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- Author or Editor: Martha Shulski x
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Abstract
Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s−1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4–0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40%–90% of the total potential in south-central and western areas of the state.
Abstract
Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s−1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4–0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40%–90% of the total potential in south-central and western areas of the state.
The Nebraska Mesonet: Technical Overview of an Automated State Weather NetworkAbstract
The Nebraska Mesonet was established in 1981 as one of the nation’s first automated state weather networks. “Automated” is defined by the nature of the observations being made and recorded by machine, as opposed to observations made and recorded manually. At the time of inception, the five observing locations were geared toward servicing agricultural production applications. The Nebraska Mesonet has grown to 69 stations (as of 2018) and is now a multipurpose environmental observing network under the Nebraska State Climate Office (NSCO). The network is composed of environmental observation stations, sited using best practices for mesoscale and microscale environment situations. Precise observations are acquired using high-quality instrumentation, following manufacturer recommendations for calibrations and maintenance. Calibrations are performed in the NSCO calibration laboratory. Uses for the data include but are not limited to water management, drought monitoring, energy production, health, environmental research, animal management, and crop pest management. This paper provides a technical overview and history of the network, outlining current practices for station siting, maintenance, data quality assurance, and data utility.
Abstract
The Nebraska Mesonet was established in 1981 as one of the nation’s first automated state weather networks. “Automated” is defined by the nature of the observations being made and recorded by machine, as opposed to observations made and recorded manually. At the time of inception, the five observing locations were geared toward servicing agricultural production applications. The Nebraska Mesonet has grown to 69 stations (as of 2018) and is now a multipurpose environmental observing network under the Nebraska State Climate Office (NSCO). The network is composed of environmental observation stations, sited using best practices for mesoscale and microscale environment situations. Precise observations are acquired using high-quality instrumentation, following manufacturer recommendations for calibrations and maintenance. Calibrations are performed in the NSCO calibration laboratory. Uses for the data include but are not limited to water management, drought monitoring, energy production, health, environmental research, animal management, and crop pest management. This paper provides a technical overview and history of the network, outlining current practices for station siting, maintenance, data quality assurance, and data utility.
Abstract
During the early winter of 2002 and late winter of 2007, the Alaskan sector of the North Pacific Ocean region experienced record-breaking temperature anomalies. The duration of these episodes was unusually long, with each lasting more than 1 month: 55 days for the warm anomaly of October–December 2002 and 37 days for the cold anomaly of February–March 2007. Temperature departures over each respective period were the largest for the continental climate of interior Alaska (>10°C) and the smallest for the maritime regions of Alaska (<4°C). Mean temperatures over the event periods in 2002 and 2007 easily ranked as the record warmest and coldest, respectively, for many surface observing stations. In addition, heating degree-day anomalies were on the order of 700 units for these periods. Atmospheric circulation patterns at the surface and upper levels for the circum-Arctic proved to be the driver for these persistent events. The 2002 warm anomaly was driven by enhanced southerly advection associated with an unusually strong Aleutian low and a positive Pacific decadal oscillation index, which resulted in a large area of anomalous temperatures in Alaska and northern Canada. The 2007 cold anomaly was driven by a weakening of the circulation pattern in the subpolar Pacific sector and a strengthening of the Siberian high, with the strongest temperature anomalies in Alaska and northwestern Canada.
Abstract
During the early winter of 2002 and late winter of 2007, the Alaskan sector of the North Pacific Ocean region experienced record-breaking temperature anomalies. The duration of these episodes was unusually long, with each lasting more than 1 month: 55 days for the warm anomaly of October–December 2002 and 37 days for the cold anomaly of February–March 2007. Temperature departures over each respective period were the largest for the continental climate of interior Alaska (>10°C) and the smallest for the maritime regions of Alaska (<4°C). Mean temperatures over the event periods in 2002 and 2007 easily ranked as the record warmest and coldest, respectively, for many surface observing stations. In addition, heating degree-day anomalies were on the order of 700 units for these periods. Atmospheric circulation patterns at the surface and upper levels for the circum-Arctic proved to be the driver for these persistent events. The 2002 warm anomaly was driven by enhanced southerly advection associated with an unusually strong Aleutian low and a positive Pacific decadal oscillation index, which resulted in a large area of anomalous temperatures in Alaska and northern Canada. The 2007 cold anomaly was driven by a weakening of the circulation pattern in the subpolar Pacific sector and a strengthening of the Siberian high, with the strongest temperature anomalies in Alaska and northwestern Canada.
Abstract
The story of the winter of 1880/81 in the central United States has been retold in historical fiction, including Laura Ingalls Wilder’s The Long Winter, as well as in local histories and folklore. What story does the meteorological data tell, and how does it measure up when compared to the fiction and folklore? What were the contributing factors to the severity of the Long Winter, and has it been or could it be repeated? Examining historical and meteorological data, reconstructions, and reanalysis, including the Accumulated Winter Season Severity Index, the Long Winter emerges as one of the most severe since European-descended settlers arrived to the central United States and began documenting weather. Contributing factors to its severity include an extremely negative North Atlantic Oscillation pattern, a mild to moderate El Niño, and a background climate state that was much colder than the twentieth-century average. The winter began early and was particularly cold and snowy throughout its duration, with a sudden spring melt that caused subsequent record-setting flooding. Historical accounts of the winter, including The Long Winter, prove to be largely accurate in describing its severity, as well as its impacts on transportation, fuel availability, food supplies, and human and livestock health. Being just one of the most severe winters on record, there are others in the modern historical record that do compare in severity, providing opportunity for comparing and contrasting the impacts of similarly severe winters.
Abstract
The story of the winter of 1880/81 in the central United States has been retold in historical fiction, including Laura Ingalls Wilder’s The Long Winter, as well as in local histories and folklore. What story does the meteorological data tell, and how does it measure up when compared to the fiction and folklore? What were the contributing factors to the severity of the Long Winter, and has it been or could it be repeated? Examining historical and meteorological data, reconstructions, and reanalysis, including the Accumulated Winter Season Severity Index, the Long Winter emerges as one of the most severe since European-descended settlers arrived to the central United States and began documenting weather. Contributing factors to its severity include an extremely negative North Atlantic Oscillation pattern, a mild to moderate El Niño, and a background climate state that was much colder than the twentieth-century average. The winter began early and was particularly cold and snowy throughout its duration, with a sudden spring melt that caused subsequent record-setting flooding. Historical accounts of the winter, including The Long Winter, prove to be largely accurate in describing its severity, as well as its impacts on transportation, fuel availability, food supplies, and human and livestock health. Being just one of the most severe winters on record, there are others in the modern historical record that do compare in severity, providing opportunity for comparing and contrasting the impacts of similarly severe winters.
Abstract
The detailed mesoscale climatology of surface winds in the Chukchi–Beaufort Seas and adjacent Arctic Slope region is analyzed using the recently developed Chukchi–Beaufort High-Resolution Atmospheric Reanalysis (CBHAR). Within the study area, surface winds are mainly driven by the prevailing synoptic weather patterns of the Beaufort high and Aleutian low and are further modulated by local geographic features through thermodynamic and dynamic processes. Sea breezes, up- or downslope winds, and the mountain barrier jets are all clearly captured by CBHAR. Sea breezes emerge in June–September and last most of the day, with a maximum spatial extent 100 km inland and 50 km offshore and maximum speed around 1–3 m s−1 in the late afternoon [~1500 Alaska standard time (AKST)]. Thermodynamic impacts of mountains on the surface winds vary from time to time. Drainage flows begin to build at the mountaintop in September and reach the strongest during November–February, occupying the entire slope. Upslope winds demonstrate a clear diurnal cycle during summer, starting to build around 0900 local time, reaching the maximum strength around 1500 local time and continuing until 2100 local time. The mountain barrier jets (MBJs) are found to be most active around the Chukotka Mountains during cold seasons. Both sea breezes and MBJs are also subject to variations and changes in response to adjusted large-scale atmosphere circulation. Storm activities can inhibit the development of sea breezes. Different responses from the Beaufort high and Aleutian low to anomalies in large-scale circulations play a vital role in the variations of MBJ activities over the Chukotka Mountains.
Abstract
The detailed mesoscale climatology of surface winds in the Chukchi–Beaufort Seas and adjacent Arctic Slope region is analyzed using the recently developed Chukchi–Beaufort High-Resolution Atmospheric Reanalysis (CBHAR). Within the study area, surface winds are mainly driven by the prevailing synoptic weather patterns of the Beaufort high and Aleutian low and are further modulated by local geographic features through thermodynamic and dynamic processes. Sea breezes, up- or downslope winds, and the mountain barrier jets are all clearly captured by CBHAR. Sea breezes emerge in June–September and last most of the day, with a maximum spatial extent 100 km inland and 50 km offshore and maximum speed around 1–3 m s−1 in the late afternoon [~1500 Alaska standard time (AKST)]. Thermodynamic impacts of mountains on the surface winds vary from time to time. Drainage flows begin to build at the mountaintop in September and reach the strongest during November–February, occupying the entire slope. Upslope winds demonstrate a clear diurnal cycle during summer, starting to build around 0900 local time, reaching the maximum strength around 1500 local time and continuing until 2100 local time. The mountain barrier jets (MBJs) are found to be most active around the Chukotka Mountains during cold seasons. Both sea breezes and MBJs are also subject to variations and changes in response to adjusted large-scale atmosphere circulation. Storm activities can inhibit the development of sea breezes. Different responses from the Beaufort high and Aleutian low to anomalies in large-scale circulations play a vital role in the variations of MBJ activities over the Chukotka Mountains.
Abstract
Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.
Abstract
Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.
Abstract
The character of a winter can be defined by many of its features, including temperature averages and extremes, snowfall totals, snow depth, and the duration between onset and cessation of winter-weather conditions. The accumulated winter season severity index incorporates these elements into one site-specific value that defines the severity of a particular winter, especially when examined in the context of climatological values for that site. Thresholds of temperature, snowfall, and snow depth are assigned points that accumulate through the defined winter season; a parallel index uses temperature and precipitation to provide a snow proxy where snow data are unavailable or unreliable. The results can be analyzed like any other meteorological parameter to examine relationships to teleconnection patterns, determine trends, and create sector-specific applications, as well as to analyze an ongoing winter or any individual winter season to place its severity in context.
Abstract
The character of a winter can be defined by many of its features, including temperature averages and extremes, snowfall totals, snow depth, and the duration between onset and cessation of winter-weather conditions. The accumulated winter season severity index incorporates these elements into one site-specific value that defines the severity of a particular winter, especially when examined in the context of climatological values for that site. Thresholds of temperature, snowfall, and snow depth are assigned points that accumulate through the defined winter season; a parallel index uses temperature and precipitation to provide a snow proxy where snow data are unavailable or unreliable. The results can be analyzed like any other meteorological parameter to examine relationships to teleconnection patterns, determine trends, and create sector-specific applications, as well as to analyze an ongoing winter or any individual winter season to place its severity in context.
Abstract
In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3 K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas.
Abstract
In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3 K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas.
