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The first TOAR assessment encountered several observational challenges that limited the confidence in estimates of the burden, short-term variability and long-term changes of ozone in the free troposphere. One of these challenges is the difficulty to interpret tropospheric observations from space, especially when combining data records from multiple satellites with differences in vertical sensitivity, prior information, resolution and spatial domain. Additional confounding factors are time-varying biases and the lack of harmonisation of geophysical quantities, units and definition of the tropospheric top level. All together, these increased the uncertainty on the distribution and trends of tropospheric ozone, impeding firm assessments relevant for policy and science. These challenges motivated the Committee on Earth Observation Satellites (CEOS) to initiate a coordinated activity on improving assessments of tropospheric ozone measured from space. Here, we report on work that contributes to this CEOS activity and to various Working Groups (SOWG, OPT, HEGIFTOM, TOP, ROSTEES, ...) of the ongoing second TOAR assessment. Our primary objective is to harmonise the vertical perspective of different satellite data records. A first class of tropospheric ozone products is obtained through an inversion of spectral measurements by nadir-viewing sounders into a vertical profile. We describe two complementary approaches (Prior Replacement and Complete Data Fusion) to harmonise the differing profile retrieval set-up for GOME-2, IASI and other UV-visible and infrared nadir sensors, using information conveyed in the prior and the averaging kernels. A second class of products is obtained through subtraction of the stratospheric component from total column retrievals. The stratospheric column is derived with various methods, resulting in differing spatial coverage, tropospheric top level, sampling frequency, etc… We present how all tropospheric ozone products, from both classes, are harmonised to a common tropospheric top level. We then intercompare all harmonised satellite records, and report on the differences and how these reduce upon harmonisation. Finally, we reflect on the importance of the vertical harmonisation process to improve constraints of the spatial distribution and trends in tropospheric ozone. Acknowledgements : We are grateful to the sustained effort and committment of the teams, institutes and agencies that collect and provide satellite and ozonesonde data records of high quality.

The status and the most recent developments of the algorithm of the operational TROPOMI (Tropospheric Monitoring Instrument) Ozone Profile product will be presented in this contribution. TROPOMI is the payload onboard of the single-satellite Sentinel-5 Precursor (S5P), an atmospheric composition mission that is part of the EU Copernicus program. The aim of the TROPOMI ozone profile product is to continue the record of the stratospheric ozone, monitor changes, improve the accuracy of the retrieved stratospheric profiles and focus on the tropospheric ozone, which is also important for climate studies. The monitoring of the evolution of stratospheric and tropospheric ozone is important as the ozone plays an important role in atmospheric chemistry and radiative balance throughout the atmosphere. The stratospheric ozone layer from 15 to 50 km absorbs the harmful UV radiation, protecting life at the surface. Tropospheric ozone is a greenhouse gas affecting the climate. In addition, ozone in the lower troposphere is a toxic component of air pollution with significant public health and agricultural impacts.The operational ozone profile product of TROPOMI provides the ozone profile at 33 pressure levels in the atmosphere and as 6 sub-columns with a vertical sampling depending on the altitude. The ozone profile is derived from the UV spectral range (270-330 nm), with an horizontal spatial resolution of approximately 28x28 km2. The derived ozone profile contains 6-7 independent pieces of information, providing a vertical resolution in the range 7 – 15 km. The retrieval is based on the Optimal Estimation method, which combines the information from the measured spectra with the a-priori information. In addition to the a-priori profile, the retrieved profiles and their errors, the algorithm also provides diagnostic information, such as the averaging kernel matrix of the ozone profile elements, an essential parameter also for the product validation operated by the ESA/Copernicus Atmospheric Mission Performance Cluster/Validation Data Analysis Facility (ATM-MPC/VDAF).The algorithm also relies on accurate Level 1B radiometric calibration of both radiance and irradiance data, which addresses in particular the spectral region below 310 nm. From comparison to other satellites sensors as well as to forward models, it is known that the TROPOMI UV spectral bands show systematic radiometric deviations. To address this issue, a radiometric correction based on a comparison with forward models has been developed. The derived correction parameters are updated regularly in the operational product in order to follow the changes of the instrument over time due to its optical degradation.The improvements in the operational algorithm, regarding the radiometric calibration and the choice of a new climatology for the ozone profile a-priori information and its uncertainty, are crucial aspects for the ozone retrieval and they will be shown during this talk.Finally, with the availability of the reprocessed ozone profile data from the beginning of the mission, this contribution will also show the results of the algorithm performances throughout the TROPOMI mission, in ozone hole conditions and for tropospheric enhancements.

The retrieval of atmospheric composition from space-based measurements, by e.g., Sentinel-5p TROPOMI, is strongly affected by radiative interferences with clouds. Dedicated cloud data products, typically retrieved from measurements by the same sounder, are therefore essential. Cloud information is used to filter data and as input to the modelling of atmospheric radiative transfer and the conversion of slant column densities into vertical column densities. The three main TROPOMI cloud retrieval algorithms are: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5P support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). The cloud variables provided by these products (radiometric cloud fraction, cloud (top) height, and cloud albedo/cloud optical thickness) are subsequently used in the retrieval of the TROPOMI trace gas products. The quality of cloud products and trace gas products is routinely assessed by the ESA/Copernicus Atmospheric Mission Performance Cluster (ATM-MPC) validation service, with ad hoc support from Sentinel-5p Validation Team (S5PVT) AO projects. Version upgrades have had a significant impact on the characteristics of S5P cloud data. The change of the wavelength window in the FRESCO product since version 1.4 (‘FRESCO-wide’) leads to a clear increase in the height of low clouds with a large impact on the tropospheric NO2 retrieval (van Geffen, 2022), and improving the validation results regarding the tropospheric and total NO2 column. The first upgrades of the ROCINN products (from v1 to v2.1-v2.3) led to an increase in correlation with CLOUDNET cloud height, but to a more negative bias for the low clouds, with ROCINN CRB cloud height even dropping below the CLOUDNET cloud base height on average. However, this effect seems alleviated with the latest upgrade to v2.4. The impact on the HCHO validation results is investigated but is less clear compared to the NO2 case. To resolve the discontinuities due to the processor version jumps, a full mission reprocessing is currently ongoing and largely carried out for the L2_CLOUD and FRESCO-S products. The reprocessed ROCINN data have a lower dispersion and higher correlation with respect to the CLOUDNET cloud heights. The bias of the L2_CLOUD OCRA/ROCINN CAL CTH becomes more negative, but that of L2_CLOUD OCRA/ROCINN CRB CH bias improves. Finally, we also discuss the impact of the FRESCO-S reprocessing on the validation results.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

The QA4ECV (Quality Assurance for Essential Climate Variables) version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDRs) from the OMI (Ozone Monitoring Instrument) satellite sensor are validated using NDACC (Network for the Detection of Atmospheric Composition Change) zenith-scattered light differential optical absorption spectroscopy (ZSL-DOAS) and multi-axis DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCDs have a small bias of ∼0.2 Pmolec.cm-2 (5 %–10 %) and a dispersion of 0.2 to 1 Pmolec.cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few peta molecules per square centimetre (Pmolec.cm-2) up to −10 Pmolec.cm-2 (−40 %) in one extreme high-pollution case. The QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec.cm-2), which is a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite measurements and ground-based data greatly exceed the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and the difference in vertical smoothing and a priori profile assumptions.

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.

Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators, enabling users to verify the fitness-for-purpose of the S5P data.

Space-born atmospheric composition measurements, like those from Sentinel-5p TROPOMI, are strongly affected by the presence of clouds. Dedicated cloud data products, typically retrieved with the same sensor, are therefore an important tool for the provider of atmospheric trace gas retrievals. Cloud products are used for filtering and modification of the modelled radiative transfer.In this work, we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor TROPOMI radiance measurements. Three cloud products are considered: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (same; Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). These cloud products are used in the retrieval of several S5p trace gas products (e.g., ozone columns and profile, total and tropospheric nitrogen dioxide, sulfur dioxide, formaldehyde). The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.Cloud height data from the three S5p cloud products is compared to radar/lidar based cloud profile information from the ground-based networks CLOUDNET and ARM. The cloud height from S5p CLOUD CRB and S5p FRESCO are on average 0.6 km below the cloud mid-height of CLOUDNET measurements, and the cloud top height from S5p CLOUD CAL is on average 1 km below CLOUDNET’s cloud top height. However, the comparison is different for low and high clouds, with S5p CLOUD CAL cloud top height being only 0.3 km below CLOUDNET’s for low clouds. The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O2-O2. While the latitudinal variation is often similar, offsets are encountered.Recently, major S5p cloud product upgrades were released for S5p OCRA/ROCINN (July 2020) and for S5p FRESCO (December 2020), leading to a decrease of the ROCINN CRB cloud height and an increase of the FRESCO cloud height on average. Moreover, a major change in the ROCINN surface albedo treatment leads to a clear improvement of the comparison with CLOUDNET at the complicated sea/land/ice/snow site Ny-Alesund.

Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing wealth of information delivered by spaceborne observing systems requires addressing the technological challenges for developing new strategies and tools that are capable to deal with these huge data volumes. The H2020 AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project investigated a novel approach for synergistic use of ozone profile measurements acquired at different frequencies (ultraviolet, visible, thermal infrared) by sensors onboard Geostationary Equatorial Orbit (GEO) and Low Earth Orbit (LEO) satellites in the framework of the Copernicus Sentinel-4 and Sentinel-5 missions. This paper outlines the main features of the technological infrastructure, designed and developed to support the AURORA data processing chain as a distributed data processing and describes in detail the key components of the infrastructure and the software prototype. The latter demonstrates the technical feasibility of the automatic execution of the full processing chain with simulated data. The Data Processing Chain (DPC) presented in this work thus replicates a processing system that, starting from the operational satellite retrievals, carries out their fusion and results in the assimilation of the fused products. These consist in ozone vertical profiles from which further modules of the chain deliver tropospheric ozone and UV radiation at the Earth&rsquo, s surface. The conclusions highlight the relevance of this novel approach to the synergistic use of operational satellite data and underline that the infrastructure uses general-purpose technologies and is open for applications in different contexts.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor (S5P) TROPOMI radiance measurements: cloud top height and cloud optical thickness (retrieved with the S5P OCRA/ROCINN_CAL algorithm), cloud height (S5P OCRA/ROCINN_CRB and S5P FRESCO) and radiometric cloud fraction (all three algorithms). The analysis combines: (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS and OMI O2-O2), and (iii) ground-based validation with respect to correlative observations (2018-04-30 to 2020-02-27) from the CLOUDNET network of ceilometers, lidars and radars. Peculiar geographical patterns were identified, and will be mitigated in future releases of the cloud data products. Zonal mean latitudinal variation of S5P cloud properties are similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud top height and cloud optical thickness, and with CLOUDNET cloud top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud top height is below the cloud top height of VIIRS and of CLOUDNET, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the CLOUDNET cloud mean height for the low clouds, but match on an average better with the CLOUDNET cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the CLOUDNET observations.

Tropospheric ozone damages ecosystems and causes human health problems. The high spatial and temporal variability of ozone concentrations in the troposphere challenges global observing systems to monitor ozone at all relevant scales. TROPOMI is a nadir-viewing UV-Vis-NIR-SWIR sensor that combines a high spatial resolution, a large swath width and the spectral measurement characteristics required to deliver trace gas data records at unprecedented detail. The first tropospheric data product was publicly released in Fall 2018, a year after launch on the Sentinel-5p platform (S5p). It is based on the convective-cloud differential technique (CCD) to infer 0.5°x1° resolved daily maps of 3-day moving mean values of the tropospheric ozone column (surface to 270 hPa) between 20°S and 20°N in clear-sky conditions. This makes it the highest resolved tropospheric ozone data set currently available for the tropical belt. About two years of data have been collected since the end of the commissioning phase in April 2018.We present an assessment of the quality of the Sentinel-5p TROPOMI convective-cloud differential tropospheric ozone column data products (O3_TCL OFFL v01.01.05-01.01.07), carried out within the context of ESA’s Sentinel-5p Mission Performance Center (MPC) and the S5PVT AO project CHEOPS-5p. Our assessment of the first two years of TROPOMI data is based on comparisons with (a) quality-assured co-located in-situ measurements by the SHADOZ ozonesonde network, and, (b) satellite data by the GOME-2 and OMI sensors. These well-characterized observational data records serve as references to evaluate the bias and the dispersion of S5p data, and their dependence on influence quantities. Additional visual inspections of the S5p tropospheric ozone maps unveiled non-geophysical structures introduced by the sampling pattern of sensor and clouds. We conclude by assessing the compliance of S5p tropospheric ozone data with respect to mission and user requirements for key data applications.

For more than two years now the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve operationally the total, tropospheric and stratospheric column abundance of atmospheric NO2. In support of these routine operations, the S5P Mission Performance Centre (MPC) performs continuous QA/QC of these data products and produces key Quality Indicators enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in monitoring mode in the S5P Automated Validation Server (AVS) and globally in more complex in-depth analyses. Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons. After two years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO2 data products delivered publicly.S5P NO2 data are compared routinely to ground-based network measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Direct-sun measurements from the Pandonia Global Network (PGN) serve as a reference for total NO2 validation, Multi-Axis DOAS network data for tropospheric NO2 validation, and NDACC zenith-scattered-light DOAS network data for stratospheric NO2 validation. Comparison methods are optimized to limit spatial and temporal mismatch to a minimum (information-based spatial co-location strategy, photochemical adjustment to account for local time measurement difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and irreducible mismatch errors, and (3) the dependence of bias and dispersion on key influence quantities (surface albedo, cloud cover…)Intercomparison of S5P products (NRTI vs. OFFL) and comparison to other satellite data, including a similar processing of OMI measurements, complement the ground-based validation with relative biases and spatio-temporal patterns/artefacts related to instrumental issues (e.g. striping) and to the sensitivity to geophysical features (e.g. clouds and sea/ice albedo contrast). Overall, the MPC quality assessment of S5P NO2 data concludes to an excellent performance for the stratospheric column data (bias2 vs. ground-based data. This dispersion larger than the mission requirement on data precision can partly be attributed to comparisons errors such as those due to differences in horizontal resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm2), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ in specific cases of direct comparisons.

Satellite measurements of tropospheric or total column trace gas species, including those from Sentinel-5p TROPOMI, are affected by the presence of clouds. Therefore, cloud data products retrieved with the same sensor play an essential role, as they allow the data provider to take an estimated cloud impact on the trace gas retrieval into account. Examples are the modification of the radiative transfer and associated quantities such as the air mass factor, and the partial masking of the measurement scene. Evidently, the accuracy of these corrections relies on the accuracy of the retrieved cloud properties, like radiometric cloud fraction (CF), cloud top height (CTH) or cloud height (CH), and cloud optical thickness (COT) or cloud albedo (CA).We consider here three S5p TROPOMI-based cloud products: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands). These are input to the S5p operational processors for several trace gas products, including ozone columns and profile, total and tropospheric NO2, formaldehyde, sulfur dioxide. The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective trace gases.In this work, cloud height (from S5p CLOUD CRB and S5p FRESCO algorithms) and cloud top height (from S5p CLOUD CAL) S5p data is validated with radar/lidar-based cloud profile information from the ground-based networks CLOUDNET and ARM at 17 sites. For some sites the comparison is difficult due to e.g., orography or snow/ice cover. S5P and CLOUDNET report similar cloud height variations at several sites, and the correlation between the S5p cloud products and CLOUDNET can be high (Pearson R up to 0.9). However, there is a site-dependent negative bias of the S5p cloud (top) height with respect to the CLOUDNET data: up to -2.5 km for S5p CLOUD CAL cloud top height and -1.5 km for S5p CLOUD CRB and S5p FRESCO cloud height. The dependence on other parameters measured by S5p and CLOUDNET (e.g., radiometric cloud fraction, cloud phase,…) is investigated.

The QA4ECV version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDR) from the satellite sensor OMI are validated, using NDACC zenith scattered light DOAS (ZSL-DOAS) and Multi Axis-DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCD have a small bias of ~ 0.2 Pmolec cm-2 (5–10 %) and a dispersion of 0.2 to 1 Pmolec cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few Pmolec cm-2 up to −10 Pmolec cm-2 (−40 %) in one extreme high-pollution case. QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec cm-2), a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite and ground-based data exceed by far the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and to the difference in vertical smoothing and a priori profile assumptions.

AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) is a three-year project supported by the European Union in the frame of its H2020 Call (EO-2-2015) for &ldquo, Stimulating wider research use of Copernicus Sentinel Data&rdquo, The project addresses key scientific issues relevant for synergistic exploitation of data acquired in different spectral ranges by different instruments on board the atmospheric Sentinels. A novel approach, based on the assimilation of geosynchronous equatorial orbit (GEO) and low Earth orbit (LEO) fused products by application of an innovative algorithm to Sentinel-4 (S-4) and Sentinel-5 (S-5) synthetic data, is adopted to assess the quality of the unique ozone vertical profile obtained in a context simulating the operational environment. The first priority is then attributed to the lower atmosphere with calculation of tropospheric columns and ultraviolet (UV) surface radiation from the resulting ozone vertical distribution. Here we provide details on the surface UV algorithm of AURORA. Both UV index (UVI) and UV-A irradiance are provided from synthetic satellite measurements, which in turn are based on atmospheric scenarios from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) re-analysis. The UV algorithm is implemented in a software tool integrated in the technological infrastructure developed in the context of AURORA for the management of the synthetic data and for supporting the data processing. This was closely linked to the application-oriented activities of the project, aimed to improve the performance and functionality of a downstream application for personal UV dosimetry based on satellite data. The use of synthetic measurements from MERRA-2 gives us also a &ldquo, ground truth&rdquo, against which to evaluate the performance of our UV model with varying inputs. In this study we both describe the UV algorithm itself and assess the influence that changes in ozone profiles, due to the fusion and assimilation, can cause in surface UV levels.

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. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License

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 accounting for retrieval differences as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry-transport modelling 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.

Tropospheric ozone is a significant greenhouse gas (GHG) formed and destroyed by chemical reactions involving other atmospheric species and a key constituent for understanding the interactions between climate and chemistry. In contrast to most GHGs, tropospheric ozone is produced photo-chemically via its precursors, notably nitrogen oxides (NOx), carbon monoxide (CO), CH4 and non-methane hydrocarbons, all of which have large anthropogenic sources. Crucial uncertainties, however, remain in the assessment of the tropospheric ozone budget, its precursors, and the chemical and physical processes involved. Large spatial and temporal variability is observed in tropospheric ozone, resulting from important regional differences in the factors controlling its concentration. For all these reasons, accurate global measurements of ozone vertical profiles in the troposphere are essential. AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) is a three-year project (ending in July 2019) supported by the European Union in the frame of its H2020 Call EO-2-2015. Its overarching objective is to simulate the provision of synergistic data products, having unprecedented accuracy, for the vertical profiling of atmospheric ozone and to assess their quality with respect to the one expected for the operational products of the geostationary (GEO) mission Sentinel-4 and of the Low Earth Orbit (LEO) missions Sentinel-5p and Sentinel-5. In particular, the main scientific purpose of AURORA is to investigate the potential of synergistic exploitation of complementary ozone measurements acquired in different spectral regions - from the UV over the visible to the thermal infrared - through the assimilation of fused GEO and LEO ozone profile products resulting from application of the innovative method of complete data fusion. The impact of combining complementary capabilities of the atmospheric Sentinels observations, especially in terms of vertical sensitivity, might result in advanced performances that are of specific relevance for tropospheric ozone monitoring. In the frame of the project it has been demonstrated that the complete data fusion method is able to reduce the complexity of managing the high volume of Copernicus S-4 and S-5 data and to increase its quality with respect to the operational outcome of individual instruments. In particular, the fused products show higher degrees of freedom and an enhanced vertical sensitivity. In this project, the 3D ozone analyses and forecasts obtained from the data assimilation systems after assimilating the fused ozone observations are used to derive tropospheric ozone columns and profiles. An extensive ozone product validation system has been developed in the frame of the project to assure that the AURORA tropospheric ozone and data products will be warily verified and documented by means of in-depth QA/validation studies. In this presentation we will discuss the first results of the AURORA synergistic approach, based on data fusion and assimilation, applied to the tropospheric ozone profiling.

With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available from geostationary (GEO) and low Earth orbit (LEO) observations. Enhanced observational capabilities are expected to foster deeper insight than ever before on key issues relevant for air quality, stratospheric ozone, solar radiation, and climate. A major potential strength of the Sentinel observations lies in the exploitation of complementary information that originates from simultaneous and independent satellite measurements of the same air mass. The core purpose of the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project is to investigate this exploitation from a novel approach for merging data acquired in different spectral regions from on board the GEO and LEO platforms. A data processing chain is implemented and tested on synthetic observations. A new data algorithm combines the ultraviolet, visible and thermal infrared ozone products into S-4 and S-5(P) fused profiles. These fused products are then ingested into state-of-the-art data assimilation systems to obtain a unique ozone profile in analyses and forecasts mode. A comparative evaluation and validation of fused products assimilation versus the assimilation of the operational products will seek to demonstrate the improvements achieved by the proposed approach. This contribution provides a first general overview of the project, and discusses both the challenges of developing a technological infrastructure for implementing the AURORA concept, and the potential for applications of AURORA derived products, such as tropospheric ozone and UV surface radiation, in sectors such as air quality monitoring and health.

This paper assesses the quality of IASI/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 nine years (2008–2017) through an extensive inter-comparison and validation exercise using independent observations (satellite, ground-based and ozonesonde). IASI-A and IASI-B Total O3 Columns (TOCs) are generally consistent, with a global mean difference less than 0.3 % for both day- and nighttime measurements, IASI-A being slightly higher than IASI-B. A global difference less than 2.4 % is found for the tropospheric (TROPO) O3 column product (IASI-A being lower than IASI-B), which is partly due to a temporary issue related to IASI-A viewing angle in 2015. Our validation shows that IASI-A and IASI-B TOCs are consistent with GOME-2, Dobson, Brewer and SAOZ retrieved ones, with global mean differences in the range 0.1–2 % depending on the instruments. The IASI-A and ground-based TOC comparison for the period 2008–July 2017 shows good long-term stability (negative trends within 3 % decade−1). The comparison results between IASI-A and IASI-B against smoothed ozonesonde partial O3 columns vary in altitude and latitude, with maximum standard deviation for the 300–150 hPa column (20–40 %) due to strong ozone variability and a priori uncertainty. The worst agreement with the ozonesondes and with UV-vis retrieved TOC [satellite and ground] is found at the southern high latitudes. Compared to ozonesonde data, IASI-A and IASI-B O3 products overestimate the O3 abundance in the stratosphere (up to 20 % for the 150–25 hPa column) and underestimates the O3 abundance in the troposphere (within 10 % for the mid-latitudes and ~ 18 % for the tropics). Based on the period 2011–2016, non-significant drift is found for the northern hemispheric tropospheric columns while a small drift prevails for the period before 2011.

Atmospheric ozone plays a key role in air quality and the radiation budget of the Earth, both directly and through its chemical influence on other trace gases. Assessments of the atmospheric ozone distribution and associated climate change therefore demand accurate vertically-resolved ozone observations with both stratospheric and tropospheric sensitivity, both on the global and regional scales, and both in the long term and at shorter timescales. Such observations have been acquired by two series of European nadir-viewing ozone profilers, namely the scattered-light UV-visible spectrometers of the GOME family, launched regularly since 1995 (GOME, SCIAMACHY, OMI, GOME-2A/B, TROPOMI, and the upcoming Sentinel-5 series), and the thermal infrared emission sounders of the IASI type, launched regularly since 2006 (IASI on Metop platforms and the upcoming IASI-NG on Metop-SG). In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been improved and harmonised in the context of the ozone project of the European Space Agency’s Climate Change Initiative (ESA Ozone_cci). To verify their fitness-for-purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical and temporal domains of validity, (b) quantification of their potential bias, noise and drift and their dependences on major influence quantities, and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA/validation system Multi-TASTE which has been developed in the context of several heritage projects (ESA’s Multi-TASTE, EUMETSAT’s O3M-SAF, and the European Commission’s FP6 GEOmon and FP7 QA4ECV). This work, as the second in a series of four Ozone_cci validation papers, reports for the first time on data content studies, information content studies and ground-based validation for both the GOME- and IASI-type climate data records combined. The ground-based reference measurements have been provided by the Network for the Detection of Atmospheric Composition Change (NDACC), NASA’s Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other ozonesonde and lidar stations contributing to the World Meteorological Organisation’s Global Atmosphere Watch (WMO GAW). Dependence of the Ozone_cci data quality on major influence quantities – resulting in data screening suggestions to users – and perspectives for the Copernicus Sentinel missions are discussed.

The exploitation of complementary and redundant information on atmospheric targets simultaneously observed by multiple independent data sources, such as the atmospheric Sentinels, has intrinsic potential to extend the quality of geophysical products beyond the limits already set by the challenging requirements of the Copernicus program for the atmosphere. Synergistic use of measurements from the Sentinel-5p satellite, launched on October 13th, 2017, from the Sentinel-4 and Sentinel-5 and their contributing missions MTG and MSG, can take advantage of different spectral coverage from the UV, to the Visible, to the Thermal Infrared region and observation geometry on Low Earth Orbit and Geostationary platforms. The use of simultaneous retrieval or data fusion techniques is fit for purpose of merging datasets from different measurements and combination with state-of-the-art modeling capabilities is offered by data assimilation systems. The Horizon 2020 project AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) is now investigating a novel approach based on sequential application of the innovative Complete Data Fusion technique and of data assimilation models (TM5, IFS and C-IFS) to synthetic data of Sentinel-4 and Sentinel-5 to obtain a unique product for the vertical profile of atmospheric ozone. First results from assimilation experiments conducted using the TM5 data assimilation systems applied to Sentinel-4 and Sentinel-5 standard retrieval products and to the corresponding fused profiles will be presented and discussed. An estimate of the impact of the advanced quality attained for the ozone profiles will be given for derived products such as tropospheric ozone column and ultraviolet surface radiation of immediate relevance for applications. The presentation will be finally focusing on the progress status of the activities for development of two applications - HappySun and AirPortal - using UV surface radiation and Ozone products from AURORA for demonstration purposes.

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a Level-3 data record, which combines individual sensor products into one single cohesive record covering the 22 year period from 1995 to 2017, generated in the frame of the European Space Agency's Climate Change Initiative Phase-II. It is based on Level-2 total ozone data produced by the GODFIT v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/MetopA and /MetopB observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate-chemistry modelling community for decadal stability, long-term and short term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC), both at Level-2 and Level-3. The individual Level-2 data sets show excellent inter-sensor consistency with mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the Level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4&htinsp;% (for SCIAMACHY). Very similar findings are reported for the Level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments; the mean bias between GODFIT v4 satellite TOC and ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % to 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges between ~ 1 % for GOME and OMI, to ~ 2 % for SCIAMACHY. For the Level-3 validation, as a first step the aim was to show that the Level-3 CRDP produces consistent findings as the Level-2 individual sensor comparisons. We show an excellent agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade in the Northern Hemisphere differences at −0.11 ± 0.10 % per decade for Dobson and +0.22 ± 0.08 % per decade for Brewer collocations. The exceptional quality of the Level-3 GTO-ECV v3 TOC record temporal stability well satisfies the requirements for the total ozone measurement decadal stability of between 1–3 % and the short term and long-term accuracy requirements of 2 % and 3 % respectively, showing an excellent inter-sensor consistency both in the Level-2 GODFIT v4 as well as in the Level-3 GTO-ECV v3 datasets and thus can be used for longer term analysis of the ozone layer, such as decadal trend studies, chemistry-climate model evaluation and data assimilation applications.

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.

The electrical-thermal-optical characteristics of AlGaInP yellow and InGaN/GaN blue LEDs under electrical stresses were studied. Since the increase of effective acceptor concentration on p-type side, the forward voltages of AlGaInP decrease after 3155 h aging. And the operating voltage of high forward bias expansion for InGaN/GaN is due to the increase of the series resistance. Compared with InGaN/GaN, AlGaInP LEDs display different trend for the relationship between optical output and ideality factors. The relationship between ideality factor and radiative recombination is also studied and established. The characteristic of different intermediate adhesive is compared during aging period based on transient thermal test.

The MIPAS spectrometer onboard the Envisat platform observed infrared emission from the Earth's limb between 2002 and 2012. It recorded high-resolution spectra during day and night, from pole to pole and between 6 and 70 km altitude in the nominal measurement mode or up to 170 km in special measurement modes, producing daily more than 1000 vertical profiles of various trace gases. The operational Level-2 data are processed by ESA/DLR but there exist three other, independent research Level-2 processors that are hosted by ISAC-CNR/University of Bologna, Oxford University, and KIT IMK/IAA. All four Level-2 processors rely on the same Level-1b data provided by ESA but their retrieval schemes differ. As part of ESA's Ozone Climate Change Initiative project, an intercomparison of the four MIPAS processors took place, in which vertical ozone profiles retrieved by these four processors from MIPAS nominal mode measurements were compared for 2007 and 2008. We present the results of this comparison exercise, which consisted of five parts: an information content study of the vertical averaging kernels, an intercomparison of zonal seasonal means and spreads, a determination of biases through comparison to ozonesonde and lidar measurements, a comparison to other satellite records (bias estimation and precision assessment with respect to ACE-FTS and Aura-MLS data), and a geophysical validation of the provided error bars using MIPAS–MIPAS collocations. The four processors demonstrate similar performance. All processors use the same Level-1b data from ESA, apply global fits, and use microwindows instead of the full spectrum. The main differences in the processing schemes include the choice of microwindows, the regularization approach, the treatment of negative retrieved values, and the cloud detection threshold. The different regularization schemes lead to a different trade-off between noise and resolution, but without a clear average advantage for any particular data set. The vertical resolution is typically 3–5 km and the single profile precision is about 2–3%. In the middle and upper stratosphere, at 25–45 km, all four MIPAS processors clearly show a high bias of 2 to 5% relative to all reference instruments. The similarity of the structure and magnitude of the bias among the MIPAS data sets indicates that the bias is most likely linked to the use of microwindows of the MIPAS AB band. The satellite intercomparisons show furthermore that for the KIT dataset, the onset of the high bias starts at a somewhat higher altitude (only above 35 km) than for the other three datasets. This is likely due to the more restrictive use of the AB band by the KIT processor, which comes at the cost of a coarser vertical resolution near the ozone volume mixing ratio (vmr) peak. In the troposphere, the Level-2 algorithms that suppress negative ozone values in the iterative retrieval process produce a larger positive bias than the algorithm that does not follow such a strategy. Our main conclusion is that the four MIPAS processors are more similar to each other than to any other reference instrument. This indicates that the observed biases are very likely instrument-related.

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.

High-power light-emitting diode (LED) efficiencies are strongly dependent on device type and operating conditions. Electrical and optical power measurements are performed on five commercially available phosphor-white LED types for a number of independently enforced forward currents and junction tem-peratures. The experimental results allow for a comparative evaluation of dominant loss mechanisms and their current and temperature dependences. ispartof: Journal of Light & Visual Environment vol:35 issue:3 pages:1-8 status: published

The relative spectral radiant flux error caused by phosphor fluorescence during integrating sphere measurements is investigated both theoretically and experimentally. Integrating sphere and goniophotometer measurements are compared and used for model validation, while a case study provides additional clarification. Criteria for reducing fluorescence errors to a degree of negligibility as well as a fluorescence error correction method based on simple matrix algebra are presented. Only remote phosphor type LED light sources are studied because of their large phosphor surfaces and high application potential in general lighting. ispartof: Proceedings of SPIE vol:8082 ispartof: Optical Metrology location:Munich date:23 May - 26 May 2011 status: published

In contrast to the spatial (luminous) intensity distribution of high power light-emitting diodes (LEDs), little effort has been made to examine the spatial colour distribution of these light sources, i.e. the values of CIE colour coordinates as a function of direction in space. The spatial colour variation is negligible for single colour emitters, but this is not the case for bichromatic white LEDs using phosphor for wavelength conversion. As the latter diode types are most often used for high colour rendering applications, a quantitative description of their colour distribution is necessary. Therefore, photogoniometer measurements have been performed on a variety of white light-emitting diodes incorporating a planar (remote) phosphor. In this paper measurement results are used to discuss and model the spatial colour distribution of phosphor-white LEDs. Such LEDs appear to show an intrinsic and inevitable spatial colour variation. Furthermore, the measurement data and constructed model allow evaluating the visibility of spatial colour differences and the relevance of colour binning measurements at the end of LED package production lines. Using insights on spatial colour distribution gathered throughout this paper, a design proposal is made to vastly decrease the colour variation of phosphor-white LEDs.

The forward voltage Uf for single-die high-power light-emitting diodes (LEDs) driven at currents within a specific current interval is proportional to the diode junction temperature T . This correlation can be used to determine junction temperatures in lots of practical applications. However, multi-die high-power LED modules with multiple series or parallel connections of diode chips are believed to have a much greater potential to be used in general lighting than single-die packages. The current-voltage characteristics of a variety of multi-die LEDs, ranging from two to a few hundred dies, are recorded at different ambient temperatures. The results are used to model the forward voltage as a function of a generalized junction temperature. In multi-die LED modules these models allow analogous junction temperature determination as in single-die packages. The influence of drive current and drive mode (DC or PWM) on junction temperature is examined and compared for both single-die and multi-die packages. Apparently, junction temperature only significantly increases when a certain current level is exceeded, depending on the internal series resistance of the complete LED package. Moreover, combining Uf (T) models for single-die and multi-die LEDs allows for the characterization of thermal interactions between different dies of multi-die packages, whether they are switched on or not. The junction temperature of separate LED dies in multi-die modules can then be predicted and used for further diode characterization.

Spectral radiant flux is the primary optical characteristic of a light source, determining the luminous flux and color. Much research is dedicated to the modeling of light-emitting diode (LED) spectra and their temperature dependence, allowing for the simulation of optical properties in various applications. Most of the spectral radiant flux models that have been published so far are purely mathematical. For this paper, spectral radiant fluxes of commercial single color LED packages have been measured in a custom made integrating sphere at several junction temperatures by active cooling and heating with a Peltier element. A spectrum model at 300 K is constructed where the Boltzmann free carrier distribution and carrier temperature are included. Subsequently, the model is extended with the carrier temperature variation, the band gap energy shift, and the nonradiative recombination rate decrease with junction temperature. As a result, the skewness variation, peak frequency shift, and peak value change in th...

Light-emitting diode (LED) technology is developing very quickly and may be considered an alternative for traditional light sources. However, at this moment, manufacturers and end users of LEDs are facing a rather basic but major problem. The lack of standardization regarding optical and electrical characterization of LEDs appears to compromise a successful implementation. In particular, numbers quoted for the luminous flux, and consequently for the efficacy of LEDs, are very sensitive data because they are used to impress and push the LED market. In this paper, the most was made of the typical hemispherical radiation of high-power LEDs to increase the accuracy of the flux determination using a custom-made integrating sphere. Recently developed measurement techniques such as the use of an external spectral irradiance standard and an optimized spectral irradiance detection head are combined with a very particular port geometry and a minimized baffle area. This results in a uniform spatial response distribution function (SRDF), which guarantees an accurate radiant and luminous flux determination, irrespective of the spatial intensity distribution of the LED package or luminaire. The effect of the directional response of the detector head on the SRDF has been explored. Measurements on LED devices with and without external optics are presented, illustrating the possibilities of the measurement setup.

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.