scholarly journals Discrimination between Winter Precipitation Types Based on Spectral-Bin Microphysical Modeling

2016 ◽  
Vol 55 (8) ◽  
pp. 1747-1761 ◽  
Author(s):  
Heather Dawn Reeves ◽  
Alexander V. Ryzhkov ◽  
J. Krause
Keyword(s):  
Drop Size ◽  
Winter Precipitation ◽  
Horizontal Distribution ◽  
Ensemble Model ◽  
Freezing Rain ◽  
New Approach ◽  
Water Fraction ◽  
Precipitation Type ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

AbstractA new approach for distinguishing precipitation types at the surface, the spectral bin classifier (SBC), is presented. This algorithm diagnoses six categories of precipitation: rain (RA), snow (SN), a rain–snow mix (RASN), freezing rain (FZRA), ice pellets (PL), and a freezing rain–ice pellet mix (FZRAPL). It works by calculating the liquid-water fraction fw for a spectrum of falling hydrometeors given a prescribed temperature T and relative humidity profile. Demonstrations of the SBC output show that it provides reasonable estimates of fw of various-sized hydrometeors for the different categories of precipitation. The SBC also faithfully represents the horizontal distribution of precipitation type inasmuch as the model analyses and surface observations are consistent with each other. When applied to a collection of observed soundings associated with RA, SN, FZRA, and PL, the classifier has probabilities of detection (PODs) that range from 62.4% to 98.3%. The PODs do decrease when the effects of model uncertainty are accounted for. This decrease is modest for RA, SN, and PL but is large for FZRA as a result of the fact that this form of precipitation is very sensitive to small changes in the thermal profile. The effects of the choice of the degree of riming above the melting layer, the drop size distribution, and the assumed temperature at which ice nucleates are also examined. Recommendations on how to mitigate all forms of uncertainty are discussed. These include the use of dual-polarized radar observations, incorporating output from the microphysical parameterization scheme, and the use of ensemble model forecasts.

Improving Predictions of Precipitation Type at the Surface: Description and Verification of Two New Products from the ECMWF Ensemble

2018 ◽  
Vol 33 (1) ◽  
pp. 89-108 ◽  
Author(s):  
Estíbaliz Gascón ◽  
Tim Hewson ◽  
Thomas Haiden
Keyword(s):  
New Products ◽  
Winter Precipitation ◽  
Precipitation Rate ◽  
Lower Probability ◽  
Winter Period ◽  
Freezing Rain ◽  
Medium Range ◽  
Precipitation Type ◽  
Forecasting System ◽  
Output Information

Abstract The medium-range ensemble (ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) is used to create two new products intended to face the challenges of winter precipitation-type forecasting. The products themselves are a map product that represents which precipitation type is most likely whenever the probability of precipitation is >50% (also including information on lower probability outcomes) and a meteogram product, showing the temporal evolution of the instantaneous precipitation-type probabilities for a specific location, classified into three categories of precipitation rate. A minimum precipitation rate is also used to distinguish dry from precipitating conditions, setting this value according to type, in order to try to enforce a zero frequency bias for all precipitation types. The new products differ from other ECMWF products in three important respects: first, the input variable is discretized, rather than continuous; second, the postprocessing increases the output information content; and, third, the map-based product condenses information into a more accessible format. The verification of both products was developed using four months’ worth of 3-hourly observations of present weather from manual surface synoptic observation (SYNOPs) in Europe during the 2016/17 winter period. This verification shows that the IFS is highly skillful when forecasting rain and snow, but only moderately skillful for freezing rain and rain and snow mixed, while the ability to predict the occurrence of ice pellets is negligible. Typical outputs are also illustrated via a freezing-rain case study, showing interesting changes with lead time.

On the Characteristics of and Processes Producing Winter Precipitation Types near 0°C

2015 ◽  
Vol 96 (4) ◽  
pp. 623-639 ◽  
Author(s):  
Ronald E. Stewart ◽  
Julie M. Thériault ◽  
William Henson
Keyword(s):  
Precipitation Amount ◽  
Winter Precipitation ◽  
Phase Changes ◽  
Wind Fields ◽  
Freezing Rain ◽  
Wet Snow ◽  
Physically Based ◽  
Precipitation Type ◽  
Background Temperature ◽  
The Many

Abstract This article examines the types of winter precipitation that occur near 0°C, specifically rain, freezing rain, freezing drizzle, ice pellets, snow pellets, and wet snow. It follows from a call by M. Ralph et al. for more attention to be paid to this precipitation since it represents one of the most serious wintertime quantitative precipitation forecasting (QPF) issues. The formation of the many precipitation types involves ice-phase and/or liquid-phase processes, and thresholds in the degree of melting and/or freezing often dictate the types occurring at the surface. Some types can occur simultaneously so that, for example, ensuing collisions between supercooled raindrops and ice pellets that form ice pellet aggregates can lead to substantial reductions in the occurrence of freezing rain at the surface, and ice crystal multiplication processes can lead to locally produced ice crystals in the subfreezing layer below inversions. Highly variable fall velocities within the background temperature and wind fields of precipitation-type transition regions lead to varying particle trajectories and significant alterations in the distribution of precipitation amount and type at the surface. Physically based predictions that account for at least some of the phase changes and particle interactions are now in operation. Outstanding issues to be addressed include the impacts of accretion on precipitation-type formation, quantification of melting and freezing rates of the highly variable precipitation, the consequences of collisions between the various types, and the onset of ice nucleation and its effects. The precipitation physics perspective of this article furthermore needs to be integrated into a comprehensive understanding involving the surrounding and interacting environment.

Differentiating Freezing Drizzle and Freezing Rain in HRRR Model Forecasts

2021 ◽  
Author(s):  
Sarah Tessendorf ◽  
Allyson Rugg ◽  
Alexei Korolev ◽  
Ivan Heckman ◽  
Courtney Weeks ◽  
...  
Keyword(s):  
Drop Size ◽  
Prediction Models ◽  
Water Drop ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Drop Diameter ◽  
Aircraft Icing ◽  
Freezing Rain ◽  
Primary Focus

AbstractSupercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (> 500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in-situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

scholarly journals More on the Scale Dependence of the Predictability of Precipitation Patterns: Extension to the 2009–13 CAPS Spring Experiment Ensemble Forecasts

2017 ◽  
Vol 145 (9) ◽  
pp. 3625-3646 ◽  
Author(s):  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
M. K. Yau ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Quasi Equilibrium ◽  
Relative Role ◽  
Ensemble Forecasts ◽  
Adjustment Time ◽  
Convective Adjustment ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

This paper analyzes the scale and case dependence of the predictability of precipitation in the Storm-Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms (CAPS) during the NOAA Hazardous Weather Testbed Spring Experiments of 2008–13. The effect of different types of ensemble perturbation methodologies is quantified as a function of spatial scale. It is found that uncertainties in the large-scale initial and boundary conditions and in the model microphysical parameterization scheme can result in the loss of predictability at scales smaller than 200 km after 24 h. Also, these uncertainties account for most of the forecast error. Other types of ensemble perturbation methodologies were not found to be as important for the quantitative precipitation forecasts (QPFs). The case dependences of predictability and of the sensitivity to the ensemble perturbation methodology were also analyzed. Events were characterized in terms of the extent of the precipitation coverage and of the convective-adjustment time scale [Formula: see text], an indicator of whether convection is in equilibrium with the large-scale forcing. It was found that events characterized by widespread precipitation and small [Formula: see text] values (representative of quasi-equilibrium convection) were usually more predictable than nonequilibrium cases. No significant statistical relationship was found between the relative role of different perturbation methodologies and precipitation coverage or [Formula: see text].

scholarly journals Verifying Forecast Precipitation Type with mPING*

2015 ◽  
Vol 30 (3) ◽  
pp. 656-667 ◽  
Author(s):  
Kimberly L. Elmore ◽  
Heather M. Grams ◽  
Deanna Apps ◽  
Heather D. Reeves
Keyword(s):  
Critical Infrastructure ◽  
Current Model ◽  
Model Performance ◽  
Transportation Systems ◽  
Forecast System ◽  
Freezing Rain ◽  
The North ◽  
Skill Scores ◽  
Precipitation Type ◽  
Surface Observations

Abstract In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

scholarly journals Improved Estimates of the Vertical Structures of Rain Using Single Frequency Doppler Radars

2021 ◽  
Author(s):  
A. R. Jameson ◽  
Michael Larsen ◽  
David Wolff
Keyword(s):  
Drop Size ◽  
Physical Structure ◽  
Single Frequency ◽  
Fall Rate ◽  
Size Distributions ◽  
New Approach ◽  
Rain Fall ◽  
Temporal And Spatial ◽  
Air Motion ◽  
Drop Size Distributions

It is important to understand the statistical-physical structure of the rain in the vertical so that observations aloft can be translated meaningfully into what will occur at the surface. In order to achieve this understanding, it is necessary to gather high temporal and spatial resolution observations of rain in the vertical. This can only be accomplished using radars. It can be achieved by translating radar Doppler spectra into drop size distributions which can then be integrated to calculate variables such as the rain fall rate. A long-standing difficulty in using such measurements, however, is the problem of vertical air motion which can shift the Doppler spectra, and, therefore, significantly alter the deduced drop size distributions and integrated variables. In this work, we illustrate the improvement in measured rain structures using a new approach for removing the effect of mean vertical air motion. It is demonstrated that the new approach proposed here not only produces what appear to be better estimates of the rainfall rates, but, also as a consequence, provides estimates of the temporal and spatial regionally coherent updraft and downdrafts occurring in the precipitation. Furthermore, the technique is readily applicable to other radars especially those operating at non-attenuating frequencies.

A Parameterization of the Microphysical Processes Forming Many Types of Winter Precipitation

2010 ◽  
Vol 67 (5) ◽  
pp. 1492-1508 ◽  
Author(s):  
Julie M. Thériault ◽  
Ronald E. Stewart
Keyword(s):  
Liquid Fraction ◽  
Winter Precipitation ◽  
The Other ◽  
Mass Content ◽  
Microphysics Scheme ◽  
Winter Storms ◽  
Precipitation Characteristics ◽  
Wet Snow ◽  
Precipitation Type ◽  
Microphysical Processes

Abstract Several types of precipitation, such as freezing rain, ice pellets, and wet snow, are commonly observed during winter storms. The objective of this study is to better understand the formation of these winter precipitation types. To address this issue, detailed melting and refreezing of precipitation was added onto an existing bulk microphysics scheme. These modifications allow the formation of mixed-phase particles and these particles in turn lead to, or affect, the formation of many of the other types of precipitation. The precipitation type characteristics, such as the mass content, liquid fraction, and threshold diameters formed during a storm over St John’s, Newfoundland, Canada, are studied and compared with observations. Many of these features were reproduced by the model. Sensitivity experiments with the model were carried out to examine the dependence of precipitation characteristics in this event on thresholds of particle evolution in the new parameterization.

scholarly journals Polarimetric Radar Observations of Precipitation Type and Rate from the 2–3 March 2014 Winter Storm in Oklahoma and Arkansas

2016 ◽  
Vol 31 (4) ◽  
pp. 1179-1196 ◽  
Author(s):  
Matthew S. Van Den Broeke ◽  
Dana M. Tobin ◽  
Matthew R. Kumjian
Keyword(s):  
Winter Storm ◽  
Freezing Rain ◽  
South Central ◽  
Central United States ◽  
Precipitation Type ◽  
Media Reports ◽  
High Base ◽  
Surface Ice ◽  
Convective Cells ◽  
Differential Reflectivity

Abstract A powerful winter storm affected the south-central United States in early March 2014, accompanied by elevated convective cells with hail and high rates of sleet, freezing rain, and snow. During portions of the event the thermal profile exhibited a shallow surface cold layer and warm, unstable air aloft. Precipitation falling into the cold layer refroze into ice pellets and was accompanied by a polarimetric refreezing signature and numerous crowdsourced surface ice pellet reports. Quasi-vertical profiles of the polarimetric variables indicated an enhanced reflectivity factor ZHH below the melting layer bright band and enhanced low-level differential reflectivity ZDR values coincident with surface ice pellet reports. Freezing rain rate was highest in areas with high ZHH and specific differential phase KDP values at low levels. High snow rates were most closely associated with 1- and 1.5-km ZHH values, though KDP and ZDR also appeared to show some ability to distinguish high snow rate. Numerous elevated convective cells contained rotating updrafts that appeared to contribute to storm longevity and intensity. Most contained well-defined ZDR maxima or columns and relatively high base-scan ZDR values. Several contained polarimetric signatures consistent with heavy mixed-phase precipitation and hail; social media reports indicated that large hail was produced by some of the storms.

Applications of Uncrewed Aerial Vehicles (UAVs) in Winter Precipitation-Type Forecasts

2021 ◽  
Vol 60 (3) ◽  
pp. 361-375
Author(s):  
Daniel D. Tripp ◽  
Elinor R. Martin ◽  
Heather D. Reeves
Keyword(s):  
High Resolution ◽  
Winter Precipitation ◽  
Mixed Phase ◽  
Winter Weather ◽  
Vertical Column ◽  
Aerial Vehicles ◽  
Crucial Information ◽  
Precipitation Type ◽  
Bin Microphysics ◽  
Weather Risks

AbstractTemperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type, which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds to observed ice pellets at the surface. This is in contrast to the High-Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.

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