Freezing Rain Research Articles

Evaluating NEX-GDDP-CMIP6 performance in complex terrain for forecasting key freezing rain factors

Evaluating NEX-GDDP-CMIP6 performance in complex terrain for forecasting key freezing rain factors

Improved freezing rain forecast using machine learning

Freezing rain is one of the most damaging weather phenomena in winter or early spring in many parts of the world, affecting traffic, power lines and agriculture. Thus, reliable and computationally efficient prediction of its occurrence is urgently needed in weather forecast operations. However, there are different thermodynamic processes that can lead to freezing rain, resulting in unsatisfactory forecasting performance of the state-of-the-art Numerical Weather Prediction (NWP) models. Here a data-driven forecasting method for freezing rain using machine learning technologies is proposed. Observations of weather phenomenon collected from 2 515 national weather stations of China for winter of 2016–2019 and the corresponding atmospheric predictors derived from ERA5 reanalysis are used. The prediction function is constructed based on the classification and regression tree, and the predicting variables include temporal and vertical profiles of fundamental thermodynamic and kinematic parameters from 500 hPa to 1000 hPa, with a total dimension of 2 304. The LightGBM (Light Gradient Boosting Machine) framework is adopted to train our prediction model and an algorithm-level approach of modifying the loss function is used to address the imbalance of classes to improve forecasting skill. Results show that the data-driven prediction model, namely DDFR (data driven forecast of freezing rain), out-performs the benchmark NWP, i.e., ECMWF IFS product. It's improvements in terms of TS score range from 120% to 258% depending on different forecast leading times, which range from 0 to 12 h. In addition, DDFR is applied in an operational NWP model of China. The problem of domain adaptation is tackled and transfer learning method is employed to adapt the original DDFR to this NWP model. The effectiveness of such adaptation has been demonstrated by its performance on both training and testing datasets.

Secondary Ice Production Improves Simulations of Freezing Rain

AbstractWeather forecasts and climate projections of precipitation phase and type in winter storms are challenging due to the complicated underlying microphysical and dynamical processes. In the Canadian numerical weather prediction model, explicit freezing rain (FR) at the surface is often overestimated during the winter season for situations in which snow is observed. For a case study simulated using this model with the Predicted Particle Properties (P3) microphysics scheme, the secondary ice production (SIP) process has a major impact on the surface precipitation type. Parameterized SIP substantially reduces FR due to increased collection of supercooled drops with ice particles formed by rime splintering. Hindcast simulations of 40 winter cases show that these results are systematic, and the decreased frequency of FR leads to improved forecast skill relative to observations. Thus, accounting for SIP in the model is critical for accurately simulating precipitation types.

Predictive Analysis of Freezing Rain Events: Statistical Insights and Meteorological Impact

Predictive Analysis of Freezing Rain Events: Statistical Insights and Meteorological Impact

The Effects of Freezing Rain on Forest Stands Administered by Zalău Forestry Department During 2014-2022 Period

Freezing rain is a rare and extremely dangerous meteorological phenomenon, consisting of the fall of liquid precipitation, while the air temperature at ground level falls below 0oC, favoring the freezing of raindrops and forming an ice sheet on the surfaces on which they are deposited. In Romania, the areas affected by the freezing rain phenomenon undergo significant changes regarding the structure and composition of the forest vegetation. The main purpose of this work is to figure out how to manage the forest stands affected by the freezing rain identifying optimal solutions for ecological reconstruction and prevention of significant damages. For the research of the affected areas, we placed 7 experimental areas, having the size of 500 m2 each, all the trees that were in this radius were inventoried. In this research work we evaluate the presence of this phenomenon which was reported in the Stejarul Zalău Forest District, in 2014, then in 2017, but with a lower intensity. Out of a total of 1627.6 ha, 500 ha were affected by the freezing rain, most of them being surfaces with beech forest stands with a consistency of 0.9, having a high slenderness index. Following the results obtained, it was observed that the most affected trees were the youngest. These having a reduced diameter, thin branches and a high slenderness coefficient.

An Observation of Precipitation during Cooling with Ka-Band Cloud Radar in Wuhan, China

Exploiting their sensitivity to cloud droplets and small raindrops, Ka-band cloud radar observations are used to investigate weak stratiform precipitation over Wuhan during cooling on 16–17 February 2022. During cooling, the surface temperature drops by about 8 °C with the lowest value less than 0 °C because of the strong cold air from the north. The cold air lifts the warm and humid air transported by the southeasterly and southwesterly winds, causing thick stratiform clouds and persistent weak precipitation. The Ka-band radar captures the full process of stratiform cloud occurrence; light rain and then mixed rain and snow; and the characteristics of clouds and precipitation at each stage due to its fine sensitivity to small hydrometeors, whereas the reanalysis data alone cannot capture the transition to the mixed rain and snow regime, which can cause dangerous freezing rain or sleet on the ground. Hence, a detailed analysis of cooling and cold surges and their hazards to society, and their reproduction in numerical predictions, needs to use high-sensitivity radar data as much as possible.

Correction: response of gap wind-driven freezing rain and ice accretion in the Willamette Basin, Oregon, to global warming

Correction: response of gap wind-driven freezing rain and ice accretion in the Willamette Basin, Oregon, to global warming

Response of gap wind-driven freezing rain and ice accretion in the Willamette Basin, Oregon, to global warming

Response of gap wind-driven freezing rain and ice accretion in the Willamette Basin, Oregon, to global warming

Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network

Abstract Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts. Significance Statement Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.

Occurrence of heavy precipitation influenced by solar wind high-speed streams through vertical atmospheric coupling

A tendency of heavy rainfall-induced floods in Canada to follow arrivals of solar wind high-speed streams (HSSs) from coronal holes is observed. Precipitation events during the winter, including extreme freezing rain events in the province of New Brunswick, also tend to occur following HSSs. More direct evidence is provided using the satellite-based gridded precipitation dataset Integrated Multi-satellitE Retrievals for GPM (IMERG) in the superposed epoch analysis of high-rate precipitation. The results show an increase in the high-rate daily precipitation occurrence over Canada following arrivals of major HSSs. This is consistent with previously published results for other mid-latitude geographic regions. The ERA5 meteorological reanalysis is used to evaluate the slantwise convective available potential energy (SCAPE) that is of importance in the development of storms. The role of the solar wind-magnetosphere-ionosphere-atmosphere coupling, mediated by globally propagating aurorally excited atmospheric gravity waves releasing the conditional symmetric instability in the troposphere leading to convection and precipitation, is proposed.

Smart controlling on the bi-stable state of bio-inspired multifunctional coatings for anti-/de-icing applications

Outdoor equipment can suffer from functional failure in winter due to ice accumulation. Although functional materials are widely recognized as promising candidates for anti-icing and de-icing applications, ice inevitably accretes on surfaces at extremely low temperatures and high humidity. If surfaces display reversible superhydrophobic and slippery performance, similar to Lotus leaves and fish skin, it will significantly improve the anti-icing and de-icing performance. This study proposes a novel type of multifunctional coating that could smartly reverse between superhydrophobic and slippery properties on demand. The superhydrophobic coatings with diverse morphologies were obtained by employing different sizes and shapes of nanoparticles. Due to the low adhesion and photothermal properties, the superhydrophobic samples presented good freezing rain prevention and snow-melting performances by outdoor tests in winter. The samples with slippery surfaces were produced by infusing dodecane on the as-prepared superhydrophobic surface. Both the superhydrophobic and slippery surfaces can effectively propel the water droplets, prolong the freezing time and enhance ice removal performance in an extremely cold environment. Therefore, the coated samples with bi-stable states (i.e., superhydrophobic and slippery states) were successfully achieved with the potential to be widely applied under different service environments. This facile strategy will provide the possibility for developing novel materials with smart reversible wettability for anti-icing and de-icing applications.

The Criterion of Supercooled Bionic Lotus Surfaces Repelling Impacting Droplets for Anti‐Icing Engineering

AbstractIcephobic material is of great importance in power transportation, communication, aerospace, and so on. Bionic lotus superhydrophobic surfaces show good application prospects in anti‐icing by repelling impacting droplets. However, the boundary (criterion) between droplet bounce and deposition on superhydrophobic surface under cold freezing rain is unclear. Here, from the view of statistics, the boundary and internal heat transfer mechanism of the droplet bounce, pinning, and three kinds of deposition including icing at later, early retraction, and spread. are clarified Droplet viscosity increase reduces droplet contact with surface and inhibited splash at high We. Surfaces with temperature above −20.6 °C showed 100% droplet bounce after impact on We = 64.7, and the probability of water adhesion and ice nucleation decreases to ≈0%, resulting in completely anti‐icing. Droplet bounce transition mainly ascribe to the decrease retraction driven force caused by interface freezing and transition temperature increases approximately linearly from −25.9 to −21.1 °C within 21.6 < We < 75.5. Ice nuclei brought by condensation in humid environment (60–100 RH%) further cause droplet eccentric retraction and transition temperature increases to −6 to −10 °C. The fundamental understanding of droplet behaviors on supercooled superhydrophobic surface is beneficial for icephobic surfaces applications.

Variability of Raindrop Size Distribution during a Regional Freezing Rain Event in the Jianghan Plain of Central China

Variability of Raindrop Size Distribution during a Regional Freezing Rain Event in the Jianghan Plain of Central China

Estimating and Mapping Extreme Ice Accretion Hazard and Load Due to Freezing Rain at Canadian Sites

The ice accretion load in Canadian structural design codes is developed based on an operational ice accretion prediction model. In the present study, three models are employed to predict the ice accretion amount on a flat surface and horizontal wire at Canadian sites. The results confirm that the model used by Canadian practice for predicting ice accretion leads to a conservative estimate as compared to the remaining two models. The results also indicate that the use of the Gumbel distribution for the annual maximum ice accretion is adequate for regions prone to ice accretion and that the lognormal distribution may be considered for regions with a moderate or negligible amount of ice accretion. Maps of the ice accretion hazard at five selected Canadian sites are developed. Statistical analysis of an equivalent wind speed that is concurrent with the iced wire is carried out, showing that the concurrent wind speed for the 50-year return period value of the annual maximum ice accretion amount is smaller than the 50-year return period value of the annual maximum wind speed. It is shown that the statistical characteristics of the annual maximum concurrent wind speed on iced wire differ from that of the annual maximum wind speed.

Influence of key parameters of ice accretion model under coexisting rain and fog weather

Based on 30 complete wire icing processes lasted longer than 24 h observed from the Enshi, Jinsha, Dacaoping and Shennongding of Shennongjia in mountainous areas of Hubei province during the winter of 2008–2016, the macroscopic effects of rain–fog weather on the ice accretion process were analyzed. Furthermore, the distribution characteristics of key simulation parameters in supercooled fog (SF) and freezing rain (FR) were discussed according to the physical model of icing process. Finally, the evolution characteristics of the simulated ice thickness in rain–fog weather were proposed. Results showed that the duration of ice accretion in mountainous areas is the key factor affecting the maximum ice thickness; the freezing rain is most frequent during the glaze icing process, which leads to the substantial growth of ice thickness. The average growth rates of ice thickness with and without freezing rain are 1.26 mm h−1 and -0.11 mm h−1, respectively. Collision rate is the main parameter for inhibiting ice accretion of SF, with an average value of ∼ 0.1, while freezing rate is the main parameter for inhibiting ice accretion of FR, with an average value of ∼ 0.6. The ice accretion of SF shows the characteristics of periodic growth, while the ice accretion of FR shows the explosive growth of ice thickness, which makes the simulated values of icing closer to the observations. The ice formation efficiency of FR was more than twice that of SF, with a negative feedback mechanism to the ice accumulation of SF.

Attributing icing precipitations trend (1951-2098) in the context of climate change in Europe

Freezing rain and wet snow, both mentioned as “icing precipitation” in this study, are wintertime climatic events that can lead severe damages for environment and societies. At the European scale, only few studies focused on these climatic events, in comparison with North America. The objectives of this study is (i) to apprehend the actual and future spatio-temporal variability of the “high-impact Icing Precipitation favourable Days” (IPDs), and (ii) to explore the dominating climate variable controlling the IPD trends between the temperature (thermal conditions) and the precipitation (vulnerability conditions), because of the uncertainties of the future projections. Daily minimum, maximum near surface temperatures and accumulated precipitations from the E-OBS (historical period; 1951-2018) and from the Euro-Cordex initiative (future simulations; 2026-2098) are used to apprehend the IPDs. For the historical period, no clear trend emerges, either for the IPDs evolution and for the influential climate variable. For both the near- and long-term horizons, models simulate a decrease in the frequencies of IPDs that should affect almost all of Europe, except for the Scandinavia region. In addition, there would be a strong contribution of the temperature, climatic variable well simulated by regional models, as the most influential climatic conditions in the future variability of the IPDs.

Analysis on the Cause and Forecast Deviation of a Low-Temperature Freezing Rain and Snow Disaster in Changsha in 2022

Analysis on the Cause and Forecast Deviation of a Low-Temperature Freezing Rain and Snow Disaster in Changsha in 2022

Meteorological Factors Responsible for Major Power Outages during a Severe Freezing Rain Storm over Eastern Canada

Abstract 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.

Quantifying the Impact of Precipitation-Type Algorithm Selection on the Representation of Freezing Rain in an Ensemble of Regional Climate Model Simulations

Abstract 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.

A Multi‐Algorithm Analysis of Projected Changes to Freezing Rain Over North America in an Ensemble of Regional Climate Model Simulations

AbstractFreezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events.