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.

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.

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.

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.

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.

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.

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.

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.