Inverse problems in stellar occultation

Remote sensing plays a key role in monitoring the atmosphere, which becomes increasingly important for the Earth's changing environment. The stellar occultation technique is a novel method to monitor vertical distribution of ozone and other trace gases from the troposphere to the upper mesosphere. GOMOS (Global Ozone Monitoring by Occultation of Stars) flying on board the European Space Agency's ENVISAT satellite is the first operational stellar occultation instrument. Satellite measurements are indirect, and therefore inversion methods play a key role in the retrieval of atmospheric parameters. This dissertation is dedicated to inversion methods for stellar occultation measurements and to the optimization of retrievals. Optimization of retrievals by inclusion of a priori information about smoothness of atmospheric profiles is considered. Two methods are developed. One of them –"the target resolution method"– develops the classical Tikhonov regularization. The second method includes smoothness a priori information in the form of Bayesian optimal estimation. The methodology for creating a priori information about smoothness of atmospheric profiles is developed. This dissertation considers optimal selection of measurements based on information theory, aiming at optimal design of future instruments, as well as at improved efficiency of the current data processing. Two optimization problems, both taking the information content of the measurements as a criterion, are defined and discussed. The selecting procedures were developed, compared with each other and existing methodologies and applied to selection of the most informative channels for GOMOS measurements in the UV-Visible wavelength range. The dissertation presents a feasibility study for retrieval of temperature and density profiles from pointing measurements by stellar occultation instruments. This study introduces extra geophysical parameters that can be obtained from GOMOS measurements. The inversion methods developed are applied to GOMOS measurements. However, the methods are formulated in a general form that allows their application beyond the GOMOS mission.reviewe

Validation of Copernicus Sentinel-3/OLCI Level 2 Land Integrated Water Vapour product

Validation of the Integrated Water Vapour (IWV) from Sentinel-3 Ocean and Land Colour Instrument (OLCI) was performed as a part of the “ESA/Copernicus Space Component Validation for Land Surface Temperature, Aerosol Optical Depth and Water Vapour Sentinel-3 Products” (LAW) project. High-spatial-resolution IWV observations in the near-infrared spectral region from the OLCI instruments aboard the Sentinel-3A and Sentinel-3B satellites provide continuity with observations from MERIS (Medium Resolution Imaging Spectrometer). The IWV was compared with reference observations from two networks: GNSS (Global Navigation Satellite System) precipitable water vapour from the SuomiNet network and integrated lower tropospheric columns from radio-soundings from the IGRA (Integrated Radiosonde Archive) database. Results for cloud-free matchups over land show a wet bias of 7 %–10 % for OLCI, with a high correlation against the reference observations (0.98 against SuomiNet and 0.90 against IGRA). Both OLCI-A and OLCI-B instruments show almost identical results, apart from an anomaly observed in camera 3 of the OLCI-B instrument, where observed biases are lower than in other cameras in either instrument. The wavelength drift in sensors was investigated, and biases in different cameras were found to be independent of wavelength. Effect of cloud proximity was found to have almost no effect on observed biases, indicating that cloud flagging in the OLCI IWV product is sufficiently reliable. We performed validation of random uncertainty estimates and found them to be consistent with the statistical a posteriori estimates, but somewhat higher

A method for random uncertainties validation and probing the natural variability with application to TROPOMI on board Sentinel-5P total ozone measurements

In this paper, we discuss the method for validation of random uncertainties in the remote sensing measurements based on evaluation of the structure function, i.e., root-mean-square differences as a function of increasing spatiotemporal separation of the measurements. The limit at the zero mismatch provides the experimental estimate of random noise in the data. At the same time, this method allows probing of the natural variability of the measured parameter. As an illustration, we applied this method to the clear-sky total ozone measurements by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite. We found that the random uncertainties reported by the TROPOMI inversion algorithm, which are in the range 1–2 DU, agree well with the experimental uncertainty estimates by the structure function. Our analysis of the structure function has shown the expected results on total ozone variability: it is significantly smaller in the tropics compared to mid-latitudes. At mid-latitudes, ozone variability is much larger in winter than in summer. The ozone structure function is anisotropic (being larger in the latitudinal direction) at horizontal scales larger than 10–20 km. The structure function rapidly grows with the separation distance. At mid-latitudes in winter, the ozone values can differ by 5 % at separations 300–500 km. The method discussed is a powerful tool in experimental estimates of the random noise in data and studies of natural variability, and it can be used in various applications.Peer reviewe

MIPAS observations of ozone in the middle atmosphere

This work is distributed under the Creative Commons Attribution 4.0 License.In this paper we describe the stratospheric and mesospheric ozone (version V5r-O3-m22) distributions retrieved from MIPAS observations in the three middle atmosphere modes (MA, NLC, and UA) taken with an unapodized spectral resolution of 0.0625 cm from 2005 until April 2012. O is retrieved from microwindows in the 14.8 and 10 μm spectral regions and requires non-local thermodynamic equilibrium (non-LTE) modelling of the O and vibrational levels. Ozone is reliably retrieved from 20 km in the MA mode (40 km for UA and NLC) up to ∼105 km during dark conditions and up to ∼95 km during illuminated conditions. Daytime MIPAS O has an average vertical resolution of 3-4 km below 70 km, 6-8 km at 70-80 km, 8-10 km at 80-90, and 5-7 km at the secondary maximum (90-100 km). For nighttime conditions, the vertical resolution is similar below 70 km and better in the upper mesosphere and lower thermosphere: 4-6 km at 70-100 km, 4-5 km at the secondary maximum, and 6-8 km at 100-105 km. The noise error for daytime conditions is typically smaller than 2% below 50 km, 2-10% between 50 and 70 km, 10-20% at 70-90 km, and ∼30% above 95 km. For nighttime, the noise errors are very similar below around 70 km but significantly smaller above, being 10-20% at 75-95 km, 20-30% at 95-100 km, and larger than 30% above 100 km. The additional major O errors are the spectroscopic data uncertainties below 50 km (10-12 %) and the non-LTE and temperature errors above 70 km. The validation performed suggests that the spectroscopic errors below 50 km, mainly caused by the O air-broadened half-widths of the band, are overestimated. The non-LTE error (including the uncertainty of atomic oxygen in nighttime) is relevant only above ∼85 km with values of 15-20 %. The temperature error varies from ∼3% up to 80 km to 15-20% near 100 km. Between 50 and 70 km, the pointing and spectroscopic errors are the dominant uncertainties. The validation performed in comparisons with SABER, GOMOS, MLS, SMILES, and ACE-FTS shows that MIPAS O has an accuracy better than 5% at and below 50 km, with a positive bias of a few percent. In the 50-75 km region, MIPAS O has a positive bias of ∼10 %, which is possibly caused in part by O spectroscopic errors in the 10 μm region. Between 75 and 90 km, MIPAS nighttime O is in agreement with other instruments by 10 %, but for daytime the agreement is slightly larger, ∼10-20 %. Above 90 km, MIPAS daytime O is in agreement with other instruments by 10 %. At night, however, it shows a positive bias increasing from 10% at 90 km to 20% at 95-100 km, the latter of which is attributed to the large atomic oxygen abundance used. We also present MIPAS O distributions as function of altitude, latitude, and time, showing the major O features in the middle and upper mesosphere. In addition to the rapid diurnal variation due to photochemistry, the data also show apparent signatures of the diurnal migrating tide during both day-and nighttime, as well as the effects of the semi-Annual oscillation above ∼70 km in the tropics and mid-latitudes. The tropical. daytime O at 90 km shows a solar signature in phase with the solar cycle. © Author(s) 2018.The IAA team was supported by the Spanish MICINN under the project ESP2014-54362-P and EC FEDER funds. The IAA and IMK teams were partially supported by ESA O3-CCI and MesosphEO projects. Maya Garcia-Comas was financially supported by MINECO through its >Ramon y Cajal> subprogram. Funding for the Atmospheric Chemistry Experiment comes primarily from the Canadian Space Agency. Work at the Jet Propulsion Laboratory was performed under contract with the National Aeronautics and Space Administration

Updated merged SAGE-CCI-OMPS+ dataset for the evaluation of ozone trends in the stratosphere

In this paper, we present the updated SAGE-CCI-OMPS+ climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments – SAGE II (Stratospheric Aerosol and Gases Experiment II), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), GOMOS (Global Ozone Monitoring by Occultation of Stars), ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer), OMPS-LP (Ozone Monitor Profiling Suite Limb Profiler), POAM (Polar Ozone and Aerosol Measurement) III, and SAGE III/ISS (Stratospheric Aerosol and Gases Experiment III on the International Space Station). Compared to the original version of the SAGE-CCI-OMPS dataset (Sofieva et al., 2017b), the update includes new versions of MIPAS, ACE-FTS, and OSIRIS datasets and introduces data from additional sensors (POAM III and SAGE III/ISS) and retrieval processors (OMPS-LP). In this paper, we show detailed intercomparisons of ozone profiles from different instruments and data versions, with a focus on the detection of possible drifts in the datasets. The SAGE-CCI-OMPS+ dataset has a better coverage of polar regions and of the upper troposphere and the lower stratosphere (UTLS) than the previous dataset. We also studied the influence of including new datasets on ozone trends, which are estimated using multiple linear regression. The changes in the merged dataset do not change the overall morphology of post-1997 ozone trends; statistically significant trends are observed in the upper stratosphere. The largest changes in ozone trends are observed in polar regions, especially in the Southern Hemisphere

Updated trends of the stratospheric ozone vertical distribution in the 60° S–60° N latitude range based on the LOTUS regression model

This study presents an updated evaluation of stratospheric ozone profile trends in the 60° S–60° N latitude range over the 2000–2020 period using an updated version of the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) regression model that was used to evaluate such trends up to 2016 for the last WMO Ozone Assessment (2018). In addition to the derivation of detailed trends as a function of latitude and vertical coordinates, the regressions are performed with the datasets averaged over broad latitude bands, i.e. 60–35° S, 20° S–20° N and 35–60° N. The same methodology as in the last assessment is applied to combine trends in these broad latitude bands in order to compare the results with the previous studies. Longitudinally resolved merged satellite records are also considered in order to provide a better comparison with trends retrieved from ground-based records, e.g. lidar, ozonesondes, Umkehr, microwave and Fourier transform infrared (FTIR) spectrometers at selected stations where long-term time series are available. The study includes a comparison with trends derived from the REF-C2 simulations of the Chemistry Climate Model Initiative (CCMI-1). This work confirms past results showing an ozone increase in the upper stratosphere, which is now significant in the three broad latitude bands. The increase is largest in the Northern and Southern Hemisphere midlatitudes, with ∼2.2 ± 0.7 % per decade at ∼2.1 hPa and ∼2.1 ± 0.6 % per decade at ∼3.2 hPa respectively compared to ∼1.6 ± 0.6 % per decade at ∼2.6 hPa in the tropics. New trend signals have emerged from the records, such as a significant decrease in ozone in the tropics around 35 hPa and a non-significant increase in ozone in the southern midlatitudes at about 20 hPa. Non-significant negative ozone trends are derived in the lowermost stratosphere, with the most pronounced trends in the tropics. While a very good agreement is obtained between trends from merged satellite records and the CCMI-1 REF-C2 simulation in the upper stratosphere, observed negative trends in the lower stratosphere are not reproduced by models at southern and, in particular, at northern midlatitudes, where models report an ozone increase. However, the lower-stratospheric trend uncertainties are quite large, for both measured and modelled trends. Finally, 2000–2020 stratospheric ozone trends derived from the ground-based and longitudinally resolved satellite records are in reasonable agreement over the European Alpine and tropical regions, while at the Lauder station in the Southern Hemisphere midlatitudes they show some differences

Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe

Pollen exposure weakens the immunity against certain seasonal respiratory viruses by diminishing the antiviral interferon response. Here we investigate whether the same applies to the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is sensitive to antiviral interferons, if infection waves coincide with high airborne pollen concentrations. Our original hypothesis was that more airborne pollen would lead to increases in infection rates. To examine this, we performed a crosssectional and longitudinal data analysis on SARS-CoV-2 infection, airborne pollen, and meteorological factors. Our dataset is the most comprehensive, largest possible worldwide from 130 stations, across 31 countries and five continents. To explicitly investigate the effects of social contact, we additionally considered population density of each study area, as well as lockdown effects, in all possible combinations: without any lockdown, with mixed lockdown−no lockdown regime, and under complete lockdown. We found that airborne pollen, sometimes in synergy with humidity and temperature, explained, on average, 44% of the infection rate variability. Infection rates increased after higher pollen concentrations most frequently during the four previous days. Without lockdown, an increase of pollen abundance by 100 pollen/m3 resulted in a 4% average increase of infection rates. Lockdown halved infection rates under similar pollen concentrations. As there can be no preventive measures against airborne pollen exposure, we suggest wide dissemination of pollen−virus coexposure dire effect information to encourage high-risk individuals to wear particle filter masks during high springtime pollen concentrations. COVID-19 | pollen | viral infection | aerobiology</p