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Several configurations of the Canadian Precipitation Analysis system (CaPA) currently produce precipitation analyses at Environment and Climate Change Canada (ECCC). To improve CaPA’s performance during the winter season, the impact of assimilating the IMERG V06 product (IMERG: Integrated Multi-satellitE Retrievals for GPM—Global Precipitation Measurement mission) into CaPA is examined in this study. Tests are conducted with CaPA’s 10 km deterministic version, evaluated over Canada and the northern part of the United States (USA). Maps from a case study show that IMERG plays a contradictory role in the production of CaPA’s precipitation analyses for a synoptic-scale winter storm over North America’s eastern coast. While its contribution appears to be physically correct over southern portions of the meteorological system, and early in its intensification phase, IMERG displays unrealistic spatial structures over land later in the system’s life cycle when it is located over northern (colder) areas. Objective evaluation of CaPA’s analyses when IMERG is assimilated without any restrictions shows an overall decrease in precipitation, which has a mixed effect (positive and negative) on the bias indicators. But IMERG’s influence on the Equitable Threat Score (ETS), a measure of CaPA’s analyses accuracy, is clearly negative. Using IMERG’s quality index (QI) to filter out areas where it is less accurate improves CaPA’s objective evaluation, leading to better ETS versus the control experiment in which no IMERG data are assimilated. Several diagnostics provide insight into the nature of IMERG’s contribution to CaPA. For the most successful configuration, with a QI threshold of 0.3, IMERG’s impact is mostly found in the warmer parts of the domain, i.e., in northern US states and in British Columbia. Spatial means of the temporal sums of absolute differences between CaPA’s analyses with and without IMERG indicate that this product also contributes meaningfully over land areas covered by snow, and areas where air temperature is below −2 °C (where precipitation is assumed to be in solid phase).

Abstract. A fundamental issue associated with the dynamical downscaling technique using limited-area models is related to the presence of a “spatial spin-up” belt close to the lateral boundaries where small-scale features are only partially developed. Here, we introduce a method to identify the distance from the border that is affected by the spatial spin-up (i.e., the spatial spin-up distance) of the precipitation field in convection-permitting model (CPM) simulations. Using a domain over eastern North America, this new method is applied to several simulations that differ on the nesting approach (single or double nesting) and the 3-D variables used to drive the CPM simulation. Our findings highlight three key points. Firstly, when using a single nesting approach, the spin-up distance from lateral boundaries can extend up to 300 km (around 120 CPM grid points), varying across seasons, boundaries and driving variables. Secondly, the greatest spin-up distances occur in winter at the western and southern boundaries, likely due to strong atmospheric inflow during these seasons. Thirdly, employing a double nesting approach with a comprehensive set of microphysical variables to drive CPM simulations offers clear advantages. The computational gains from reducing spatial spin-up outweigh the costs associated with the more demanding intermediate simulation of the double nesting. These results have practical implications for optimizing CPM simulation configurations, encompassing domain selection and driving strategies.

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6- and 24-h precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process because of the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates are improved as the equitable threat score is improved because of a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics vary from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.

Abstract 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. The amount and the phase of cold-season precipitation accumulating in the upper Saint John River (SJR) basin are critical factors in determining spring runoff, ice jams, and flooding. To study the impact of winter and spring storms on the snowpack in the upper SJR basin, the Saint John River Experiment on Cold Season Storms (SAJESS) was conducted during winter–spring 2020–2021. Here, we provide an overview of the SAJESS study area, field campaign, and data collected. The upper SJR basin represents 41 % of the entire SJR watershed and encompasses parts of the US state of Maine and the Canadian provinces of Quebec and New Brunswick. In early December 2020, meteorological instruments were co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick. This included a separate weather station for measuring standard meteorological variables, an optical disdrometer, and a micro rain radar. This instrumentation was augmented during an intensive observation period that also included upper-air soundings, surface weather observations, a multi-angle snowflake camera, and macrophotography of solid hydrometeors throughout March and April 2021. During the study, the region experienced a lower-than-average snowpack that peaked at ∼ 65 cm, with a total of 287 mm of precipitation (liquid-equivalent) falling between December 2020 and April 2021, a 21 % lower amount of precipitation than the climatological normal. Observers were present for 13 storms during which they conducted 183 h of precipitation observations and took more than 4000 images of hydrometeors. The inclusion of local volunteers and schools provided an additional 1700 measurements of precipitation amounts across the area. The resulting datasets are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2023). We also include a synopsis of the data management plan and a brief assessment of the rewards and challenges of conducting the field campaign and utilizing community volunteers for citizen science.

Abstract Temperatures near 0°C represent a critical threshold for many environmental processes and socio‐economic activities. This study examines surface air temperatures ( T ) near 0°C (−2°C ≤  T  ≤ 2°C) across much of southern Canada over a 13 year period (October 2000–September 2013). It utilized hourly data from 39 weather stations and from 4‐km resolution Weather Research and Forecasting model simulations that were both a retrospective simulation as well as a pseudo‐global warming simulation applicable near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% from 985 hr in the current climate to 1,035 hr within the future one. Near‐0°C occurrences with precipitation vary from <5% to approximately 50% of these values. Near‐0°C occurrences are sometimes higher than values of neighboring temperatures. These near‐0°C peaks in temperature distributions can occur in both the current and future climate, in only one, or in neither. Only 4.3% of southern Canada is not associated with a near‐0°C peak and 65.8% is associated with a near‐0°C peak in both climates. It is inferred that latent heat exchanges from the melting and freezing of, for example, precipitation and the snowpack contribute significantly to some of these findings. , Plain Language Summary Our changing climate is spurring the development of huge efforts to improve resiliency. For many regions of the world, these efforts must account for potential changes in near‐0°C conditions within which both melting and freezing can occur and the accompanying latent heat exchanges can push air temperature toward 0°C. This article focuses on the occurrence of near‐0°C surface temperatures across southern Canada through an examination of observational and model information including projections in a future warmer (average 6.1°C increase) climate near the end of the 21st century. Average annual occurrences of near‐0°C conditions increase by a relatively small amount of 5.1% in the future climate and highest values continue to be along the Pacific coast or within the Western Cordillera and lowest values continue to be within central and northern areas. Near‐0°C occurrences are often higher than those of neighboring temperatures in the present climate and some of these elevated occurrences persist into the future one despite dramatic warming. It is inferred that latent heat exchanges from the melting and freezing of precipitation and snowcover contribute to these findings. , Key Points Near‐0°C surface air temperatures were examined over southern Canada using retrospective and pseudo‐global warming simulations Overall average occurrences increase slightly and their spatial patterns are largely maintained in the warmer climate Near‐0°C occurrences sometimes exceed those of neighboring temperatures and this feature often persists despite dramatic warming

Solid precipitation falling near 0 °C, mainly snow, can adhere to surface features and produce major impacts. This study is concerned with characterizing this precipitation over the Canadian Prairie provinces of Manitoba and Saskatchewan in the current (2000–2013) and pseudo-global warming future climate, with an average 5.9 °C temperature increase, through the use of high resolution (4 km) model simulations. On average, simulations in the current climate suggest that this precipitation occurs within 11 events per year, lasting 33.6 h in total and producing 27.5 mm melted equivalent, but there are wide spatial variations that are partly due to enhancements arising from its relatively low terrain. Within the warmer climate, average values generally increase, and spatial patterns shift somewhat. This precipitation consists of four categories covering its occurrence just below and just above a wet-bulb temperature of 0 °C, and with or without liquid precipitation. It generally peaks in March or April, as well as in October, and these peaks move towards mid-winter by approximately one month within the warmer climate. Storms producing this precipitation generally produce winds with a northerly component during or shortly after the precipitation; these winds contribute to further damage. Overall, this study has determined the features of and expected changes to adhering precipitation across this region.

Abstract Accurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent ( SWE ) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Abstract Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

Abstract 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 This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

Abstract Freezing 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. , Plain Language Summary Freezing rain events, or ice storms, can have major impacts on electrical infrastructure, agriculture, and road and air travel. Despite these impacts, relatively little research has been done on how these events may change as the Earth warms. We therefore examine several climate model simulations to determine how the frequency of freezing rain may change at different levels of future global warming. We focus in particular on how sensitive the projected changes are to the method used to identify freezing rain in the model output, as well as to the choice of climate model used to produce the projections. We find strong agreement among methods and models on a decrease in freezing rain frequency over much of the United States (from Texas northeastward to Maine) and an increase in freezing rain frequency over portions of western Canada (Alberta, Saskatchewan, Manitoba). In many other areas, however, the different methods and simulations disagree on the direction of projected change. Our findings highlight the importance of using many different climate models, rather than single simulations, to paint a clearer picture of the level of certainty in projections of freezing rain in the context of global warming. , Key Points Freezing rain is projected to increase in frequency over portions of western and central Canada and decrease over most of the United States The sign of projected changes is not highly sensitive to the precipitation‐type algorithm used to diagnose freezing rain The choice of driving global climate model is a key source of uncertainty in both the sign and magnitude of projected changes

Abstract 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 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 μ m) 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.