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Abstract
Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.
Abstract
Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.
Abstract
This study provides an assessment of changes in the occurrence frequency of four types of adverse-weather (freezing precipitation, blowing snow, fog, and low ceilings) and no-weather (i.e., no precipitation or visibility obscuration) events as observed at 15 Canadian Arctic stations of good hourly weather observations for 1953–2004. The frequency time series were subjected to a homogenization procedure prior to a logistic regression–based trend analysis.
The results show that the frequency of freezing precipitation has increased almost everywhere across the Canadian Arctic since 1953. Rising air temperature in the region has probably resulted in more times that the temperature is suitable for freezing precipitation. On the contrary, the frequency of blowing snow occurrence has decreased significantly in the Canadian Arctic. The decline is most significant in spring. Changes in fog and low ceiling (LC) occurrences have similar patterns and are most (least) significant in summer (autumn). Decreases were identified for both types of events in the eastern region in all seasons. In the southwest, however, the fog frequency has increased significantly in all seasons, while the LC frequency has decreased significantly in spring and summer. The regional mean rate of change in the frequency of the four types of adverse weather was estimated to be 7%–13% per decade.
The frequency of no-weather events has also decreased significantly at most of the 15 sites. The decrease is most significant and extensive in autumn. Comparison with the adverse-weather trends above indicates that the decline in no-weather occurrence (i.e., increase in weather occurrence) is not the result of an increase in blowing snow or fog occurrence; it is largely the result of the increasing frequency of freezing precipitation and, most likely, other types of precipitation as well. This is consistent with the reported increases in precipitation amount and more frequent cyclone activity in the lower Canadian Arctic.
Abstract
This study provides an assessment of changes in the occurrence frequency of four types of adverse-weather (freezing precipitation, blowing snow, fog, and low ceilings) and no-weather (i.e., no precipitation or visibility obscuration) events as observed at 15 Canadian Arctic stations of good hourly weather observations for 1953–2004. The frequency time series were subjected to a homogenization procedure prior to a logistic regression–based trend analysis.
The results show that the frequency of freezing precipitation has increased almost everywhere across the Canadian Arctic since 1953. Rising air temperature in the region has probably resulted in more times that the temperature is suitable for freezing precipitation. On the contrary, the frequency of blowing snow occurrence has decreased significantly in the Canadian Arctic. The decline is most significant in spring. Changes in fog and low ceiling (LC) occurrences have similar patterns and are most (least) significant in summer (autumn). Decreases were identified for both types of events in the eastern region in all seasons. In the southwest, however, the fog frequency has increased significantly in all seasons, while the LC frequency has decreased significantly in spring and summer. The regional mean rate of change in the frequency of the four types of adverse weather was estimated to be 7%–13% per decade.
The frequency of no-weather events has also decreased significantly at most of the 15 sites. The decrease is most significant and extensive in autumn. Comparison with the adverse-weather trends above indicates that the decline in no-weather occurrence (i.e., increase in weather occurrence) is not the result of an increase in blowing snow or fog occurrence; it is largely the result of the increasing frequency of freezing precipitation and, most likely, other types of precipitation as well. This is consistent with the reported increases in precipitation amount and more frequent cyclone activity in the lower Canadian Arctic.
Abstract
On 1–2 February 1992 a major storm produced a prolonged period (6 h) of ice pellets over St. John's, Newfoundland. At least two key features contributed to the prolonged duration. First, a subsaturated region within an inversion led to a reduction in the melting rate of particles that eventually meant that they could completely refreeze in the lower subfreezing region. This subsaturated region formed within descending air aloft identified by Doppler radar observations. Second, a cold core of air between the surface and the inversion was critically important for the refreezing of partially melted particles. Results from an airmass transformation model were used to show that the ice pellet duration was extended as a result of air traveling over sea ice as opposed to over the ocean. In addition, this study showed that Doppler radar velocity information may be capable of estimating the base height of the above freezing temperature regime during freezing rain/drizzle. Furthermore, the Doppler velocity information may also be used as a warning for possible freezing rain/drizzle conditions. A conceptual model of this storm has been developed to integrate all of the observations and it was also compared to other storms producing ice pellets. Only one other storm possessed a period of sole ice pellets and it was also the only other storm that exhibited a pronounced subsaturated region within the inversion.
Abstract
On 1–2 February 1992 a major storm produced a prolonged period (6 h) of ice pellets over St. John's, Newfoundland. At least two key features contributed to the prolonged duration. First, a subsaturated region within an inversion led to a reduction in the melting rate of particles that eventually meant that they could completely refreeze in the lower subfreezing region. This subsaturated region formed within descending air aloft identified by Doppler radar observations. Second, a cold core of air between the surface and the inversion was critically important for the refreezing of partially melted particles. Results from an airmass transformation model were used to show that the ice pellet duration was extended as a result of air traveling over sea ice as opposed to over the ocean. In addition, this study showed that Doppler radar velocity information may be capable of estimating the base height of the above freezing temperature regime during freezing rain/drizzle. Furthermore, the Doppler velocity information may also be used as a warning for possible freezing rain/drizzle conditions. A conceptual model of this storm has been developed to integrate all of the observations and it was also compared to other storms producing ice pellets. Only one other storm possessed a period of sole ice pellets and it was also the only other storm that exhibited a pronounced subsaturated region within the inversion.
Abstract
The time series of daily averaged cloud fractions (CFs) collected from different platforms—two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP) model, a Vaisala 25K laser ceilometer, and ground-based manual observations (manobs)—above the winter camp of the Canadian Arctic Shelf Exchange Study (CASES) field experiment are analyzed in this study. Taking the manobs as standard, the authors conclude that 1) the NCEP products considerably underestimated CFs in spring (e.g., from April to May) and 2) the performance of two MODIS products depends on the variation of solar zenith angle (SZA). Aqua MODIS misrepresents the snow-covered surface as clouds with almost randomly distributed CFs during the dark winter [cos(SZA) < 0], leading to the overestimation of CFs in winter while Terra MODIS has good agreement with manobs. When 0.1 < cos(SZA) < 0.4, both MODIS products regularly misrepresent the snow-covered background as clouds, leading to the significant overestimation of CFs in late winter (February) and early spring (March). When cos(SZA) > 0.4, both MODIS products have good performance in detecting cloud masks over snow backgrounds. If the sky is slightly cloudy, surface-based meteorological observers tend to underestimate cloud amounts when there is a lack of light. Comparing the CFs from Terra and manobs, the authors conclude that this bias can be over 10%. Power spectral analysis and wavelet analysis show three results: 1) High clouds more frequently appear in winter than in spring with periods between 8 and 16 days, indicating their close connection with synoptic events. Current NCEP products can predict this periodicity but have a phase lag. 2) Middle and low clouds are more local and are common in mid- and late spring (April and May) with periods between 2 and 4 days. At the CASES winter and spring field site, the periodicity of high clouds is dominant. 3) The time-scale-dependent correlation coefficients (CCs) between both MODIS products, NCEP and manobs, show that with high frequent CF sampling per day, the CCs are stable when the time scale varies between 1 and 4 days: with Terra MODIS and NCEP, the value is about 0.6; with Aqua MODIS, between 0.4 and 0.5. All CCs get smaller when the time scale increases beyond 8 days: with respect to both MODIS products, the CCs get closer with values between 0.3 and 0.4; with respect to NCEP, the CC dramatically decreases from positive values to negative values, indicating the lack of accuracy in current NCEP cloud schemes.
Abstract
The time series of daily averaged cloud fractions (CFs) collected from different platforms—two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP) model, a Vaisala 25K laser ceilometer, and ground-based manual observations (manobs)—above the winter camp of the Canadian Arctic Shelf Exchange Study (CASES) field experiment are analyzed in this study. Taking the manobs as standard, the authors conclude that 1) the NCEP products considerably underestimated CFs in spring (e.g., from April to May) and 2) the performance of two MODIS products depends on the variation of solar zenith angle (SZA). Aqua MODIS misrepresents the snow-covered surface as clouds with almost randomly distributed CFs during the dark winter [cos(SZA) < 0], leading to the overestimation of CFs in winter while Terra MODIS has good agreement with manobs. When 0.1 < cos(SZA) < 0.4, both MODIS products regularly misrepresent the snow-covered background as clouds, leading to the significant overestimation of CFs in late winter (February) and early spring (March). When cos(SZA) > 0.4, both MODIS products have good performance in detecting cloud masks over snow backgrounds. If the sky is slightly cloudy, surface-based meteorological observers tend to underestimate cloud amounts when there is a lack of light. Comparing the CFs from Terra and manobs, the authors conclude that this bias can be over 10%. Power spectral analysis and wavelet analysis show three results: 1) High clouds more frequently appear in winter than in spring with periods between 8 and 16 days, indicating their close connection with synoptic events. Current NCEP products can predict this periodicity but have a phase lag. 2) Middle and low clouds are more local and are common in mid- and late spring (April and May) with periods between 2 and 4 days. At the CASES winter and spring field site, the periodicity of high clouds is dominant. 3) The time-scale-dependent correlation coefficients (CCs) between both MODIS products, NCEP and manobs, show that with high frequent CF sampling per day, the CCs are stable when the time scale varies between 1 and 4 days: with Terra MODIS and NCEP, the value is about 0.6; with Aqua MODIS, between 0.4 and 0.5. All CCs get smaller when the time scale increases beyond 8 days: with respect to both MODIS products, the CCs get closer with values between 0.3 and 0.4; with respect to NCEP, the CC dramatically decreases from positive values to negative values, indicating the lack of accuracy in current NCEP cloud schemes.
Abstract
The purpose of this study was to focus on how anomalies in the normalized difference vegetation index (NDVI; a proxy for soil moisture) over the Canadian Prairies can condition the convective boundary layer (CBL) so as to inhibit or facilitate thunderstorm activity while also considering the role of synoptic-scale forcing. This study focused on a census agricultural region (CAR) over central Alberta for which we had observed lightning data (proxy for thunderstorms), remotely sensed NDVI data, and in situ rawinsonde data (to quantify impacts of vegetation vigor on the CBL characteristics) for 11 summers from 1999 to 2009. The authors’ data suggest that the occurrence of lightning over the study area is more likely (and is of longer duration) when storms develop in an environment in which the surface and upper-air synoptic-scale forcing are synchronized. On days when surface forcing and midtropospheric ascent are present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. Additionally, the authors found the response of thunderstorm duration to NDVI anomalies to be asymmetric. That is, the response of lightning duration to anomalies in NDVI is marked when NDVI is below average but is not necessarily discernible when NDVI is above average. The authors propose a conceptual model, based largely on observations, that integrates all of the above findings to describe how a reduction in vegetation vigor—in response to soil moisture deficits—modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating lifting condensation level heights, which in turn affect lightning activity.
Abstract
The purpose of this study was to focus on how anomalies in the normalized difference vegetation index (NDVI; a proxy for soil moisture) over the Canadian Prairies can condition the convective boundary layer (CBL) so as to inhibit or facilitate thunderstorm activity while also considering the role of synoptic-scale forcing. This study focused on a census agricultural region (CAR) over central Alberta for which we had observed lightning data (proxy for thunderstorms), remotely sensed NDVI data, and in situ rawinsonde data (to quantify impacts of vegetation vigor on the CBL characteristics) for 11 summers from 1999 to 2009. The authors’ data suggest that the occurrence of lightning over the study area is more likely (and is of longer duration) when storms develop in an environment in which the surface and upper-air synoptic-scale forcing are synchronized. On days when surface forcing and midtropospheric ascent are present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. Additionally, the authors found the response of thunderstorm duration to NDVI anomalies to be asymmetric. That is, the response of lightning duration to anomalies in NDVI is marked when NDVI is below average but is not necessarily discernible when NDVI is above average. The authors propose a conceptual model, based largely on observations, that integrates all of the above findings to describe how a reduction in vegetation vigor—in response to soil moisture deficits—modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating lifting condensation level heights, which in turn affect lightning activity.
Abstract
Linkages between the terrestrial ecosystem and precipitation play a critical role in regulating regional weather and climate. These linkages can manifest themselves as positive or negative feedback loops, which may either favor or inhibit the triggering and intensity of thunderstorms. Although the Canadian Prairies terrestrial system has been identified as having the potential to exert a detectable influence on convective precipitation during the warm season, little work has been done in this area using in situ observations.
The authors present findings from a novel study designed to explore linkages between the normalized difference vegetation index (NDVI) and lightning duration (DUR) from the Canadian Lightning Detection Network for 38 census agricultural regions (CARs) on the Canadian Prairies. Statistics Canada divides the prairie agricultural zone into CARs (polygons of varying size and shape) for the purpose of calculating agricultural statistics. Here, DUR is used as a proxy for thunderstorm activity. Statistical analyses were undertaken for 38 CARs for summers [June–August (JJA)] between 1999 and 2008. Specifically, coefficients of determination were calculated between pairs of standardized anomalies of DUR and NDVI by season and by month. Correlations were also calculated for CARs grouped by size and/or magnitude of the NDVI anomalies.
The main findings are as follows: 1) JJA lightning activity is overwhelmingly below average within larger dry areas (i.e., areas with below-average NDVI); that is, the linkages between NDVI and DUR increased significantly as both the area and magnitude of the dry anomaly increased. 2) In contrast, CARs with above-average NDVI did not consistently experience above-average lightning activity, regardless of the CAR size. 3) The lower threshold for the length scale of the dry anomalies required to affect the boundary layer sufficiently to reduce lightning activity was found to be approximately 150 km (~18 000 km2). 4) The authors’ analysis suggests that the surface-convection feedback appears to be a real phenomenon, in which drought tends to perpetuate drought with respect to convective storms and associated rainfall, within the limits found in 1) and 3).
Abstract
Linkages between the terrestrial ecosystem and precipitation play a critical role in regulating regional weather and climate. These linkages can manifest themselves as positive or negative feedback loops, which may either favor or inhibit the triggering and intensity of thunderstorms. Although the Canadian Prairies terrestrial system has been identified as having the potential to exert a detectable influence on convective precipitation during the warm season, little work has been done in this area using in situ observations.
The authors present findings from a novel study designed to explore linkages between the normalized difference vegetation index (NDVI) and lightning duration (DUR) from the Canadian Lightning Detection Network for 38 census agricultural regions (CARs) on the Canadian Prairies. Statistics Canada divides the prairie agricultural zone into CARs (polygons of varying size and shape) for the purpose of calculating agricultural statistics. Here, DUR is used as a proxy for thunderstorm activity. Statistical analyses were undertaken for 38 CARs for summers [June–August (JJA)] between 1999 and 2008. Specifically, coefficients of determination were calculated between pairs of standardized anomalies of DUR and NDVI by season and by month. Correlations were also calculated for CARs grouped by size and/or magnitude of the NDVI anomalies.
The main findings are as follows: 1) JJA lightning activity is overwhelmingly below average within larger dry areas (i.e., areas with below-average NDVI); that is, the linkages between NDVI and DUR increased significantly as both the area and magnitude of the dry anomaly increased. 2) In contrast, CARs with above-average NDVI did not consistently experience above-average lightning activity, regardless of the CAR size. 3) The lower threshold for the length scale of the dry anomalies required to affect the boundary layer sufficiently to reduce lightning activity was found to be approximately 150 km (~18 000 km2). 4) The authors’ analysis suggests that the surface-convection feedback appears to be a real phenomenon, in which drought tends to perpetuate drought with respect to convective storms and associated rainfall, within the limits found in 1) and 3).
Abstract
On 30 September 1994 an Arctic low pressure system passed over the southern Beaufort Sea area of northern Canada and research aircraft observations were made within and around the warm front of the storm. This study is unique in that the warm front contained subzero centigrade temperatures across the entire frontal region. The overall structure of the warm front and surrounding region was similar to midlatitude storms; however, the precipitation rates, liquid water content magnitudes, horizontal and vertical winds, vertical wind shear, turbulence, and thermal advection were very weak. In addition, a low-level jet and cloud bands were aligned parallel to the warm front, near-neutral stability occurred within and around the front, and conditional symmetric instability was likely occurring. A steep frontal region resulted from strong Coriolis influences that in turn limited the amount of cloud and precipitation ahead of the system. The precipitation efficiency of the storm was high (60%) but is believed to be highly dependent on the stage of development. The mesoscale frontogenetic forcing was primarily controlled by the tilting of isentropic surfaces with confluence/convergence being the secondary influence. Sublimation contributions may have been large in the earlier stages of storm development. Satellite and aircraft radiometers underestimated cloud top heights by as much as 4 km and this was mostly due to the near transparency of the lofted ice layer in the upper portion of the storm. Maximum surface solar radiation deficits ranged between 91 W m−2 and 187 W m−2 at two surface observing sites. This common type of cloud system must have a major impact on the water and energy cycles of northern Canada in the autumn and therefore must be well accounted for within climate models.
Abstract
On 30 September 1994 an Arctic low pressure system passed over the southern Beaufort Sea area of northern Canada and research aircraft observations were made within and around the warm front of the storm. This study is unique in that the warm front contained subzero centigrade temperatures across the entire frontal region. The overall structure of the warm front and surrounding region was similar to midlatitude storms; however, the precipitation rates, liquid water content magnitudes, horizontal and vertical winds, vertical wind shear, turbulence, and thermal advection were very weak. In addition, a low-level jet and cloud bands were aligned parallel to the warm front, near-neutral stability occurred within and around the front, and conditional symmetric instability was likely occurring. A steep frontal region resulted from strong Coriolis influences that in turn limited the amount of cloud and precipitation ahead of the system. The precipitation efficiency of the storm was high (60%) but is believed to be highly dependent on the stage of development. The mesoscale frontogenetic forcing was primarily controlled by the tilting of isentropic surfaces with confluence/convergence being the secondary influence. Sublimation contributions may have been large in the earlier stages of storm development. Satellite and aircraft radiometers underestimated cloud top heights by as much as 4 km and this was mostly due to the near transparency of the lofted ice layer in the upper portion of the storm. Maximum surface solar radiation deficits ranged between 91 W m−2 and 187 W m−2 at two surface observing sites. This common type of cloud system must have a major impact on the water and energy cycles of northern Canada in the autumn and therefore must be well accounted for within climate models.
Severe thunderstorms are a common occurrence in summer on the Canadian prairies, with a large number originating along the Alberta, Canada, foothills, just east of the Rocky Mountains. Most of these storms move eastward to affect the Edmonton–Calgary corridor, one of the most densely populated and fastest-growing regions in Canada. Previous studies in the United States, Europe, and Canada have stressed the importance of mesoscale features in thunderstorm development. However, such processes cannot be adequately resolved using operational observation networks in many parts of Canada. Current conceptual models for severe storm outbreaks in Alberta were developed almost 20 years ago and do not focus explicitly on mesoscale boundaries that are now known to be important for thunderstorm development.
The Understanding Severe Thunderstorms and Alber ta Boundary Layers Experiment (UNSTABLE) is a field and modeling study aiming to improve our understanding of the processes associated with the initiation of severe thunderstorms, to refine associated conceptual models, and to assess the ability of convectivescale NWP models to simulate relevant physical processes. As part of UNSTABLE in 2008, Environment Canada and university scientists conducted a pilot field experiment over the Alberta foothills to investigate mesoscale processes associated with the development of severe thunderstorms. Networks of fixed and mobile surface and upper-air instrumentation provided observations of the atmospheric boundary layer at a level of detail never before seen in this region. Preliminary results include the most complete documentation of a dryline in Canada and an analysis of variability in boundary layer evolution across adjacent forest and crop vegetation areas. Convective-scale NWP simulations suggest that although additional information on convective mode may be provided, there is limited benefit overall to downscaling to smaller grid spacing without assimilation of mesoscale observations.
Severe thunderstorms are a common occurrence in summer on the Canadian prairies, with a large number originating along the Alberta, Canada, foothills, just east of the Rocky Mountains. Most of these storms move eastward to affect the Edmonton–Calgary corridor, one of the most densely populated and fastest-growing regions in Canada. Previous studies in the United States, Europe, and Canada have stressed the importance of mesoscale features in thunderstorm development. However, such processes cannot be adequately resolved using operational observation networks in many parts of Canada. Current conceptual models for severe storm outbreaks in Alberta were developed almost 20 years ago and do not focus explicitly on mesoscale boundaries that are now known to be important for thunderstorm development.
The Understanding Severe Thunderstorms and Alber ta Boundary Layers Experiment (UNSTABLE) is a field and modeling study aiming to improve our understanding of the processes associated with the initiation of severe thunderstorms, to refine associated conceptual models, and to assess the ability of convectivescale NWP models to simulate relevant physical processes. As part of UNSTABLE in 2008, Environment Canada and university scientists conducted a pilot field experiment over the Alberta foothills to investigate mesoscale processes associated with the development of severe thunderstorms. Networks of fixed and mobile surface and upper-air instrumentation provided observations of the atmospheric boundary layer at a level of detail never before seen in this region. Preliminary results include the most complete documentation of a dryline in Canada and an analysis of variability in boundary layer evolution across adjacent forest and crop vegetation areas. Convective-scale NWP simulations suggest that although additional information on convective mode may be provided, there is limited benefit overall to downscaling to smaller grid spacing without assimilation of mesoscale observations.
Abstract
Nocturnal convection initiation (NCI) is more difficult to anticipate and forecast than daytime convection initiation (CI). A major component of the Plains Elevated Convection at Night (PECAN) field campaign in the U.S. Great Plains was to intensively sample NCI and its near environment. In this article, we summarize NCI types observed during PECAN: 1 June–16 July 2015. These NCI types, classified using PECAN radar composites, are associated with 1) frontal overrunning, 2) the low-level jet (LLJ), 3) a preexisting mesoscale convective system (MCS), 4) a bore or density current, and 5) a nocturnal atmosphere lacking a clearly observed forcing mechanism (pristine). An example and description of each of these different types of PECAN NCI events are presented. The University of Oklahoma real-time 4-km Weather Research and Forecasting (WRF) Model ensemble forecast runs illustrate that the above categories having larger-scale organization (e.g., NCI associated with frontal overrunning and NCI near a preexisting MCS) were better forecasted than pristine. Based on current knowledge and data from PECAN, conceptual models summarizing key environmental features are presented and physical processes underlying the development of each of these different types of NCI events are discussed.
Abstract
Nocturnal convection initiation (NCI) is more difficult to anticipate and forecast than daytime convection initiation (CI). A major component of the Plains Elevated Convection at Night (PECAN) field campaign in the U.S. Great Plains was to intensively sample NCI and its near environment. In this article, we summarize NCI types observed during PECAN: 1 June–16 July 2015. These NCI types, classified using PECAN radar composites, are associated with 1) frontal overrunning, 2) the low-level jet (LLJ), 3) a preexisting mesoscale convective system (MCS), 4) a bore or density current, and 5) a nocturnal atmosphere lacking a clearly observed forcing mechanism (pristine). An example and description of each of these different types of PECAN NCI events are presented. The University of Oklahoma real-time 4-km Weather Research and Forecasting (WRF) Model ensemble forecast runs illustrate that the above categories having larger-scale organization (e.g., NCI associated with frontal overrunning and NCI near a preexisting MCS) were better forecasted than pristine. Based on current knowledge and data from PECAN, conceptual models summarizing key environmental features are presented and physical processes underlying the development of each of these different types of NCI events are discussed.
Abstract
The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night.
To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.
Abstract
The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night.
To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.
