Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset

This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/)

Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data

In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i.e., the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data. The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.DFG, 322579844, Hitzewellen in Berlin, Deutschland - StadtklimamodifkationenBMBF, 01LP1602A, Verbundprojekt Stadtklima: Evaluierung von Stadtklimamodellen (Modul B), 3DO Teilprojekt 1: Dreidimensionales Monitoring atmosphärischer Prozesse in Berli

Polarization due to rotational distortion in the bright star Regulus

This is the full published article (retrieved from the 6 months post-publication posting on arXiv) including the Methods and Supplementary Information sections: 33 pages, 10 figures, 8 tablesPolarization in stars was first predicted by Chandrasekhar [1] who calculated a substantial linear polarization at the stellar limb for a pure electron-scattering atmosphere. This polarization will average to zero when integrated over a spherical star but could be detected if the symmetry is broken, for example by the eclipse of a binary companion. Nearly 50 years ago, Harrington and Collins [2] modeled another way of breaking the symmetry and producing net polarization - the distortion of a rapidly rotating hot star. Here we report the first detection of this effect. Observations of the linear polarization of Regulus, with two different high-precision polarimeters, range from +42 parts-per-million (ppm) at a wavelength of 741 nm to -22 ppm at 395 nm. The reversal from red to blue is a distinctive feature of rotation-induced polarization. Using a new set of models for the polarization of rapidly rotating stars we find that Regulus is rotating at 96.5(+0.6/-0.8)% of its critical angular velocity for breakup, and has an inclination greater than 76.5 degrees. The rotation axis of the star is at a position angle of 79.5+/-0.7 degrees. The conclusions are independent of, but in good agreement with, the results of previously published interferometric observations of Regulus [3]. The accurate measurement of rotation in early-type stars is important for understanding their stellar environments [4], and course of their evolution [5].Peer reviewedFinal Accepted Versio

SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences

While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201