Radiosonde soundings from the GCOS Reference Upper-Air Network (GRUAN) data record are shown to be consistent with Infrared Atmospheric Sounding Instrument (IASI)-measured radiances via LBLRTM (Line-By-Line Radiative Transfer Model) in the part of the spectrum that is mostly affected by water vapour absorption in the upper troposphere (from 700 hPa up). This result is key for climate data records, since GRUAN, IASI and LBLRTM constitute reference measurements or a reference radiative transfer model in each of their fields. This is specially the case for night-time radiosonde measurements. Although the sample size is small (16 cases), daytime GRUAN radiosonde measurements seem to have a small dry bias of 2.5 % in absolute terms of relative humidity, located mainly in the upper troposphere, with respect to LBLRTM and IASI. Full metrological closure is not yet possible and will not be until collocation uncertainties are better characterized and a full uncertainty covariance matrix is clarified for GRUAN.
Temperature and water vapour are two of the essential climate variables
(ECVs) from Global Climate Observing System (GCOS). The ECVs are variables
that are required to support the work of the United Nations Framework
Convention on Climate Change (UNFCC) and the Intergovernmental Panel on
Climate Change (IPCC) and that are technically and economically feasible for
systematic observation. The required performance for satellite-based
upper-air temperature and water vapour data products for climate from GCOS
are very demanding
GCOS target requirements for the satellite-based essential climate
variable (ECV) of water vapour
n/a
Temperature and water vapour are ECVs for which satellite observations can make a significant contribution – in particular from operational meteorological satellites by means of (passive) top-of-atmosphere (TOA) radiance measurements. Observations from space have several advantages, in particular (i) spatial coverage, which can be global, and (ii) continuous sampling of the atmosphere at regular intervals. Their main disadvantage is that they do not directly observe the Earth system but rather indirectly sense it by measuring the radiance from the Earth impinging on the satellite instrument. To measure the ECVs, it is necessary to convert measured radiances into atmospheric temperature and water vapour profiles. This is usually accomplished by modelling the pathways of radiation in the atmosphere via radiative transfer models (RTMs). The inverse process allows for profiles of temperature and water vapour to be retrieved from the satellite-measured radiances. The inversion can be performed either as a straightforward inversion or, in the case of numerical weather prediction (NWP), by assimilating radiances into short- and medium-range forecasting models. The retrieval or assimilation method may contain inaccuracies either due to one or more of the following: (i) imperfect modelling of the atmosphere, (ii) auxiliary data used or (iii) inaccuracies inherent to the assumptions made by the technique itself, such as Gaussian uncertainty distribution assumptions, trace gases concentrations or others.
Whether radiances or temperature and water vapour profiles are measured, for
them to be useful for climate or many other application, they need to be
adequately calibrated. The science of metrology defines best practices to
achieve this goal. One key element in calibrating is traceability, by which
various measurements can be compared. Metrological traceability is a property
of a measurement result whereby the result can be related to a reference
through a documented unbroken chain of calibrations, each contributing to the
measurement uncertainty. In simple terms metrological traceability is a
direct link between the result of a measurement made in the field and the
result of the best possible measurement made in a calibration laboratory. It
ensures that different measurement methods and instruments used at different
locations and at different times produce reliable, repeatable, reproducible,
compatible and comparable measurement results. When a measurement result is
metrologically traceable, it can be confidently linked to the internationally
accepted measurement references. Traceability of metrological measurement
results are assured by ensuring a documented, unbroken chain of instrument
calibrations, from the operational instruments used for field measurements
all the way up the metrological hierarchy pyramid to the primary standard. At
the top of the pyramid is an internationally defined and accepted reference,
in most cases the International System of Units (SI), whose technical and
organizational infrastructure has been developed by the Bureau International
des Poids et Mesures – BIPM (
For the case described here, the measurement process consists of three
fundamental elements: (i) the radiance measurement from the satellite
instrument, (ii) the temperature and water vapour measurements from the
radiosondes and (iii) the RTM that establishes the link between them.
Throughout this measurement process, not all elements in the traceability
chain are usually used comprehensively. In operational meteorological
satellites, instruments are usually calibrated against well-defined standards
on the ground before launch. It is often the case that these instruments, and
particularly their components, have critical properties which vary with time,
degrading once the satellite is in space – effectively breaking the full
traceability chain. RTM simulations of the observed TOA radiances usually do
not propagate uncertainties arising from gaps in knowledge about the
spectroscopy, therefore breaking again the traceability chain. Radiosonde
measurements provided by the GCOS Reference Upper-Air Network (GRUAN) adhere
to metrology best practices as they provide an accurate estimation of all
uncertainties involved in the measurements
With the goal of achieving an unbreakable chain of calibrations in the future, the satellite community is establishing a set of standards to which all other measurements can use as reference. The objective is to ultimately have these references calibrated through an unbroken traceability chain to primary standards. These current standards are described in the following:
The Global Space-based Inter-Calibration System (GSICS) is an international collaborative effort initiated in
2005 by the World Meteorological Organzation (WMO) and Coordination Group for Meteorological
Satellites (CGMS) to monitor, improve and harmonize the quality of observations from operational weather and environmental
satellites of the Global Observing System (GOS). GSICS aims at ensuring consistent accuracy among space-based
observations worldwide for climate monitoring, weather forecasting and environmental applications.
For infrared (IR) sensors, the standard instrument being adopted by GSICS is the Infrared Atmospheric Sounding Instrument (IASI) For radiative transfer models the satellite community working with IR sensors commonly uses line-by-line
radiative transfer models. They make use of laboratory measurements of gas absorption spectra to perform
their calculations, simulating the radiative transfer that occurs in the real atmosphere. One of such de facto
standards is LBLRTM (Line-By-Line Radiative Transfer Model), which is the one tested in this paper The GRUAN community takes great care to keep the chain of traceability
unbroken (e.g.
When transforming IR measured radiances into atmospheric parameters – effectively performing what are known as retrievals, or as a component of data assimilation, where radiances are used to improve the original atmospheric profile estimation from NWP – it is necessary to keep the chain of traceability between all its elements unbroken. A first step into this direction is checking that all these elements are effectively consistent. That is, the consistency between IASI measurements, GRUAN sondes and LBLRTM calculations is a necessary condition to have an adequate chain of traceability. The consistency of all these components, with the elements available today, is the main subject of this paper.
Comparisons of measurements are usually done in temperature and humidity
profile space, where a retrieval is compared to a radiosonde measurement
(e.g.
In order for different components to be consistent, their measurements need
to lie (on average) between their uncertainties. This is described by the
The different components that are verified in this paper to be consistent are described in the following subsections.
Space-borne IR hyperspectral instruments typically measure Earth views in a
spectral range from 600 to 3000 cm
IASI has been compared with various calibration references, both pre-flight
and in orbit. However, reference values with associated uncertainties that
are traceable to SI standards have not been assigned. Moreover, while
in orbit the instrument has no SI source, and hence the traceability to an SI
standard once the satellite is launched is lost. Despite this, due to its
quality and long-term radiometric stability, the GSICS community has declared
IASI as a standard to which all other IR satellite sensors can reference
Spectra at the top of the atmosphere were generated using the reference
Line-By-Line Radiative Transfer Model (
GCOS has established and is continuing to develop a reference network for upper-air climate observations (GRUAN). GCOS is a joint undertaking of the WMO, the Intergovernmental Oceanographic Commission (IOC) of the United Nations Educational Scientific and Cultural Organization (UNESCO), the United Nations Environment Programme (UNEP) and the International Council for Science (ICSU). Its goal is to provide comprehensive information on the total climate system, involving a multidisciplinary range of physical, chemical and biological properties, and atmospheric, oceanic, hydrological, cryospheric and terrestrial processes.
GRUAN is a ground-based network for reference observations of upper-air climate parameters. GRUAN is expected to provide long-term, highly accurate measurements of atmospheric profiles, complemented by ground-based state-of-the-art instrumentation to constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks). Two of GRUAN's primary goals are to fully characterize the properties of the atmospheric column and their changes. GRUAN is envisaged as a network of 30–40 high-quality, long-term upper-air observing stations, building on existing observational networks.
The data that are currently certified as meeting the GRUAN standards are the
Vaisala RS92 radiosonde data, which are the data that will be used in this
paper. The specific GRUAN data used in this paper are the “RS92 GRUAN Data
Product Version 2”, which has the “RS92-GDP.2” key
In order to verify the consistency of all the elements involved in the
comparison, ideally a collocation uncertainty close to 0 is desired
(
The IASI instrument flies on board Metop which is in a mid-morning orbit,
overpassing the Equator at around 09:00 h local solar time. Since the GRUAN
radiosondes are mostly launched at synoptic times (00:00 Z and 12:00 Z), the
locations on the globe where IASI and the GRUAN radiosondes would coincide
are located over the middle of the Atlantic or the western Pacific
(Fig.
IASI complete orbit (red) on 4 November 2011 at 23:20:57 Z over the observatory location, Manus island (green dot).
Radiative transfer models are in practice accurately characterized for
clear-sky cases, making it therefore necessary to select the clear-sky scenes.
There are a total of 597 coincident IASI overpasses and GRUAN radiosonde
launches over Manus during this period. From these a further selection of
clear-sky cases is needed. The cloud flag available in the standard IASI L1c
product is used for a first screening, leaving 76 clear-sky cases. To perform
the radiation matching between GRUAN-derived and IASI radiances, a perfectly
clear sky scene is needed. Since the IASI L1c cloud flag does not have an
efficiency of 100 % in detecting clear-sky cases, a further visual
screening of the scenes as seen by AVHRR (Advanced Very High Resolution
Radiometer) has been performed. This instrument is flown on board the
same satellite (Metop) and has the advantage of having a much higher spatial
resolution of around 1 km at nadir, which makes it specially useful for
cloud detection. After this second clear-sky screening is done, only 27 cases
are left. These cases are the ones used in the remainder of this paper. All
cases with a GRUAN and IASI collocation over Manus, which are clear sky
according to the IASI L1c cloud fraction, are listed in Table
GRUAN RS92 sondes and IASI collocation cases over Manus, where only the clear-sky cases according to IASI L1c cloud fraction are listed.
According to
The useability of the RS92 humidity profiles is largely determined by the amount of water vapour present. Above the tropopause the water vapour level drops by approximately 2 orders of magnitude. The intrinsic uncertainty of the radiosonde humidity profile is 1 % RH or more, meaning that at low relative humidity (RH) levels, which typically occur in the stratosphere, the relative uncertainty of the measurement is 100 %, which renders the data of little use in the present exercise. In the examples in this paper, humidity measurements from the GRUAN radiosondes are taken as useful when they are physically below 100 hPa, which, for these cases, is just below the tropopause. Regarding temperature, the burst of the balloon is what limits their altitude. The GRUAN objective is to aim for a maximum altitude of 5 hPa. For thicker balloons, in the range of 600 to 1200 g, the burst of the balloons reaches heights between 10 and 4 hPa. For radiosondes launched from Manus they are typically limited to an altitude between 30 and 10 hPa due to the use of thinner balloons. This would then be the limit for temperature measurements of these GRUAN data. Because of these upper limitations on temperature and humidity measurements and in order to be able to apply the radiative transfer to the radiosonde profiles, it is necessary to extend them above this altitude up to the TOA. This is done by complementing them in this upper region with European Centre for Medium-Range Weather Forecasts (ECMWF) fields, by taking the nearest operational analysis to the radiosonde launch location in space and time. In the sample dealt with in this paper, there are no big discontinuities between GRUAN measurements and ECMWF profiles; therefore no further processing at the intersection point has been done.
The RS92 sensor measures the relative humidity of the ambient air, whereas
the RTM needs as input the water vapour concentration, typically specific
humidity. It is therefore necessary to convert the humidity measurements from
relative humidity to specific humidity. To do this, a water vapour saturation
curve is needed. The final calculated radiances – especially for channels
which are most sensitive to upper-air regions, such as the high troposphere, or
which have low water vapour concentrations, such as the ones used in this
paper – are very much dependent on the type of formulation which is selected
Finally, the radiosonde profiles are smoothed with a mean filter of
100 points in the vertical. The reason for this is that the original
radiosonde data exhibit high oscillations and spikes which are either
extremely oscillatory or spurious, and it is therefore not recommended to feed
these raw data as input to the RTM. It must be considered that, in any case,
IASI-measured radiances or retrievals are not sensitive to particularly small
scales in the vertical. Figure
Individual sample of a raw GRUAN sonde (green), the ECMWF profile (blue) and the final profile after pre-processing before being fed as input to LBLRTM (red). The red, green and blue lines to the right show the temperature profiles, while the ones to the left show the humidity profiles represented as dew point temperature.
Once the profiles are prepared, they are used as input to LBLRTM. To avoid
surface effects in the calculated radiances, only the higher absorptive water
vapour channels are used in this study. The channels used range from
The radiosonde profile uncertainties provided by GRUAN
There are several ways to propagate the uncertainties from atmospheric profile into radiance space. The most straightforward way of propagating uncertainties is by using the parameter derivatives. In this case, the Jacobians of the radiances with respect to the atmospheric profiles from the radiative transfer equations could be multiplied by the atmospheric profile uncertainties to obtain the radiance uncertainties. These Jacobians are usually available as an output of the RTM. Due to the large number of IASI spectral points and the number of levels in the GRUAN profiles, this method is computationally expensive and impractical for this study. Also, the Jacobian of the radiances is needed, which for the case of LBLRTM can be quite impractical to use and obtain. Added to this, the fact that the uncertainty covariances between levels are not available for GRUAN profiles, it is not evident how to use the Jacobians for this purpose. In this paper, a more practical approach has been taken. The uncertainty propagation has been performed assuming two extreme cases: uncertainty is completely uncorrelated between levels, and there is a perfect correlation between uncertainties from all levels. The truth most likely lies in between these two extremes.
IASI observed minus calculated radiances (OBS
To propagate the uncertainties, assuming no uncertainty correlation between
levels, a Monte Carlo method was applied. For each level and variable a
random perturbation is added, having a Gaussian distribution with zero bias
and a standard deviation equal to the corresponding GRUAN global uncertainty
on that level. Each level is perturbed totally independently from the next.
After this perturbation is applied, the radiances at the top of the
atmosphere are calculated using LBLRTM. This process is repeated several
times to obtain the standard deviation of the radiances within the Monte
Carlo approach. Since only an estimation of the standard deviation is needed,
not too many repetitions are necessary. Eleven have been used in this work,
which is a sufficient number for an accurate estimation of the standard deviation.
This final standard deviation is taken as the uncertainty of the GRUAN
profiles in radiance space. One result for a particular profile is shown in
Fig.
The propagation of uncertainties when assuming a perfect correlation of
uncertainties between levels is done by perturbing the temperature and
humidity variables by plus or minus the uncertainty as given by GRUAN from
that parameter and level consistently over the complete profile. In other
words if the temperature is perturbed by plus one GRUAN uncertainty at the
surface, the rest of the temperature profile is also perturbed by plus one
GRUAN uncertainty for each level. Therefore, there are a total of four
different profiles: two coming from the plus and minus addition of one GRUAN
uncertainty times another two coming from the two variables, temperature and
water vapour. Radiances are then calculated for these four profiles using
LBLRTM. To derive a radiance uncertainty from these calculations, all four
calculated radiances are subtracted pairwise, giving a total of six
differences. Of these six, the greatest difference is taken as the final
uncertainty for uncertainty-correlated levels. The combination that provides
the greatest uncertainty in this case consisted of plus one GRUAN uncertainty
in temperature and minus one GRUAN uncertainty in humidity. Results are shown
in Fig.
IASI observed minus calculated radiances (OBS
The differences between calculated radiances obtained from the results of
LBLRTM applied to the GRUAN radiosondes and the IASI-measured radiances are
computed for the comparison. For illustrative purposes, the calculated
radiances obtained from the ECMWF operational analysis profile that is nearest
in space and time are also compared to IASI. It is worth recalling that all
cases analysed in this paper are clear-sky scenes. Figure
Several radiance uncertainties. IASI overall instrument uncertainty (black); GRUAN instrument uncertainty propagated into radiance space assuming no uncertainty correlation between levels for the 21 January 2011 at 11:41:31 case (orange); GRUAN instrument uncertainty propagated into radiance space assuming perfect uncertainty correlation between levels for the 21 January 2011 at 11:41:31 case (dark green); calculated radiance standard deviation from GRUAN sondes for all the completely clear sky scenes and night-time cases (red); calculated radiance standard deviation from ECMWF profiles for all the clear-sky scenes and night-time cases (blue); estimated collocation uncertainty plus any remaining RTM uncertainties (light green).
Average radiance difference (bias) between IASI observed and
calculated radiances for the completely clear sky scenes and night-time
cases. Calculated radiances are derived from GRUAN sondes (red) and ECMWF
profiles (blue). The thickness of the red line (hardly noticeable) denotes
the GRUAN uncertainty propagated into bias radiance space assuming no
uncertainty correlation between levels. The solid black line shows the IASI
instrument uncertainty for this bias. The light green line
indicates the collocation uncertainty of this average or bias.
The dotted black line indicates 2 times the square root of the squares of the IASI overall instrument plus GRUAN uncertainties
plus collocation uncertainties for the bias (the
Average radiance difference (bias) between IASI observed and
calculated radiances for the completely clear sky scenes and daytime cases.
Calculated radiances are derived from GRUAN sondes (red) and ECMWF profiles
(blue). The thickness of the red line (hardly noticeable) denotes the GRUAN
uncertainty propagated into bias radiance space assuming no uncertainty
correlation between levels. The solid black line shows the IASI instrument
uncertainty for this bias. The light
green line indicates the collocation uncertainty of this average or bias.
The dotted black line indicates 2 times the square root of the squares of the IASI overall instrument plus GRUAN
uncertainties plus collocation uncertainties for the bias (the
Figure
For further reference, note that the standard deviation of the differences
for all samples indicates the total uncertainty in the comparison, including
collocation, instrument and RTM uncertainties. These are shown in
Fig.
Since the collocation uncertainty is not negligible, all terms from
Eq. (
The IASI instrument uncertainty is available. The GRUAN uncertainty in radiance space is only partially available, since the uncertainty correlations
between levels is not perfectly known. To have a firm value for the GRUAN uncertainty in radiance space,
it will be assumed that there is no uncertainty correlation between GRUAN levels. Illustrating this in
Fig. The collocation uncertainty is completely unknown. To overcome this, it will be estimated from the standard
deviation of the observed minus calculated radiances from the complete clear-sky and night-time sample.
Subtracting from the square of this value, the IASI and GRUAN squared uncertainty, the remaining uncertainties,
mostly the collocation uncertainty, squared should be left. Illustrating this in Fig.
Note that by making the assumption of the uncertainty correlation between
levels for GRUAN and by calculating the collocation uncertainty from the data
themselves, the ideal closure of Eq. (
Same as Fig.
To minimize the collocation uncertainty, the average of the radiance
difference for different cases was calculated. The expectation is that the
random perturbations due to collocation uncertainties would average out. For
this to happen, these perturbations need to have a normal random
distribution. The IASI, GRUAN and collocation uncertainties also need to be
re-calculated in order to normalize them with the square root of the size of
the averaged sample. Results are shown in Fig.
Figure
It is interesting to note how the sample size shrinks as we select the data more and more. The initial number of collocations of IASI with GRUAN over Manus during the period this station was operational (2011–2013) was 597 cases. Once only clear-sky cases are selected, following the cloud flag present in the IASI L1 product, 76 cases are left. After visual inspection of the scenes, to remove potential residual cloudy cases, only 27 cases remain. Of these, 11 cases are measured during night-time, which are the ones that provide a good match between IASI and GRUAN, and the other 16 daytime cases do not provide a reasonable match-up. This stresses the need for having high-quality radiosonde observations, such as those provided by GRUAN, collocated with satellite overpasses.
It has been verified that GRUAN, LBLRTM and IASI are indeed consistent with
each other, specially for night-time ascents. This is the main result of this
paper. This is a key finding when using these measurements in fields where a
high accuracy is needed, like climate science. Even though the consistency
between GRUAN and IASI cannot be proven on cloudy scenes, it can be expected
that GRUAN quality remains unchanged under any conditions, serving its main
purpose as a reference network for climate and other applications.
Consistency is also necessary for applications such as obtaining accurate
retrievals from IASI measurements
Adequate collocations are needed. Scale lengths and times of water vapour are
extremely small as To achieve a full metrological closure, the full GRUAN uncertainty matrix is
needed. This matrix would show the uncertainty correlations between levels
and between parameters, temperature and water vapour. To fully characterize
the comparison, a method to estimate the collocation uncertainty would be
desirable. This method should not depend on the data being used for the study
and should be independent from them. The water vapour saturation function used to convert from relative humidity
measured by the radiosonde to some form of water concentration, such as
specific humidity, is highly critical. In this case, following
It is also very important to correct the RS92 radiosonde measurements from
all potential systematic errors they might have. For this, the GRUAN processing
plays a key role, removing such biases and providing the necessary
uncertainties to make a meaningful comparison. Proper cloud detection is also critical. A few cases with spurious clouds
will adversely affect the consistency results. In this paper, an additional
visual cloud detection was done on the data with the help of AVHRR images. GRUAN processing seems to still have a remaining bias of around 2.5 % in
absolute terms of relative humidity for radiosondes flown during daytime,
which is corroborated by the fact that this effect does not seem to show up
in night-time sondes nor in ECMWF profiles. Results from this paper are drawn with very limited sample sizes (11
night-time and 16 daytime), so they should be taken with care. A study with more
cases should be performed in the future. The need for more radiosonde launches
coincident with satellite overpasses should also be stressed. The results shown in this paper would have been impossible with other data of
lower quality than GRUAN data. The fact that the GRUAN community strives to
provide bias-free data and an uncertainty associated with each measurement
is what has made this study possible.
GRUAN radiosonde data are available from GRUAN, IASI data are available from EUMETSAT and the LBLRTM model is available from AER.
The authors declare that they have no conflict of interest.
We acknowledge the editor Domenico Cimini and the anonymous referees for their helpful comments, which have greatly improved the paper. Edited by: Domenico Cimini Reviewed by: three anonymous referees