PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

TOI-1431b/MASCARA-5b: A Highly Irradiated Ultrahot Jupiter Orbiting One of the Hottest and Brightest Known Exoplanet Host Stars

We present the discovery of a highly irradiated and moderately inflated ultrahot Jupiter, TOI-1431b/MASCARA-5 b (HD 201033b), first detected by NASA’s Transiting Exoplanet Survey Satellite mission (TESS) and the Multi-site All-Sky Camera (MASCARA). The signal was established to be of planetary origin through radial velocity measurements obtained using SONG, SOPHIE, FIES, NRES, and EXPRES, which show a reflex motion of K = 294.1 ± 1.1 m s−1. A joint analysis of the TESS and ground-based photometry and radial velocity measurements reveals that TOI-1431b has a mass of M p = 3.12 ± 0.18 M J (990 ± 60 M ⊕), an inflated radius of R p = 1.49 ± 0.05 R J (16.7 ± 0.6 R ⊕), and an orbital period of P = 2.650237 ± 0.000003 days. Analysis of the spectral energy distribution of the host star reveals that the planet orbits a bright (V = 8.049 mag) and young ( 0.29 − 0.19 + 0.32 Gyr) Am type star with T eff = 7690 − 250 + 400 K, resulting in a highly irradiated planet with an incident flux of 〈 F 〉 = 7.24 − 0.64 + 0.68 × 109 erg s−1 cm−2 ( 5300 − 470 + 500 S ⊕ ) and an equilibrium temperature of T eq = 2370 ± 70 K. TESS photometry also reveals a secondary eclipse with a depth of 127 − 5 + 4 ppm as well as the full phase curve of the planet’s thermal emission in the red-optical. This has allowed us to measure the dayside and nightside temperature of its atmosphere as T day = 3004 ± 64 K and T night = 2583 ± 63 K, the second hottest measured nightside temperature. The planet’s low day/night temperature contrast (∼420 K) suggests very efficient heat transport between the dayside and nightside hemispheres. Given the host star brightness and estimated secondary eclipse depth of ∼1000 ppm in the K band, the secondary eclipse is potentially detectable at near-IR wavelengths with ground-based facilities, and the planet is ideal for intensive atmospheric characterization through transmission and emission spectroscopy from space missions such as the James Webb Space Telescope and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey.

Compact Thermal Imager (CTI) for Atmospheric Remote Sensing

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations modulated by small-scale atmospheric gravity waves. The CTI’s high-resolution imaging was used to infer vertical cloud temperature profiles from a side-viewing geometry. For nadir imaging, the combined high-resolution and high-sensitivity capabilities allowed the CTI to better separate cloud and surface emissions, including those in the planetary boundary layer (PBL) that had small contrast against the background surface. Finally, based on the ISS’s orbit, the stable detector performance and robust calibration algorithm produced valuable diurnal observations of cloud and surface emissions with respect to solar local time during May–October 2019, when the CTI had nearly continuous operation.

Ariel - The ESA M4 Space Mission to Focus on the Nature Of Exoplanets

<p>Ariel, the atmospheric remote-sensing infrared exoplanet large-survey, is the recently adopted M4 mission within the Cosmic Vision science programme of ESA. The goal of Ariel is to investigate the atmospheres of planets orbiting distant stars in order to address the fundamental questions on how planetary systems form and evolve and to investigate in unprecedented detail the composition of a large number of exoplanetary atmospheres. During its 4-year mission, Ariel will observe hundreds of exoplanets ranging from Jupiter- and Neptune-size down to super-Earth size, in a wide variety of environments, in the visible and the infrared. The main focus of the mission will be on warm and hot planets in orbits close to their star. Some of the planets may be in the habitable zones of their stars, however. The analysis of Ariel spectra and photometric data will allow to extract the chemical fingerprints of gases and condensates in the planets’ atmospheres, including the elemental composition for the most favourable targets. The Ariel mission has been developed by a consortium of more than 60 institutes from 15 ESA member state countries, including UK, France, Italy, Poland, Spain, the Netherlands, Belgium, Austria, Denmark, Ireland, Hungary, Sweden, Czech Republic, Germany, Portugal, with an additional contribution from NASA. In this talk, we will review the science goals of the mission and give insight into the current status, both from the ESA and the Ariel Mission Consortium point of view.  </p>

PAOS, the Physical Optics Propagation model of the Ariel optical system

<p>Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, is a medium-class space mission part of ESA's Cosmic Vision programme, due for launch in 2029. Ariel will survey a diverse sample of about 1000 extrasolar planets in the visible and infrared spectrum to answer questions about their composition, formation and evolution. Ariel mounts an off-axis Cassegrain telescope with a 1100 mm x 730 mm elliptical mirror and has two separate instruments (FGS and AIRS) that cover the 0.5-7.8 micron spectral range. To study the Ariel optical performance and related systematics, we developed PAOS, the Proper Ariel Optical Simulator, an End-to-End physical optics propagation model of the Ariel Telescope and subsystems based on PROPER, an optical propagation library for IDL, Python and Matlab. PAOS is a Python code that consists of a series of calls to PROPER library functions and procedures that reproduces the Ariel optical design, interleaved with additional code that can be specified according to the simulation. Using PAOS, we can investigate how diffraction affects the electromagnetic wavefront as it travels through the Ariel optical systems and the resulting PSFs in the photometric and spectroscopic channels of the mission. This enables to perform a large number of detailed analyses, both on the instrument side and on the optimisation of the Ariel mission. In particular, PAOS can be used to support the requirement on the maximum amplitude of the aberrations for the manufacturing of the Ariel primary mirror, as well as to develop strategies for in-flight calibration, e.g. focussing procedures for the FGS and AIRS focal planes, and to tackle systematics such as pointing jitter and vignetting. With the Ariel mission now in the process of finalizing the instrument design and the data analysis techniques, PAOS will greatly contribute in evaluating the Ariel payload performance with models to be included in the existing Ariel simulators such as ArielRad, the Ariel Radiometric model, and ExoSim, the Exoplanet Observation simulator, for the purpose of studying and optimising the science return from Ariel.</p>

Observability of Exo-Atmospheres in emission using Ariel

<p>Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, is a medium-class space mission part of ESA's Cosmic Vision program, due for launch in 2029. Ariel is the first mission dedicated to the spectroscopic observation of a diverse, statistical sample of about 1000 transiting exoplanets, obtaining spectra in transit, eclipse, or both, to answer questions about their composition, formation and evolution. Ariel has adopted a four-tiered approach in which all targets are observed with different SNRs to optimise the science return from the mission. Ariel has two separate instruments (FGS and AIRS) that will perform simultaneous observations across the 0.5-7.8 micron spectral range, which encompasses both the peak emission of exoplanets and the spectral signatures of key molecules. This will enable Ariel to collect statistical information on the composition and the thermal structure of exo-atmospheres, allowing it to reveal underlying trends in exoplanetary populations. In particular, transit spectroscopy is expected to provide the bulk of information on the chemical composition of exo-atmospheres, while eclipses are necessary to constrain their thermodynamic state. In this framework, I report a preliminary study of Ariel targets observed in emission: at first, I investigate the information content from Tier 1 data, where spectra from the full population of Ariel targets are observed with low SNR, and binned as if Ariel were a multi-band photometer to increase the SNR. I then investigate the effectiveness of Ariel in detecting chemical-physical trends in exoplanetary populations observed in Tier 2, designed to reach SNR in excess of 7 on spectra binned to roughly half the spectral resolution of the focal planes, as specified by the mission requirements.</p>