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This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macrophotography, and laser-optical disdrometer data. First, various ice pellet fall speeds and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter–fall speed relations measured. Second, the measurements of particles' fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s−1 slower than raindrops of the same size. In addition, the fall speed of a small fraction of ice pellets was < 2 m s−1 regardless of their size, as compared with a fall speed > 3 m s−1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer. Significance Statement: Ice pellets are challenging to forecast and to detect automatically. In this study, we documented the fall speed and physical characteristics of ice pellets during various atmospheric conditions using a combination of a laser-optical disdrometer, manual observations, and macrophotography images. Relationships were found between the shape and fall speed of ice pellets. These findings could be used to refine the parameterization of ice pellets in atmospheric models and, consequently, improve the forecast of impactful winter precipitation types such as freezing rain. Furthermore, they will also help to physically interpret laser-optical disdrometer data during ice pellets and freezing rain. [ABSTRACT FROM AUTHOR]

During near-0°C surface conditions, diverse precipitation types (p-types) are possible, including rain, drizzle, freezing rain, freezing drizzle, ice pellets, wet snow, snow, and snow pellets. Near-0°C precipitation affects wide swaths of the United States and Canada, impacting aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Fundamental challenges remain in observing, diagnosing, simulating, and forecasting near-0°C p-types, particularly during transitions and within complex terrain. Motivated by these challenges, the field phase of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted from 1 February to 15 March 2022 to better understand how multiscale processes influence the variability and predictability of p-type and amount under near-0°C surface conditions. WINTRE-MIX took place near the U.S.–Canadian border, in northern New York and southern Quebec, a region with plentiful near-0°C precipitation influenced by terrain. During WINTRE-MIX, existing advanced mesonets in New York and Quebec were complemented by deployment of 1) surface instruments, 2) the National Research Council Convair-580 research aircraft with W- and X-band Doppler radars and in situ cloud and aerosol instrumentation, 3) two X-band dual-polarization Doppler radars and a C-band dual-polarization Doppler radar from the University of Illinois, and 4) teams collecting manual hydrometeor observations and radiosonde measurements. Eleven intensive observing periods (IOPs) were coordinated. Analysis of these WINTRE-MIX IOPs is illuminating how synoptic dynamics, mesoscale dynamics, and microscale processes combine to determine p-type and its predictability under near-0°C conditions. WINTRE-MIX research will contribute to improving nowcasts and forecasts of near-0°C precipitation through evaluation and refinement of observational diagnostics and numerical forecast models. [ABSTRACT FROM AUTHOR]

Finally, stakeholders expressed the need for midterm (30-yr) projections of freezing rain changes, which is a time scale that is more actionable than longer-term (end-of-century) projections though also challenging given the large natural variability of freezing rain. Keywords: Winter/cool season; Freezing precipitation; Renewable energy; Risk assessment; Societal impacts; Transportation meteorology EN Winter/cool season Freezing precipitation Renewable energy Risk assessment Societal impacts Transportation meteorology E1265 E1267 3 08/24/23 20230701 NES 230701 B I What i b : Freezing rain scientists and stakeholders generated a road map of risk-relevant knowledge gaps in freezing rain science and critical avenues to fill those gaps B I When i b : 11-12 October 2021 B I Where i b : Online On 11-12 October 2021, Purdue University hosted the Purdue Ice Storm Risk Workshop, a 2-day interdisciplinary workshop bringing together over 20 experts from across weather and climate science and a wide range of sectors, including meteorology, climate, forestry, energy, aviation, agriculture, and economics. Finally, given the acute nature of freezing rain impacts, continued dialogue between on-the-ground stakeholders and weather/climate scientists is critical to ensure the science is addressing decision-relevant needs. [Extracted from the article]

Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement: Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain. [ABSTRACT FROM AUTHOR]

Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement: A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain. [ABSTRACT FROM AUTHOR]

Forecasts of precipitation type are of high priority, as they have a large influence on human safety, the economy and the environment. In recent decades, methods of statistical post-processing of numerical weather prediction (NWP) outputs were only applied beside the experience of the operational forecasters. In the last few years, numerical models developed significantly; thus, precipitation type has become a variable directly calculated in some models. In the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system (IFS) model, a new method has been used since 2015 to predict the type of precipitation. In this study, we examine the forecast of the ECMWF IFS ensemble model concerning precipitation type through ensemble verification and a case study on a freezing-rain situation for the territory of Hungary. We put emphasis on the investigation of the usability of ensemble forecasts. We introduce the developed forms of visualization supporting the interpretation of ensemble precipitation-type forecasts. [ABSTRACT FROM AUTHOR]

The predictability of precipitation type in a January 2017 winter storm over the northeastern United States and southeastern Canada is examined using a convective-scale initial-condition ensemble with the Weather Research and Forecasting (WRF) Model. Real-time forecasts of the event by Environment and Climate Change Canada predicted 15–25 cm of snow accumulation in Montreal, Quebec, Canada. However, the initial 4 h of the event had 5–8 mmof freezing rain instead, followed by 7 cm of snow. While the total liquid-equivalent precipitation was consistent with the forecast, the unexpected freezing rain caused significant disruption in the Montreal region. The fraction of freezing precipitation (freezing rain and/or ice pellets) over the initial 4 h in Montreal varied greatly across the ensemble, with some members producing nearly all snow and others producing nearly all freezing precipitation. In members with larger fractions of freezing precipitation (as opposed to snow), the cyclone’s midlevel trough was displaced slightly to the northwest, and its downstream (eastern) edge was narrower, the latter of which was traced back to model initialization. These differences increased the midlevel southerly flow into southern Quebec, which both enhanced the horizontal warm advection and decreased the vertical cold advection leading up to the event. The consequent midlevel warming over Montreal in these members produced an above-zero layer that melted falling precipitation, leading to freezing upon contact with the ground. This case study highlights the value of convective-scale ensembles for identifying mechanisms by which initial synoptic-scale uncertainties lead to high-impact localized errors in precipitation type. [ABSTRACT FROM AUTHOR]

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests. [ABSTRACT FROM AUTHOR]

New regulations are being issued by the Federal Aviation Administration (FAA) that require three-dimensional hydrometeor-phase diagnosis, including discrimination between freezing rain (FZRA) and freezing drizzle (FZDZ), for all commercial airports in the United States. Herein, a novel hydrometeor-phase algorithm, the spectral bin classifier (SBC), is adapted to meet these requirements. First, the SBC's particle size distribution (PSD) is upgraded to be variable rather than fixed. This, along with some changes to the logic, allows for drizzle (DZ)/FZDZ to be diagnosed. Second, the SBC is modified to provide a low-altitude (LA), aboveground diagnosis (SBC-LA). Last, necessary changes to account for resolution issues in NWP thermal profiles are presented. Adding a dynamic-PSD capability improves the probability of detection (POD) by about 12%, but adapting the algorithm to include DZ/FZDZ worsens the PODs. This is due to potentially errant reports of rain (RA)/FZRA in environments that are more conducive to DZ/FZDZ. Assuming a diagnosis of DZ is a hit when RA is observed, and likewise for FZRA/FZDZ, increases the POD by between 35% and 60%. Although performance statistics for SBC-LA cannot be computed, about one-third of all RA and DZ soundings herein have an elevated layer of FZRA/FZDZ, underscoring the importance of an aboveground diagnosis for the aviation sector. The comparatively low vertical resolution of NWP profiles is found to degrade the SBC's performance. Interpolating to a higher resolution helps to mitigate this problem. Several case studies of mixed phases at different airports are presented to highlight the enhanced decision support made possible by the above modifications. [ABSTRACT FROM AUTHOR]

Freezing rain and ice pellets are particularly difficult to forecast when solid precipitation is completely melted aloft. This study addresses this issue by investigating the processes that led to a long-duration ice pellet event in Montreal, Québec, Canada, on 11–12 January 2020. To do so, a benchmark model initialized with ERA5 data is used to show that solid precipitation was completely melted below the melting layer, which discards partial melting from the possible ice pellet formation processes. Macro photography of precipitation reveals that small columnar crystals (∼200 μm) and ice pellets occurred simultaneously for more than 10 h. The estimation of ice crystal number concentration using macro photographs and laser-optical disdrometer data suggests that all supercooled drops could have refrozen by contact freezing with ice crystals. Rimed ice pellets also indicate ice supersaturation in the subfreezing layer. Given these observations, the formation of ice pellets and ice crystals was probably promoted by secondary ice production and the horizontal advection of ice crystals below the melting layer, as we illustrate using a conceptual model. Overall, these findings demonstrate how ice nucleation processes at temperatures near 0°C can drastically change the precipitation phase and the impact of a storm. Significance Statement: Ice pellets are generally formed when snow particles partially melt while falling through a warm layer aloft before completely refreezing in a cold layer closer to the surface. Ice pellets can also be formed when snow particles completely melt aloft, but freezing rain is often produced in such conditions. On 11–12 January 2020, ice pellets were produced during more than 10 h in Montreal, Quebec, Canada. Macro photographs of the precipitation particles show that ice pellets occurred simultaneously with small ice crystals. Most of the ice pellets were produced while snow particles were completely melted aloft. The supercooled drops probably refroze due to collisions with the ice crystals that could have been advected by the northeasterly winds near the surface. [ABSTRACT FROM AUTHOR]

Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted. [ABSTRACT FROM AUTHOR]

The process of glaciation in mixed-phase stratiform clouds was investigated by a novel Lagrangian–Eulerian model (LEM) in which thousands of adjoining Lagrangian parcels moved within a turbulent-like velocity field with statistical parameters typical of the Arctic boundary layer. We used detailed bin microphysics to describe the condensation/evaporation processes in each parcel, in which droplets, aerosols, and ice particles were described using size distributions of 500 mass bins. The model also calculated aerosol mass inside droplets and ice particles. Gravitational sedimentation of droplets and ice particles was also accounted for. Assuming that droplet freezing is the primary source of ice particles, the Arctic clouds observed in Indirect and Semi-Direct Aerosol Campaign (ISDAC) were successfully simulated. The model showed that at a low ice particle concentration typical of ISDAC, large vortices (eddies) led to a quasi-stationary regime, in which mixed-phase St existed for a long time. The large eddies controlled the water partitioning in the mixed-phase clouds. Droplets formed and grew in updrafts, typically reaching the cloud top, and evaporated in downdrafts. Ice particles grew in updrafts and downdrafts. The Wegener–Bergeron–Findeisen (WBF) mechanism was efficient in downdrafts and some parts of updrafts, depending on ice concentration and vertical velocity. At low ice concentrations, the effect of ice on the phase partitioning was negligible. In this regime, liquid droplets were found near the cloud top, whereas ice particles precipitated through the cloud base. When ice concentration exceeded about 10 L−1, the WBF mechanism led to glaciation of almost the entire cloud, with the exception of narrow cloud regions associated with strong updrafts. At ice particle concentrations of a few tens per liter, the oscillatory regime took place due to the ice–liquid interaction. The microphysical structure of mixed-phase St forms as a combined effect of cloud dynamics (large eddies) and the WBF mechanism. [ABSTRACT FROM AUTHOR]

Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain, using models to estimate equivalent radial ice thicknesses from historical weather data. The conservative simple flux model assumes that all the freezing rain that impinges on a horizontal cylinder, representing vegetation or components of the built infrastructure, freezes. Here I present a simplified heat-balance formulation to calculate the fraction of the impinging precipitation that freezes, using parameters measured at ASOS weather stations and an estimate of solar heating. Radial ice thickness estimates from this approach are compared with the simple model and those generated from the ASOS icing sensor. These estimates can all be tested by comparing to measurements on cylinders at weather stations. A link to an Excel spreadsheet that calculates freezing fraction using user-input weather data is provided. In forecast freezing rain events, this tool could be used by utility crews and emergency response teams to estimate the likely range of equivalent radial ice thicknesses over the affected region and plan their response accordingly. Significance Statement: Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain calculated from historical weather data. This paper provides an algorithm for computing ice loads on trees and power lines, using weather data to determine the fraction of the precipitation that freezes on them rather than dripping off. This freezing fraction result is compared to estimates reported by weather stations and to a simple model that assumes all the wind-blown freezing rain freezes on the wires, twigs, and branches. A link is provided to an Excel tool that calculates freezing fraction. This could be used with freezing rain forecasts to estimate the likely severity of the event. [ABSTRACT FROM AUTHOR]

Improved forecasts of atmospheric river (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate quantitative precipitation forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in February–March 2019 and compare them to quantitative precipitation estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while integrated water vapor (IWV), wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a neighborhood maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest gridbox evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events. [ABSTRACT FROM AUTHOR]

In the winter, orographic precipitation falls as snow in the mid- to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute's operational forecast system and two simulations with adjusted cloud microphysics. Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces +10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0%, +43.8%, and +59.2%, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by −1071.4% and −523.7%, respectively. The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in situ and vertically profiling remote sensing instruments to identify biases in numerical weather prediction. [ABSTRACT FROM AUTHOR]

The accurate detection and monitoring of freezing rain and icing conditions at the surface is a notoriously challenging but important problem. This work attempts to enhance icing detection and characterization utilizing data from the New York State Mesonet (NYSM). NYSM is the first operational network measuring winds at 10 m from two independent sensors: propeller and sonic anemometers. During and after freezing rain events, large wind speed differences are frequently reported between the two anemometers because the propeller develops a coating of ice, thus either stopping or slowing its rotation. Such errors of propeller data provide a signal for identifying icing conditions. An automated method for identifying "active freezing rain" (AFR) and a continuation of "frozen surface" (FS) conditions is developed. Hourly maps of AFR and FS sites are generated using four criteria: 1) a wind speed difference (sonic − propeller) of >1 m s−1 or 0 m s−1 propeller wind speed for at least a half hour, 2) a temperature threshold of −5° to 2°C for AFR and less than 2°C for FS, 3) insignificant hourly snow accumulation, and 4) with (without) significant hourly precipitation accumulation for AFR (FS). The AFR events detected by the automated method for last four winters (2017–21) show very good agreements in starting and ending times with that from the Automated Surface Observing System (ASOS) data. A case study of the ice storm during 14–16 April 2018 further demonstrates the validity of the methodology and highlights the benefit of NYSM profiler and camera data. [ABSTRACT FROM AUTHOR]

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway, and Kiruna, Sweden, are documented using ground-based in situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large-scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 kmAGL and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR re- flectivity toward the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites. [ABSTRACT FROM AUTHOR]

Understanding the key dynamical and microphysical mechanisms driving precipitation in the Snowy Mountains region of southeast Australia, including the role of orography, can help improve precipitation forecasts, which is of great value for efficient water management. An intensive observation campaign was carried out during the 2018 austral winter, providing a comprehensive range of ground-based observations across the Snowy Mountains. We used data from three vertically pointing rain radars, cloud radar, a PARSIVEL disdrometer, and a network of 76 pluviometers. The observations reveal that all of the precipitation events were associated with cold front passages. About half accumulated during the frontal passage associated with deep, fully glaciated cloud tops, while the rest occurred in the postfrontal environment and were associated with clouds with supercooled liquid water (SLW) tops. About three-quarters of the accumulated precipitation was observed under blocked conditions, likely associated with blocked stratiform orographic enhancement. Specifically, more than a third of the precipitation resulted from moist cloudless air being lifted over stagnant air, upwind from the barrier, creating SLW-top clouds. These SLW clouds then produced stratiform precipitation mostly over the upwind slopes and mountain tops, with hydrometeors reaching the mountain tops mostly as rimed snow. Two precipitation events were studied in detail, which showed that during unblocked conditions, orographic convection invigoration and unblocked stratiform enhancement were the two main mechanisms driving the precipitation, with the latter being more prevalent after the frontal passage. During these events, ice particle growth was likely dominated by vapor deposition and aggregation during the frontal periods, while riming dominated during the postfrontal periods. [ABSTRACT FROM AUTHOR]

The review of the studies dealing with the effects of icing on air transport operation is presented. The problems of ground-based icing leading to the deterioration of runways and complication of pre-flight aerodrome operations are discussed. The reasons for the in-flight aircraft icing, the tools and methods of icing observation, the approaches to the forecasting of icing in clouds and precipitation are considered. The methods for predicting the icing of aircraft engines in the zones with high concentration of ice crystals are analyzed. [ABSTRACT FROM AUTHOR]

A unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in copolar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle and the orientation of this liquid with respect to the horizontal. [ABSTRACT FROM AUTHOR]

This study quantifies how far snow can fall into the melting layer (ML) before all snow has melted by examining a combination of in situ observations from aircraft measurements in Lagrangian spiral descents from above through the ML and descents and ascents into the ML, as well as an extensive database of NOAA surface observer reports during the past 50 years. The airborne data contain information on the particle phase (solid, mixed, or liquid), population size distributions and shapes, along with temperature, relative humidity, and vertical velocity. A wide range of temperatures and ambient relative humidities are used for both the airborne and ground-based data. It is shown that an ice-bulb temperature of 0°C, together with the air temperature and pressure (altitude), are good first-order predictors of the highest temperature snowflakes can survive in the melting layer before completely melting. Particle size is also important, as is whether the particles are graupel or hail. If the relative humidity is too low, the particles will sublimate completely as they fall into the melting layer. Snow as warm as +7°C is observed from aircraft measurements and surface observations. Snow pellets survive to even warmer temperatures. Relationships are developed to represent the primary findings. [ABSTRACT FROM AUTHOR]

Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR's brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records. [ABSTRACT FROM AUTHOR]

Though prolonged freezing rain events are rare, they can result in substantial damage when they occur. While freezing rain occurs less frequently in the south-central United States than in some regions of North America, a large number of extremely long-duration events lasting at least 18 h have been observed there. We explore the key synoptic--dynamic conditions that lead to these extreme events through a comparison with less severe short-duration events. We produce synoptic--dynamic composites and 7-day backward trajectories for parcels ending in the warm and cold layers for each event category. The extremely long-duration events are preferentially associated with a deeper and more stationary 500-hPa longwave trough centered over the southwestern United States at event onset. This trough supports sustained flow of warm, moist air from within the planetary boundary layer over the Gulf of Mexico northward into the warm layer. The short-duration cases are instead characterized by a more transient upper-level trough axis centered over the south-central U.S. region at onset. Following event onset, rapid passage of the trough leads to quasigeostrophic forcing for descent and the advection of cold, dry air that erodes the warm layer and ends precipitation. While trajectories ending in the cold layer are very similar between the two categories, those ending in the warm layer have a longer history over the Gulf of Mexico in the extreme cases compared with the short-duration ones, resulting in warmer and moister onset warm layers. [ABSTRACT FROM AUTHOR]

A newly developed precipitation phase metric is used to detect the impact of urbanization on the nature of precipitation at Toronto, Ontario, Canada, by contrasting the relative amounts of rain and snow. A total of 162 years of observed precipitation data were analyzed to classify the nature of winter-season precipitation for the city of Toronto. In addition, shorter records were examined for nearby climate stations in less-urbanized areas in and near Toronto. For Toronto, all winters from 1849 to 2010 as well as three climate normal periods (1961–90, 1971–2000, and 1981–2010) were thus categorized for the Toronto climate record. The results show that Toronto winters have become increasingly "rainy" across these time periods in a statistically significant fashion, consistent with a warming climate. Toronto was compared with the other less urban sites to tease out the impacts of the urban heat island from larger-scale warming. This yielded an estimate of 19%–27% of the Toronto shift in precipitation type (from snow to rain) that can be attributed to urbanization for coincident time periods. Other regions characterized by similar climates and urbanization with temperatures near the freezing point are likely to experience similar climatic changes expressed as a change in the phase of winter-season precipitation. [ABSTRACT FROM AUTHOR]

Using vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 08C consist of positive energy; those colder than 08C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters. [ABSTRACT FROM AUTHOR]

Temperature 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. [ABSTRACT FROM AUTHOR]

In this two-part paper, influences from environmental factors on lightning in a convective storm are assessed with a model. In Part I, an electrical component is described and applied in the Aerosol–Cloud model (AC). AC treats many types of secondary (e.g., breakup in ice–ice collisions, raindrop-freezing fragmentation, rime splintering) and primary (heterogeneous, homogeneous freezing) ice initiation. AC represents lightning flashes with a statistical treatment of branching from a fractal law constrained by video imagery. The storm simulated is from the Severe Thunderstorm Electrification and Precipitation Study (STEPS; 19/20 June 2000). The simulation was validated microphysically [e.g., ice/droplet concentrations and mean sizes, liquid water content (LWC), reflectivity, surface precipitation] and dynamically (e.g., ascent) in our 2017 paper. Predicted ice concentrations (~10 L−1) agreed—to within a factor of about 2—with aircraft data at flight levels (−10° to −15°C). Here, electrical statistics of the same simulation are compared with observations. Flash rates (to within a factor of 2), triggering altitudes and polarity of flashes, and electric fields, all agree with the coincident STEPS observations. The “normal” tripole of charge structure observed during an electrical balloon sounding is reproduced by AC. It is related to reversal of polarity of noninductive charging in ice–ice collisions seen in laboratory experiments when temperature or LWC are varied. Positively charged graupel and negatively charged snow at most midlevels, charged away from the fastest updrafts, is predicted to cause the normal tripole. Total charge separated in the simulated storm is dominated by collisions involving secondary ice from fragmentation in graupel–snow collisions. [ABSTRACT FROM AUTHOR]

In its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS 1-min-observation (OMO) data to infer the presence of freezing drizzle. This freezing drizzle derivation algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g., Doppler radar hydrometeor classification algorithms, and improved numerical modeling methods) and impact studies that utilize archived datasets [e.g., National Transportation Safety Board (NTSB) investigations of transportation accidents in which freezing drizzle may have played a role]. Ten years of archived OMO data (2005–14) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Aviation routine weather reports (METARs) from human-augmented ASOS observations were used to evaluate and quantify the FDDA's ability to infer freezing drizzle conditions. Advantages and drawbacks to the method are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS but rather to determine periods for which freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS. [ABSTRACT FROM AUTHOR]

Fully polarimetric scanning and vertically pointing Doppler spectral data from the state-of-the-art Stony Brook University Ka-band Scanning Polarimetric Radar (KASPR) are analyzed for a long-duration case of ice pellets over central Long Island in New York from 12 February 2019. Throughout the period of ice pellets, a classic refreezing signature was present, consisting of a secondary enhancement of differential reflectivityZDR beneath the melting layer within a region of decreasing reflectivity factor at horizontal polarization ZH and reduced copolar correlation coefficient rhv. The KASPR radar data allow for evaluation of previously proposed hypotheses to explain the refreezing signature. It is found that, upon entering a layer of locally generated columnar ice crystals and undergoing contact nucleation, smaller raindrops preferentially refreeze into ice pellets prior to the complete freezing of larger drops. Refreezing particles exhibit deformations in shape during freezing, leading to reduced rhv, reduced co-to-cross-polar correlation coefficient rxh, and enhanced linear depolarization ratio, but these shape changes do not explain the ZDR signature. The presence of columnar ice crystals, though apparently crucial for instigating the refreezing process, does not contribute enough backscattered power to affect the ZDR signature, either. [ABSTRACT FROM AUTHOR]

Freezing rain is an especially hazardous winter weather phenomenon that remains particularly challenging to forecast. Here, we identify the salient thermodynamic characteristics distinguishing long-duration (six or more hours) freezing rain events from short-duration (2–4 h) events in three regions of the United States and Canada from 1979 to 2016. In the northeastern United States and southeastern Canada, strong surface cold-air advection is not common during freezing rain events. Colder onset temperatures at the surface and in the near-surface cold layer support longer-duration events there, allowing heating mechanisms (e.g., the release of latent heat of fusion when rain freezes at the surface) to act for longer periods before the surface reaches 0°C and precipitation transitions to rain. In the south-central United States, cold air at the surface is replenished via continuous cold-air advection, reducing the necessity of cold onset surface temperatures for event persistence. Instead, longer-duration events are associated with warmer and deeper >0°C warm layers aloft and stronger advection of warm and moist air into this layer, delaying its erosion via cooling mechanisms such as melting. Finally, in the southeastern United States, colder and especially drier onset conditions in the cold layer are associated with longer-duration events, with evaporative cooling crucial to maintaining the subfreezing surface temperatures necessary for freezing rain. Through an improved understanding of the regional conditions supporting freezing rain event persistence, we hope to provide useful information to forecasters in their attempt to predict these potentially damaging events. [ABSTRACT FROM AUTHOR]

The partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming. [ABSTRACT FROM AUTHOR]

Snowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1. [ABSTRACT FROM AUTHOR]

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures. [ABSTRACT FROM AUTHOR]

Data from the National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS) database were used to identify vehicle-related fatalities that occurred during active precipitation from 2013 to 2017. Changes to FARS for 2013–present allow the identification of freezing rain, in addition to rain, snow, sleet, and precipitation mixtures as prevailing precrash atmospheric conditions. The characteristics of vehicle-related fatalities for each precipitation type identified in FARS were assessed in terms of total numbers, roadway surface conditions, location, and annual and diurnal variability. Vehicle-related fatalities were also matched to nearby Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) precipitation-type reports to determine their agreement with precipitation types documented in FARS. Of the vehicle-related fatalities examined, 8.6% occurred during precipitation, with these fatalities further divided by precipitation type of approximately 81% rain, 14% snow, and 5% sleet, freezing rain, and mixtures of precipitation. Unexpected discrepancies between the numbers of sleet- versus freezing-rain-related fatalities reveal that caution should be taken when using FARS to identify these precipitation types. ASOS/AWOS precipitation-type reports have moderate agreement with FARS at 20 mi (32.2 km), and are capable of distinguishing precipitation and nonprecipitation indicated in FARS. Rain and snow have good agreement between the databases, whereas sleet, freezing rain, and precipitation mixtures have significantly reduced agreement due to a combination of ASOS/AWOS limitations and suspected FARS limitations. To provide a more accurate account of precipitation types for fatal crashes, the use of crashes where FARS-identified precipitation types are confirmed by nearby ASOS/AWOS reports is suggested. [ABSTRACT FROM AUTHOR]

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth's atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat's Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM's Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach. [ABSTRACT FROM AUTHOR]

The ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind "upslope" events. Larger biases of at least 50% were found for high wind "pulsed" events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

This paper describes an observational study for the geometrical properties of hydrometeors during a refreezing process initiated by a winter storm in the northern Kanto Plain, Japan, on 29 January 2016, in which a subfreezing layer developed below a melting layer. The observations by using dual-polarization radar showed consistency between high values of differential reflectivity ZDR signals in midair and ice-pellet reports at the surface. The high ZDR was indicative of the sideways-oriented particles with a small axis ratio. The low ZDR signals in midair corresponded with the reports of rain or rain/ice-pellet mixtures. Observations by using a two-dimensional video disdrometer (2DVD) near the ground showed different microphysics corresponding to high ZDR and low ZDR periods. The high ZDR periods of 2DVD observations indicated that the hydrometeors exhibited dual modes of fall velocities, namely, fast-falling and slow-falling modes. The fast-falling particles were found to be deformed ice pellets with long sideways orientations that contributed to the high ZDR. The slow-falling particles were also deformed ice pellets but with a variety of orientations. This feature was rather close to that of general dry conditions of ice particles in the atmosphere. Meanwhile, the low ZDR periods of 2DVD observations indicated that the hydrometeors exhibited a single mode of fall velocity close to the terminal velocity of raindrops, but with a more spherical shape compared to raindrops. Hence, it is suggested that the high ZDR signal occurs during freezing between the initial stage of spherical ice forming and completely freezing stage of ice pellets with a variety of orientations. [ABSTRACT FROM AUTHOR]

Analytical solutions are derived for the steady-state size distributions of precipitating rain and snow particles assuming growth via collection of suspended cloud particles. Application of the Liouville equation to the transfer of precipitating mass through size bins in a "cascade" yields a characteristic gamma distribution with a Marshall–Palmer exponential tail with respect to particle diameter. For rain, the principle parameters controlling size distribution shape are cloud droplet liquid water path and cloud updraft speed. Stronger updrafts lead to greater concentrations of large precipitating drops and a peak in the size distribution. The solutions provide a means for relating size distributions measured in the air or on the ground to cloud bulk microphysical and dynamic properties. [ABSTRACT FROM AUTHOR]

Bulk microphysics parameterizations that are used to represent clouds and precipitation usually allow only solid and liquid hydrometeors. Predicting the bulk liquid fraction on ice allows an explicit representation of mixed-phase particles and various precipitation types, such as wet snow and ice pellets. In this paper, an approach for the representation of the bulk liquid fraction into the predicted particle properties (P3) microphysics scheme is proposed and described. Solid-phase microphysical processes, such as melting and sublimation, have been modified to account for the liquid component. New processes, such as refreezing and condensation of the liquid portion of mixed-phase particles, have been added to the parameterization. Idealized simulations using a one-dimensional framework illustrate the overall behavior of the modified scheme. The proposed approach compares well to a Lagrangian benchmark model. Temperatures required for populations of ice crystals to melt completely also agree well with previous studies. The new processes of refreezing and condensation impact both the surface precipitation type and feedback between the temperature and the phase changes. Overall, prediction of the bulk liquid fraction allows an explicit description of new precipitation types, such as wet snow and ice pellets, and improves the representation of hydrometeor properties when the temperature is near 0°C. [ABSTRACT FROM AUTHOR]

Atmospheric ice accretion caused by freezing precipitation (FP) can lead to severe damage and failure of buildings and infrastructure. This study investigates projected changes to extreme ice loads - those used to design infrastructure over North America (NA) - for future periods of specified global mean temperature change (GMTC), relative to a recent 1986-2016 period, using a large 50 member initial condition ensemble of the CanRCM4 regional climate model driven by CanESM2 under the RCP8.5 scenario. The analysis is based on three-hourly ice accretions on horizontal, vertical, and radial surfaces calculated based on FP diagnosed by the offline Bourgouin algorithm as well as wind speed during FP. The CanRCM4 ensemble projects an increase in future design ice loads for most of northern NA and decreases for most of southern NA and some northeastern coastal regions. These changes are mainly caused by regional increases in future upper level and surface temperatures associated with global warming. Projected changes in design ice thickness are also affected by changes in future precipitation intensity and surface wind speed. Changes in upper level and surface temperature conditions for FP occurrence in CanRCM4 are in broad agreement with those from nine global climate models, but display regional differences under the same level of global warming, indicating that a larger multi-model, multi-scenario ensemble may be needed to better account for additional sources of structural and scenario uncertainty. Increases in ice accretion for latitudes higher than 40° N are substantial and would have clear implications for future building and infrastructure design. [ABSTRACT FROM AUTHOR]

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the "new Arctic"). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic. [ABSTRACT FROM AUTHOR]

A real-time, hourly updated, online graphical data product that displays the depth and strength of easterly gap flow in the Columbia River Gorge using a 915-MHz Doppler wind profiler is presented. During precipitation events, this data product also displays observed precipitation accumulation and diagnosed precipitation type using measurements provided by a collocated heated tipping-bucket rain gauge, an optical disdrometer, and temperature and relative humidity sensors. Automated algorithms that determine the existence and depth of the gap flow, as well as precipitation type, are described. The Columbia River Gorge is the only major gap in the Cascade Mountains of Oregon and Washington. Consequently, both easterly and westerly directed gap-flow events are common in this region. Especially during late autumn and winter, easterly gap flow can cause hazardous and damaging weather (e.g., snow, freezing rain, and strong winds) in the Portland, Oregon–Vancouver, Washington metropolitan area. The product described here was developed to help forecasters at the Portland National Weather Service Forecast Office monitor cool-season easterly gap-flow events in order to provide situational awareness and guide warnings to the public about potential weather-related hazards. [ABSTRACT FROM AUTHOR]

The variations in several climatological characteristics are studied on the basis of hourly (half-hourly) meteorological terminal observations at 51 aerodromes of the Russian Federation in 2001-2015. For every aerodrome extreme temperature, wind speed and gusts, and QNH are analyzed for the above period. Using data for three consecutive 5-year periods, variations in the number of days with temperature above 30°C or below -30°C, with wind speed of ≥10 m/s and gusts of ≥15 m/s are considered. The occurrence frequency of significant weather events affecting the takeoff and landing (fog, blizzard, freezing precipitation, thunderstorm) is investigated. The results for aerodromes with positive or negative trends in the occurrence frequency of weather phenomena in 2001-2015 are presented. [ABSTRACT FROM AUTHOR]

Detection of ice precipitation is one of the objectives in the Global Precipitation Measurement (GPM) mission. The dual-frequency precipitation radar (DPR) can provide precipitation echoes at two different frequencies, which may enable differentiating solid precipitation echoes from liquid precipitation echoes. A simple algorithm that flags the pixels that contain intense ice precipitation above the height of 2108C is implemented in version 5 of the DPR products. In the inner swath of DPR measurements in which both Kuand Ka-band radar echoes are available, the measured dual-frequency ratio (DFRm) together with the measured radar reflectivity factor is used to judge the existence of intense ice precipitation. Comparisons of the flagged pixels with surface measurements show that the algorithm correctly identifies relatively intense ice precipitation regions. The global distribution of the flagged pixels indicates an interesting difference between land and ocean, in particular in the distribution of ice precipitation that reaches the surface. The flag is also expected to be useful for improving precipitation retrieval algorithms by microwave radiometers. [ABSTRACT FROM AUTHOR]

Radiosonde observations are used the world over to provide critical upper-air observations of the lower atmosphere. These observations are susceptible to errors that must be mitigated or avoided when identified. One source of error not previously addressed is radiosonde icing in winter storms, which can affect forecasts, warning operations, and model initialization. Under certain conditions, ice can form on the radiosonde, leading to decreased response times and incorrect readings. Evidence of radiosonde icing is presented for a winter storm event in Norman, Oklahoma, on 24 November 2013. A special sounding that included a particle imager probe and a GoPro camera was flown into the system producing ice pellets. While the iced-over temperature sensor showed no evidence of an elevated melting layer (ML), complementary Particle Size, Image, and Velocity (PASIV) probe and polarimetric radar observations provide clear evidence that an ML was indeed present. Radiosonde icing can occur while passing through a layer of supercooled drops, such as frequently found in a subfreezing layer that often lies below the ML in winter storms. Events that have warmer/deeper MLs would likely melt any ice present off the radiosonde, minimizing radiosonde icing and allowing the ML to be detected. This paper discusses the hypothesis that the absence of an ML in the radiosonde data presented here is more likely to occur in winter storms that produce ice pellets, which tend to have cooler/shallower MLs. Where sounding data do appear to be compromised by icing, polarimetric radar data might be used to identify MLs for nowcasting purposes and numerical model initialization. [ABSTRACT FROM AUTHOR]

Wide cold regions with high air density as well as wind speed are much beneficial for the wind energy exploitation. However, wind turbines in these regions always suffer a series of icing issues, especially the blade aerodynamics degradation and much production loss in winter. Therefore, the paper aims to study the ice accretion feature on blades and the effect of ice severity on the power performance of wind turbines at natural icing environment. The ice distribution on the blade is analyzed quantitatively in the image processing method, and it is found that the ice thickness on the blade increases rapidly from the root to the middle of the blade, and then slowly from the middle to the blade tip. In the icing duration period, both the pitch angle and rotor speed of the wind turbine fall remarkably at the same wind speed, and the pitch angle shows more sensitive to the blade icing for wind turbines with the traditional control strategy. Based on the statistical operating data of the 300 kW wind turbine in different icing cases, the rotor performance deteriorates more seriously as the ice thickness of the blade increases, which changes the original matching relation between the rotor and generator. In addition, the ice thickness of the leading edge at r / R  = 0.95 shows great correlation with power loss in given icing cases, and this correlation still needs to be studied further at more icing events. The results can provide a reference for the operation performance of wind turbines in cold regions. [ABSTRACT FROM AUTHOR]

The effects of aerosol perturbations on deep convective clouds have received considerable attention in the recent literature, especially from a modeling perspective. The published responses in precipitation amount and updraft strength vary in both sign and magnitude and may be the result of different models and parameterizations. Here, a simple numerical framework is employed to determine the potential effects of warming both below the freezing level (warm invigoration) and above the freezing level (mixed-phase invigoration) due to increased aerosol loading. The role of updraft width and slope in the same framework is also examined, highlighting the relative importance of each factor on the resulting updraft strength. The results show that the potential effects of warm invigoration are 2-3 times larger than for mixed-phase invigoration. However, a relatively small response in updraft velocity to warming is found, especially in comparison with the predicted changes in updraft velocity due to small differences in system slope and width, with °7.7% and 96.4% of the subadiabatic and adiabatic realizations, respectively, showing changes in updraft velocity of less than 15% for warmings of no more than 2°C. This result suggests that observations of the aerosol effect will be largely muddled by the natural variability of convective updraft width and slope (which are related to environmental wind shear). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]