Recently, the reprocessed Advanced Television Infrared Observation Satellite
(TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and
temperature data record was released by the EUMETSAT Satellite
Application Facility on Climate Monitoring (CM SAF). ATOVS observations from
infrared and microwave sounders onboard the National Oceanic and Atmospheric
Agency (NOAA)-15–19 satellites and EUMETSAT's Meteorological Operational
(Metop-A) satellite have been consistently reprocessed to generate 13 years
(1999–2011) of global water vapour and temperature daily and monthly means
with a spatial resolution of 90 km
TPW and LPW products were compared to corresponding products from the Global
Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde
observations and from the Atmospheric Infrared Sounder (AIRS) version 5
satellite data record. TPW shows a good agreement with the GUAN
radiosonde data: average bias and root mean square error (RMSE) are
Although the atmospheric CO
The Global Climate Observing System (GCOS) is a user-driven operational system intended for long-term use whose role it is to ensure availability of global observations for monitoring the
climate system, detecting and attributing climate change, assessing impacts
of and supporting adaptation to climate variability and change, and
supporting climate research. GCOS was established in 1990 as an
outcome of the second world climate conference, and it is sponsored by
international and intergovernmental organisations such as the World
Meteorological Organization, the Intergovernmental Oceanographic Commission,
the United Nations Environment Programme, and the International Council for
Science. The GCOS Second Adequacy Report (GCOS-82, 2003) established a
priority list of 44 essential climate variables and called for integrated
global analysis products. GCOS essential climate variables are classified into the three domains, atmospheric, oceanic, and terrestrial. Within the atmospheric domain, a distinction is made between the surface, the upper air, and the composition variables. Water vapour is one of the atmospheric surface and upper air essential climate variables because of its key role in the radiation budget, the structure of tropospheric diabatic heating, the water cycle and the atmospheric chemistry. The objective of the World Climate Research Programme's Global Energy and
Water Cycle Experiment (GEWEX) is to fully understand the water cycle for predicting climate
change. GEWEX has initiated a series of projects and assessments to produce
long time series of parameters linked to the water cycle and to evaluate
the current maturity of such products. The Global Water Vapor Project
was one of GEWEX's projects dealing with water vapour, the primary goals of which were the
accurate global measurement, modelling, and long-term prediction of water
vapour. Furthermore, the GEWEX Data and Assessment Panel has initiated the
GEWEX Water Vapor Assessment, G-VAP (
The Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) suite onboard the TIROS-N and National Oceanic and Atmospheric Agency (NOAA)-6–14 satellites consists of three sounders – one infrared sounder, the High Resolution Infrared Radiation Sounder (HIRS), and two microwave sounders, the Microwave Sounding Unit (MSU) and the Stratospheric Sounding Unit (SSU). The MSU and SSU have since been replaced with improved instruments – Advanced Microwave Sounding Unit A and Unit B (AMSU-A and AMSU-B) – and more recently AMSU-B was replaced by the Microwave Humidity Sounder (MHS). The Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) suite, AMSU-A, AMSU-B and HIRS are onboard the NOAA-15–17 satellites. Onboard NOAA-18, NOAA-19 and Metop-A, AMSU-B has been replaced by MHS. The TOVS/ATOVS observations allow the retrieval of water vapour and temperature profiles. The TOVS/ATOVS observations started in 1978/1998 and are among the longest time series available from satellites.
Retrieval methods can be separated into statistical/semi-physical and physical schemes. The semi-physical schemes retrieve the water vapour content by applying a statistical scheme (linear regression or neural networks) based on a training data set. The physical schemes mostly use a first guess, often coming from a numerical weather forecast model or reanalysis, as the basis for the forward computation, and then vary the first-guess profile until the computed set of radiances best matches the observed radiances. Processes in the atmosphere complicate the retrieval task, e.g. the co-existence of the three thermodynamic phases of water on Earth, interaction with aerosols, and uncertainties in surface emissivities and temperatures, particularly over land. The error characteristics of the retrieval or analysis will critically depend on the a priori or training data utilised. Several retrievals for TOVS and in particular ATOVS have been developed. An important aspect in this context is that synchronised infrared and microwave observations can be used. This way the information content increases and both clear-sky and cloudy-sky conditions are sampled. An example of TOVS retrieval is described in Scott et al. (1999) and forms the basis for a data record of atmospheric profiles. Retrieval algorithms for ATOVS are described in, for example, Li et al. (2000) and Reale et al. (2008). Boukabara et al. (2011) developed the Microwave Integrated Retrieval System, which uses AMSU-A and MHS observations and is currently being updated to also include Special Sensor Microwave Imager/Sounder observations. These retrieval schemes are presently applied operationally and have not been used so far to reprocess the ATOVS record.
With the availability of hyperspectral infrared sounders which are jointly
installed with microwave radiometers onboard the NASA Aqua, the EUMETSAT
Metop-A/Metop-B, and the Joint Polar Satellite System's Suomi National
Polar-orbiting Partnership (Suomi NPP) platforms, the retrieval capacity has
been enhanced. This development started with the Atmospheric Infrared Sounder
(AIRS) onboard Aqua, which has been in orbit since 2002. AIRS covers the
infrared spectrum from 3.7 to 15.4
A few long-term satellite-based water vapour profile data records have been
generated and publicly released. To give an example, the NASA Water Vapor
Project total precipitable water vapour (TPW) and layer-integrated precipitable water
vapour (LPW) products are based on a combination of the Special
Sensor Microwave Imager (SSM/I), TOVS and radiosonde data for the time
period between 1988 and 1999 (Randel et al., 1996) and have contributed to the
GEWEX Global Water Vapor Project. The NASA Water Vapor Project has
recently been reanalysed and extended to cover the period 1988–2009 as part
of NASA's Making Earth System Data Records for Use in Research
Environments programme (Vonder Haar et al., 2012). An overview of available
satellite and reanalysis records is provided in the G-VAP plan available at
This paper introduces the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) ATOVS tropospheric humidity and temperature
data record. The ATOVS observations are consistently reprocessed with a
fixed processing chain. The main elements of the processing chain are the
AVHRR and ATOVS Pre-processing Package (AAPP; Atkinson, 2011), the
International ATOVS Processing Package (IAPP) retrieval algorithm (Li et
al., 2000) and the Kriging algorithm (Schröder et al., 2013). The ATOVS
data record is freely available from
The ATOVS data record contains tropospheric water vapour and temperature
products and is defined at all longitudes and for latitudes between
80
The following products are included in the ATOVS data record:
Vertically integrated water vapour or total precipitable water vapour (TPW) in kg m Layered products for five layers:
layer vertically integrated precipitable water vapour (LPW) in kg m layer mean temperature in K. Products at six pressure levels:
specific humidity in g kg temperature in K.
Relative humidity for five layers is provided as additional, auxiliary data.
The layer and level definitions are given in Table 1 and TPW is integrated from the surface to 100 hPa. The ATOVS data
are provided on a fixed vertical grid to ease utilisation. However, the actual
vertical resolution of an individual retrieval differs from pixel to pixel
and time to time because the information content is a function of local
surface and atmospheric conditions. The origin of the observed radiation is
best described by so-called Jacobians, and in addition to atmospheric
conditions these are a function of the instrument characteristics. Examples
of Jacobians are given in Li et al. (2000) for AMSU-A and HIRS and in
Kleespies and Watts (2006) and Buehler et al. (2004) for AMSU-B. The full
ATOVS time series has been reprocessed with a fixed preprocessing,
retrieval and post-processing scheme described below. The reprocessed ATOVS
data record was released in 2013. Though consistently reprocessed, the ATOVS
data record may not be considered as a consistent data record, mainly
because the input data require improved quality control and
intercalibration.
TPW (left panel), extra-daily standard deviation (middle panel) and number of valid observations per grid point (right panel) for September 2007.
TPW (left panel), Kriging error (middle panel) and number of valid observations per grid point (right panel) for 20 September 2007.
Layer and level definitions for the ATOVS data record.
Examples of the ATOVS data record products are shown in Figs. 1 and 2. In Fig. 1, the monthly mean TPW for September 2007 is shown together with the corresponding extra-daily standard deviation and the corresponding number of observations per grid box. Figure 2 shows LPW for the layer between 500 and 700 hPa for 27 September 2007, with the Kriging error expressed in terms of standard deviation (see Schröder et al., 2013, for a definition) and the corresponding number of observations per grid box.
Associated level 2 data are available on request. The level 2 data contain,
among other information, dew point temperature on the 42 IAPP level (using the CO
Average profiles of specific humidity from ATOVS (left panel) and
mean difference (bias) between ATOVS and ERA-Interim (right panel) for
September 2007. The regions are defined as follows: Northern Hemisphere (NH), within 20 and 50
The average differences between the ATOVS and the ERA-Interim profiles are
shown in the right panel of Fig. 3. This figure
illustrates the adjustment made by the retrieval to the input profiles. At
near-surface layers the changes are minimal, which is likely due to the rather
low information content in the observation. It is noticeable that this extends
up to 650 hPa in the Southern Hemisphere. Largest reductions of up to
Finally, it should be noted here that CM SAF also provides an
“operational” version of the ATOVS products with a maximum timeliness of
2 months. These data have been operational since 2009 and cover the period 2004–present.
The operational processing scheme has used ECMWF Integrated Forecast System forecasts since March 2012, does not apply simultaneous nadir
overpasses (SNOs) and is based on various retrieval versions. Currently, the
implementation of IAPP version 4 is carried out to allow the processing
of Metop-B data. The operational ATOVS products are routinely compared
against GUAN observations and the results of this comparison are subject to
an annual review and are published at
The operationally processed ATOVS data record is freely available from
ATOVS is a sounding instrument system composed of three sounders. Two of these are microwave sounders, AMSU-A and AMSU-B, onboard NOAA-15, NOAA-16, and NOAA-17, with MHS replacing AMSU-B onboard NOAA-18, NOAA-19, and Metop-A. The third sounder is an infrared sounder, HIRS.
ATOVS has been onboard NOAA and Metop polar-orbiting
satellites since 13 May 1998. So far, seven platforms have carried the ATOVS instruments, namely
NOAA-15–19, Metop-A, and Metop-B. AMSU-A and AMSU-B
are cross-track-scanning total power radiometers with instantaneous fields of
view of 3.3 and 1.1
Observations from a specific satellite are used for the processing if all
three ATOVS instruments are declared operational on the NOAA polar-orbiting
environmental satellite status page:
Satellite combinations used to generate the ATOVS humidity and temperature data record together with the corresponding time period.
The retrieval of the geophysical parameters is done using IAPP software version 3.0b (see Sect. 3.3). IAPP uses the following ATOVS channels: HIRS channels 1 to 17, AMSU-A channels 1 to 15, and AMSU-B channels 17 to 20. When an instrument channel experienced a malfunction on a specific satellite, this channel was removed from the retrieval for the entire reprocessing time period for that particular satellite. Such channels are AMSU-A channels 11 and 14 on NOAA-15, AMSU-A channel 4 on NOAA-16, and AMSU-A channel 7 on Metop-A.
The IAPP relies on the use of a priori data. The following European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis fields (Dee et al., 2011) are used as a priori information: temperature profile, relative humidity profile, 2 m dew point, 2 m temperature, skin temperature, surface pressure, geopotential height, sea ice cover, land–sea mask, and total column water vapour.
The input data preprocessing is carried out in two steps. First, AAPP is used to convert the geo-referenced and calibrated brightness temperatures (level 1c, taken from ECMWF's Meteorological Archival and Retrieval System) into mapped data (level 1d). During this process the scan lines are also sorted according to time. Furthermore, the AAPP Binary Universal Form for the Representation of Meteorological Data decoding tool is used to read the l1c data. The AAPP software is developed and maintained by the EUMETSAT Satellite Application Facility for Numerical Weather Prediction. An overview of AAPP is given in Atkinson (2011), a scientific description is available from Labrot et al. (2011), and the software description can be found in Labrot et al. (2012). The default AAPP version was used. The HIRS pixel definition defines the “grid” for AAPP preprocessing.
Secondly, SNO coefficients are applied to the data of the four AMSU-B channels used for the retrieval (channels 17 to 20) to intercalibrate observations from the different satellites. The SNO coefficients used to process the ATOVS data record are described in John et al. (2012) and were provided (V. John, personal communication, 2010) as monthly mean brightness temperature differences for the satellites NOAA-15 to NOAA-18 and Metop-A, covering the period January 2001–December 2010. Since NOAA-16 exhibits temporal overlap with all other satellites that have ATOVS instruments onboard, it has been used as a reference satellite for the SNO intercalibration. John et al. (2012) emphasise that the quality of the intercalibration using classical SNO approaches is hampered due to the overrepresentation of cold scenes. The biases between the satellites are dependent on the scene radiance, which is itself dependent on the latitude at which the observation is made. Improvements to classical SNO approaches were suggested by John et al. (2012) for AMSU-B and developed by Shi and Bates (2011) for HIRS. Unfortunately, at the time of the data record processing, no intercalibration coefficients based on the conclusions of John et al. (2012) were available. In general, intercalibration coefficients are also available for AMSU-A (see Zou and Wang, 2011, for details) and HIRS (see Shi and Bates, 2011, for details). However, they are applicable to limb-corrected observations and thus not useable for the processing of the ATOVS data record as IAPP requires non-limb-corrected radiances as input. Consequently, intercalibration coefficients have not been applied to the HIRS and AMSU-A data for the processing of the CM SAF ATOVS record.
The retrieval software used to generate the ATOVS data record is IAPP version
3b developed by the University of Wisconsin in Madison, WI, USA (Li et al.,
2000). The default version of IAPP was used, as no parameters can be tuned in
the IAPP configuration file, which mostly contains path definitions for the
different data needed for the retrieval. The IAPP retrieves, among other
atmospheric parameters, temperature and moisture profiles in both clear and
cloudy atmospheres at 42 pressure levels. The IAPP algorithm can be
decomposed into the following steps: the HIRS cloud detection and removal
procedures, the bias adjustment relative to collocated radiosonde
observations, and the actual retrieval. The bias adjustment scheme is
applicable to NOAA-15 data only. It has not been applied here because it has
been anticipated that its application will lead to a breakpoint in the time
series of the final products. The goal of a bias correction is to account for
calibration uncertainties of the satellite data, radiative transfer
uncertainties and uncertainties of the input to the radiative transfer. The
deactivated bias correction can impact the number of convergent retrievals
and the systematic and random uncertainties of the retrieved parameters. The
retrieval involves two steps. In the first, the initial temperature, water
vapour, ozone profiles, and the surface skin temperature are obtained by
statistical regression between the ATOVS measurements and the ERA-Interim
reanalysis. The second part of the retrieval is the computation of an
iterative physical solution of the radiative transfer equation using the
first-guess results and the ERA-Interim reanalysis as background information.
The physical iterative retrieval algorithm, the cloud detection procedure and
the bias adjustment method are described in detail in Li et al. (2000) and
are reiterated in Courcoux and Schröder (2013).
Here, we note that the HIRS cloud detection algorithm is applied
to
The retrieval outputs are first quality-controlled according to the following criteria:
TPW between 0 and 90 kg m temperature between 180 and 340 K, specific humidity between 0 and 55 g kg surface emissivity between 0 and 1, surface pressure between 0 and 1050 hPa (on basis of input data).
If profile or surface values are outside these ranges or if the profile exhibits super-adiabaticity, the full profile is set to undefined. After quality control, the 42 level profiles are integrated and averaged to obtain the final products described in Sect. 2.
Finally, an objective interpolation technique commonly called Kriging is
applied to the quality-controlled and integrated products. The advantage of
applying Kriging is that it fills data gaps and that uncertainty estimates
at grid level are computed. The principle of Kriging is that an estimate or
prediction for an unobserved location is computed by using the observations
from locations in its vicinity. The optimal estimate at each grid point is
found by a weighted average of the information from the surrounding points.
The challenge is to determine these optimal weights. The weights depend on
the distance-dependent spatial correlation function and the error of the
observation used. The Kriging algorithm used for the ATOVS data record is
described in detail in Schröder et al. (2013). The only parameter
tunable by the user in the Kriging algorithm is the grid resolution – here up to 90 km
The ATOVS tropospheric humidity and temperature data record is compared to
GUAN radiosonde observations in order to guarantee consistent and comparable
evaluation results between the operational and the reprocessed ATOVS data
records. To further allow a global comparison we also use the AIRS data
record. AIRS observations have a large temporal overlap with the ATOVS data
record. Many other ground-based, in situ and satellite observations are
available for comparison. An extensive list of such data records is given in
the appendix of G-VAP plan, available at
The goal of the comparison of the ATOVS data record with GUAN radiosonde and AIRS data record is to identify and understand potential issues in the ATOVS data record and to provide an overall characterisation of the ATOVS data record in a relative sense. An accuracy assessment is not carried out. Furthermore, the impact of background information and uncertainty on the observed quality is not analysed here, and we refer the reader to, for example, Eyre and Hilton (2013) for further reading.
In Sect. 4.1 the GUAN and AIRS data records are described. The comparison considers TPW and LPW and the results are presented in three subsections of Sect. 4.2. In the first, the TPW time series from ATOVS, GUAN and AIRS data records are presented and discussed. In the second and third, the comparison results between ATOVS and GUAN data records and between ATOVS and AIRS data records are discussed.
The GUAN radiosonde network has been established by GCOS in order to make
current and historical upper air data available for climate change detection
and climate monitoring. GUAN provides global radiosonde observations, from
homogeneously distributed upper air stations, that have a specific record
length in addition to meeting the continuity requirement and data quality
requirements as defined by GCOS (Daan, 2002). At present there are 171 GUAN
stations worldwide. A station map and a station list can be found at
The quality of radiosonde observations is affected by a series of issues such as temporally and spatially varying radiosonde types and national practice (e.g. Soden and Lanzante, 1996; Christy and Norris, 2009; Moradi et al., 2010), as well as issues and differences in calibration procedures (e.g. Miloshevich et al., 2006; Vömel et al., 2006). Among the strongest impacts is the dry bias caused by solar radiation (Vömel et al., 2006), which leads to significant underestimations of humidity in the upper troposphere if not corrected. A series of correction algorithms have been developed by, for example, Miloshevich et al. (2004), Leiterer et al. (2005) and Miloshevich et al. (2009), which mainly focus on RS80 and RS92 radiosonde observations. Such corrections have not been applied to the utilised GUAN observations.
Examples of reprocessed radiosonde archives which include temperature and
water vapour are the integrated global radiosonde archive (Durre et al.,
2006) and its homogenised version (Dai et al., 2011). Dai et al. (2011)
describe a few known discontinuities in humidity observations from
radiosondes. These are as follows: the dew point depression was set to 30
AIRS is an infrared cross-track-scanning instrument onboard the NASA Aqua satellite which also carries an AMSU-A radiometer. The NASA Aqua satellite has been in orbit since 2002. The level
2 AIRS data record which is used for comparison is the AIRX2RET product
provided by the NASA Goddard Earth Science Data and Information Service
Center (
An evaluation of the AIRS version 5 TPW products' accuracy is given in Bedka
et al. (2010), who compared the satellite products to ground-based
observations at selected Atmospheric Radiation Measurement (ARM) sites.
Using ground-based microwave radiometer observations at Barrow, Southern
Great Plains–Lamont (SGP) and Nauru, the authors found an average relative error
which is typically smaller than 5 % for all sites, except at SGP, where
AIRS products are too moist when TPW is less than 10 kg m
Recently, the AIRS version 6 products have been released. Improvements over
version 5 are described at
Three data records are used for the evaluation: ATOVS and GUAN data records
for the period 1999–2011 and AIRS data record for the period 2003–2011.
The data have not been collocated and GUAN data have only been used when at
least two observations per day are available. ATOVS and AIRS data records
exhibit similar annual cycles. However, a systematic difference between both
data records is evident. This is discussed in Sect. 4.2.3. The annual cycle of the GUAN time series has
larger amplitudes than the annual cycles of the satellite time series (not
shown). The GUAN stations are located on islands and over land, with the
majority of stations in the continental Northern Hemisphere. Schröder
and Lockhoff (2013) show that the strength of the annual cycle is a function
of region: strongest annual cycles are associated with the monsoon regions
and the propagation of the ITCZ, largest regions of minimum strength are
found over the oceans of the Southern Hemisphere, and land areas typically exhibit strong
annual cycles. The former explains the presence of an annual cycle in the
satellite data due to the imbalance in strength between the Northern and the
Southern Hemisphere and the latter in combination with the asymmetric
sampling between the Northern and Southern Hemisphere explains the
annual cycle in the GUAN data. The annual cycle in TPW from GUAN has
slightly larger amplitudes in 1999 and 2000 than from 2001 onwards (not
shown). The larger amplitudes in the first 2 years are caused by stronger
minima during boreal winters. When looking at the time series of
deseasonalised anomalies (not shown), a breakpoint is found between June and
July 2001. The strength of the breakpoint is computed as the difference
between the average TPW using a period of 24 months prior to and after the break. Through use of this
strength and the averaged standard deviation over the two periods as input
to a two-sided
The ATOVS TPW data record exhibits a breakpoint between January 2001 and
February 2001 (not shown). The difference in TPW between the years 1999–2000
and 2002–2003 is 2.8 kg m
First, the breakpoint does not temporally coincide with the start of the use of SNOs in January 2001. Moreover, no breakpoint is visible between December 2010 and January 2011, when the use of SNOs ends.
Histogram of the TPW values for June 2002. The dashed line represents the histogram of the CM SAF ATOVS TPW product using the data from the NOAA-15 and NOAA-16 satellites and the Kriging method for averaging. The red line represents the histogram of the data from the NOAA-15 and NOAA-16 satellites being averaged using the arithmetic averaging method. The solid black and green lines represent the data from the NOAA-15 and NOAA-16 satellites, respectively (averaged also using the arithmetic averaging method).
Second, we assess the impact of Kriging on the homogeneity of the time
series. We compared the PDF based on the CM SAF ATOVS data record products
and ATOVS products, which have been arithmetically averaged on basis of daily
values. Figure 4 shows the PDFs of TPW values for
June 2002 separately for the CM SAF ATOVS data record products and for the
arithmetically averaged monthly means. Obviously, the distribution of the CM
SAF ATOVS data record products exhibits an increased number of TPW values at
the high end of the distribution. This is reflected in the monthly mean TPW
of 25.3 kg m
Finally, a specific feature of Kriging is discussed. Kriging requires two independent measurements such as those from different satellites or from the morning and afternoon overpasses of a single satellite. For the period January 1999 to February 2001, only NOAA-15 was available. Then, it may happen that the same location is not observed twice a day, e.g. due to the occurrence of strong precipitation events. When this happens, Kriging is not applied and the daily average is flagged as undefined. For the June 2000 case study, the number of valid observations is 12 % smaller in the Kriging product than in the arithmetically averaged product and the number of undefined values is 9 % larger in the Kriging product than in the arithmetically averaged product. Indeed, it is visible that the positions of minima in the number of valid observations and of undefined values coincide with the position of the Intertropical Convergence Zone (ITCZ) (not shown).
The methodology for the comparison of the ATOVS data record against the GUAN radiosonde data record is as follows. First, the GUAN data record is integrated to match the vertical layer and level definitions of the ATOVS data record water vapour products. For each day, only stations with at least two radiosonde launches per day are used and averaged to daily values. The ATOVS data record is spatially collocated to the position of the GUAN stations using a nearest-neighbour algorithm. The collocated daily averages form the basis for the comparison. We analyse the monthly bias and the bias-corrected root mean square error (RMSE) between ATOVS and GUAN data records. The number of valid collocations per month is greater than or equal to 450. The results shown in this section show bias and RMS based on all valid daily averages. Note that potential dependencies on climate regimes, TPW, and other regional dependencies are not resolved here. We expect occasionally larger bias and larger RMS on regional scale.
TPW bias and bias-corrected RMSE between the ATOVS and the GUAN data records: reprocessed data set from January 1999 to December 2011 (left panel) and operational data set from January 2004 to December 2011 (right panel). Note the difference in temporal coverage.
First of all, the difference in quality between the reprocessed and operational ATOVS products is discussed. Figure 5 presents the comparison results between TPW from the reprocessed and operational ATOVS data records and the GUAN data record. Figure 5 clearly shows that the TPW product from the reprocessed ATOVS data record exhibits a better quality and stability than the TPW from the operational ATOVS product. The bias of the operational ATOVS product compared to the GUAN data record shows a significant breakpoint between April and May 2009. At this time the following changes had been implemented in the operational ATOVS processing chain: migration of the processing chain, update of AAPP and IAPP, removal of NOAA-15 and NOAA-18 observations from the retrieval, and implementation of Metop-A and NOAA-19 observations in the retrieval. The obvious improvement for the reprocessed data record is that the breakpoint in the bias time series is largely reduced, and this also leads to an improved averaged bias. See Sect. 4.2.3 for further discussion. Moreover, the RMSE is slightly smaller for the reprocessed data record than for the operational product.
We now focus on the comparison between the reprocessed ATOVS data record
and the GUAN data record that is shown in the left panel of
Fig. 5. The TPW bias is typically smaller than
1 kg m
The averaged RMSE is 3.25 kg m
LPW bias (left panel) and bias-corrected RMSE (right panel) between the ATOVS and the GUAN data records for the time period January 1999 to December 2011. The upper panels show the time series for the three lowermost layers and the lower panels show the time series for the two uppermost layers.
Figure 6 presents the comparison results between
the ATOVS and the GUAN LPW products, again in terms of bias and RMSE. The
LPW bias for layer 5 (surface–850 hPa) is around
In view of the results shown in Fig. 3 we briefly want to characterise the relative bias of the ATOVS specific humidity product (not shown). The relative bias increases with height and ranges from 4 % at 1000 hPa to approximately 90 % at 200 hPa, with this latter value being 3 or more times larger than the values of the other levels and with ATOVS being more humid than the radiosondes. This may again partly be explained by the dry bias in radiosondes. However, the relative bias is of similar order to the maximum values given in Fig. 3. This may point to a wet bias in the ATOVS product in the upper troposphere. However, a verification is hard to accomplish due to the lack of fully independent and high-quality reference data.
When relative values are considered (not shown), the bias is the smallest
(largest) for LPW5 (LPW1) with average values of
The GUAN radiosonde data and ATOVS are assimilated in the ERA-Interim
reanalysis. Consequently, the bias and RMS of the comparison between the
ATOVS data record and the GUAN radiosonde data might be underestimated due
to this dependency. Although it is outside of the focus of this work, we briefly
note again that various uncertainties contribute to the observed differences.
Here, we compare point measurements with areal observations. Thus, the
representativity uncertainty impacts the observed differences. To our
knowledge the representativeness uncertainty is not known at each GUAN
station. However, for assimilation purposes, high-resolution models have been
used to assess such uncertainties. An analysis example is given in Waller et
al. (2014) for specific humidity – they found a strong dependence of the
representativity uncertainty on height and weather state. Furthermore, the
comparison of the ATOVS and the GUAN data is based on daily averages and the
differences in sampling between the radiosonde and the satellite
observations contribute to the observed differences. To give an example of
the diurnal sampling uncertainty, we use the work of Dai et al. (2002).
Using high-temporal-resolution Global Positioning System data from stations
over North America, they found that the uncertainty in seasonally averaged
TPW is within
In order for it to be possible to compare the ATOVS data record with the AIRS data record, the AIRS profiles are vertically integrated according to the ATOVS layer definitions (see Sect. 2). Then, the swath-based products are gridded onto the ATOVS spatial grid, and finally all data are averaged to obtain monthly means, which form the basis for the comparison. The number of valid collocations per month is typically larger than 60 000.
TPW bias and bias-corrected RMSE between the ATOVS and the AIRS V5 data records for the time period January 2003 to December 2011.
Figure 7 presents the comparison results of AIRS
and ATOVS TPW products. It can be seen that the TPW bias changes from
approximately 1 kg m
Similar to Fig. 6 but for the bias and bias-corrected RMSE between the ATOVS and the AIRS V5 data records for the time period January 2003 to December 2011.
The LPW bias is shown in Fig. 8 and exhibits similar features as the bias
for TPW, except for the LPW bias for layer 5, which exhibits an annual cycle.
The breakpoint observed in the comparison of TPW in early 2009 is also
evident for LPW for layers 3 to 5. Relative to the TPW bias and the LPW bias
for layers 3 to 5, the RMSE is stable over time. The LPW bias for layer 1 and
the LPW RMSE for layer 3 exhibit a distinct feature between late 2005 and
early 2009. This coincides with the use of NOAA-18 observations in the
retrieval while MHS onboard this particular satellite experienced a series of
technical issues (see
The RMSE between the ATOVS and the AIRS data records does not exhibit a
pronounced annual cycle and is typically smaller than the RMSE between the
ATOVS and the GUAN data records likely because the number of valid
collocations is larger and equally distributed over the Northern and
Southern Hemisphere and because the comparison of point measurements with
areal observations likely exhibits larger representativity uncertainties
than the comparison of two areal observations. However, the biases for TPW
and LPW are larger between the ATOVS and the AIRS data records than between
the ATOVS and the GUAN data records. The relatively large bias between the
ATOVS and the AIRS data records is discussed and analysed in more detail.
Schneider et al. (2012) compared the TPW of a SSM/I
Bedka et al. (2010) compared the AIRS V5 data record to ARM observations at
Nauru, Barrow and SGP. The average RMSE values are between 2.0
and 3.4 kg m
Mean TPW bias between ATOVS and AIRS V5 data records for the time period January 2003 to December 2011.
Finally, Fig. 9 shows a map of the TPW bias between ATOVS and the AIRS data records. Obviously the bias is dominated by regions of strong precipitation and frequent cloud occurrences such as in the ITCZ and storm track regions. Relatively large values are observed at mountainous areas such as the Alps, but the maximum differences are observed over tropical land surfaces with relative differences of about 15 %. Of course, differences in retrieval setup and associated uncertainties contribute to this bias. Of particular relevance in view of the spatial distribution of the bias are differences in cloud detection, in cloud clearing (not applied for the ATOVS data record) and in the handling of scattering events (screened in the ATOVS data record). In the ATOVS data record, AMSU-B observations are used which also allow a retrieval under cloudy conditions, while in the AIRS data record, cloud clearing needs to be applied to the AIRS data in order to retrieve TPW. In general clear-sky observations exhibit a systematic underestimation of TPW relative to almost all-sky observations (e.g. Sohn and Bennartz, 2008). Thus, the different instrumentation might contribute to the observed differences.
As outlined earlier, the gap-filling process of the Kriging contributes to the observed difference between the ATOVS and the AIRS TPW products with the TPW from ATOVS being larger in precipitating areas than the TPW from AIRS. Schröder et al. (2013) compared the CM SAF SSM/I TPW product to the SSM/I TPW product from the University of Hamburg and from the Max Planck Institute for Meteorology (Andersson et al., 2010). The only difference in the generation of both products is again that the CM SAF product is based on post-processing using Kriging. The spatial distribution of their results is very similar to spatial distribution in Fig. 9. Thus, Kriging is a significant contributor to the observed bias between the ATOVS and the AIRS data records.
As precipitation over tropical land surfaces exhibits a pronounced diurnal
cycle with maxima in the late afternoon and evening (e.g. Yang and Slingo,
2001) the differences between TPW from Metop-A (Equator-crossing time of
ascending node:
Because the TPW bias between the ATOVS and AIRS data records exhibits a
breakpoint in early 2009, we had a closer look at the TPW time series from
the ATOVS data record and at the TPW bias time series between the ATOVS and
the GUAN data records. Between April and May 2009, the ATOVS TPW anomaly
time series exhibits a breakpoint with a strength of 0.75 kg m
Bias between the ATOVS data record, derived from each satellite separately, and the AIRS V5 data record for the time period January 2003 to December 2011.
TPW bias between the ATOVS and the AIRS V5 data records, as in Fig. 7. Furthermore, the figure shows the bias between the ATOVS data record without the use of NOAA-15 data from June 2008 onwards (instead of May 2009).
The ATOVS data record was processed using a frozen processing system and up-to-date tools and retrievals. The ATOVS data record is suitable for the following applications: process studies, variability analysis, and model evaluation if the assumptions and limitations described in this section are kept in mind.
The data record is not independent of the ERA-Interim model fields since those are used as input to the retrieval. Considering the weighting functions of the ATOVS instruments, the results in the lower troposphere over land surface may be significantly influenced by the model fields. Another related limitation is that the ERA-Interim model fields are not independent of ATOVS since the ATOVS data are assimilated in the reanalysis model.
Different satellites are used to generate the data record, and the number of satellites which are used for the processing also varies from one to four. The satellites have different local overpass times and some of them drifted with time – these two factors might affect the performance of the data record. Furthermore, the data exhibit a lower quality if only one satellite is used to generate the data record because the Kriging routine then uses morning and afternoon orbits to estimate the local variance. This is only possible if the morning and afternoon observations are valid at the same location, which reduces the number of valid observations. This impacts the quality of the first 2 years of the ATOVS data record.
The quality of the product depends on the intercalibration of the AMSU-A, AMSU-B/MHS, and HIRS brightness temperatures. A missing or nonoptimal intercalibration might lead to artifact trends. A feasible intercalibration for AMSU-A and HIRS brightness temperatures was not available at the time of processing. Only intercalibration coefficients for AMSU-B channels have been applied for the time period 2001 to 2010 (John et al., 2012). AMSU-B/MHS brightness temperatures are intercalibrated using the SNO method described in John et al. (2012). It is shown in John et al. (2012) that the measurements taken into account in the SNO occur only at the poles, and thus only a small part of the dynamic range of the global measurements is represented in the SNO. Consequently, potential non-linear effects as a function of scene brightness temperature are not considered. It has also been shown that there might be scan asymmetry in the AMSU-B brightness temperatures (Buehler et al., 2005; John et al., 2013), which has not been accounted for here.
The impacts of the Kriging and the lack of intercalibration reduce the stability of the ATOVS product.
This, in combination with missing bias correction, has a complex impact on the systematic error of the product and, together with the limited temporal coverage, makes this product unsuitable for climate change analysis.
The water vapour retrieval is not reliable in the case of very elevated terrain (mostly in the Himalaya region), because in such regions the sounders “see” through the entire atmosphere down to the surface and the signal is contaminated with surface contributions.
We introduced the recently released global CM SAF ATOVS tropospheric
temperature and water vapour data record. The data record has a spatial
resolution of 90 km
The analysis of the global TPW average from the ATOVS data record revealed a significant breakpoint between January and February 2001 which coincides with the change from the use of observations from one satellite for the retrieval to the use of two satellites. An example of the monthly mean PDF analysis shows that the Kriging systematically fills the PDF at large values. As gaps typically occur in association with precipitation, and consequently in areas of high humidity content, this is reasonable. Thus, the gap filling process through Kriging largely explains the breakpoint. We do not recommend using the ATOVS data record for the period from January 1999 to January 2001 for variability analysis due to a questionable applicability of the Kriging algorithm in the presence of data from a single satellite. Further analysis is needed to quantify the bias potentially caused by sampling gaps in the presence of precipitation, as also recommended in Schröder et al. (2013) as a result of the third G-VAP workshop.
The TPW and LPW products from the ATOVS data record have been compared to
corresponding radiosonde observations at GUAN stations and to the AIRS
version 5 data record in order to identify potential issues in the ATOVS
data record and to characterise the ATOVS data record in a relative sense.
The breakpoint between January and February 2001 is not evident in the bias
between ATOVS and GUAN due to the collocation procedure. Based on the
comparison to the GUAN data record, we find an averaged bias and an averaged
RMSE of
The provision of vertically resolved data in the upper troposphere is crucial for, among other things, the analysis of the water vapour feedback. In order to ease comparisons and to enhance the reliability of related conclusions, the provision of the retrieval uncertainty and averaging kernels at the pixel level would be beneficial. In the case of the gridded CM SAF product, the first of the next steps will be the implementation of the retrieval error and error propagation into the gridded product.
Finally, it became obvious that the ATOVS data record will benefit from carefully quality-controlled, recalibrated and intercalibrated sensor data. Such high-quality level 1 data are being generated in cooperation between CM SAF and EUMETSAT and within the European Union project “Fidelity and Uncertainty in Climate data records from Earth Observations”.
Metop-B is the last satellite carrying the ATOVS sensor suite. The processing needs to be adapted to account for the replacement of HIRS with IASI. The quality and usability would benefit from the inclusion of data from other hyperspectral infrared and microwave sounders and from a backward extension of the processing by implementing TOVS data.
The work presented in this paper was performed within the EUMETSAT CM SAF framework. The authors acknowledge the financial support of the EUMETSAT member states. The authors also acknowledge the Cooperative Institute for Meteorological Satellite Study of the University of Wisconsin for developing IAPP and making it available, and more particularly Chia Moeller for her support. Acknowledgments also go to the EUMETSAT Satellite Application Facility for Numerical Weather Prediction for the development and the provision of AAPP, with special thanks to Nigel Atkinson for his support. The authors also thank Viju John for providing the SNO coefficients for AMSU-B and for his advice. The data record has been generated using the computer and database facility of the ECMWF. ECMWF and the NASA Goddard Earth Science Data and Information Service Center are acknowledged for providing the ERA-Interim and AIRS version 5 data, respectively. Finally, we thank Nathalie Selbach and Stephan Finkensieper from the Deutscher Wetterdienst for valuable discussions and technical support.Edited by: V. Sinha