This paper presents a new technique to determine temperature in the thermosphere from observations of far ultraviolet radiation emitted by molecular nitrogen. The technique utilizes a ratio of two far ultraviolet spectral channels to capture the thermosphere temperature signal. Applying the technique to NASA GOLD observations results in temperatures that agree well with other thermosphere observations during a geomagnetic disturbance.

The key points are the following. (i) Integrative observing system design can multiply the information gain of surface–atmosphere field measurements. (ii) Catalyzing numerical simulations and first-principles machine learning open up observing system simulation experiments to novel applications. (iii) Use cases include natural climate solutions, emission inventory validation, urban air quality, and industry leak detection.

To understand the sources and fate of particulate matter in the atmosphere, the ability to quantitatively describe its chemical composition is essential. In this work, we developed a calibration method for a state-of-the-art measurement technique without the need for chemical standards. Statistical analyses identified the driving factors behind instrument sensitivity variability towards individual components of particulate matter.

This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.

A broad supersaturation scanning CCN (BS2-CCN) system, in which particles are exposed to a range of supersaturation simultaneously, can measure a broad range of CCN activity distribution with a high time resolution. We describe how the BS2-CCN system can be effectively calibrated and which factors can affect the calibration curve. Intercomparison experiments between typical DMA-CCN and BS2-CCN measurements to evaluate the BS2-CCN system showed high correlation and good agreement.

A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.

We investigate the ability of the hyperspectral infrared satellite instrument IASI to resolve moist layers in the tropical free troposphere in a model framework. Previous observational results indicated major deficiencies of passive satellite instruments in resolving moist layers around the freezing level. We conduct a first systematic hyperspectral infrared retrieval analysis of such moist layers and conclude that no inherent satellite blind spot for moist layers exists.

We have designed an unmanned aerial vehicle (UAV) sampling platform for operation in extreme polar environments that is capable of sampling atmospheric water vapor for subsequent measurement of water isotopes. During flight, we measure location, temperature, humidity, and pressure to determine the height of the planetary boundary layer (PBL) using algorithms, allowing for strategic decision-making by the pilot to collect samples in glass flasks contained in the nose cone of the UAV.

Large microwave surface emissivities (ε) cause difficulties in widely using satellite microwave data over land. Usually, ground-based radiometers are fixed to a scan field to obtain the temporal evolution of ε over a single land-cover area. To obtain the long-term temporal evolution of ε over different land-cover surfaces simultaneously, we developed a ground mobile observation system to enhance in situ ε observations and presented some preliminary results.

Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.

Instrumental temperature records are fundamental to climate science. There are spatial gaps in the distribution of these measurements across the globe. This lack of spatial coverage introduces coverage error. In this research, a methodology is developed and used to quantify the coverage errors. It results in a data product that, for the first time, provides a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps.

Two commercial quadcopters (DJI Matrice 100 and M210 RTK) were equipped with an air temperature measurement system. They were flown at the Polar Environment Atmospheric Research Laboratory, Eureka, Nunavut, Canada, at 80° N latitude to study surface-based temperature inversion during February–March field campaigns in 2017 and 2020. It was demonstrated that the drones can be effectively used in the High Arctic to measure vertical temperature profiles up to 75 m off the ground.

A field study tested two footprint models for calculating surface emissions from downwind flux measurements. Emission rates from a 10 × 10 m synthetic source were estimated with the simple Kormann–Meixner model and a sophisticated Lagrangian stochastic model. Both models underestimated emissions by approximately 30 %, and no statistical differences were observed between the models. Footprint models are critically important for interpreting eddy covariance measurements.

A method to form SO₂ profiles in the stratosphere with high vertical resolution following volcanic eruptions is introduced. The method combines space-based high-resolution vertical aerosol profiles and SO₂ measurements the first 2 weeks after an eruption with air mass trajectory analyses. The SO₂ is located at higher altitude than in most previous studies. The detailed resolution of the SO₂ profile is unprecedented compared to other methods.

This paper summarizes the identification and correction of one of the most important systematic error sources for the wind measurements of the ESA satellite Aeolus. It depicts the effects of small temperature variations in the primary telescope mirror on the quality of the wind products and describes the approach to correct for it in the near-real-time processing. Moreover, the performance of the correction approach is assessed, and alternative approaches are discussed.

A novel simulation chamber, PASSxS (Planetary Atmosphere Simulation System for Spectroscopy), has been developed for absorption measurements with a Fourier transform spectrometer (FTS) and possibly a cavity ring-down (CRD) spectrometer, with a sample temperature ranging from 100 K up to 550 K, while the pressure of the gas can be varied up to 60 bar. These temperature and pressure ranges cover a significant part of the planetary atmospheres in the solar system and possibly extrasolar planets.

We introduce a new way of estimating winds in the upper atmosphere (about 80 to 100 km in altitude) from the observed Doppler shift of meteor trails using a statistical method called Gaussian process regression. Wind estimates and, critically, the uncertainty of those estimates can be evaluated smoothly (i.e., not gridded) in space and time. The effective resolution is set by provided parameters, which are limited in practice by the number density of the observed meteors.

The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments. This study introduces a seven-step methodology for the field calibration of low-cost sensors with user-friendly guidelines, open-access code, and a discussion of common barriers. Our goal with this work is to push for standardized reporting of methods, make critical data processing steps clear for users, and encourage responsible use in the scientific community and beyond.

A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.

The first space-based Doppler wind lidar on board the Aeolus satellite was launched on 22 August 2018 to obtain global horizontal wind profiles. In this study, wind profilers, ground-based coherent Doppler wind lidars, and GPS radiosondes were used to validate the quality of Aeolus Level 2B wind products over Japan during three different periods. The results show that Aeolus can measure the horizontal winds over Japan accurately.

A neural-network-based approach was suggested to estimate CO₂ emissions using satellite-based net primary productivity (NPP) and XCO₂ retrievals. XCO₂ anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO₂ anomalies, and ODIAC CO₂ emissions from 2015 to 2018 were used as a training dataset; and, finally, CO₂ emissions were predicted for 2019 based on the NPP and XCO₂ anomalies calculated for the same year.

Very small CO₂ fluxes are measured at night in Arctic regions. If the sensible heat flux is not close to zero under these conditions, the WPL correction will take values on the order of the flux. A special quality control is proposed for these cases.

In this work, an improved tropospheric NO₂ retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented with correction for the dependency of the stratospheric NO₂ on the viewing geometry. The AMF calculation is implemented using improved surface albedo, a priori NO₂ profiles, and cloud correction. The improved tropospheric NO2 data show good correlations with ground-based MAX-DOAS measurements.

This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.

We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.

A new minimum curvature algorithm has been proposed for tomographic mapping of air chemicals using optical remote sensing based on the seminorms in variational interpolation. The algorithm was evaluated by using multiple test maps. It shows significant improvement compared with the nonsmoothed algorithm, requires only approximately 65 % computation time of the low third derivative algorithm, and is simple to implement by directly using high-resolution grids.

Understanding and improving the quality of data generated from low-cost air quality sensors are crucial steps in using these sensors. This work investigates how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can impact the perceived performance of low-cost sensors in an evaluation. The influence of these factors should be considered when comparing one sensor to another or determining if a sensor can produce data that fit a specific need.

Small particles in the air have important effects on visibility, clouds, and human health. For the ORACLES project we got a new particle sizing instrument that is fast, works over the most important particle sizes, and avoids some of the issues that plague other optical particle sizers. Unfortunately it sees some particles much smaller than they really are, likely because they heat up and evaporate. We show a crude correction and speculate why these particles heat up much more than expected.