Despite extensive research, emission detection and quantification of greenhouse gases (GHGs) remain an open problem. This article presents a novel statistical framework for detecting and quantifying methane emissions and showcases its efficacy on data collected from different instruments in the 2015 Ginninderra controlled-release experiment. The developed techniques can be used to aid GHG emission reduction schemes by, for example, detecting and quantifying leaks from carbon storage facilities.

Turbulent fluxes are usually measured by the eddy covariance method using a combination of a costly gas analyser and a sonic anemometer. In this paper we present an eddy covariance set-up of low cost to measure evapotranspiration over agricultural fields. The method is shown to be comparable to a conventional eddy covariance set-up and is a viable alternative when spatial replicates are required.

Traffic-related air pollutants are highly variable in space. To determine their spatial distribution in relation to human exposure, portable black carbon and PM_2.5 mass concentration sensors aboard mobile platforms can be used. High-spatial-resolution data can help improve exposure estimates. The quality of these data becomes increasingly important. This study provides a detailed methodology on how to achieve highly quality assured data from the abovementioned mobile measurements.

Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. We propose a new automatic cloud segmentation algorithm to segment cloud pixels from diurnal and nocturnal ASI images called an enhancement fully convolutional network (EFCN). Experiments showed that the proposed EFCN was much more accurate in cloud segmentation for diurnal and nocturnal ASI images.

High-volume air sampling is a key measurement technique for investigating the concentration of atmospheric pollutants. Despite being a mature technique, a detailed description of how to derive flow rates and how to calibrate these samplers is missing in the literature. This paper uses basics in fluid mechanics to derive the equations in question. This allows for a deeper understanding of the measurement process and opens up for a more differentiated assessment of potential error sources.

We constructed total-ozone-dependent and tropopause-dependent climatologies from MERRA-2 ozone data to describe the dynamic variations in the ozone profile in response to changing meteorological conditions. The new climatologies contain the first quantitative characterization of ozone profile covariances, which facilitate a new approach to improve ozone profiles using the most probable patterns of profile adjustments represented by the empirical orthogonal functions of the covariance matrices.

The analytical performances of SE-GC-MS and TD-GC-MS for the determination of n-alkanes, PAHs and hopanes were evaluated and compared. The two methods show a good agreement with a high correlation efficient (R² > 0.98) and a slope close to unity. The concentrations of n-alkanes, PAHs and hopanes are found to be much higher in Beijing than those in Chengdu, Shanghai and Guangzhou, most likely due to emissions from coal combustion for wintertime heating in Beijing.

We simulate the ability of a recently installed multistation meteor detection radar to measure characteristics of turbulence in the Earth's lower ionosphere. After verifying that it performs reasonably well, we use the radar's data to study an interaction between turbulence and tidal effects. We performed the study because no one has yet applied a multistation radar to this problem before and because multistation radars like this are becoming increasingly common worldwide.

Clouds may be composed of more than one particle population even at the smallest scales. Cloud radar observations can contain information on multiple particle species, showing up as distinct peaks and subpeaks in the Doppler spectrum. We propose the use of binary tree structures to recursively structure these peaks. Two case studies from different locations and instruments illustrate how this approach can be used to disentangle particle populations in multilayered mixed-phase clouds.

In the study, the potential of GNSS tomography as an important supplementary data source for numerical weather prediction models was examined. We used two GNSS tomography models (TUW, WUELS) in different configurations. The GNSS tomography outputs were assimilated into the WRF model using a radio occultation observations operator (non-standard approach). Promising results show improvement in the weather forecasting of relative humidity and temperature during heavy-precipitation events.

We present a novel, field-deployable extractive electrospray time-of-flight mass spectrometer (EESI-TOF), which provides real-time, near-molecular measurements of organic aerosol at atmospherically relevant concentrations, addressing a critical gap in existing measurement capabilities. Successful deployments of the EESI-TOF for laboratory measurements, ground-based ambient sampling, and aboard a research aircraft highlight the versatility and potential of the EESI-TOF system.

In this paper, the boundary layer structure, and especially turbulence characteristics, were studied during a severe pollution episode. The data were taken from multiple observation techniques, such as lidar, wind profiler radar, radiosonde and a 325 m meteorological tower. Vertical distribution of wind and temperature, evolution of the atmospheric boundary layer (ABL) height, and turbulent flux quantities were compared and analyzed.

Satellite radiance observations have only recently become usable in conditions of cloud and precipitation for the initialization of weather forecasts. The move to all-sky assimilation started with data from the microwave part of the spectrum, with substantial benefit to the quality of operational forecasts. The current work shows a framework in which cloudy infrared data, with its stronger and more non-linear sensitivity, can also benefit operational-quality forecasts.

This article describes the three newly deployed second-generation radar of the Atmospheric Radiation Measurement program. Techniques to retrieve precipitation rate from their measurements are presented: noise and clutter filtering, gas and liquid attenuation correction, and radar reflectivity calibration. Rain rate for a 40 km radius domain around Graciosa estimated from all three radar differ, which highlights the need to consider sensor capabilities when interpreting radar measurements.

Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based observatories. Calibrating these radars is important for establishing a long record of observations suitable for model evaluation and improvement. Here, the CloudSat CPR is used to assess the calibration of a record over 10 years long of ARM cloud radar observations (a total of 44 years). The results indicate that correction coefficients are needed to improve record reliability and usability.

The soft chemical ionization analytical technique of selected ion flow tube mass spectrometry, SIFT-MS, was enhanced by a fast GC pre-separation unit to identify individual isomers. Experiments were carried out with two GC columns, MXT-1 and MXT-Volatiles, using two reagent ions, H₃O⁺ and NO⁺, on monoterpene samples (an artificial mixture and coniferous needles). Analyses of product ion ratios allowed for quantification of multiple monoterpenes in partially separated chromatograms.

Evaluating the on-orbit geolocation accuracy of the ATMS is of great importance. The widely used Earth-target-dependent methods are crippled by the strong atmospheric absorption at sounding channels and cloud contamination at window channels. To solve these issues, this study developed a geolocation evaluation algorithm based on a unique 2-D lunar scan dataset captured by the ATMS during a NOAA-20 pitch-over maneuver operation. The results are validated by the coastline inflection point method.

The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.

An improved understanding of Arctic mixed-phase clouds and their contribution to Arctic warming can be achieved by observations from airborne platforms with remote sensing instruments. Such an instrument is MiRAC combining active and passive techniques to gain information on the distribution of clouds, the occurrence of precipitation, and the amount of liquid and ice within the cloud. Operated during a campaign in Arctic summer, it could observe lower clouds often not seen by spaceborne radars.

This study demonstrates that ice cloud emissivity uncertainties at 11, 12, and 13.3 µm can be used to provide a reasonable range of ice cloud layer boundaries. We test this methodology using MODIS Collection 6 cloud properties over the western North Pacific Ocean during August 2015. The cloud boundaries for single-layer optically thin ice clouds show good agreement with those from CALIOP version 4 products, with biases increasing for optically thick and multilayered clouds.

This paper evaluates the rain rate estimates from the Global Precipitation Measurement (GPM) mission's radar instrument by comparing them to the data of the WegenerNet, a local-scale high-resolution network of meteorological stations. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased.

A better understanding of polar clouds is needed for predicting climate change, including cloud thickness and the sizes and amounts of liquid droplets and ice crystals. These properties can be estimated from an instrument (an infrared spectrometer) that sits on the surface and measures how much infrared radiation is emitted by the cloud. In this work we use model data to investigate how well such an instrument could retrieve cloud properties for different instrument and error characteristics.

Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.

Satellite–based detection of lofted mineral via infrared–window channels, well established in the literature, faces significant challenges in the presence of atmospheric moisture. Here, we consider a case featuring the juxtaposition of two dust plumes embedded within dry and moist air masses. The case is considered from the vantage points of numerical modeling, multi–sensor observations, and radiative transfer theory arriving at a new method for mitigating the water vapor masking effect.

A new algorithm is described, which was used to derive direct radiative effects of aerosols above clouds. These effects are among the largest uncertainties in global climate model simulations, and observations are needed to constrain these simulations. A recently developed method was applied to a combination of satellite reflectance measurements to cover the entire shortwave (solar) spectrum. Radiative effects of aerosols over the south-east Atlantic are presented, where the effects are largest.

This article describes a new discrete wavelength algorithm, MS_SO2, which has been used operationally to retrieve global daily volcanic SO₂ vertical column densities and the UV volcanic ash index from the Total Ozone Mapping Spectrometer (TOMS) data collected by NASA’s Nimbus-7 satellite from 1978 to 1991. We examine the sensitivity of the algorithm to the detection of SO₂, evaluate potential sources of error and compare results from MS_SO2 with the Principal Component Analysis (PCA) algorithm.

To avoid unnecessary data traffic it is sometimes desirable to apply mean averaging kernels to mean profiles of atmospheric state variables. Unfortunately, the application of individual averaging kernels to individual profiles and subsequent averaging will, in general, lead to different results than averaging of the original profiles prior to the application of the mean averaging kernels. This effect is investigated and a correction scheme is proposed.

Here we present a simultaneous Gaussian process regression (GPR) and linear regression pipeline to calibrate and monitor dense wireless low-cost particulate matter sensor networks (WLPMSNs) on the fly by using all available reference monitors across an area. Our approach can achieve an overall 30 % prediction error at a 24 h scale, can differentiate malfunctioning nodes, and track drift. Our solution can substantially reduce manual labor for managing WLPMSNs and prolong their lifetimes.

Total column water vapor (TCWV) is retrieved from the spectra obtained by the Ozone Monitoring Instrument (OMI). Data filtering criteria are recommended. The OMI data generally compare well with reference datasets over both land and the oceans. The data are useful for a variety of applications spanning a range of spatial and temporal scales, such as atmospheric rivers, corn sweat and El Niño.

GEMS will be launched in late 2019 on board the GeoKOMPSAT (Geostationary Korea Multi-Purpose Satellite) to measure O₃, NO₂, SO₂, H₂CO, CHOCHO, and aerosols in East Asia. To support the development of the GEMS ozone profile algorithm, we perform the cross-evaluation of simulated GEMS ozone profile retrievals based on optimal estimation and ozonesonde measurements within the GEMS domain.