In July 2024, Sunrise completed its third successful science flight. The Sunrise iii observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013, to tackle the most recent science challenges concerning the solar atmosphere. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet (309 – 417 nm) and the near-infrared (765 – 855 nm) regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46\times 46$ 46 × 46 Mm² in the photosphere and the chromosphere in the visible (525 and 517 nm). The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005”. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise iii was launched successfully on 10 July 2024, from the Esrange Space Center of the Swedish Space Corporation near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise iii observatory and its instruments.

Aerosols play an important role in key atmospheric processes and feature high spatial and temporal variabilities. This has motivated scientific interest in the development of networks capable of measuring aerosol properties over large geographical areas in near-real time. In this work we present and discuss results of an aerosol optical depth (AOD) algorithm applied to instruments of the European Brewer Network. This network is comprised of close to 50 Brewer spectrophotometers, mostly located in Europe and adjacent areas, although instruments operating at, for example, South America and Australia are also members. Although we only show results for instruments calibrated by the Regional Brewer Calibration Center for Europe, the implementation of the AOD algorithm described is intended to be used by the whole network in the future. Using data from the Brewer intercomparison campaigns in the years 2013 and 2015, and the period in between, plus comparisons with Cimel sun photometers and UVPFR instruments, we check the precision, stability, and uncertainty of the Brewer AOD in the ultraviolet range from 300 to 320 nm. Our results show a precision better than 0.01, an uncertainty of less than 0.05, and, for well-maintained instruments, a stability similar to that of the ozone measurements. We also discuss future improvements to our algorithm with respect to the input data, their processing, and the characterization of the Brewer instruments for the measurement of AOD.

Plenoptic cameras are a new type of sensor that extend the possibilities of current commercial cameras allowing 3D refocusing or the capture of 3D depths. One of the limitations of plenoptic cameras is their limited spatial resolution. In this paper we describe a fast, specialized hardware implementation of a super-resolution algorithm for plenoptic cameras. The algorithm has been designed for field programmable graphic array (FPGA) devices using VHDL (very high speed integrated circuit (VHSIC) hardware description language). With this technology, we obtain an acceleration of several orders of magnitude using its extremely high-performance signal processing capability through parallelism and pipeline architecture. The system has been developed using generics of the VHDL language. This allows a very versatile and parameterizable system. The system user can easily modify parameters such as data width, number of microlenses of the plenoptic camera, their size and shape, and the super-resolution factor. The speed of the algorithm in FPGA has been successfully compared with the execution using a conventional computer for several image sizes and different 3D refocusing planes.

Sunrise iii is a balloon-borne solar observatory dedicated to the investigation of the processes governing the physics of the magnetic field and the plasma flows in the lower solar atmosphere. The gondola hosts a 1-m aperture telescope that feeds three post-focus instruments. One of these instruments, the Tunable Magnetograph (TuMag), is a tunable imaging spectropolarimeter in visible wavelengths. It is designed to probe the vector magnetic field, $\bf{B}$ , and the line-of-sight (LoS) velocity, $v_{\mathrm{LoS}}$ v LoS , of the photosphere and the lower chromosphere. It provides polarized images with a $63^{\prime \prime }\times \, 63^{\prime \prime }$ 63 ″ × 63 ″ field of view (FoV) of the Sun in four polarization states. These images can later be processed on ground to retrieve maps of the aforementioned solar physical quantities. The quasi-simultaneous observation of two spectral lines sensitive to $\bf{B}$ and $v_{\mathrm{LoS}}$ v LoS in the photosphere and lower chromosphere provides excellent diagnostic measurements of the magnetic and dynamic coupling in these layers. When combined with the other two instruments on board, observing in the infrared and ultraviolet regions of the spectrum, TuMag’s diagnostic potential is expected to be greatly enhanced. Building upon heritage of instruments like IMaX and SO/PHI, the key technologies employed for TuMag are a liquid-crystal-variable-retarder-based polarimeter and a solid, LiNbO ₃ Fabry–Pérot etalon as a spectrometer. However, it also incorporates several innovative features, such as in-house-made, high-sensitivity scientific cameras and a double filter wheel. The latter makes TuMag the first balloon-borne instrument of its type capable of tuning between spectral lines. Specifically, it can sequentially observe any two out of the three spectral lines of Fe i at 525.02 and 525.06 nm and of Mg i at 517.3 nm. Time cadences range from 30 to 100 seconds, depending on the observing mode and the specific pair of spectral lines targeted. Laboratory measurements have demonstrated good image quality, spectral resolution, and polarimetric efficiency. Here we report on the concept, design, calibration, and integration phases of the instrument, as well as on the data reduction pipeline.

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.