Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned ...activities at the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1∘ × 1∘ TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (-2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.
On board the Copernicus Sentinel-5 Precursor (S5P) platform, the
TROPOspheric Monitoring Instrument (TROPOMI) is a double-channel,
nadir-viewing grating spectrometer measuring solar back-scattered ...earthshine
radiances in the ultraviolet, visible, near-infrared, and shortwave infrared
with global daily coverage. In the ultraviolet range, its spectral resolution
and radiometric performance are equivalent to those of its predecessor OMI,
but its horizontal resolution at true nadir is improved by an order of
magnitude. This paper introduces the formaldehyde (HCHO) tropospheric
vertical column retrieval algorithm implemented in the S5P operational
processor and comprehensively describes its various retrieval steps.
Furthermore, algorithmic improvements developed in the framework of the EU
FP7-project QA4ECV are described for future updates of the processor.
Detailed error estimates are discussed in the light of Copernicus user
requirements and needs for validation are highlighted. Finally, verification
results based on the application of the algorithm to OMI measurements are
presented, demonstrating the performances expected for TROPOMI.
Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the ...uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting.
A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from ...the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels.
Many applications of atmospheric composition and climate data involve the comparison or combination of vertically resolved atmospheric state variables. Calculating differences and combining data ...require harmonization of data representations in terms of physical quantities and vertical sampling at least. If one or both datasets result from a retrieval process, knowledge of prior information and averaging kernel matrices in principle allows retrieval differences to be accounted for as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry transport modeling support. In this work an overview of harmonization or matching operations for atmospheric profile observations is provided. The effect of these manipulations on the information content of the original data and on the uncertainty budget of data comparisons is examined and discussed.
Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural ...resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications.
Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ...ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model.This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes.On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from -6 % to -3 %), tropospheric (from -32 % to -23 %) and total (from -12 % to -5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.
The TROPOspheric Monitoring Instrument (TROPOMI), launched in
October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the
composition of the Earth's atmosphere at an unprecedented ...horizontal
resolution as fine as 3.5 × 5.5 km2. This paper assesses the performances
of the TROPOMI formaldehyde (HCHO) operational product compared to its
predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal
scales. The parallel development of the two algorithms favoured the
consistency of the products, which facilitates the production of long-term
combined time series. The main difference between the two satellite products
is related to the use of different cloud algorithms, leading to a positive
bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show
that after switching off the explicit correction for cloud effects, the two
datasets come into an excellent agreement. For medium to large HCHO vertical
columns (larger than 5 × 1015 molec. cm−2) the median bias between
OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present
a remaining positive bias of about 20 % (< 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also
use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite
sensors for a large range of HCHO columns. This work complements the study
by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate
the TROPOMI HCHO operational product. Consistent with the FTIR validation
study, we find that for elevated HCHO columns, TROPOMI data are
systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column
values. We further show that OMI and TROPOMI data present equivalent biases
for large HCHO levels. However, TROPOMI significantly improves the precision
of the HCHO observations at short temporal scales and for low HCHO columns.
We show that compared to OMI, the precision of the TROPOMI HCHO columns is
improved by 25 % for individual pixels and by up to a factor of 3 when
considering daily averages in 20 km radius circles. The validation precision
obtained with daily TROPOMI observations is comparable to the one obtained
with monthly OMI observations. To illustrate the improved performances of
TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO
column enhancements related to shipping emissions in the Indian Ocean. This
is achieved by averaging data over a much shorter period (3 months) than
required with previous sensors (5 years) and opens new perspectives to
study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical
interactions.
Contrary to the statements put forward in “Evaluation of measurement data – Guide to the expression of uncertainty in measurement”, edition 2008
(GUM-2008), issued by the Joint Committee for Guides ...in Metrology, the error concept and the uncertainty concept are the same. Arguments in favor of the contrary have been analyzed and found not to be compelling. Neither was any evidence presented in GUM-2008 that “errors” and “uncertainties” define a different relation between the measured and true values of the variable of interest, nor does this document refer to a Bayesian account of uncertainty beyond the mere endorsement of a degree-of-belief-type conception of probability.
In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO.sub.2) ...pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO.sub.2 tropospheric columns (TrC-NO.sub.2) over the Iberian Peninsula during 2018-2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NO.sub.x anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %-45 % during April and November to 70 %-80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO.sub.2 hotspots are located over Madrid and Barcelona, while TrC-NO.sub.2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO.sub.2 appear reasonably well correlated with collocated surface NO.sub.2 mixing ratios, with correlations around 0.7-0.8 depending on the averaging time.
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