Estimates of hail size have been collected by a network of hail sensors, installed in three regions of Switzerland, since September 2018. In this study, we use a technique called “double-moment normalization” to model the distribution of diameter sizes. The parameters of the method have been defined over 70 % of the dataset and tested over the remaining 30 %. An independent distribution of hail sizes, collected by a drone, has also been used to evaluate the method.

Microwave radiometers are usually not able to provide atmospheric quantities such as temperature profiles during rain. We present a method based on a selection of specific frequencies and elevation angles from microwave radiometer observations. A comparison with a numerical weather prediction model shows the presented method allows low-level temperature profiles during rain to be resolved, with rain rates of up to 2.5 mm h^−1_,, which was not possible before with state-of-the-art retrievals.

This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.

The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.

Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.

We describe a low-cost and small research-grade spectrometer for measurements of water vapor in the boundary layer. The instrument uses small Arduino microcontrollers and inexpensive laser diodes to reduce cost while maintaining high performance comparable to more expensive instruments. Performance was assessed with intercomparisons between commercially available instruments outdoors. The design's simplicity, performance, and price point allow it to be accessible to a variety of users.

The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back into space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.

Methane leaks into houses that use natural gas from appliances, pipes, and fittings. We measured methane emitted from a manufactured house under different ventilation conditions using indoor and outdoor concentration measurements. We injected methane at prescribed rates into the house and then measured the emissions using our method. We report the error in the calculation based on these tests. We also describe the method and provide guidance on conducting this type of experiment.

Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.

This paper presents a design of a UV wind lidar, made with a UV fiber laser and a Quadri Mach-Zehnder interferometer as a spectral analyzer, used to measure the wind in front of future low-consumption aircraft. The article details the optimization of the different elements of the instrument with simulations. This paper also presents a method to optimize laser angles for determining wind direction and strength and shows a 50 % improvement over the current angles used.

This paper focuses on the BMA-FLX processor, in which thermal and solar top-of-atmosphere radiative fluxes are obtained from longwave and shortwave radiances measured along track by the EarthCARE Broadband Radiometer (BBR). The BBR measurements, at three fixed viewing angles (fore, nadir, aft), are co-registered either at the surface or at a reference level. A combined flux from the three BRR views is obtained. The algorithm has been successfully validated against test scenes.

Ozone measurements from the ACE-FTS satellite instrument have been compared to worldwide balloon-borne ozonesonde profiles using pairs of closely spaced profiles and monthly averaged profiles. ACE-FTS typically measures more ozone in the stratosphere by up to 10 %. The long-term stability of the ACE-FTS ozone data is good, exhibiting small (but non-significant) drifts of less than 3 % per decade in the stratosphere. Lower in the profiles, the calculated drifts are larger (up to 10 % per decade).

This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.

We tested a method to identify airborne microplastics (MPs), merging imaging, fluorescence, and machine learning of single particles. We examined whether combining imaging and fluorescence data enhances classification accuracy compared to using each method separately and tested these methods with other particle types. The tested MPs have distinct fluorescence, and a combined imaging and fluorescence method improves their detection, making meaningful progress in monitoring MPs in the atmosphere.

Three-dimensional (3D) wind velocity measurements are of major importance for the characterization of atmospheric turbulence. This paper presents a detailed study of the measurement uncertainty of a three-beam wind lidar designed for mounting on airborne platforms. Considering the geometrical constraints, the analysis provides quantitative estimates for the measurement uncertainty of all components of the 3D wind vector. As a result, we propose optimized post-processing for error reduction.

This study analyzes precipitation features over the Qinghai–Tibet Plateau, focusing on drop size distribution (DSD), terminal velocity, number density, radar reflectivity (Z), and rainfall intensity (I) at four sites. Terminal velocity is fastest at Nyalam and slowest at Lhasa.  Five microphysical characteristics exhibit different correlations with altitude. Z–I relationships differ by site, and both exponential and gamma distributions fit DSD, with gamma parameters varying with altitude.

A new millimetre-wavelength radar is used to improve methods of retrieving information about the smallest droplets that exist within clouds. The radar is shown to be able to retrieve the vertical wind speed more accurately and more frequently and to retrieve the cloud properties for clouds with lower rainfall rates and smaller droplets than would be possible using longer-wavelength radars.

This study explores differences between remote sensing and in situ instruments in terms of their vertical, horizontal, and temporal sampling differences. Understanding and resolving these differences are critical for future analyses linking satellite, ground-based remote sensing, and in situ observations in air quality monitoring. It shows that the meteorological conditions (wind directions, speed, and boundary layer conditions) will strongly affect the agreement between the two measurements.

This work provides a comprehensive sensitivity calibration of a chemical ionization instrument commonly used in field measurements for the measurement of the toxic isomers methyl isocyanate and hydroxyacetonitrile that are found in the atmosphere. The results from this work have demonstrated that the hydroyacetonitrile isomer was observed in previous field studies rather than the stated identification of methyl isocyanate.

The effect of ducting due to the presence of the planetary boundary layer (PBL) is prevalent over the northeastern Pacific Ocean from Los Angeles to Honolulu, USA. The ducting-induced refractivity bias in the radiosonde climatology and ERA5 data is highly correlated with the height of the PBL. The magnitude of bias is linearly dependent on the strength of ducting but not the location, and the overall reanalysis data systematically underestimate the height of the PBL by as much as 120 m.

Different ground-based ozone measurements from the last 2 decades at Lauder are compared to each other. We want to know why different trends have been observed in the stratosphere. Also, the quality and relevance of tropospheric datasets need to be evaluated. While remaining drifts are still present, our study explains roughly half of the differences in observed trends in previous studies and shows the necessity for continuous review and improvement of the measurements.

The importance of the consideration of cloud motion for the stereographic determination of cloud top height from aircraft observations is demonstrated using measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). A method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts is presented and validated using both real measurements and realistic radiative transfer simulations.

This study explores the effectiveness of the Horiba PX-375 monitor for analysing the elemental composition of airborne particulate matter (PM). Understanding this composition of PM is important for identifying its sources, assessing potential health risks, and developing strategies to reduce air pollution. The PX-375 monitor proved to be a valuable tool for ongoing air quality monitoring studies and could be particularly useful as pollution levels and sources change in the future.

We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.

A Japanese team developed the Level 2 atmospheric radiation flux and heating rate product for EarthCARE, which offers vertical profiles of longwave and shortwave radiative fluxes and heating rates. This study outlines the algorithm for the radiative product and its comparative validation against satellite and ground-based observations. It also analyzes errors in radiative fluxes at various scales and their dependence on cloud type, with ice-containing clouds identified as a primary error source.

The main source of measurement error from particulate matter PurpleAir sensors is relative humidity. Recent bias correction methods have not focused on the humid southeastern United States (US). To provide high-quality spatial and temporal data to inform community exposure in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We found improved performance metrics, with error metrics decreasing by 16–23 % for our models.

This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.

We developed a new algorithm to retrieve vertical distributions of aerosol extinction coefficients in the stratosphere. The algorithm is applied to measurements of scattered solar light from the spaceborne OMPS-LP (Ozone Mapper and Profiler Suite Limb Profiler) instrument. The retrieval results are compared to data from other spaceborne instruments and used to investigate the evolution of the aerosol plume following the eruption of the Hunga Tonga–Hunga Ha'apai volcano in January 2022.

The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.

This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP)  algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.