Improved Measurements of Molecular Cloud Distances Based on Global Search

The principle of the background-eliminated extinction-parallax (BEEP) method is examining the extinction difference between on- and off-cloud regions to reveal the extinction jump caused by molecular clouds, thereby revealing the distance in complex dust environments. The BEEP method requires high-quality images of molecular clouds and high-precision stellar parallaxes and extinction data, which can be provided by the Milky Way Imaging Scroll Painting (MWISP) CO survey and the Gaia DR2 catalog, as well as supplementary A V extinction data. In this work, the BEEP method is further improved (BEEP-II) to measure molecular cloud distances in a global search manner. Applying the BEEP-II method to three regions mapped by the MWISP CO survey, we collectively measured 238 distances for 234 molecular clouds. Compared with previous BEEP results, the BEEP-II method measures distances efficiently, particularly for those molecular clouds with large angular size or in complicated environments, making it suitable for distance measurements of molecular clouds in large samples.

Estimation of Aerosol Columnar Size Distribution from Spectral Extinction Data in Coastal and Maritime Environment

Aerosol columnar size distributions (SDs) are commonly provided by aerosol inversions based on measurements of both spectral extinction and sky radiance. These inversions developed for a fully clear sky offer few SDs for areas with abundant clouds. Here, we estimate SDs from spectral extinction data alone for cloudy coastal and maritime regions using aerosol refractive index (RI) obtained from chemical composition data. Our estimation involves finding volume and mean radius of lognormally distributed modes of an assumed bimodal size distribution through fitting of the spectral extinction data. We demonstrate that vertically integrated SDs obtained from aircraft measurements over a coastal site have distinct seasonal changes, and these changes are captured reasonably well by the estimated columnar SDs. We also demonstrate that similar seasonal changes occur at a maritime site, and columnar SDs retrieved from the combined extinction and sky radiance measurements are approximated quite well by their extinction only counterparts (correlation exceeds 0.9) during a 7-year period (2013–2019). The level of agreement between the estimated and retrieved SDs depends weakly on wavelength selection within a given spectral interval (roughly 0.4–1 µm). Since the extinction-based estimations can be performed frequently for partly cloudy skies, the number of periods where SDs can be found is greatly increased.

Investigation of CATS aerosol products and application toward global diurnal variation of aerosols

We present a comparison of 1064 nm aerosol optical depth (AOD) and aerosol extinction profiles from the Cloud-Aerosol Transport System (CATS) level 2 aerosol product with collocated Aerosol Robotic Network (AERONET) AOD, Moderate Imaging Spectroradiometer (MODIS) Aqua and Terra Dark Target AOD and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD and extinction data for the period of March 2015–October 2017. Upon quality-assurance checks of CATS data, reasonable agreement is found between aerosol data from CATS and other sensors. Using quality-assured CATS aerosol data, for the first time, variations in AODs and aerosol extinction profiles are evaluated at 00:00, 06:00, 12:00 and 18:00 UTC (and/or 00:00, 06:00, 12:00 and 18:00 local time or LT) on both regional and global scales. This study suggests that marginal variations are found in AOD from a global mean perspective, with the minimum aerosol extinction values found at 18:00 LT near the surface layer for global oceans, for both the June–November and December–May seasons. Over land, below 500 m, the daily minimum and maximum aerosol extinction values are found at 12:00 and 00:00/06:00 LT, respectively. Strong diurnal variations are also found over north Africa, the Middle East and India for the December–May season, and over north Africa, south Africa, the Middle East and India for the June–November season.

Isochrone fitting in the Gaia era

Context. Currently, galactic exploration is being revolutionized by a flow of new data: Gaia provides measurements of stellar distances and kinematics; growing numbers of spectroscopic surveys provide values of stellar atmospheric parameters and abundances of elements; and Kepler and K2 missions provide asteroseismic information for an increasing number of stars. Aims. In this work, we aim to determine stellar distances and ages using Gaia and spectrophotometric data in a consistent way. We estimate precisions of age and distance determinations with Gaia end-of-mission (EoM) and Tycho-Gaia astrometric solution (TGAS) parallax precisions. Methods. To this end, we incorporated parallax and extinction data into the isochrone fitting method used in the Unified tool to estimate Distances, Ages, and Masses (UniDAM). We prepared datasets that allowed us to study the improvement of distance and age estimates with the inclusion of TGAS and Gaia EoM parallax precisions in isochrone fitting. Results. Using TGAS parallaxes in isochrone fitting, we are able to reduce distance and age estimate uncertainties for TGAS stars for distances up to 1 kpc by more than one third compared to results based only on spectrophotometric data. With Gaia EoM parallaxes in isochrone fitting, we will be able to further decrease our distance uncertainties by about a factor of 20 and age uncertainties by a factor of 2 for stars up to 10 kpc away from the Sun. Conclusions. We demonstrate that we will be able to improve our distance estimates for about one third of stars in spectroscopic surveys and to decrease log(age) uncertainties by about a factor of two for over 80% of stars as compared to the uncertainties obtained without parallax priors using Gaia EoM parallaxes consistently with spectrophotometry in isochrone fitting.

Strengths and limitations of the NATALI code for aerosol typing from multiwavelength Raman lidar observations

A Python code was developed to automatically retrieve the aerosol type (and its predominant component in the mixture) from EARLINET’s 3 backscatter and 2 extinction data. The typing relies on Artificial Neural Networks which are trained to identify the most probable aerosol type from a set of mean-layer intensive optical parameters. This paper presents the use and limitations of the code with respect to the quality of the inputed lidar profiles, as well as with the assumptions made in the aerosol model.