The use of thermal–optical analysis for the determination of elemental carbon (EC) and organic carbon (OC) can be biased by mineral dust (MD). We present a method that utilizes this interference to quantify iron contained in MD in snow samples. Possibilities and limitations of applying this method to particulate matter samples are presented. The influence of light-absorbing iron compounds in MD on the transmittance signal can be used to identify samples experiencing a bias of the OC / EC split.

The estimate of atmospheric CO₂ from space measurement is difficult. Current methods are based on a detailed description of the atmospheric radiative transfer. These are affected by significant biases and errors and are very computer intensive. Instead we have proposed using a neural network approach. A first attempt led to confusing results. Here we provide an interpretation for these results and describe a new version that leads to high-quality estimates.

We used aerosol profiles from the SAGE III/ISS instrument to develop an aerosol classification method that was tested on four case-study events (two volcanic, two fire) and supported with CALIOP aerosol products. The method worked well in identifying smoke and volcanic aerosol in the stratosphere for these events. Raikoke is presented as a demonstration of the limitations of this method.

Atmospheric inversion at local–regional scales over Europe and pseudo-data assimilation are used to evaluate how CO₂ and ¹⁴CO₂ ground-based measurement networks could complement satellite CO₂ imagers to monitor fossil fuel (FF) CO₂ emissions. This combination significantly improves precision in the FF emission estimates in areas with a dense network but does not strongly support the separation of the FF from the biogenic signals or the spatio-temporal extrapolation of the satellite information.

The aim of this study was to provide global characterisation of a new SYNERGY aerosol product derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Over ocean, the performance of SYNERGY-retrieved AOD is good. Reduced performance over land was expected since the surface reflectance and angular distribution of scattering are more difficult to treat. Validation statistics are often slightly better for S3B and in the Southern Hemisphere.

Floating lidar systems are used for measuring wind speeds offshore, and their motion influences the measurements. This study describes the motion-induced bias on mean wind speed estimates by simulating the lidar sampling pattern of a moving lidar. An analytic model is used to validate the simulations. The bias is low and depends on amplitude and frequency of motion as well as on wind shear. It has been estimated for the example of the Fugro SEAWATCH wind lidar buoy carrying a ZX 300M lidar.

This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m⁻².

Ice nucleation in the atmosphere influences cloud properties and lifetimes. Microfluidic instruments have recently been used to investigate ice nucleation, but these instruments are typically made out of a polymer that contributes to droplet instability over extended timescales and relatively high temperature uncertainty. To address these drawbacks, we develop and validate a new microfluidic instrument that uses fluoropolymer tubing to extend droplet stability and improve temperature accuracy.

Satellite observations of global carbon monoxide (CO) are essential for understanding atmospheric chemistry and pollution sources. This paper describes a new data product using radiance measurements from the Cross-track Infrared Sounder (CrIS) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite that provides vertical profiles of CO from single-field-of-view observations. We show how these satellite CO profiles compare to aircraft observations and evaluate their biases.

TANSO-FTS-2 onboard GOSAT-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration of TIR and its validation. The multi-satellite sensor and multi-angle comparison results suggest that the spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data.

Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.

This research demonstrates a new method for determination of the chemical reactivity of volatile organic compounds that are emitted from the leaves and needles of trees. These measurements allow elucidating if and how much of these emissions and their associated reactivity are captured and quantified by currently applicable chemical analysis methods.

Nitrous acid (HONO) is an important source of the main atmospheric oxidant – the hydroxyl radical (OH). Advances in nitrous acid measurement techniques and calibration methods therefore improve our understanding of atmospheric oxidation processes. In this paper, we present two calibration methods based on photo-dissociating water vapor. These calibration methods are useful alternatives to conventional calibrations that involve a reacting hydrogen chloride vapor with sodium nitrite.

Multicopter uncrewed aerial systems (UAS, also known as drones) are very easy to use systems for collecting data in the lowest part of the atmosphere. Wind and turbulence are parameters that are particularly important for understanding the dynamics in the atmosphere. Only with three-dimensional measurements of the wind can a full understanding can be achieved. In this study, we show how even the vertical wind through the UAS can be measured with good accuracy.

This work presents the differences resulting from two techniques (rolling and seasonal) of the positive matrix factorisation model that can be run for organic aerosol source apportionment. The current state of the art suggests that the rolling technique is more accurate, but no proof of its effectiveness has been provided yet. This paper tackles this issue in the context of a synthetic dataset and a multi-site real-world comparison.

Satellite observations provide information on the sources of SO₂, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO₂. Here, we use a machine learning technique to analyze satellite SO₂ observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO₂ data.

Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.

Visibility is how well we can see something. Visibility sensors, such as employed in meteorological observatories and airports, measure at a point at the instrument location, which may not be representative of visibilities further away, e.g. near the sea surface during sea spray. Light detecting and ranging (lidar) can measure visibility further away. We find wind lidar to be a viable tool to measure visibility with low accuracy, which could suffice for safety-uncritical applications.