MAX IV Emittance Reduction and Brightness Improvement

With MAX IV construction well underway and beam commissioning expected to commence in July 2015, first studies have been launched on improving the optics of the MAX IV 3 GeV storage ring with the goal of further reducing the emittance from the baseline design (320 pm rad) towards 150 pm rad while improving the matching of the electron beam to insertion devices to further improve the resulting photon brightness. We report on progress in the development of this new optics as well as studies of the impact of many strong insertion devices on beam emittance. The latter is an crucial issue in ultralow-emittance rings where dipole radiation losses are low and hence the equilibrium emittance is determined to large extent by the insertion devices. Additionally, large stored current can lead to a significant increase of emittance via strong intrabeam scattering in both transverse and longitudinal planes. Hence, the anticipated insertion devices, stored bunch current, and RF cavity tuning all have to be taken into account when optimizing for highest brightness. We present initial results and sketch a path towards a first MAX IV upgrade

Coupling and Brightness Considerations for the MAX IV 3 GeV Storage Ring

It is often suggested that the emittance coupling of a storage ring should be adjusted to the so-called diffraction limit corresponding to the shortest wavelength of interest. For 1 Å radiation this leads to a typical requirement of 8 pm rad vertical emittance. In ultralow-emittance storage rings like the MAX IV 3 GeV storage ring this corresponds to a comparably large setting of the emittance coupling (2.5%). This approach, however, does not produce the brightest radiation and needs to be revisited taking into account that overall photon brightness depends on the emittances of the electron beam and the intrinsic photon beam. This paper summarizes an analytic approach to maximizing brightness as a function of emittance coupling while retaining sufficient lifetime. Instead of "meeting the diffraction limit", we further reduce the coupling, thus increasing both the brightness and transverse coherence of the emitted radiation. We derive that reducing the MAX IV 3 GeV storage ring's vertical emittance to 2 pm rad (0.6% coupling) will increase brightness and transverse coherence by almost a factor two while 10 h overall lifetime can still be achieved

Compact Model Representation for 3D Reconstruction

3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however that only one image cannot provide enough information for such a reconstruction. A prior knowledge that has been entertained are 3D CAD models due to its online ubiquity. A fundamental question is how to compactly represent millions of CAD models while allowing generalization to new unseen objects with fine-scaled geometry. We introduce an approach to compactly represent a 3D mesh. Our method first selects a 3D model from a graph structure by using a novel free-form deformation FFD 3D-2D registration, and then the selected 3D model is refined to best fit the image silhouette. We perform a comprehensive quantitative and qualitative analysis that demonstrates impressive dense and realistic 3D reconstruction from single images.Comment: 9 pages, 6 figure

Study to support the definition of Arctic Weather Satellite (AWS) high frequency channels

This study compares the options of having a channel at 229 GHz or having some around\ua0325 GHz from a single perspective, cloud filtering/correction of 183 GHz data.Final report of EUMETSAT study under contract :\ua0 EUM/CO/20/4600002417/CJ

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) will provide unprecedented measurements at sub-millimetre frequencies, employing channels up to 664 GHz. However, the utilization of such measurements requires detailed data on how individual ice particles scatter and absorb radiation, i.e. single scattering data. Several single scattering databases are currently available, with the one by Eriksson et al. (2018) specifically tailored to ICI. This study attempts to validate and constrain the large set of particle models available in this database to a smaller and more manageable set. A combined active and passive model framework is developed and employed, which converts CloudSat observations to simulated brightness temperatures (TBs) measured by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and ICI. Simulations covering about 1 month in the tropical Pacific Ocean are performed, assuming different microphysical settings realized as combinations of the particle model and particle size distribution (PSD). Firstly, it is found that when the CloudSat inversions and the passive forward model are considered separately, the assumed particle model and PSD have a considerable impact on both radar-retrieved ice water content (IWC) and simulated TBs. Conversely, when the combined active and passive framework is employed instead, the uncertainty due to the assumed particle model is significantly reduced. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (166, 186.31, and 190.31 GHz), indicating the robustness of the simulations. However, it is difficult to identify a particle model that outperforms any other. One aggregate particle model, composed of columns, yields marginally better agreement with GMI compared to the other particles, mainly for the most severe cases of deep convection. Of the tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement with GMI and also yields radar dBZ–IWC relationships closely matching measurements by Protat et al. (2016). Only one particle, modelled as an air–ice mixture spheroid, performs poorly overall. On the other hand, simulations at the higher ICI frequencies (328.65, 334.65, and 668.2 GHz) show significantly higher sensitivity to the assumed particle model. This study thus points to the potential use of combined ICI and 94 GHz radar measurements to constrain ice hydrometeor properties in radiative transfer (RT) using the method demonstrated in this paper

Solaris Storage Ring Lattice Optimization with Strong Insertion Devices

The Solaris synchrotron radiation facility under construction in Krakow will be a replica of the 1.5 GeV storage ring of MAX IV. This compact 3rd generation light source has been designed to have an emittance of 6 nm rad and operate with 500 mA stored current for VUV and and soft X-ray production.The lattice consists of 12 double- bend achromats (DBA) where each DBA cell is integrated into one solid iron block. Twelve 3.5 m long straight sections are available of which 10 will be equipped with various insertion devices. These devices will differ from those adopted by MAX IV. For X-ray production one or more superconducting wigglers will be used, while APPLE-II type undulators will be used for variable polarised light production. The linear and nonlinear beam dynamics have been studied with these perturbing insertion devices included in the lattice and results are presented in this paper

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work applies machine learning to improve the IWP retrieval from Meteosat-9 observations, with a focus on low latitudes, training the models against retrievals based on CloudSat. Advantages of machine learning include avoiding explicit physical assumptions on the data, an efficient use of information from all channels, and easily leveraging spatial information. Thermal infrared (IR) retrievals are used as input to achieve a performance independent of the solar angle. They are compared with retrievals including solar reflectances as well as a subset of IR channels for compatibility with historical sensors. The retrievals are accomplished with quantile regression neural networks. This network type provides case-specific uncertainty estimates, compatible with non-Gaussian errors, and is flexible enough to be applied to different network architectures. Spatial information is incorporated into the network through a convolutional neural network (CNN) architecture. This choice outperforms architectures that only work pixelwise. In fact, the CNN shows a good retrieval performance by using only IR channels. This makes it possible to compute diurnal cycles, a problem that CloudSat cannot resolve due to its limited temporal and spatial sampling. These retrievals compare favourably with IWP retrievals in CLAAS, a dataset based on a traditional approach. These results highlight the possibilities to overcome limitations from physics-based approaches using machine learning while providing efficient, probabilistic IWP retrieval methods. Moreover, they suggest this first work can be extended to higher latitudes as well as that geostationary data can be considered as a complement to the upcoming Ice Cloud Imager mission, for example, to bridge the gap in temporal sampling with respect to space-based radars

Can machine learning correct microwave humidity radiances for the influence of clouds?

A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels - the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %-6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements