Atmospheres Laboratory Environments, Observations Spatiales

IntroductionWISDOM (Water Ice Subsurface Deposits Observation on Mars) will be the Ground Penetrating Radar of the Rosalind Franklin rover of the ExoMars 2022 mission. This rover will be the first able to collect samples down to 2 m in the Martian subsurface, and analyse them in search of potential traces of life [1]. Prior to any drilling operations, the WISDOM instrument will provide an insight into the shallow subsurface structure and composition, in order to guide the sample collection and understand its geological context [2].In order to guide precisely the rover drill, an accurate estimation of the speed of electromagnetic waves in the subsurface is required to convert time delays of echoes from underground interfaces into distances. In the case of subsurface materials to be sounded by WISDOM, this speed is expected to be directly driven by their dielectric constants. Additionally, before drilling, subsurface hazards such as buried boulders must be detected by WISDOM to be avoided.The automated interpretation chain we developed will be able to detect diffraction curves corresponding to potential subsurface hazards in WISDOM radargrams, and characterize the shape of these diffraction curves to estimate the dielectric constant of the subsurface, and therefore the depths of the hazards.For most GPR radargrams operating on Earth, the antennas being directly coupled to the surface, the diffraction curves from point scatters are mathematically perfect hyperbolas (see Figure 1). In the case of WISDOM, the engineering constraints of the ExoMars mission imposed a distance of 38 cm between the bottom of the antennas and the Martian surface. In this air-coupled situation, with the refraction of electromagnetic waves at the surface, the diffraction curves are no longer perfect hyperbolas [3], and another model is required (see Figure 2).As a consequence, we adapted the Hough transform that was already implemented to detect hyperbolas in GPR radargrams in ground-coupled situations [4], for the detection of diffraction curves in WISDOM radargrams. Promising preliminary results have been obtained on numerically simulated radargrams, and applications to experimental radargrams is ongoing.MethodThe Hough transform is a classic image processing technique, originally invented to detect lines in an image, and later adapted to the detection of any shape with an analytic definition, such as hyperbolas. The idea behind the detection of hyperbolas is the following one: any hyperbola can be defined by a unique triplet of parameters, or by any triplet of points in the hyperbola. For a hyperbola in ground-coupled GPR radargram, the 3 parameters are the horizontal position of the antennas X, the time delay at the apex of the hyperbolas T, and the dielectric constant of the subsurface 𝜀.First a detection of echoes is applied to the radargram, to obtain a cloud of points with horizontal and vertical positions corresponding to the horizontal distance and time delays of these echoes. For every triplet of points in the radargram, the parameters (X, T, 𝜀) of the hyperbola passing through this triplet is calculated, and a 3D accumulator is incremented +1 at the position corresponding to (X, T, 𝜀). Maxima in the accumulator are expected to correspond to the hyperbolas present in the radargram.To adapt the technique to WISDOM radargrams, where diffraction curves are not perfect hyperbolas, the presented “air-coupled” model had to be employed to determine a first estimate of parameters (X, T, 𝜀) of the diffraction curves corresponding to the different triplets of points. The equation presented in Figure 2 being far more complicated to analytically inverse than the equation of a hyperbola, an optimization technique (the Nelder-Mead algorithm [5]) is used.Preliminary resultsIn order to validate the technique before its application to experimental WISDOM radargrams, different numerical simulations (with a Finite Difference in Time Domain code) have been performed on subsurface models with a selected homogeneous dielectric constant, and buried targets. The geometry of the antennas, and the equivalent time-domain signal emitted by the radar have been modelled for these simulations to be as representative as possible of the WISDOM radargrams.As an example, we show here a simulation generated for 5 underground targets in a subsurface of dielectric constant 4 (see Figure 3). In addition to the echoes detected in the simulated radargram, 500 parasitic points with random positions have been added to the cloud of points to which we applied the Hough transform (see Figure 4). The idea is to see if the implemented tool is able to detect diffraction curves among other echoes.With the classic Hough transform (using the model presented in Figure 1), the estimated dielectric constants were 1.3, 1.8, 2.2, 1.9 and 2.3, leading to estimated depths of 0.88, 1.23, 1.47, 1.78, and 2 m, to be compared to the expected 0.50, 0.90, 1, 1.35, and 1.5 m. An over-estimation of the depth of a subsurface hazard could obviously be dangerous for the rover drill during the ExoMars 2022 mission.After the application of the corrected Hough transform we implemented, the estimated dielectric constants are 4, 3.8, 3.8, 3.7 and 3.8 (much closer to the expected 4), and the estimated depths are now 0.50, 0.85, 1.04, 1.37 and 1.56 m (see Figure 5). The estimation is now accurate with error levels of only a few cm. This result validates the ability of the implemented technique to automatically detect diffraction curves in WISDOM radargrams, accurately estimate the dielectric constant of the associated subsurface material, and eventually estimate the depth of the associated reflector on Mars.Will be presented the results of this interpretation tool applied to other simulations, some of them generated with more complex subsurface models (heterogeneous materials, two underground layers…), and eventually to experimental WISDOM radargrams where diffraction curves are visible.

In the Arctic, global warming is 2–3 times faster than over other regions of the globe. As a result, noticeable changes are already being recorded in all areas of the environment. However, there is very little data on such changes in the Russian Arctic. Therefore, to fill the gap in the data on the vertical distribution of the gas and aerosol composition of air in this region, an experiment was carried out on the Tu-134 Optik flying laboratory in September 2020 to sound the atmosphere and water surface over the water areas of all seas in the Russian Arctic. This paper analyzes the spatial distribution of methane. It is shown that during the experiment its concentration was the highest over the Kara Sea (2090 ppb) and the lowest over the Chukchi Sea (2005 ppb). The East Siberian and Bering Seas were slightly different from the Chukchi Sea in terms of the methane concentration. Average values of CH4 are characteristic of the Barents (2030 ppb) and the Laptev Seas (2040 ppb). The difference between the concentrations at an altitude of 200 meters and in the free troposphere attained 150 ppb over the Kara Sea, decreased to 91 and 94 ppb over the Barents and Laptev Seas, and further decreased over the East Siberian, Chukchi, and Bering Seas to 66, 63, and 74 ppb, respectively. Horizontal heterogeneity in the distribution of methane over the Arctic seas is the greatest over the Laptev Sea, where it attained 73 ppb. It is two times higher than over the Barents and Kara Seas, and 5–7 times higher than over the East Siberian and Bering Seas.

Abstract. Sulfate aerosol in the stratosphere is an important climate driver, causing solar dimming in the years after major volcanic eruptions. Hence, a growing number of general circulation models are adapting interactive sulfur and aerosol schemes to improve the representation of relevant chemical processes and associated feedbacks. However, uncertainties of these schemes are not well constrained. Stratospheric sulfate is modulated by natural emissions of sulfur-containing species, including volcanic eruptive, and anthropogenic emissions. Model intercomparisons have examined the effects of volcanic eruptions, whereas the background conditions of the sulfur cycle have not been addressed in a global model intercomparison project. Assessing background conditions in global models allows us to identify model discrepancies as they are masked by large perturbations such as volcanic eruptions, yet may still matter in the aftermath of such a disturbance. Here, we analyze the atmospheric burden, seasonal cycle, and vertical and meridional distribution of the main sulfur species among nine global atmospheric aerosol models that are widely used in the stratospheric aerosol research community. We use observational and reanalysis data to evaluate model results. Overall, models agree that the three dominant sulfur species in terms of burdens (sulfate aerosol, OCS, and SO2) make up about 98 % of stratospheric sulfur and 95 % of tropospheric sulfur. However, models vary considerably in the partitioning between these species. Models agree that anthropogenic emission of SO2 strongly affects the sulfate aerosol burden in the Northern Hemispheric troposphere, while its importance is very uncertain in other regions. The total deposition of sulfur varies among models, deviating by a factor of two, but models agree that sulfate aerosol is the main form in which sulfur is deposited. Additionally, the partitioning between wet and dry deposition fluxes is highly model dependent. We investigate the areas of greatest variability in the sulfur species burdens and find that inter-model variability is low in the tropics and increases towards the poles. Seasonality in the southern hemisphere is depicted very similar among models. Differences are largest in the dynamically active northern hemispheric extratropical region, hence some of the differences could be attributed to the differences in the representation of the stratospheric circulation among underlying general circulation models. This study highlights that the differences in the atmospheric sulfur budget among the models arise from the representation of both chemical and dynamical processes, whose interplay complicates the bias attribution. Several problematic points identified for individual models are related to the specifics of the chemistry schemes, model resolution, and representation of cross-tropopause transport in the extratropics. Further model intercomparison research is needed focusing on the clarification of the reasons for biases, given also the importance of this topic for the stratospheric aerosol injection studies.

Abstract Despite the growing importance of planetary Space Weather forecasting and radiation protection for science and robotic exploration and the need for accurate Space Weather monitoring and predictions, only a limited number of spacecraft have dedicated instrumentation for this purpose. However, every spacecraft (planetary or astronomical) has hundreds of housekeeping sensors distributed across the spacecraft, some of which can be useful to detect radiation hazards produced by solar particle events. In particular, energetic particles that impact detectors and subsystems on a spacecraft can be identified by certain housekeeping sensors, such as the Error Detection and Correction (EDAC) memory counters, and their effects can be assessed. These counters typically have a sudden large increase in a short time in their error counts that generally match the arrival of energetic particles to the spacecraft. We investigate these engineering datasets for scientific purposes and perform a feasibility study of solar energetic particle event detections using EDAC counters from seven European Space Agency Solar System missions: Venus Express, Mars Express, ExoMars‐Trace Gas Orbiter, Rosetta, BepiColombo, Solar Orbiter, and Gaia. Six cases studies, in which the same event was observed by different missions at different locations in the inner Solar System are analyzed. The results of this study show how engineering sensors, for example, EDAC counters, can be used to infer information about the solar particle environment at each spacecraft location. Therefore, we demonstrate the potential of the various EDAC to provide a network of solar particle detections at locations where no scientific observations of this kind are available.

Abstract. In this study, we use a synergy of in-situ and remote sensing measurements collected during the Southwest FOGs 3D experiment for processes study (SOFOG3D) field campaign in autumn 2019 and winter 2020, to analyze the thermodynamic and turbulence processes related to fog formation, evolution, and dissipation across southwestern France. Based on a unique dataset with a very high resolution and a fog conceptual model, an analysis of the four heaviest fog episodes (two radiation fogs and two advection-radiation fogs) is conducted. The results show that radiation and advection-radiation fogs form under deep and thin temperature inversion, respectively. For both fog categories, the transition period from stable to adiabatic fog and the fog adiabatic phase are driven by vertical mixing associated with an increase in turbulence in the fog layer due to mechanical production (turbulence kinetic energy (TKE) up to 0.4 m2 s-2 and vertical velocity variance (σw2) up to 0.04 m2 s-2) generated by brisk wind at the supersite (advection). The dissipation time is observed at night for the advection-radiation fog case studies and during the day for the radiation fog case studies. Night-time dissipation is driven by horizontal advection generating mechanical turbulence (TKE at least 0.3 m2 s-2 and σw2 larger than 0.04 m2 s-2). Daytime dissipation is linked to the combination of thermal and mechanical turbulence related respectively to solar heating (near surface sensible heat flux larger than 10 W m-2) and advection. Through a deficit of the fog reservoir of liquid water path, the fog conceptual model estimates the dissipation time at least one hour before the observed dissipation for radiation fog cases. It gives a better estimate of the fog dissipation time for advection-radiation cases. This study also demonstrates the importance of using instrumental synergy (with microwave radiometer, wind lidar, weather station, and cloud radar) and a fog conceptual model to better predict fog characteristics and dissipation time at nowcasting ranges.

Abstract On 1 October 2021, Bepi‐Colombo performed its first flyby of Mercury. During the maneuver, the short wavelength channel (55–155 nm) of “Probing the Hermean Exosphere by UV Spectroscopy” (PHEBUS) was activated for a total duration of 1 hr. The helium resonance line at 58.4 nm was clearly observed during the whole sequence. At large distance from the planet, the emission was due to helium atoms in the interplanetary medium (interplanetary UV glow). Just after crossing the terminator of the planet and entering the dawn side of the exosphere, PHEBUS observed a clear additional emission due to scattering of solar photons by helium atoms in the exosphere of Mercury. The first detection of the 58.4 nm line in the exosphere of Mercury was reported by Broadfoot et al. (1976, https://doi.org/10.1029/gl003i010p00577) following the Mariner 10 flybys in 1974. The PHEBUS observation of exospheric helium emissions is the first for this element since the UVS measurements. In this paper, we present the results of our analysis of the PHEBUS data at 58.4 nm. Calibration of both instruments are compared with observations of the interplanetary glow, showing that the measurements of both instruments are accurate. However, we find that the exospheric density of helium atoms deduced from the PHEBUS data is 4.5–7.5 times lower than the previous estimate from UVS on Mariner 10. Possible explanations are considered. We show that some of the helium atoms present in the exosphere of Mercury could originate from the local interstellar cloud.

Heatwaves are one of the most dangerous climatic hazards affecting the health of humans and ecosystems around the world. Accurate forecasts of these dramatic events can be relevant for policy makers, climate services and the local population. In this perspective, the present study addresses the predictability of heatwaves in sub-seasonal to seasonal forecasts in the West Africa region over the recent period from 2001 up to 2020. Two models from the S2S Prediction project namely ECMWF and UKMO have been analyzed. Heatwaves have been detected using minimum/maximum values of 2-m temperature as indicators over a period of at least 3 consecutive  days. The validation of the model outputs is processed using ERA5 as reference. The global skill of the models in reproducing 2-m temperature is done by calculating the Continuous Rank Probability Score (CRPS). ECMWF model shows more skill in the Guinean region for minimum and maximum values of 2-meter temperatures.  The predictability of heatwaves in the models is estimated by the computation of some probabilistic metrics such as : hit-rate and false alarm ratio (FAR).  Models show predictive skill of heatwave days greater than the climatology up to 3 weeks lead time in the 3 regions. The FAR values are high and increasing with the lead time. This suggests that the models used to predict heat wave days which are not observed in the reanalysis (ERA5) more than real events. ECMWF shows more predictive skill at weekly time scale with high hit_rate values up to 3 weeks lead time. The accurate representation of the heatwaves intensity in the models remains very challenging at any lead time.

Trends in tropospheric ozone, an important air pollutant and short-lived climate forcer (SLCF), are estimated using available surface and ozonesonde profile data for 1993-2019. Using a coherent methodology, observed trends are compared to modeled trends (1995-2015) from the Arctic Monitoring Assessment Programme SLCF 2021 assessment. Statistically significant increases in observed surface ozone at Arctic coastal sites, notably during winter, and concurrent decreasing trends in surface carbon monoxide, are generally captured by multi-model median (MMM) trends. Wintertime increases are also estimated in the free troposphere at most Arctic sites, but tend to be overestimated by the MMMs. Springtime surface ozone increases in northern coastal Alaska are not simulated while negative springtime trends in northern Scandinavia are not always reproduced. Possible reasons for observed changes and model behavior are discussed, including decreasing precursor emissions, changing ozone sinks, and variability in large-scale meteorology.

[ The mesospheric inversion layer (MIL) phenomenon refers to a temperature enhancement (10-50 K) in a vertical layer ($\sim$10 km) lasting several days and spanning thousands of kilometers within the mesosphere. As MILs govern the mesospheric variability, their study is crucial for a better understanding of the middle-atmosphere global circulation and applications in aeronautics since perturbations in the mesosphere are significant issues for the safe reentry of rockets, space shuttles, or missiles. However, the description of this phenomenon remains partial, as no observations of MIL’s effects on winds exist, preventing a complete understanding of the mechanisms responsible for their formation. Here, we first report an investigation of simultaneous wind-temperature observations in the altitude range of 30-90 km during MIL events. As a result, the profiles exhibit a strong winds deceleration occurring in the same altitude range that the temperature inversion, confirming the role of gravity waves in MIL’s formation mechanisms.]