A precipitation phase discriminator for IMPROVER

<p>Knowledge of the expected precipitation phase is crucial for mitigating the impacts snow and ice on national infrastructure. This is sensitive to the altitude of the modelled forecast grid point which varies between models.</p><p>The IMPROVER project aims to blend probabilistic model variables from different models. This presentation describes the approach used to standardise the phase change levels of falling precipitation from the Met Office UK and Global models over the high-resolution UK domain.</p><p>The method uses wet-bulb temperature profiles to identify the surface where snow changes to sleet and sleet changes to rain, interpolates these surfaces through model orography and below sea level, then extracts the predicted phase at the altitude of the standard high-resolution UK orography. This is performed for each model realization to maintain the multivariate connection between precipitation and precipitation phase.</p><p>The precipitation phase discriminators are used to categorise precipitation rate and accumulation probability data into rain, sleet and snow phases which in turn inform a categorical most-likely weather code.</p><p>We present results from a one-month trial using data from February 2020 comparing the weather code forecasts with site observations across the UK.</p>

Dynamic Compensation of Parameters in Servo Drives

In this paper, we studied a servo drive with an unlimited increase in the gain coefficients of the speed con-tour. We demonstrated dynamic compensation of parameters associated with the formation of differential com-ponents based on the coverage of the real coordinates of the control object using static model. It was shown that dynamics of the transient processes is caused by real differentiating links determining the fast and slow components of the transient process. The static velocity error in this case did not depend on the ratio of large and small time constants, as in a single integrating system of subordinate regulation of parameters. Therefore, we considered that parametric disturbances and their compensation occur due to the external astatic circuit. We demonstrated an equivalent transition to the position contour, the speed structure and position generator, corresponding to the organization of the servo drive in the CNC systems. We investigated features of the phase discriminator during the operation of the position sensor in the phase difference mode and carried out model-ing of the servo drive structures and comparative analysis of two and three-circuit systems.

A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

A new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3–32 µm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5 % for diameters >3 µm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections.

A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

A new instrument, the High Speed Particle Phase Discriminator (PPD-HS) developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in-situ analysis of the spatial intensity distribution of near forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2D scattering pattern to scattered light intensities captured onto two linear, one dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles, generated in a well-controlled laboratory setting using a Vibrating Orifice Aerosol Generator (VOAG) and covering a size range of approximately 3–32 micron. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5 % for diameters > 3 micro meter. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes independent of optical particle size. We conclude that PPD-HS constitutes a powerful new instrument to size and discriminate phase of cloud hydrometeors and thus study microphysical properties of mixed-phase clouds, that represent a major source of uncertainty in aerosol indirect effect for future climate projections.