In October 2017, the Sentinel-5 Precursor (S5P) mission was launched, carrying the TROPOspheric Monitoring Instrument (TROPOMI), which provides a daily global coverage at a spatial resolution as high as 7 km × 3.5 km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, enhancing our scientific knowledge of atmospheric processes with its unprecedented spatial resolution. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work, the TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, which form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith angle, viewing angle, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 –1.5 % and 2.5 %–4.5 %, respectively. The mean bias that results from the comparisons is well within the S5P product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements. Additionally, the TROPOMI OFFL and NRTI products are evaluated against already known spaceborne sensors, namely, the Ozone Mapping Profiler Suite, on board the Suomi National Polar-orbiting Partnership (OMPS/Suomi-NPP), NASA v2 TOCs, and the Global Ozone Monitoring Experiment 2 (GOME-2), on board the Metop-A (GOME-2/Metop-A) and Metop-B (GOME-2/Metop-B) satellites. This analysis shows a very good agreement for both TROPOMI products with well-established instruments, with the absolute differences in mean bias and mean standard deviation being below +0.7 % and 1 %, respectively. These results assure the scientific community of the good quality of the TROPOMI TOC products during its first year of operation and enhance the already prevalent expectation that TROPOMI/S5P will play a very significant role in the continuity of ozone monitoring from space.

The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of 14 limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20 and 40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5–12 % and the drifts are at most ±5 % decade⁻¹ (or even ±3 % decade⁻¹ for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10 % and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY) and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.

Daily global analyses and 5-day forecasts are generated in the context of the European Monitoring Atmospheric Composition and Climate (MACC) project using an extended version of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The IFS now includes modules for chemistry, deposition and emission of reactive gases, aerosols, and greenhouse gases, and the 4-dimensional variational data assimilation scheme makes use of multiple satellite observations of atmospheric composition in addition to meteorological observations. This paper describes the data assimilation setup of the new Composition-IFS (C-IFS) with respect to reactive gases and validates analysis fields of ozone (O₃), carbon monoxide (CO), and nitrogen dioxide (NO₂) for the year 2008 against independent observations and a control run without data assimilation. The largest improvement in CO by assimilation of Measurements of Pollution in the Troposphere (MOPITT) CO columns is seen in the lower troposphere of the Northern Hemisphere (NH) extratropics during winter, and during the South African biomass-burning season. The assimilation of several O₃ total column and stratospheric profile retrievals greatly improves the total column, stratospheric and upper tropospheric O₃ analysis fields relative to the control run. The impact on lower tropospheric ozone, which comes from the residual of the total column and stratospheric profile O₃ data, is smaller, but nevertheless there is some improvement particularly in the NH during winter and spring. The impact of the assimilation of tropospheric NO₂ columns from the Ozone Monitoring Instrument (OMI) is small because of the short lifetime of NO₂, suggesting that NO₂ observations would be better used to adjust emissions instead of initial conditions. The results further indicate that the quality of the tropospheric analyses and of the stratospheric ozone analysis obtained with the C-IFS system has improved compared to the previous "coupled" model system of MACC.

This paper assesses the quality of IASI (Infrared Atmospheric Sounding Interferometer)/Metop-A (IASI-A) and IASI/Metop-B (IASI-B) ozone (O₃) products (total and partial O₃ columns) retrieved with the Fast Optimal Retrievals on Layers for IASI Ozone (FORLI-O₃; v20151001) software for 9 years (2008–July 2017) through an extensive intercomparison and validation exercise using independent observations (satellite, ground-based and ozonesonde). Compared with the previous version of FORLI-O3 (v20140922), several improvements have been introduced in FORLI-O₃ v20151001, including absorbance look-up tables recalculated to cover a larger spectral range, with additional numerical corrections. This leads to a change of ∼ 4 % in the total ozone column (TOC) product, which is mainly associated with a decrease in the retrieved O₃ concentration in the middle stratosphere (above 30 hPa/25 km). IASI-A and IASI-B TOCs are consistent, with a global mean difference of less than 0.3 % for both daytime and nighttime measurements; IASI-A is slightly higher than IASI-B. A global difference of less than 2.4 % is found for the tropospheric (TROPO) O₃ column product (IASI-A is lower than IASI-B), which is partly due to a temporary issue related to the IASI-A viewing angle in 2015. Our validation shows that IASI-A and IASI-B TOCs are consistent with GOME-2 (Global Ozone Monitoring Experiment-2), Dobson, Brewer, SAOZ (Système d'Analyse par Observation Zénithale) and FTIR (Fourier transform infrared) TOCs, with global mean differences in the range of 0.1 %–2 % depending on the instruments compared. The worst agreement with UV–vis retrieved TOC (satellite and ground) is found at the southern high latitudes. The IASI-A and ground-based TOC comparison for the period from 2008 to July 2017 shows the long-term stability of IASI-A, with insignificant or small negative drifts of 1 %–3 % decade⁻¹. The comparison results of IASI-A and IASI-B against smoothed FTIR and ozonesonde partial O₃ columns vary with altitude and latitude, with the maximum standard deviation being seen for the 300–150 hPa column (20 %–40 %) due to strong ozone variability and large total retrievals errors. Compared with ozonesonde data, the IASI-A and IASI-B O₃ TROPO column (defined as the column between the surface and 300 hPa) is positively biased in the high latitudes (4 %–5 %) and negatively biased in the midlatitudes and tropics (11 %–13 % and 16 %–19 %, respectively). The IASI-A-to-ozonesonde TROPO comparison for the period from 2008 to 2016 shows a significant negative drift in the Northern Hemisphere of −8.6±3.4 % decade⁻¹, which is also found in the IASI-A-to-FTIR TROPO comparison. When considering the period from 2011 to 2016, the drift value for the TROPO column decreases and becomes statistically insignificant. The observed negative drifts of the IASI-A TROPO O₃ product (8 %–16 % decade⁻¹) over the 2008–2017 period might be taken into consideration when deriving trends from this product and this time period.

Optical and electrical characteristics of power light-emitting diodes (LEDs) are strongly dependent on the diode junction temperature. However, direct junction temperature determination is not possible and alternative methods must be developed. Current-voltage characteristics of commercial high power LEDs have been measured at six different temperatures ranging between 295 and 400 K. Modeling these characteristics, including variation in the bandgap with temperature, revealed a linear temperature dependence of the forward voltage if the drive current is chosen within a rather limited current range. Theoretically, the voltage intercept can be deduced from the bulk semiconductor bandgap. However, accurate junction temperature determination is only possible if at least two calibration measurements at a particular drive current are performed. The method described in this paper can be applied to calculate the thermal resistance from the junction to any other reference point for any particular LED configuration.

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.