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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]

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]

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]

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]

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]

Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer. [ABSTRACT FROM AUTHOR]

Substantial freezing rain or drizzle occurs in about 24% of winter weather events in the continental United States. Proper preparation for these freezing rain events requires accurate forecasts of ice accumulation on various surfaces. The Automated Surface Observing System (ASOS) has become the primary surface weather observation system in the United States, and more than 650 ASOS sites have implemented an icing sensor as of March 2015. ASOS observations that included ice accumulation were examined from January 2013 through February 2015. The data chosen for this study consist of 60-min periods of continuous freezing rain with precipitation rates ≥ 0.5 mm h−1 (0.02 in. h−1) and greater than a trace of ice accumulation, yielding a dataset of 1255 h of observations. Ice:liquid ratios (ILRs) were calculated for each 60-min period and analyzed with 60-min mean values of temperature, wet-bulb temperature, wind speed, and precipitation rate. The median ILR for elevated horizontal (radial) ice accumulation was 0.72:1 (0.28:1), with a 25th percentile of 0.50:1 (0.20:1) and a 75th percentile of 1.0:1 (0.40:1). Strong relationships were identified between ILR and precipitation rate, wind speed, and wet-bulb temperature. The results were used to develop a multivariable Freezing Rain Accumulation Model (FRAM) for use in predicting ice accumulation incorporating these commonly forecast variables as input. FRAM performed significantly better than other commonly used forecast methods when tested on 20 randomly chosen icing events, with a mean absolute error (MAE) of 1.17 mm (0.046 in.), and a bias of −0.03 mm (−0.001 in.). [ABSTRACT FROM AUTHOR]

Winter storms in the southern United States can significantly impact infrastructure and the economy. In this study, National Centers for Environmental Information Storm Event Database and local climate summaries, are used to develop a spatial climatology of freezing precipitation (freezing rain and ice pellets) and snow over the southern Great Plains, 1993-2011. Principal component analysis is performed on the 500-hPa height field, at the approximate onset time of precipitation, for 33 freezing precipitation and 42 snow case studies, to differentiate common synoptic flow fields associated with precipitation type. The five leading patterns for each precipitation type are retained. Composites of temperature, moisture, pressure, and wind fields are constructed and extended 24 h before and after precipitation initiation to track the storm system evolution. Many 500-hPa flow fields are similar for both precipitation types. However, snow-dominant events have stronger and/or more frequent surface cyclone development. Freezing precipitation is associated with the southward propagation of an Arctic anticyclone well ahead of precipitation, weak or absent surface cyclone formation, and a more western trough axis. High-impact ice storms in the region often have slow-moving upper-level flow, persistent isentropic ascent over a surface quasi-stationary front with strongly positive moisture anomalies, and warm layer airmass trajectories originating over the Gulf of Mexico. The results here are based on a relatively small sample size. However, this work is intended to be useful for forecasters, in particular as a pattern recognition aid in predicting the evolution of precipitation within southern Great Plains winter storms. [ABSTRACT FROM AUTHOR]

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), both operational at NOAA's National Centers for Environmental Prediction (NCEP) use the Thompson et al. mixed-phase bulk cloud microphysics scheme. This scheme permits predicted surface precipitation to simultaneously consist of rain, snow, and graupel at the same location under certain conditions. Here, the explicit precipitation-type diagnostic method is described as used in conjunction with the Thompson et al. scheme in the RAP and HRRR models. The postprocessing logic combines the explicitly predicted multispecies hydrometeor data and other information from the model forecasts to produce fields of surface precipitation type that distinguish between rain and freezing rain, and to also portray areas of mixed precipitation. This explicit precipitation-type diagnostic method is used with the NOAA operational RAP and HRRR models. Verification from two winter seasons from 2013 to 2015 is provided against METAR surface observations. An example of this product from a January 2015 south-central United States winter storm is also shown. [ABSTRACT FROM AUTHOR]

On 8-9 February 2013, the northeastern United States experienced a historic winter weather event ranking among the top five worst blizzards in the region. Heavy snowfall and blizzard conditions occurred from northern New Jersey, inland to New York, and northward through Maine. Storm-total snow accumulations of 30-61 cm were common, with maximum accumulations up to 102 cm and snowfall rates exceeding 15 cm h−1. Dual-polarization radar measurements collected for this winter event provide valuable insights into storm microphysical processes. In this study, polarimetric data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Upton, New York (KOKX), are investigated alongside thermodynamic analyses from the 13-km Rapid Refresh model and surface precipitation type observations from both Meteorological Phenomena Identification Near the Ground (mPING) and the National Weather Service (NWS) Forecast Office in Upton, New York, for interpretation of polarimetric signatures. The storm exhibited unique polarimetric signatures, some of which have never before been documented for a winter system. Reflectivity values were unusually large, reaching magnitudes >50 dB Z in shallow regions of heavy wet snow near the surface. The 0°C transition line was exceptionally distinct in the polarimetric imagery, providing detail that was often unmatched by the numerical model output. Other features include differential attenuation of magnitudes typical of melting hail, depolarization streaks that provide evidence of electrification, nonuniform beamfilling, a 'snow flare' signature, and localized downward excursions of the melting-layer bright band collocated with observed transitions in surface precipitation types. In agreement with previous studies, widespread elevated depositional growth layers, located at temperatures near the model-predicted −15°C isotherm, appear to be correlated with increased snowfall and large reflectivity factors Z H near the surface. [ABSTRACT FROM AUTHOR]

Five implicit precipitation-type algorithms are assessed using observed and model-forecast sounding data in order to measure their accuracy and to gauge the effects of model uncertainty on algorithm performance. When applied to observed soundings, all algorithms provide very reliable guidance on snow and rain (SN and RA). However, their skills for ice pellets and freezing rain (IP and FZRA) are comparatively low. Most misclassifications of IP are for FZRA and vice versa. Deeper investigation reveals that no method used in any of the algorithms to differentiate between IP and FZRA allows for clear discrimination between the two forms. The effects of model uncertainty are also considered. For SN and RA, these effects are minimal and each algorithm performs reliably. Conversely, IP and FZRA are strongly impacted. When the range of uncertainty is fully accounted for, their resulting wet-bulb temperature profiles are nearly indistinguishable, leading to very poor skill for all algorithms. Although currently available data do not allow for a thorough investigation, comparison of the statistics from only those soundings that are associated with long-duration, horizontally uniform regions of FZRA shows there are significant differences between these profiles and those that are from more transient, highly variable environments. Hence, a five-category (SN, RA, IP, FZRA, and IP-FZRA mix) approach is advocated to differentiate between sustained regions of horizontally uniform FZRA (or IP) from more mixed environments. [ABSTRACT FROM AUTHOR]

The hourly updating High-Resolution Rapid Refresh (HRRR) model is evaluated with regard to its ability to predict the areal extent of cold-season precipitation and accurately depict the timing and location of regions of snow, rain, and mixed-phase precipitation on the ground. Validation of the HRRR forecasts is performed using observations collected by the Automated Surface Observing System (ASOS) stations across the eastern two-thirds of the United States during the 2010-11 cold season. The results show that the HRRR is able to reliably forecast precipitation extent during the cold season. In particular, the location and areal extent of both snow and rain are very well predicted. Depiction of rain-to-snow transitions and freezing rain is reasonably good; however, the associated evaluation scores are significantly lower than for either snow or rain. The analyses suggest the skill in accurately depicting precipitation extent and phase (i.e., rain, snow, and mixed phase) depends on the size and organization of a weather system. Typically, larger synoptically forced weather systems are better predicted than smaller weather systems, including the associated rain-to-snow transition or freezing-rain areas. Offsets in space or time (i.e., causing misses and false alarms) have a larger effect on the model performance for smaller weather systems. [ABSTRACT FROM AUTHOR]

Winter storms, namely snowstorms and ice storms, are a major hazard and forecasting challenge across central North Carolina. This study employed a trajectory approach to analyze the ingredients (i.e., temperature, moisture, and lift) associated with heavy snowstorms and ice storms that occurred within the Raleigh, North Carolina, National Weather Service forecast region from 2000 to 2010. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) tool was used to calculate 72-h backward (i.e., upstream) air parcel trajectories from three critical vertical pressure levels at the time and location of heaviest precipitation for each storm. Analysis of composite trajectories revealed the source regions and meteorological properties of air parcels associated with heavy winter storms. Adiabatic and diabatic contributions to air parcel temperature and moisture content were also estimated along each trajectory to assess the physical processes connected with heavy winter precipitation in the region. Results indicate that diabatic warming and cooling contribute significantly to the vertical temperature profile during heavy winter storms and therefore dictate the resulting precipitation type. The main source of diabatic warming is fluxes of sensible and latent heat within the marine atmospheric boundary layer over the Gulf Stream. These fluxes contribute to a warming and moistening of air parcels associated with heavy ice storms. In contrast, heavy snowstorms are characterized by diabatic cooling in the lower troposphere above the marine atmospheric boundary layer. The most significant moisture source for heavy snowfall is the Caribbean Sea, while heavy ice storms entrain moisture from the Gulf of Mexico and Gulf Stream region near the Carolina coast. [ABSTRACT FROM AUTHOR]

Northwest flow snowfall (NWFS) impacts the southern Appalachian Mountains after the upper-level trough has departed from the region, when moist northwesterly flow near the ground is lifted after encountering the mountains. Snowfall associated with these events is highly localized and challenging to predict as the clouds generating the accumulation are mesoscale in structure and depend on rapidly varying structures of moisture, instability, and wind in the planetary boundary layer (PBL) and on the relief of the local topography. The purpose of this study is to investigate the near-term impact of heat and moisture fluxes at the ground on the evolution of an NWFS event using several simulations of the Advanced Research core of the Weather Research and Forecasting mesoscale model. Model simulations indicate that convective banding is responsible for the snowfall accumulations in the southern Appalachians during the event and the structure of the banding is sensitive to the vertical positions of maximum wind shear and minimal stability within the PBL. Sensible heat fluxes at the ground upstream of the mountains in the daytime tend to deepen the PBL, reduce cloud water content, and reduce snowfall accumulations. At the same time, however, the daytime sensible heating also increases the overall vapor of the PBL through increased turbulent mixing and the transport of vapor made available to the atmosphere through upward latent heat fluxes at the ground. Latent heat fluxes at the ground upstream of the southern Appalachian Mountains provide a source of moisture that contributes a significant fraction of the overall simulated snowfall accumulations. [ABSTRACT FROM AUTHOR]

Freezing rain is a major environmental hazard that is especially common along the St. Lawrence River valley (SLRV) in southern Quebec, Canada. For large cities such as Montreal, severe events can have a devastating effect on people, property, and commerce. In this study, a composite analysis of 46 long-duration events for the period 1979-2008 is presented to identify key synoptic-scale structures and precursors of Montreal freezing rain events. Based on the observed structures of the 500-hPa heights, these events are manually partitioned into three types-west, central, and east-depending on the location and tilt of the 500-hPa trough axis. West events are characterized by a strong surface anticyclone downstream of Montreal, an inverted trough extending northward to the Great Lakes, and a quasi-stationary area of geostrophic frontogenesis located over Quebec. Central events are characterized by a cyclone-anticyclone couplet pattern, with a deeper surface trough extending into southern Ontario, and a strong stationary anticyclone over Quebec. East events are characterized by the passage of a transient well-defined cyclone, and a weaker downstream anticyclone. In all cases, cold northeasterly winds are channeled down the SLRV primarily by pressure-driven channeling. Northeasterly surface winds are associated with strong low-level temperature inversions within the SLRV. Additionally, west events tend to have a longer duration of weaker precipitation, while east events tend to have a shorter duration of more intense precipitation. The results of this study may aid forecasters in identifying and understanding the synoptic-scale structures and precursors to Montreal freezing rain events. [ABSTRACT FROM AUTHOR]

This paper investigated the interactions between the synoptic patterns, quasi-stationary fronts, eastward-propagating cloud clusters from the Tibetan Plateau, surface conditions, and atmospheric stratification processes associated with a 20-day event of freezing precipitation over southern China from January to February 2008. It was found that the long duration of the freezing precipitation process was primarily caused by stationary and anomalous synoptic weather patterns such as a blocking high pressure system in the northern branch and a trough in the south branch of the westerlies, which resulted in the convergence of cold air from northern China and warm, moist air from the south. The cloud clusters over the Tibetan Plateau propagated eastward and showed noticeable impacts in the local areas when they moved over southern China during several similar cloud propagation processes from January to February 2008. An east–west-oriented quasi-stationary front system in southern China, which is rare during the Asian winter monsoon season, is responsible for producing freezing precipitation and snowstorms. A stronger horizontal gradient of the isolines of the pseudo-equivalent potential temperature and higher temperatures at the inversion layer in the western part of the front than that in its eastern part can be found. At the same time, low-level moisture convergence ahead of the front enhanced the formation, development, and persistence of freezing precipitation in the west part of the front. The thickness of the warm layer and the temperature inversion layer also modulated the intensity and duration of freezing rain and ice pellets. Temperature from about −1° to −3°C and weak winds were found to be favorable meteorological factors at the surface level for freezing precipitation. These analysis results are synthesized into a conceptual model that coherently describes the physics processes associated with the synoptic features and quasi-stationary front system as well as the atmospheric stratification process during the freezing precipitation event. [ABSTRACT FROM AUTHOR]

Valley cold pools (VCPs), which are trapped, cold layers of air at the bottoms of basins or valleys, pose a significant problem for forecasters because they can lead to several forms of difficult-to-forecast and hazardous weather such as fog, freezing rain, or poor air quality. Numerical models have historically failed to routinely provide accurate guidance on the formation and demise of VCPs, making the forecast problem more challenging. In some case studies of persistent wintertime VCPs, there is a connection between the movement of upper-level waves and the timing of VCP formation and decay. Herein, a 3-yr climatology of persistent wintertime VCPs for five valleys and basins in the western United States is performed to see how often VCP formation and decay coincides with synoptic-scale (∼200–2000 km) wave motions. Valley cold pools are found to form most frequently as an upper-level ridge approaches the western United States and in response to strong midlevel warming. The VCPs usually last as long as the ridge is over the area and usually only end when a trough, and its associated midlevel cooling, move over the western United States. In fact, VCP strength appears to be almost entirely dictated by midlevel temperature changes, which suggests large-scale forcing is dominant for this type of VCP most of the time. [ABSTRACT FROM AUTHOR]

Receiver operating characteristic (ROC) curves have become a common analysis tool for evaluating forecast discrimination: the ability of a forecast system to distinguish between events and nonevents. As is implicit in that statement, application of the ROC curve is limited to forecasts involving only two possible outcomes, such as rain and no rain. However, many forecast scenarios exist for which there are multiple possible outcomes, such as rain, snow, and freezing rain. An extension of the ROC curve to multiclass forecast problems is explored. The full extension involves high-dimensional hypersurfaces that cannot be visualized and that present other problems. Therefore, several different approximations to the full extension are introduced using both artificial and actual forecast datasets. These approximations range from sets of simple two-class ROC curves to sets of three-dimensional ROC surfaces. No single approximation is superior for all forecast problems; thus, the specific aims in evaluating the forecast must be considered. [ABSTRACT FROM AUTHOR]

Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique. [ABSTRACT FROM AUTHOR]

Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall. [ABSTRACT FROM AUTHOR]

A comprehensive analysis of freezing rain, freezing drizzle, and ice pellets was conducted using data from surface observations across the United States and Canada. This study complements other studies of freezing precipitation in the United States and Canada, and provides additional information about the temporal characteristics of the distribution. In particular, it was found that during this period 1) spatial variability in the annual frequency of freezing precipitation and ice pellets is large across the United States and Canada, and these precipitation types occur most frequently across the central and eastern portions of the United States and Canada, much of Alaska, and the northern shores of Canada; 2) freezing precipitation and ice pellets occur most often from December to March, except in northern Canada and Alaska where it occurs during the warm season, as well; 3) freezing rain and freezing drizzle appear to be influenced by the diurnal solar cycle; 4) freezing precipitation is often short lived; 5) most freezing rain and freezing drizzle are not mixed with other precipitation types, whereas most reports of ice pellets included other types of precipitation; 6) freezing precipitation and ice pellets occur most frequently with a surface (2 m) temperature slightly less than 0°C; and 7) following most freezing rain events, the surface temperature remains at or below freezing for up to 10 h, and for up to 25 h for freezing drizzle. [ABSTRACT FROM AUTHOR]

Develops the general applicability of an isomonogram to diagnose the position of the geographic boundary between freezing precipitation and ice pellets. Tests of the isomonogram using a 25-year database; Soundings that did not conform to the profile.

Regional and local influences on frequency and type of freezing precipitation (freezing drizzle, freezing rain, and ice pellets) are investigated via in-depth climatologies of six continental United States (CONUS) sounding sites. For each site, wind roses of precipitation type occurrences are compared with those for nearby stations and the aggregate values for the CONUS. Synoptic scenarios and sounding structures are identified for prolonged events of each precipitation type and probable formation mechanisms are discussed. Station location relative to topographic features smaller than 1 km in height, water bodies ranging in size from oceans to small bays, and dominant wintertime storm tracks are shown to play a major role in the determination of the frequency and type of freezing precipitation at each site. Results help to explain the regional maxima and minima of freezing precipitation across the CONUS, as well as the dominance of certain precipitation types and formation mechanisms in different portions thereof. Understanding these differences is necessary for proper development of techniques used to diagnose and forecast surface precipitation type and the occurrence of hazardous aircraft icing conditions associated with freezing precipitation aloft. [ABSTRACT FROM AUTHOR]