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This paper investigates the technical aspects of the 1550 nm free space optical (FSO) link in extreme Arctic weather, specifically in falling snow, which is currently understudied. We measured the time variation of the received optical power in many snowfall events and derived attenuation statistics. Continuous measurements allowed us to estimate attenuation under varying temperatures and humidity to examine the after-snow channel impairments. Our results show several shortcomings in the International Telecommunication Union-Radio Communication (ITU-R) snow attenuation model and verify that a correlation exists between snow features, snowfall rate, temperature, and humidity in determining the total snow attenuation. We demonstrated the relationship between snow properties, temperature, humidity, and attenuation under both in-snow and post-snow environments. Furthermore, based on the experimental results, we recommend several mitigation techniques to realize FSO communication during challenging Arctic weather conditions. Lastly, we present a feasibility study of FSO in Svalbard at 78°N latitude.

The Absorbing Aerosol Index (AAI) determines the presence and approximate amount of absorbing aerosol particles. The Absorbing Aerosol Sensor (AAS) equipped on the Gaofen-5B satellite was successfully launched and deployed in orbit on September 7, 2021. Following a period of initial on-orbit testing, reliable AAI observational results were collected. The nadir of the AAS had a design spatial resolution of 4 km × 4 km and a current spatial resolution of 2 km × 4 km, which presented significant benefits in the spatial resolution and clear distribution characteristics of pollutants. The results of AAS were validated by comparing with the observed data of Global Ozone Monitoring Experiment-2 (GOME-2) in the Sahara Desert and surrounding areas. In 2022 and 2023, we utilized AAS observations to study the typical pollution processes. These included the spring pollution distribution around the Bohai Sea, the observation and tracking of long-term dust pollution in China in March and April 2023, the distribution of spring pollution in the Indochina Peninsula in April 2023, and the distribution of wildfires in Canada on June 22, 2023. The results were validated and analyzed using data from Tropospheric Monitoring Instrument (TROPOMI), GOME-2, AErosol RObotic NETwork (AERONET) and Moderate-resolution Imaging Spectroradiometer (MODIS). The results of the observation and evaluation revealed that the AAS AAI can provide reliable information for precise observation of air pollutants at a high resolution.

Measurements of the vertical structure of the lower atmosphere are important to the understanding of air quality. Unmanned Aerial Systems (UASs, drones) can provide low cost, repeatable measurements of the temperature, pressure, and relative humidity. A set of inexpensive sensors controlled with an Arduino microprocessor board were tested on a UAS against a meteorology grade sensor. Two modes of operation for sampling were tested: a forward moving sampler and a vertical ascent sampler. A small particle sensor (Sensiron SPS30) was integrated and was capable of retrieving vertical aerosol distributions during an inversion event. The thermocouple-based temperature probe and the relative humidity measurement on the Bosch BME280 sensor correlated well with the meteorological sensor. The temperature and relative humidity sensors were then deployed on a rocket sounding platform. The rocket sounding system performed well up to a height of 400 m. The inexpensive sensors were found to perform adequately for low-cost development and uses in education and research.

The atmospheric electric current, “air–earth current”, flows between the low ionosphere and Earth’s surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson’s concept of the Earth’s Global Electric Circuit, in the areas of so-called fair weather, based on current measurements at the Earth’s surface, it is possible to conclude the global electrical processes in the ionosphere and higher layers. The theoretical basis for this inference is the law of continuity of electric current or the principle of conservation of electric charge. We present the results of simulations of the distribution of electric field lines for sensors with different geometries placed in a uniform electric field, representing the atmospheric electric field. The sensors are metal surfaces on which electric charges are induced or deposited. In the external measuring circuit to which the sensor is connected, an electric current [A] will flow, related to the air–earth current density [A/m2], but their relationship may be challenging to interpret. We analyze the impact of sensor geometry on the possibility of interpreting the atmospheric electric conduction and atmospheric displacement current based on the current measured in the external circuit. This present method can be used for the geometric construction of new sensors at the stage of determining the electrical characteristics of the sensor (e.g., effective collecting area). It can support the comprehensive design of a measurement system at the interface between an atmosphere, sensor, and electronic equipment.

The Meteor-M No. 2-2 meteorological satellite with the microwave radiometer MTVZA-GY on board was launched into a circular sun-synchronous orbit on July 5, 2019. The radiometer conducts a conical scanning at 65-degree incidence angle and receives the Earth's outgoing radiation in the frequency range ν ≈ 6–190 GHz. The swath width is 2500 km on ascending orbits and 1500 km on descending orbits. The parameters of the ocean, land surface, and troposphere are extracted from brightness temperatures TB(ν) measured at imager frequencies of 6.9, 10.65, 18.7, 23.8, 31.5, 36.5, 42, 48, and 91.65 GHz in vertical and horizontal polarizations. Measurements at the sounder frequencies (ten channels in the 52–58 GHz oxygen absorption band and three channels in the strong water vapor resonance line region centered at 183.31 GHz) provide information on air temperature and humidity in the troposphere and stratosphere. The structure and development of dynamic atmospheric phenomena of synoptic scale are imprinted on the global TB(ν) maps at imager frequencies. The TB(ν) time series at the sounder frequencies allowed us to detect and trace the evolution of a rare phenomenon — a sudden stratospheric warming over Antarctica in August–September 2019.

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has the advantage of measuring radiances emitted from the atmosphere at a high temporal and moderate vertical resolution. In this article, the retrieval of temperature and humidity profiles from ground-based infrared hyperspectral observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer from high computational complexity or poor performance. Motivated by the strength of the deep learning, we present a deep retrieval architecture (DReA) by skillfully designing a light-weight one-dimensional convolution neural network (CNN) to retrieve the temperature and humidity profiles. Experiments were conducted using atmospheric emitted radiance interferometer (AERI) and radiosonde data to demonstrate the superiority of the proposed DReA. The validation of the DReA with the radiosonde, using 802 profiles with 37 layers below 3 km, presents an excellent retrieval ability with a root mean square error (RMSE) of 0.87 K for the temperature and 1.06 g/kg for the water vapor mixing ratio. Furthermore, a thorough comparison with commonly used inversion methods such as the traditional back propagation (BP) and the eigenvector (EV) regression method, shows that our proposed DReA method obtains a leading solution in retrieving temperature and humidity profiles.

In this contribution, we present a comparison of three digital real-time spectrometers used in passive remote sensing of ozone and other trace gases in the middle atmosphere. During a period of six months, we connected the spectrometers to the same radiometric front-end to perform parallel observations of the ozone emission line at 110.836 GHz. This allowed us to better characterize a bias previously observed on the integrated spectra of the Acqiris AC240, a widely used digital spectrometer which has been used for more than a decade in many operational microwave radiometers. We investigated the bias under different atmospheric conditions and found that it is caused by multiple sources. Nonlinearities in the calibration are responsible for part of the bias, but a larger contribution stems from a second effect in the AC240. Although this error source is still partly unexplained, we found that a simple correction scheme simulating a spectral leakage can be applied to the integrated spectra of the AC240 and worked well on our range of observations. We also show that by applying our bias correction to the spectra, we can correct the bias in the ozone retrievals. There is still a need for further measurements to validate this approximate correction, but it could help to correct the numerous time series of ozone and other atmospheric constituents recorded by the AC240.

To mitigate the environmental impacts of synthetic halocarbons it is crucial to quantify their emissions to the atmosphere on different spatial scales. For this, top-down modelling approaches were developed, relying on atmospheric concentration observations. However, increased sensitivity on country-scale is needed, requiring a denser measurement network and models operating on a regional scale. In this light, we conducted an extensive study to assess Swiss halocarbon emissions, with high sensitivity to the regional emissions sources.

Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations are limited to frequent flight routes, and vertical profiles are provided in the vicinity of large cities. However, there are large areas with few radiosonde launches, in particular above the oceans and in the polar areas. In this article, the development and technical details of the unmanned aerial system LUCA (Lightweight Unmanned high Ceiling Aerial system) are described. LUCA has the potential to complement radiosonde and aircraft-based observations up to 10 km in altitude. The system ascends and descends (by electrical power) in spiral trajectories and returns to the launching site. This article discusses the requirements for obtaining high data availability under mid-European and Antarctic conditions, with highly automated take-offs and landings under high surface winds, the capacity to deal with icing, and the ability to operate under high wind speeds. The article presents technical solutions for the design and construction of the system and demonstrates its potential.

Temperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrieval is an ill-posed problem, and thus there are multiple thermodynamic solutions that might satisfy the observed radiances. Previous work developed a physical-iterative method called AERIoe that retrieved temperature and water vapor mixing ratio profiles from these radiance observations in both clear and cloudy conditions. The AERIoe algorithm was modified to enforce two physical constraints, namely that the derived relative humidity must be less than 100% and that the potential temperature must be monotonically increasing with height above some thin potentially subadiabatic layer after each iteration. Furthermore, additional observations including in situ surface meteorology, numerical weather prediction model output, microwave brightness temperatures, and partial profiles of water vapor from a Raman lidar were incorporated into the observation vector of the retrieval along with the infrared radiance observations. The addition of these new observations markedly improved the accuracy of the temperature profiles, especially above 2 km, and the water vapor profiles relative to radiosondes. These improvements are seen using cases from the tropics, mid-latitudes, and Arctic.

The recent decade’s rapid unconventional oil and gas development in the Eagle Ford of south-central Texas has caused increased hydrocarbon emissions, which we have previously analyzed using data from a Texas Commission on Environmental Quality air quality monitoring station located downwind of the shale area. Here, we expand our previous top-down emissions estimate and compare it to an estimated regional emissions maximum based on (i) individual facility permits for volatile organic compound (VOC) emissions, (ii) reported point source emissions of VOCs, (iii) traffic-related emissions, and (iv) upset emissions. This largely permit-based emissions estimate accounted, on average, for 86% of the median calculated emissions of C3-C6-hydrocarbons at the monitor. Since the measurement-based emissions encompass a smaller section of the shale than the calculated maximum permitted emissions, this strongly suggests that the actual emissions from oil and gas operations in this part of the Eagle Ford exceeded their permitted allowance. Possible explanations for the discrepancy include emissions from abandoned wells and high volumes of venting versus flaring. Using other recent observations, such as large fractions of unlit flares in the Permian shale basin, we suggest that the excessive venting of raw gas is a likely explanation. States such as Texas with significant oil gas production will need to require better accounting of emissions if they are to move towards a more sustainable energy economy.

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 ∘ C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

In this paper, the potential of a network consisting of low cost weather stations for validating microscale model simulations and for forcing surface-atmosphere-transfer-schemes is investigated within two case studies. Transfer schemes often do not account for small scale variabilities of the earth surface, because measurements of the atmospheric conditions do not exist in such a high spatial resolution to force the models. To overcome this issue, in this study a small scale network of meteorological stations is used to derive measurements in high spatial and temporal resolution. The observations carried out during the measurement campaign are compared to air temperature and specific humidity simulations of the mesoscale atmospheric model FOOT3DK (Flow Over Orographically-Structured Terrain - 3 Dimensional Model (Kölner Version)). This comparison indicates that FOOT3DK simulates either air temperature or specific humidity satisfactorily for each station at the lowest model level, depending on the dominating land use class within each grid cell. The influence of heterogeneous forcing and vegetation on heat flux modelling is studied using the soil-vegetation-atmosphere transfer scheme TERRA. The observations of the measurement campaign are used as input for four different runs with homogeneous and heterogeneous forcing and vegetation. Heterogeneous vegetation reduces the bias between the grid cells, heterogeneous forcing reduces the random error for each grid cell.

Since over 70% of carbon emissions are from urban areas, it is of great importance to develop an effective measurement technique that can accurately monitor atmospheric CO2 in global urban areas. Remote sensing could be an effective way to achieve this goal. However, due to high aerosol loading in urban areas, there are large, inadequately resolved areas in the CO2 products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO2-light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO2 volume mixing ratios (XCO2) using the LIDAR measurements. A performance framework consisting of a sensor model, sampling model, and environmental model was proposed to fulfill our demand. We found that both the coverage and the accuracy of the LIDAR-derived city-scale XCO2 values were highly dependent on the orbit height. With an orbit height of 450 km, random errors of less than 0.3% are expected for all four metropolitan areas tested in this work. However, random errors of less than 0.3% were obtained in only two metropolitan areas with an orbit height of 705 km. Our simulations also showed that off-nadir sampling would improve the performance of a CO2-Integrated Path Differential Absorption (IPDA) LIDAR system operating in a 705 km orbit. These results indicate that an active remote sensing mission could help to effectively measure XCO2 values in urban areas. More detailed studies are needed to reveal the potential of such equipment for improving the verification of carbon emissions and the estimation of urban carbon fluxes.