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    Conversion diagram for the six-class one-moment microphysical scheme applicable to global cloud-resolving simulations. It shows interactions among main precipitation and cloud physical and thermodynamic parameters, and processes among various parameters, e.g., autoconversion due to collision–coalescence, aggregation, and ice multiplication (adapted from Tomita 2008).

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    Snow particles collected during FRAM and SAAWSO projects that took place during 2010–15 winters. (a) Secondary ice crystal generated by splintering mechanism over Whistler Mountain, (b) small wet ice crystals, (c) graupel, (d) rimed single ice crystals, (e) light snow crystals, (f) rimed and aggregated snow crystals, and (g) high density ice pellets. Scales between 2 lines in (a)–(e) is 1 mm; (f) and (g) have a snow crystal maximum size of 3 and 1 mm, respectively.

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    Various precipitation sensors at the PUMS site near Toronto, Ontario, Canada. (a) The Geonor, Pluvio, Yonge tipping-bucket, capacitor sensor, and WXT52. (b) A double-fenced reference system with Pluvio sensor (scaled down to 1.5 times), similar to the DFIR reference platform. (c) Entire project area (PUMS site) in Oshawa.

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    (a) GCIP instrument for snow spectral measurements at sizes less than about 1 mm. (b) Pluvio instrument with a single-alter shield at 3-m height with a Metek Inc. 3D ultrasonic anemometer. (c) LPM for snow spectral measurements. (d) DMT Inc. FMD (FM100) to measure fog particle spectra between 1 and 50 μm over 16 channels.

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    (left) Forward-facing video photos repeated Learjet penetrations of the same cloud at three temperature levels given as −8°, −12°, and −15°C; (center) particle size distributions from three cloud particle probes (FFSSP, 2D-S, and HVPS) on the aircraft; and (right) composite size distributions of water drops (blue) and ice particles (red). Examples of Spec Inc. CPI and 2D-S images with particle number concentration (L−1) and mass concentration (g m−3) averaged over the updraft core are also shown in the right panels. (top left) The images of water drops and snow crystals from 2D-S probe; (top right) the particle spectra for drops and snow crystals based on CPI and 2D-S measurements (adapted from Lawson et al. 2015).

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    Radiometrics PMWR (a) temperature, (b) RH over ice, and (c) LWC (×10) profile retrievals to 1.2-km height, (d) IWV and ILW retrievals, and (e) surface temperature (Tamb) and cloud-base IR temperature (Tir) for 23 Jan 2014.

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    Time–height cross sections of the equivalent radar reflectivity factor (dBZe) (a) measured by the 95-GHz Doppler radar on board the Mirai ferry, and calculated by the radar product simulator applied to the outputs of (b) the bin (control) simulation, (c) the bin with the terminal fall velocities of snow equalized to those of hail in all size bins (rimed snow), and (d) the bulk model simulations from 1200 UTC 22 May to 1200 UTC 23 May 2001 (adapted from Iguchi et al. 2012).

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    Hourly vertical profiles of C-band (a) horizontal reflectivity Zh, (b) differential reflectivity Zdr, (c) differential phase shift Kdp, and (d) correlation coefficient ρHV colored according to their respective RCP values. The RCP quantiles (0%, 25%, 50%, 75%, and 100%) represent values of 1.1, 2.7, 3.9, 7.3, and 21.7 dB, respectively. The black (gray) thick lines represent the average of the daily profiles for stratiform (convective) events. To highlight the variations for small values, the Kdp profiles are plotted on a log axis (adapted from Bechini et al. 2013).

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    3D view of the simulated GPM orbital data over the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) project location. Color-shaded terrain represents 15-dBZ echo-top height of the DPR Ku band, and horizontal slices of color shades represent microwave brightness temperature of the GMI 37- and 166-GHz (V) channels (adapted from Matsui et al. 2013).

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    FD12P Vis vs PR for all snow events occurred during the FRAM Science of Nowcasting Winter Weather for Vancouver 2010 (SNOW-V10) project for various precipitation types shown in the legend (adapted from Gultepe et al. 2014a,b). The symbols as LSN, MSN, HSN, LIP, MIP, SG, and ICE represent light snow, moderate snow, heavy snow, light ice crystal precipitation, snow grains, and ice crystals, respectively.

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    Histogram of PR for the entire SAAWSO project that took place over Goose Bay, NL, Canada, from 1 Nov 2013 to 1 May 2014. The pdf of PR is obtained based on Weibull distribution function given by Eq. (6-22), which is shown on the figure.

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    Precipitation change (%) from the period 1980–99 to 2080–99 in the Consortium for the Application of Climate Impacts Assessments Business as Usual (ACACIA-BAU or BAU). BAU simulation for (a) DJF, (b) JJA, and (c) annual mean (adapted from Dai 2001).

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    The comparison of LSN precipitation rate from various instruments (see legend) on 23 Jan 2014 at T ~ −18°C occurred over Goose Bay during the SAAWSO project. (a) Blowing snow effects seen after 1600 UTC are consistent with Uh ~ 6 m s−1. (b) Time series in UTC of GCIP-, LPM-, Pluvio-, and PWD-based LSN PR on 3 May 2014, Goose Bay. The black dots are for 1-Hz PR obtained from GCIP. The green solid line is for 60-s averages of GCIP PR to match with LPM- and PWD-based PR scales. The Pluvio-based PR is obtained using 60-min running averages. Freezing drizzle droplets occurred at 1830 UTC is seen in the inset panel.

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    (a) Hourly catch ratios of solid precipitation vs 1.5-m-height wind speeds. Double-alter-shielded Geonor measurements are normalized by the standard hourly precipitation amount. Best-fit equation (red line) is also shown on the plot with correlation coefficient. (b) Liquid equivalent accumulation in the Geonor with DFIR, small DFIR (SDFIR), and double-alter and single-alter shields for the 17–19 Mar blizzard. Wind speed is given by the red line and is indicated by the scale on the right (adapted from Rasmussen et al. 2012).

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    (top) A blowing snow event happened during FRAM project at Roundhouse (RND) mountain site. Time series of (a) Vis, (b) PR from FD12P, and (c) Uh from 3D ultrasonic anemometer for RND, Whistler Mountain high-level (VOA), and Whistler Mountain midlevel (VOL) sites (black dots, red dots, and black solid line, respectively) show the vertical variability along a 500-m slope for 17 Jan 2010 (adapted from Gultepe et al. 2014a,b).

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    Illustration of the new Sundqvist-type parameterization along with the previous autoconversion parameterizations. The two typical examples of the new Sundqvist-type parameterization shown here correspond to μ = 2 and 4, respectively. Berry: Berry (1968); Beheng: Beheng (1994); KK: Khairoutdinov and Kogan (2000); SB: Seifert and Beheng (2001); CL: Chen and Liu (2004); P0: Liu–Daum rate function (Liu and Daum 2004) are also shown on the plot (adapted from Liu et al. 2006).

  • View in gallery

    Vertical profiles of rain mixing ratio based on various mesoscale models used for the same case study for (a) one-moment bulk models, (b) two-moment bulk models, and (c) bin models (adapted from Onishi and Takahashi 2012).

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Ice-Phase Precipitation

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  • 1 Cloud Physics and Severe Weather Research Section, Environment Canada, Toronto, Ontario, Canada
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Met Office, Exeter, and School of Earth and Environment, Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom
  • | 4 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

Ice-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.

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Corresponding author: Ismail Gultepe, ismail.gultepe@ec.gc.ca

Abstract

Ice-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.