the data due to noise
compared to the inter-pixel method, which underestimates the uncertainty for
non-white noise spectra.
The DSV count noise per channel for the years 2005 to 2008 for MHS
on NOAA-18. The red dots indicate the noise in DSV counts calculated with the
inter-pixel method. For comparison with the inter-scan-line method, we
applied this method exemplarily only for the two orbits 505 of 2005 and
4500 of 2007, for which we investigated the spectrum as well (see Fig. ).
To analyse the noise spectrum, we make use of the Allan deviation and the
general M-sample variance again. Together, they make an interesting tool to
determine the noise spectrum in a simple way
. The quotient of the M-sample variance
and the Allan variance, each averaged over the same sample size, is the so-called bias function :
B1(M)=<σM2>N<σAllan2>N.
The behaviour of B1(M) for varying M is characteristic for different
noise spectra. We let M vary from 2 to 20. We simulate white noise
(constant power spectral density) and pink noise (or 1/f noise – noise with power
spectral density proportional to the inverse of frequency, i.e. 1/f) in
MATLAB and determine their bias functions over the indicated range of M.
This serves as a comparison tool for the bias functions obtained from real data
to estimate the nature of the noise spectrum of the data. This spectral
analysis is carried out on the counts of the DSVs (DSV counts).
The bias functions B1(M) per channel for the orbits 500 of the
year 2003 and the year 2006 for AMSU-B on NOAA-17.
The DSV count noise per channel for the years 2003 to 2006 for
AMSU-B on NOAA-17. The red dots indicate the noise in DSV counts calculated
with the inter-pixel method. For comparison with the inter-scan-line method,
we applied this method exemplarily only for these two orbits, 500 of the year 2003 and 500 of the year 2006, for which we investigated the spectrum as well (see Fig. ).
In this study, we investigate three estimates of noise. We calculate the
Allan deviation on the deep space view counts to obtain the DSV count noise:
ΔCDSV=12(N-1)∑n=1N-1∑k=1K(CDSVk,n+1-CDSVk,n)2.
First, the difference in the counts CDSV from scan line n to
scan line n+1 is calculated for each view k separately. Then, the average
for all K=4 views is taken. Then the total Allan deviation is computed as
the average over all N-1 values obtained for the window of N=300 scan
lines. This estimate of count noise is then translated into a temperature. We
deduce the cold NEΔT by dividing by the gain corresponding to the
first of the two adjacent scan lines (equally one could take the gain
corresponding to the second one):
NEΔTcold=12(N-1)∑n=1N-1∑k=1KCDSVk,n+1-CDSVk,nGn2.
Similarly, we calculate the warm NEΔT by replacing the DSV counts by
the counts of the on-board calibration target (OBCT counts) in Eq. (). These three measures, i.e. the DSV count noise, and the cold
and the warm NEΔT, are monitored over the lifetime of the instruments
for each channel. The long time series that are displayed in this study
contain data only for every 50th orbit (Figs. ,
, ) in order to avoid a stronger
overlapping of symbols and to maintain readability.
Time evolution of the DSV count noise for the five frequency
channels.
ResultsAnalysis of noise spectrum
The noise spectrum for the different channels has a non-white component that
is more or less strongly pronounced for the different instruments and years. We
present the effects of this mixed spectrum on the calculation of the noise
time series. As examples, we pick two orbits from different years of MHS on
NOAA-18 (2005: orbit 505; 2007: orbit 4500). The spectrum is calculated for
these orbits with the bias function introduced in Eq. ().
The bias functions for each 300-scan-line window are further averaged over
the orbit and the four DSVs. In this way, we obtain for each of the two
orbits an averaged bias function as shown for each channel in Fig. , together with the simulated bias functions for white
and pink noise. For channels 1, 3 and 4, the bias function is close to that of pure white noise and therefore indicates for these channels a strong
white noise component that is dominant over the pink one in the count noise
of the DSV. The spectra for channels 2 and 5 look different, though – both
channels show a strong deviation from the pure white noise case,
indicating a mixture of white and pink noise for both years.
How far this affects our noise estimates can be deduced from looking at the
corresponding periods in time for the actual calculated count noise. In Fig. the time evolution of the DSV count noise for the
five channels is shown. In addition to the count noise calculated with the
inter-scan-line method, we also provide the estimates obtained from the
inter-pixel method for the two investigated orbits (red dots). The comparison
of both methods' results together with the spectra in Fig. , indicate that both methods agree as long as there is
a strong white noise component only (channels 1, 3 and 4). Hence, the jump in
DSV count noise in late 2007 in channels 3 and 4 is captured by both methods.
At this time one can observe sudden jumps in the mean counts as well as a
suddenly increased spread of the recorded counts around the mean, not only
for the counts in the DSV but also for the OBCT counts. This is probably due
to a gain adjustment for channels 3 and 4 in September 2007
.
If the noise spectrum is a mixture with a strong pink component, however, as
is the case for channels 2 and 5, the inter-scan-line method gives a
higher value than the inter-pixel method. This difference in the results of
the two methods seems reasonable, since a pink noise (1/f noise) contaminated
signal, having larger noise power at smaller frequencies, has variations due
to noise on a longer timescale than the inter-pixel timescale. Thinking of
the calibration cycle of the instruments, one can imagine the following
scenario and consequences for the uncertainty estimation. At the beginning of
the Earth scan, the signal suffers from a certain unknown portion of noise.
Later in the scan, when looking at DSV, the signal from the target (deep
space) itself is smaller of course. The portion of noise that contaminates
the signal will have changed in the meantime, too. In the case of pure white
noise, we know the range of that longer-term change, since it will be defined
by the standard deviation of the underlying distribution. This standard
deviation is described by the value of the inter-pixel count noise. However,
in the case of pink noise (or a mixture of white and pink), that longer-term
change may have a different magnitude because of the stronger contribution of
smaller frequencies to the noise. The inter-pixel noise value cannot capture
this larger, longer-term change. Executing all the processing of the measured
signal, one obtains the final brightness temperature Ta,scene.
Naturally, this Ta,scene is not the real value, but Ta,scene is an
estimate that will have an uncertainty. If we took the inter-pixel noise
value as the uncertainty due to noise, we would underestimate the
uncertainty. These longer-term variations between different targets within
one calibration cycle are captured in the inter-scan line method (as far as
they do not exceed the timescale of two scan lines) and therefore yield a
higher value as noise estimate. This possibly significant change in the
amount of noise that can happen between the measurements of the Earth views
and the calibration views due to pink noise should be included in an estimate
of uncertainty of the final brightness temperature measurement. Therefore,
avoiding underestimation of the uncertainty, we use the inter-scan-line
method for the calculation of noise.
Exemplarily we investigate the noise spectrum for the different instruments
and channels in some chosen orbits and years across their lifetime.
Naturally, this investigation cannot fully resolve the evolution of changes
in the spectrum, but our analysis provides snapshots of the overall evolution
of the spectrum. The AMSU-B and MHS instruments show in their channels either
pure white noise or a mixture of white and pink noise. The distribution of
this characteristic among the channels is not fixed. However – a certain
channel, for example the central water vapour channel 3, does not necessarily
exhibit the same noise characteristic in all AMSU-B and MHS instruments.
Furthermore, the characteristic may change as well in time. Looking at AMSU-B
on NOAA-17 in Fig. for example, channel 3 shows a
strong pink component in the year 2006, whereas 2 years before in 2003 the
pink component was less pronounced. This change in spectrum, adding some pink
component to the noise, is also captured in our noise estimation by the
inter-scan-line method. We detect a higher noise value accounting for the
increased level of uncertainty that is due to the increased pink component.
This is visible in the corresponding DSV count noise shown in Fig. .
For the SSMT-2 instruments, the bias function method as we use it here for
analysing the noise spectrum does not work properly for all times and
channels. The reason for that lies in the absolute count values that are so
small for the SSMT-2 that the digitization noise may impact and distort the
picture. To improve this bias function method for the usage on data affected
by digitization noise, one should simulate the digitization as well as the
white and pink noise, as has been presented by .
Another aspect that impacts the noise analysis even more, is the multitude of
outliers in the measurements of the SSMT-2 instruments that often disturb the
noise estimation. As mentioned above, we applied no filtering in order to get
the whole picture of the instruments' behaviour: the instability in the
performance of PRT, OBCT and DSV measurements of SSMT2 is clearly visible
in comparison to the other instruments. In the processing of the data to
level 1c FIDUCEO FCDRs, those outliers are filtered out and do not contribute
to the noise estimation executed on the fly.
The time evolution of the cold NEΔT for the five frequency
channels.
The time evolution of the warm NEΔT for the five frequency
channels.
Evolution of noise
We provide an overview over the evolution of noise in the different channels
over the lifetime of the instruments (a detailed description of the
instruments' performance is given in the Appendix). The three measures of
noise, i.e. the DSV count noise, and the cold and the warm NEΔT, are
displayed for all instruments and channels in Figs. ,
and . The DSV count noise (see Fig. ) is given in absolute counts and is therefore not suited
for a comparison of noise levels of different instruments. The individual
instrumental stability of the noise level can be observed very well, however.
Looking at channels 3 and 4 of SSMT-2 on F14, one can observe a significant
increase of the DSV count noise from 2001 on. A strong degradation of the DSV
count noise is visible also for channel 1 of AMSU-B on NOAA-17: from 2007 on,
the noise often peaks at almost 10 times higher values than its original one.
Channels 3 and 4 of MHS on Metop-A show a rather smooth change over several
years: from 2009 to 2012 the DSV count noise smoothly increased, then it
abruptly jumped back to its initial value before increasing smoothly again.
During the years 2014 to 2016 it then decreased again. The DSV count noise of
AMSU-B on NOAA-15 and NOAA-16 varies only very slightly and smoothly over the
lifetime of the instruments.
Both instruments, however, show a very different picture for the warm and
cold NEΔT. Its evolution is displayed in Figs. and . The NEΔT is influenced by the underlying count
noise and the gain used for the conversion to temperature. Therefore, the
evolution reflects the interplay of both quantities. The overall increase of
NEΔT therefore relates to an increase of the count noise or a decrease
of the gain. The increases in DSV count noise discussed above are quite
visible in the cold NEΔT as well, e.g. for the channel 1 of AMSU-B on
NOAA-17 or for channels 3 and 4 of MHS on Metop-A. The same is valid for the
count noise of the internal calibration target views and the warm NEΔT,
which is usually about 0.1 K higher than the cold NEΔT. For
channels 3 to 5 of AMSU-B on NOAA-15 and NOAA-16, which showed an almost stable
count noise, the cold and warm NEΔT show a strong increase over the
lifetime, reaching e.g. 5 K in channel 3 in 2010, superimposed with an
oscillating pattern. This increase is due to a strong degradation and
decrease of the gain that was observed by
too. The oscillating pattern is also observed
in many other measured quantities for these periods and is probably related
to the change of the solar beta angle as the orbit of the satellite drifts –
see the Appendix and . This changing pattern is
also visible for cold NEΔT of channels 3 and 4 of MHS on NOAA-18 from
late 2014 on. However, there is no steady degradation of the gain as for
NOAA-15 and NOAA-16, such that the cold NEΔT remains at rather low values.
The cold NEΔT also reflects erratic behaviour of the instrument when
the smooth evolution of the quantities is interrupted by sudden jumps. For
example, channels 3 and 4 of MHS on NOAA-19 suffer from an incident in late
2009 where NEΔT suddenly rises and falls again, but stays at an
increased level.
Discussion
In this study we used the Allan deviation to calculate the evolution of the
noise as well as the noise spectrum for the microwave sounders SSMT-2, AMSU-B
and MHS in order to assess the quality of the data with respect to
uncertainty due to noise.
The analysis of the noise spectrum showed that in some channels there is a
significant non-white component that may change during the lifetime of the
instruments. Together with the corresponding periods of count noise evolution
in time, the analysis of the spectrum revealed that the inter-scan-line
method for computing the Allan deviation is better suited for the purpose of
uncertainty estimation than the inter-pixel method that underestimates the
uncertainty if a pink noise component is present. Although the analysis of
the noise spectrum was carried out on some orbits only, it definitely shows
important aspects of the spectrum and its possible evolutions. Nonetheless, a
full analysis of the noise spectrum would require a study on all orbits to
track the evolution of the spectrum over time.
For the quality assessment of the microwave sounder data, we investigated the
evolution of noise (count noise and NEΔT) over the lifetime of the
instruments. The graphical overview we provided with Figs. – on the evolution of the noise gives a first impression
of the quality of the data. The various outliers that we did not filter out
on purpose indicate problematic periods of the instruments. The actual
reasons for the various kinds of outliers are unclear.
Degradation in quality also manifests itself in an increasingly cold
NEΔT. This degradation can have two causes. First, the actual noise
level measured in the count noise may have increased. This effect is hardly
visible on mission timescales as the count noise is rather stable for most
instruments. But on monthly timescales, the effect of increasing and
subsequent decreasing of count noise shines through in the changes of cold
NEΔT. Yet, the count noise does not cause an overall steady
degradation for the investigated instruments. The second possible reason for
degradation, however, has a strong impact on NEΔT in the observed
cases: if the gain decreases and therefore the measured counts of DSV and
OBCT converge, the NEΔT increases strongly. This reflects that the
radiometer sensitivity, which NEΔT is a measure of, strongly degrades,
and the instrument is no longer able to distinguish temperatures properly.
For example, it can only determine a temperature with an uncertainty of about 5 K, as is the case for channel 3 of AMSU-B on NOAA-16 in 2010. This
effect of gain degradation and increase of NEΔT is visible on both
short and long timescales. The pattern induced by the change of the
solar beta angle modifies NEΔT on monthly timescales and an overall
continuous degradation of the gain causes a steady increase of cold and warm
NEΔT, as seen for NOAA-15 and NOAA-16.
As intuitively obvious, an ageing satellite or sensor may degrade since its
components have a limited lifetime. Accordingly, one can observe this
degradation for many of the considered instruments. An interesting fact here
is the different evolution for the different channels: when the three water
vapour sounding channels severely degrade, the lower peaking channels may be
unchanged, i.e. they may show no sign of ageing. Or, there are events that
are visible in all channels, but only have long-lasting impact on certain
channels. For the newer satellites, some adjustments were made during
operation and this protected the instruments from degradation and kept them
at an acceptable noise level. The lowest and most stable noise, but also the
shortest data record so far, was from the MHS instrument on board the Metop-B
satellite.
Usable microwave data records with cold NEΔT< 1 K.
The five bars per satellite correspond to the channels 1 to 5 (from top to
bottom).
As an easy-to-use tool for information on noise we provided plots of the time
evolution for all individual instruments of this microwave sounder family.
These plots may help to decide on the usability of the data for a certain
application. They were given for the DSV count noise, the warm and the cold
NEΔT. Users of the data have to decide which level of uncertainty
their product generation might still bear and which threshold of
NEΔT they would set to limit the uncertainty. As a further result,
we provide a chart in Fig. , which shows the periods of
data for a threshold of cold NEΔT< 1 K.
For atmospheric product retrieval, Figs. and together with Eq. () can be used to
estimate the correct scene NEΔT. Since warm and cold NEΔT
typically differ by only approximately 0.1 K, a reasonable
approximation would be also to simply use the warm NEΔT as an estimate
for the scene NEΔT.
Conclusions
The results of our study provide users with information on the uncertainty due
to noise that they should expect when using the data sets of the
microwave sounders SSMT-2, AMSU-B and MHS.
The chart in Fig. reveals the possibility to concatenate
the available data for constructing gap-less long time series since 1994 at a
noise level below 1 K for all frequency intervals that the instruments
cover. This is of major interest for climate researchers who need long time
series with low noise levels in order to investigate possible trends.
Apart from the stand-alone results as information content for users of these
microwave sounders' data, our analysis is of direct use for the FIDUCEO
project: the method for estimating the count noise for the DSV and OBCT will
be used in the processing of level 1b to level 1c FIDUCEO FCDR in order to
provide on-the-fly input values for the uncertainty propagation. This FCDR
will provide a field-of-view-wise estimate of uncertainty in brightness
temperature due to count noise for every scan line and orbit. Additionally, the
FCDR will contain extensive information that will further close the gap of
lacking information on uncertainty.
The data from SSMT-2, AMSU-B and MHS are available from
NOAA CLASS, http://www.class.ngdc.noaa.gov/saa/products/catSearch.
Sensor time series
In the following we investigate the stability of the individual instruments
flying on different satellites by looking at the long time series of the
above-mentioned observables, mostly at the cold NEΔT as indicator of
the overall noise. For every channel, we display the cold NEΔT over
all considered missions in Fig. from 0.1 to 5 K.
We state which data we would definitely suggest to exclude, based on the
rather high threshold of 1 K. The remaining useful periods are
displayed in Fig. . We are interested in long-term
evolutions in the sensor or sudden incidents impacting the instrument. Hence,
the normal orbital variations are not investigated further, since their
effect on the cold NEΔT, even in the case of stronger changes, is only
very small by construction.
DMSP-F11 (SSMT-2)
The DMSP-F11 was launched in 1991. The NOAA CLASS data set starts at 1 April
1994 and ends on 24 April 1995 with some data gaps of several days or weeks.
The time record exhibits some issues. Sometimes the time stamp indicating the
seconds of the day is zero (without a change of day) or has values larger
than 86 400 s. The corresponding scan lines are excluded in our processing and
do not enter the time series.
OBCT temperature (2 PRT)
Both PRT sensors show normal behaviour throughout the time range. The
temperature on the black body changes in an interval of about 5 K
around 290 K.
Channels 1 and 2
Channel 1 has a stable gain and a low cold NEΔT of 0.2 K over
the whole time range (see Fig. a, black line). Channel 2,
however, is damaged from the start: the gain is constantly zero as the signal
for the OBCT and DSV counts is the same. Hence, it is of no use for research.
The cold NEΔT has infinite values and therefore does not appear in
Fig. b.
Channels 3, 4 and 5
The gain is stable at about 10 Counts K-1, except for some
erroneous outliers between -5 and 10 Counts K-1 (very similar
values for all three channels). In November 1994, there is a complete orbit
of bad outlier data spreading between -5 and 10 Counts K-1. The
cold NEΔT is quite stable at around 0.3, 0.4 and 0.5 for channels 3, 4
and 5 respectively – except for the corresponding outliers of the gain (Fig. c–e, black line). From late 1994 and early 1995 on, the cold
NEΔT of channels 3 and 5 shows more frequently higher values around 1.3 K. This is due to a most peculiar aspect: there are jumps up and down
in the OBCT and DSV counts within an orbit from the year 2001 on (it already
appears before, but rather seldom). The orbital change has the expected
smooth shape before it suddenly jumps to a higher or lower level and there
continues its course. The origin of these jumps is unclear.
DMSP-F12 (SSMT-2)
The second SSMT-2 instrument was brought to its orbit on 28 April 1994. The
NOAA CLASS data set runs from 13 October 1994 to 8 January 2001 with some
data gaps of several weeks.
Beside the time record issues mentioned for F11, the instrument on F12
revealed more wrong time stamps for many data points: the time stamp goes
back to some hours before the actual time and therefore produces artificial
abrupt rises and drops in the time evolution of the observables. Hence,
additionally to the filter used for F11, we use a second one excluding all
data whose time stamp is smaller than the previous one.
OBCT temperature (2 PRT)
The PRT sensors do not show any peculiarity, except for several groups of
outliers in 1994 (around 288 K) and more widely distributed outliers
in 1999. Both PRTs show slight oscillatory changes in the black body
temperature of about 4 K around an increasing mean of 300 to 304 K. In 1994 and later in 1999 there are several groups of outliers.
Channels 1 and 2
The lower peaking channels show the same behaviour as the water vapour
channels described below, with similar values. They cannot be used for
research purposes after 1999 either (see Fig. a, b, violet
line).
Channels 3, 4 and 5
Apart from outliers, the gain is stable until 1999 at around 10 or 9 Counts K-1 for channels 4 and 5 and channel 3 respectively. The same holds for the
DSV count noise and the cold NEΔT (0.43, 0.34, 0.38 K for
channel 3, 4, 5). From later 1999 on, there are very many outliers that are
rather widely spread such that cold NEΔT also reaches above 5 K
for those data points and the remaining line of cold NEΔT around 0.4 K appears quite thin (see Fig. c–e, violet line).
This makes the water vapour channels less suited for research purposes.
DMSP-F14 (SSMT-2)
The third SSMT-2 instrument was only launched on board the DMSP-F14 on 10
April 1997. The NOAA CLASS data set starts on 28 April 1997 and ends on 18
January 2005
OBCT temperature (2 PRT)
The black body temperature slightly oscillates with a period of about 6
months around a decreasing mean from 294 to 292 K in 2005.
Both PRT sensors agree within the random uncertainties throughout the
investigated time frame.
Channels 1 and 2
The low-peaking channels show a similar behaviour as the water vapour
channels until 1998, when the instrument suffers from several issues,
described in detail for channels 3 to 5. Channel 1 recovers from the critical
1998 phase and has a very low cold NEΔT at the level of the pre-1998
value of 0.3 K (Fig. a, blue line). Channel 2,
however, does not recover after May 1998 – instead, the signal of the OBCT and
DSV approach each other, resulting in a strongly increasing cold NEΔT
surpassing 1.5 K at the end of 1998. Afterwards, it even
reaches 8 K before decreasing slightly again, but always staying above 6 K (Fig. b, blue line). Channel 2 is therefore only
usable for 1997. Both channels also show the jumps of unclear origin, already
mentioned for F11, but to a lesser extent than the water vapour channels
described below.
Channels 3, 4 and 5
The gain remains stable at 10 and 9 Counts for channels 4 and 3, and channel 5,
respectively, throughout the lifetime. However, during the first half of 1998
the instrument suffers from some incidents: several additional levels of gain
emerge and the OBCT and DSV counts show extensive jumps. Thus it appears as
if the levels of OBCT and DSV count signals split into two branches each. Then, the branches for DSV count signals approach those for OBCT.
The resulting gain levels are lower than the original stable value or even
close to zero, which leads to many high peaks of cold NEΔT of even
> 1000 K (not visible in Fig. c–e). Data from this
period, i.e. January–May 1998, should not be used. Apart from this
period the cold NEΔT is quite stable, with slight changes around 0.5 K for channels 3 and 4, or 0.4 K for channel 5. In 2001,
however, cold NEΔT increases above 1.5 K for channel 4 (4.5 K for channel 3 and 0.7 K for channel 5) and stays at this high
level (see Fig. d, blue line). This corresponds to the
development of the DSV count noise: after 1998, the DSV count noise also
increases from initially 4 Counts to 15 Counts, slightly at first, then
more strongly in 2001 (even to 30 Counts for channel 3, and 8 Counts for channel 5). Then, the values fluctuate around this increased level. To some extent, this
correlates with the more frequent appearance of the jumps in the OBCT and DSV
counts within an orbit as mentioned already for F11. Due to the described
increase of the cold NEΔT, channels 3 and 4 should not be used from
year 2001 on. Channel 5 might be used with caution due to higher uncertainty
resulting from the jumps.
DMSP-F15 (SSMT-2)
On 12 December 1999 the DMSP-F15 satellite was launched carrying the last SSMT-2
instrument. The NOAA CLASS data set encompasses the measurements from 24
January 2000 to 18 January 2005.
OBCT temperature (2 PRT)
Throughout the considered time frame, both PRT sensors indicate a stable,
only slightly oscillating black body temperature around an increasing mean of
295 to 298 K.
Channels 1 and 2
After a stable phase at the beginning of the mission, the gain gets slightly
unstable for channel 1 and smoothly increases from 7
to 9 Counts K-1 before decreasing to 5 Counts K-1.
Accordingly, cold NEΔT increases from 0.6 to 0.8 K. In
February, March and September 2003, channel 1 suffers from very large noise
> 5 K. These periods should be excluded. Furthermore, in 2003, there
is a second level of cold NEΔT values which the measurement jumps to
and off, increasing from 3 to 4 K (Fig. a,
light blue line). This pattern can be seen in the DSV count noise as well and
relates to the same jumps of unclear origin as those mentioned below for
channels 3–5. These are also visible in the OBCT and DSV counts of channel
2. However, the gain for channel 2 already decreases from 2001 on, when the
OBCT and DSV signals become similar. Accordingly, cold NEΔT rises and
even reaches 5 K. It does not decrease below 2.3 K afterwards
(Fig. b light blue line). Hence, channel 1 could be used
with caution due to some higher uncertainty, whereas channel 2 is of no use
due to its large noise.
Channels 3, 4 and 5
The gain is quite stable at a constant value of 7 Counts K-1 for
channel 4 (8 Counts K-1 for channels 3 and 5), but has many
outliers even down to a negative gain of -3 Counts K-1. Cold
NEΔT is mostly stable at 0.5 K (0.6 K for channels 3 and
5). In 2003, cold NEΔT temporarily increases in channel 3 to 1.5 K, but decreases again to 0.8 K (see Fig. c,
light blue line). Channels 4 and 5 remain quite stable (Fig. d–e, light blue line). However, from the start, the jumps
of unclear origin, mentioned for the surface channels above and for F11 and
F14, appear in channels 4 and 5 and make the DSV count noise as well as the
cold NEΔT change suddenly between two courses.
NOAA-15 (AMSU-B)
On 13 May 1998 the NOAA-15 satellite was launched having the first AMSU-B
sensor on board as a subunit of the AMSU instrument. The operational data start
on 15 December 1998. The instrument was turned off on 28 March 2011
, but already in late 2010 the data are too noisy to be
used. Here, we investigate the NOAA CLASS data set from the start of operational
data until the end of 2010. AMSU-B was turned off due to problems with the
scan motor making measurements impossible. However, there are still data
records being sent to Earth which cannot be used, of course, since these
contain no measurement data but random numbers.
The NOAA-15 satellite started with an LECT of about 19:30, reached about
16:30 in 2010 and drifted back to 18:00. Its quick orbital drift over its
lifetime impacted on the AMSU-B instrument: a characteristic pattern of peaks
and drops becomes visible in the time evolution of many observables from 2002
on (see also NEΔT in Figs. and ). The same pattern can also be seen for the Microwave
Sounding Unit (MSU) instrument on the earlier NOAA-14 satellite
, for the AMSU-A on NOAA-15
as well as on the AMSU-A and AMSU-B on board
the successor satellite NOAA-16 (see below) which has already experienced the
same strong orbital drift as other NOAA satellites. In
, focusing on AMSU-A, a connection of
this pattern to a changing solar beta angle due to orbital drift is seen.
This angle is defined as the angle between the vector from Earth to Sun and
the orbital plane of the satellite. Hence, a changing angle will influence
the exposure of the instrument to the Sun and may therefore impact its
performance. An investigation of this is beyond the scope of this overview of
microwave data.
The AMSU-B on NOAA-15 also suffered from the radio frequency interference
(RFI), with channels 2 and 4 being impacted most. It introduced a scan-dependent bias that also affected the DSVs as well as the 90 Earth
views. The impact was not constant in time, however. For example, in the
period of October 1998 to September 1999, the measurements are biased for
half the orbit before returning to normal behaviour for the rest of the orbit
. This is also visible in the cold
NEΔT of channels 2, 4 and 5 (see Fig. b, d, e, dark
green line).
OBCT temperature (7 PRT)
From the start, the black body shows strong variations of temperature (5 to 8 K) on a monthly scale. Moreover, there are many drops to 262 K,
which are probably related to the PRT sensors. All seven sensors mostly agree
throughout the lifetime, apart from some events where they drop or jump to
different temperature levels. There are also many randomly distributed
outlier values of the different PRT sensors. From 2002 on, the orbital drift
induced the changing pattern mentioned above, which becomes clearly visible and
remains until the end of the data set.
Channels 1 and 2
The counts for the OBCT and DSV are quite stable, except for small changes on the
monthly scale. However, the counts often drop to zero (either for both
targets or for one of them) which results in constant levels of outliers in
the gain at -60, 0 or 100 Counts K-1. Yet, apart from some
random outliers the gain is mostly stable at its initial value of 30 and 20 Counts K-1 for channels 1 and 2 respectively. The changing pattern
mentioned above becomes more pronounced in the course of time, but as the
OBCT and DSV counts almost change accordingly there are only very small
changes in the gain (∼1 Count K-1) and no decline. The cold
NEΔT remains quite stable at 0.25 K (channel 1) and 0.6 K (channel 2), see the dark green line in Fig. a
and b, respectively. Filtering out the scan lines of outlier values, and
excluding channel 2 from the start until November 2000, when a phase of unstable
cold NEΔT ends, will provide a useful data set.
Channels 3, 4 and 5
The water vapour channels are subject to more quality issues. From the start,
one can observe slowly decreasing counts for the DSV signal and quicker
decreasing for the OBCT counts. For the first years up to the end of 2001, the
resulting gain still has acceptable values and cold NEΔT is about 1 K for channel 3 or 0.8 and 0.6 K
for channels 4 and 5,
respectively. From 2002 on, however, the changing pattern as seen in the
black body temperature shines through also to the cold NEΔT and the
degradation gets stronger. The recorded signals for OBCT and DSV approach each
other until the gain becomes very small (below 6 Counts K-1 for
an initial value of 20 Counts K-1) and, consequently, the cold
NEΔT rises above 2.5 K. Finally, in the middle of September
2010, the gain drops to zero, resulting in NAN values for cold NEΔT.
Data should not be used for channel 3 from 2001 on, for channel 4 from 2004
on or for channel 5 from 2007 on, as the cold NEΔT increases beyond 1 K (see Fig. c–e, dark green line).
NOAA-16 (AMSU-B)
The second AMSU-B instrument was sent to space on board the NOAA-16 satellite
on 21 September 2000. The operational data started on 20 March 2001. Finally,
NOAA-16 was decommissioned on 9 June 2014. Compared to its predecessor,
NOAA-16 was exposed to an even stronger orbital drift from about 14:00 to
22:00 LECT (see Fig. ). In 2007, the earlier mentioned
changing pattern for the observables emerges, probably related to the
solar beta angle (see above, ). This is
visible in NEΔT, too (see Figs. and ).
OBCT temperature (7 PRT)
The black body temperature only shows small oscillations on a monthly scale,
reaching about 4 K in late 2006, though. As for NOAA-15, the PRT
sensors also often drop to 262 K. There are also periods of months
when the PRT sensors differ by about 10 K for several orbits. Then, from
October 2007 on, the variations in the overall evolution become more severe
as the strong changing pattern becomes visible with an amplitude of 5 to 10 K. In 2012, the pattern ceases
and only small changes around 288 K can be seen.
Channels 1 and 2
The low peaking channels show quite acceptable data having a cold NEΔT
of 0.3 K. Nonetheless, over the whole lifetime, the OBCT and DSV
counts often drop to zero or jump to other quite stationary levels
(especially from 2004 on). This is transported to the gain and also causes
outliers of up to 2 K in cold NEΔT. In channels 1 and 2 the
changing pattern is very faint and only changes the gain by about ±1 %.
Therefore, the cold NEΔT also appears stable at the scale of Fig. a, b, green line).
Channels 3, 4 and 5
Initially, the gain is rather stable for the three water vapour channels. A
slight decreasing starts in early 2001 after higher orbit-to-orbit variations
that can be seen in OBCT and DSV counts, as well. In 2002, the OBCT counts
start to decrease more quickly than the DSV counts, and hence the gain decreases
continuously. Four years later, in 2006, the gain has decreased from
initially 22 down to 9 Counts K-1 in
channel 3 (the other channels show a similar evolution) and cold NEΔT
has risen from 0.6 to 1.4 K. The degradation for the three
channels continues further as the gain decreases (OBCT and DSV counts
getting close to one another) and cold NEΔT increases. From late 2007
on, the changing pattern shines through in the counts and the cold
NEΔT (see Fig. c–e, green line) reaches 18 K
in 2011, when the gain approaches zero, and increases beyond 50 K in
2014 as the signal recorded for the OBCT and DSV is basically the same. Doing
a two-point calibration is not sensible at this stage and produces completely
useless data due to absurdly high noise with cold NEΔT> 10 K. One should stop using NOAA-16 data from the end of 2005 when cold
NEΔT surpasses 1 K and degradation keeps advancing in channels
3–5.
NOAA-17 (AMSU-B)
On board NOAA-17 the last AMSU-B instrument was launched on 24 June 2002. Its
operational data set starts on 15 October 2002 and ends on 10 April 2013. NOAA-17
drifted from about 22:00 to 19:00 LECT over its mission (Fig. ).
OBCT temperature (7 PRT)
The seven PRT sensors indicate a stable black body temperature softly
oscillating on the yearly scale around 285 K (slightly increasing to 287 K). As for the other AMSU-B instruments, the PRT measurements also
often drop to 262 K. In 2010, the overall evolution remains, but the
measured values of the seven sensors jump between discrete levels and follow
the overall evolution with different constant offsets. There also appear
strong peaks from 2011 on, a sharp drop to 275 K in early 2013 and then an
increase again.
Channels 1 and 2
Until 2007, channel 1 has a stable gain, cold NEΔT and DSV count noise.
Then, sharp peaks (of factor >4 to stable noise value) appear in the DSV
count noise. Later the peaks reach even a value of factor of 10 times the
stable noise value and outliers even a factor of >50. Moreover, the peaks
become more frequent, such that the underlying constant DSV count noise of
initially 8 Counts becomes less visible. Hence, channel 1 gets
very noisy (cold NEΔT peaks reach up to 5 K) due to the DSV
count noise that transfers to the overall cold NEΔT, see Fig. a, light green line. The gain is also impacted from the
high DSV count noise peaks, since the DSV counts apparently have a larger
variation that becomes visible in jumps and drops of the gain to certain
levels whilst keeping the overall initial value of 24 Counts K-1.
Channel 2 shows a similar behaviour, though less pronounced, i.e. the
frequency of the appearing peaks is smaller (see Fig. b
light green line). Filtering out the scan lines of outlier values will lead
to a usable data set for channel 2. Channel 1 also needs filtering, but from
2007 on, one should not use the data at all, since they get too noisy, as
described at the beginning of the paragraph.
Channels 3, 4 and 5
Apart from small jumps and drops in channels 3 and 4 in 2003 and 2004, all
three channels have stable cold NEΔT values of 0.85, 0.7 and 0.8 K, respectively. In December 2009, however, a sharp
drop of both OBCT and DSV counts results in a gain of almost zero and a huge
cold NEΔT of 2000 K or infinite (NAN) values (see Fig. c–e, light green line). From December 2009 on, the NOAA-17
AMSU-B data for the sounding channels cannot be used for any research
questions.
NOAA-18 (MHS)
The first MHS instrument was installed on board the NOAA-18 satellite
launched on 20 May 2005. The operational data set starts on 30 August 2005.
The mission is still ongoing; however, our data set for investigation ends in
May 2016. From its start until May 2016 it drifted from 14:00 LECT to 18:00.
OBCT temperature (5 PRT)
The five PRT sensors agree in the slight oscillations on the yearly scale of the
black body temperature around 284 to 287 K. Apart from a few outlier
values of several PRTs, the measurements are quite stable and show a stable
black body temperature. However, in August 2014, the strong changing pattern
as seen for the NOAA-15 and 16 satellites emerges and leads to maximum
(minimum) temperature of 298 K (270 K). This pattern is still
visible at the end of the used data set in May 2016.
Channels 1 and 2
Apart from outlying values, both channels 1 and 2 have a stable gain and cold
NEΔT around 0.14 and 0.36 K, respectively, over the
lifetime (see Fig. a, b, dark red line). The changing
pattern visible in the black body temperature is only prominent in the OBCT
and DSV counts that change accordingly, thus resulting in a stable gain.
Channels 3, 4 and 5
A first, all three channels show a stable gain (in the range of 140 Counts K-1), with small discrete jumps and drops. The orbital variation
around the mean is larger than for channels 1 and 2, often about
±10 Counts K-1, and also shows changes over the years. Channel 5 has
very large orbital variation in 2011 and 2012 and also significant changes in
the DSV count noise for these periods, but then it is suddenly reduced by a
factor of 20 by controlled gain adjustment . Thus, channel
5 is less variable from 2013 on. The changing pattern is apparent in the
gain in 2014: its strongest impact is on channel 3 (up to 90 Counts K-1 within a month), then on channel 4 and finally on channel 5, where it is hardly
visible. Cold NEΔT is also stable at first (0.5, 0.4, 0.3 K, for channels 3 to 5), but also shows the jumps in the
gain and increases slightly until, in 2014, the changing pattern becomes
visible and increases or decreases cold NEΔT (Fig. c–e, dark red line). Temporarily, cold NEΔT reaches
0.95 K in channel 3 (0.8 K for channel 4, whereas channel 5 remains
stable since 2013 at 0.3 K). It is a usable data set, but one
should be aware of the temporarily increased noise and therefore larger
uncertainty for all three channels. Channel 5 has the fewest problems from 2013
on.
NOAA-19 (MHS)
On 6 February 2009 the NOAA-19 satellite was launched carrying the second
MHS instrument. The operational data start on 2 June 2009. So far, NOAA-19 has
drifted from 14:00 to 15:00 LECT. It is still operational.
OBCT temperature (5 PRT)
All five PRT sensors measure the same stable temperature of the black body,
oscillating slightly on the yearly scale around 285 K.
Channels 1 and 2
Throughout the lifetime both channels are stable and have a constant cold
NEΔT of 0.13 and 0.33 K respectively. In Fig. a, b, the corresponding red line is directly behind the
orange one of Metop-A.
Channels 3, 4 and 5
Channels 3 and 4 begin stable, but show erratic behaviour in July 2009. The
OBCT and DSV signal suffer from major incidents, resulting in a strongly
diminished gain. Following the drop in the gain, cold NEΔT increases
from 0.5 to 3.4 K in channel 3 (Fig. c,
red line). Yet, channel 4 recovers from the incidents in 2009 and then
remains stable at 0.58 K (Fig. d, red line).
Channel 5 is stable throughout the mission, having a low cold NEΔT of
0.27 K (Fig. e, red line). From the data set of
NOAA-19, channel 3 should not be used.
Metop-A (MHS)
The third MHS instrument was carried to orbit on board the Metop-A satellite
launched on 19 October 2006. The operational data start on 15 May 2007. The
instrument is still active. Unlike the NOAA satellites, the Metop satellites
do not exhibit orbital drift. Their local equator crossing time remains
stable at 21:30.
OBCT temperature (5 PRT)
The temperature of the black body is quite stable over the mission so far and
shows small variations on a 3-monthly scale around 283 K. There
are a few orbits with outlier values, mainly in the first years of the
mission, and there is a larger data gap in spring 2014.
Channels 1 and 2
Both channels do not show any anomalies and remain stable at their initial
cold NEΔT values of 0.13 and 0.31 K respectively (see
Fig. a, b, orange line). The latter one increases slightly
to 0.34 K in 2016.
Channels 3, 4 and 5
The gain is constantly adjusted during operation to correct for decreases
and increases and to keep it within certain limits . Overall,
the resulting cold NEΔT is quite stable around 0.5 or 0.6 K for channel 3, peaking at 0.7 K in late 2011. For channel 4
there is a slightly lower noise of 0.3 K, peaking at 0.5 K in
late 2011. Channel 5 is stable throughout the mission, with low cold
NEΔT of 0.27 K (see Fig. c–e, orange line).
As for channel 5 of the MHS instrument on NOAA-18, the DSV count noise
changes over the mission in channels 3 and 4. This is visible in the cold
NEΔT as well.
Metop-B (MHS)
On 17 September 2012 the Metop-B satellite was launched with the fourth MHS
instrument on board. The first operational data are available for 29 January
2013 when it replaced the Metop-A for operational purposes .
The mission is envisaged to end after 2018. As Metop-A, Metop-B has no
orbital drift either.
OBCT temperature (5 PRT)
Until the end of the considered time frame (May 2016), the temperature of the
black body varies with an amplitude of about 2 K on a 3-monthly
scale around 281 K. There are only four events of outlier values so
far.
Channels 1 and 2
A small decrease of the gain can be observed for channel 2. However, this
degradation is always corrected for by adjusting the gain and resetting it to
higher values. The cold NEΔT is stable at 0.18 K for channel 1
and 0.36 K for channel 2 (see Fig. a, b, yellow
line).
Channels 3, 4 and 5
The adjustment of the gain to keep it at a quasi-constant level is also
prominent for channels 3 to 5 (with the smallest adjustments for channel 5).
The cold NEΔT is stable at 0.35, 0.27 and 0.25 K for channels 3, 4 and 5
respectively (see Fig. c–e, yellow line).
IH developed the MATLAB code for the processing and noise determination and analysis,
carried out the analysis and prepared the paper. MB supported the
development of the MATLAB code for reading the raw data, contributed to the
discussion and preparation of the paper. VJ created Fig. ,
contributed to the discussion and preparation of the paper. JM suggested the method
for noise analysis, advised the analysis, contributed to the discussion and
preparation of the paper. SB accompanied and advised the investigation, contributed
to the discussion and preparation of the paper.
The authors declare that they have no conflict of interest.
Acknowledgements
Imke Hans, Martin Burgdorf, Viju O. John, Jonathan Mittaz and Stefan A. Buehler gratefully
acknowledge support from the FIDUCEO project (“Fidelity and Uncertainty in
Climate data records from Earth Observation”) which has received funding from
the European Union's Horizon 2020 Programme for Research and Innovation,
under Grant Agreement no. 638822. Viju O. John was also supported by the UK
Department of Energy and Climate Change (DECC) and Department of Environment,
Food and Rural Affairs (DEFRA) Integrated Climate Programme (GA01101) and the
EUMETSAT CMSAF. Stefan A. Buehler was also supported through the Cluster of
Excellence “CliSAP” (EXC177), Universität Hamburg, funded through the
German Science Foundation (DFG). The authors would like to thank Oliver Lemke
for helpful tips on reading the raw data records.
Edited by: Tanvir Islam
Reviewed by: two anonymous referees
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