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Abstract
A tornado climatology for Finland is constructed from 1796 to 2007. The climatology consists of two datasets. A historical dataset (1796–1996) is largely constructed from newspaper archives and other historical archives and datasets, and a recent dataset (1997–2007) is largely constructed from eyewitness accounts sent to the Finnish Meteorological Institute and news reports. This article describes the process of collecting and evaluating possible tornado reports. Altogether, 298 Finnish tornado cases compose the climatology: 129 from the historical dataset and 169 from the recent dataset. An annual average of 14 tornado cases occur in Finland (1997–2007). A case with a significant tornado (F2 or stronger) occurs in our database on average every other year, composing 14% of all tornado cases. All documented tornadoes in Finland have occurred between April and November. As in the neighboring countries in northern Europe, July and August are the months with the maximum frequency of tornado cases, coincident with the highest lightning occurrence both over land and sea. Waterspouts tend to be favored later in the summer, peaking in August. The peak month for significant tornadoes is August. The diurnal peak for tornado cases is 1700–1859 local time.
Abstract
A tornado climatology for Finland is constructed from 1796 to 2007. The climatology consists of two datasets. A historical dataset (1796–1996) is largely constructed from newspaper archives and other historical archives and datasets, and a recent dataset (1997–2007) is largely constructed from eyewitness accounts sent to the Finnish Meteorological Institute and news reports. This article describes the process of collecting and evaluating possible tornado reports. Altogether, 298 Finnish tornado cases compose the climatology: 129 from the historical dataset and 169 from the recent dataset. An annual average of 14 tornado cases occur in Finland (1997–2007). A case with a significant tornado (F2 or stronger) occurs in our database on average every other year, composing 14% of all tornado cases. All documented tornadoes in Finland have occurred between April and November. As in the neighboring countries in northern Europe, July and August are the months with the maximum frequency of tornado cases, coincident with the highest lightning occurrence both over land and sea. Waterspouts tend to be favored later in the summer, peaking in August. The peak month for significant tornadoes is August. The diurnal peak for tornado cases is 1700–1859 local time.
Abstract
Over the last 50 yr, the number of tornadoes reported in the United States has doubled from about 600 per year in the 1950s to around 1200 in the 2000s. This doubling is likely not related to meteorological causes alone. To account for this increase a simple least squares linear regression was fitted to the annual number of tornado reports. A “big tornado day” is a single day when numerous tornadoes and/or many tornadoes exceeding a specified intensity threshold were reported anywhere in the country. By defining a big tornado day without considering the spatial distribution of the tornadoes, a big tornado day differs from previous definitions of outbreaks. To address the increase in the number of reports, the number of reports is compared to the expected number of reports in a year based on linear regression. In addition, the F1 and greater Fujita-scale record was used in determining a big tornado day because the F1 and greater series was more stationary over time as opposed to the F2 and greater series. Thresholds were applied to the data to determine the number and intensities of the tornadoes needed to be considered a big tornado day. Possible threshold values included fractions of the annual expected value associated with the linear regression and fixed numbers for the intensity criterion. Threshold values of 1.5% of the expected annual total number of tornadoes and/or at least 8 F1 and greater tornadoes identified about 18.1 big tornado days per year. Higher thresholds such as 2.5% and/or at least 15 F1 and greater tornadoes showed similar characteristics, yet identified approximately 6.2 big tornado days per year. Finally, probability distribution curves generated using kernel density estimation revealed that big tornado days were more likely to occur slightly earlier in the year and have a narrower distribution than any given tornado day.
Abstract
Over the last 50 yr, the number of tornadoes reported in the United States has doubled from about 600 per year in the 1950s to around 1200 in the 2000s. This doubling is likely not related to meteorological causes alone. To account for this increase a simple least squares linear regression was fitted to the annual number of tornado reports. A “big tornado day” is a single day when numerous tornadoes and/or many tornadoes exceeding a specified intensity threshold were reported anywhere in the country. By defining a big tornado day without considering the spatial distribution of the tornadoes, a big tornado day differs from previous definitions of outbreaks. To address the increase in the number of reports, the number of reports is compared to the expected number of reports in a year based on linear regression. In addition, the F1 and greater Fujita-scale record was used in determining a big tornado day because the F1 and greater series was more stationary over time as opposed to the F2 and greater series. Thresholds were applied to the data to determine the number and intensities of the tornadoes needed to be considered a big tornado day. Possible threshold values included fractions of the annual expected value associated with the linear regression and fixed numbers for the intensity criterion. Threshold values of 1.5% of the expected annual total number of tornadoes and/or at least 8 F1 and greater tornadoes identified about 18.1 big tornado days per year. Higher thresholds such as 2.5% and/or at least 15 F1 and greater tornadoes showed similar characteristics, yet identified approximately 6.2 big tornado days per year. Finally, probability distribution curves generated using kernel density estimation revealed that big tornado days were more likely to occur slightly earlier in the year and have a narrower distribution than any given tornado day.
Abstract
The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.
Abstract
The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.
Abstract
Southeast U.S. cold season severe weather events can be difficult to predict because of the marginality of the supporting thermodynamic instability in this regime. The sensitivity of this environment to prognoses of instability encourages additional research on ways in which mesoscale models represent turbulent processes within the lower atmosphere that directly influence thermodynamic profiles and forecasts of instability. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling and proceeds with a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. It is demonstrated that simultaneous representation of both nonlocal and local mixing in the Asymmetric Convective Model, version 2 (ACM2), scheme has the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense for the low-level lapse rate and depth of the PBL, on average. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing.
Abstract
Southeast U.S. cold season severe weather events can be difficult to predict because of the marginality of the supporting thermodynamic instability in this regime. The sensitivity of this environment to prognoses of instability encourages additional research on ways in which mesoscale models represent turbulent processes within the lower atmosphere that directly influence thermodynamic profiles and forecasts of instability. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling and proceeds with a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. It is demonstrated that simultaneous representation of both nonlocal and local mixing in the Asymmetric Convective Model, version 2 (ACM2), scheme has the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense for the low-level lapse rate and depth of the PBL, on average. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing.
Abstract
Retreat of the Laurentide Ice Sheet (LIS) following the Last Glacial Maximum 21 000 yr BP affected regional to global climate and accounted for the largest proportion of sea level rise. Although the late Pleistocene LIS retreat chronology is relatively well constrained, its Holocene chronology remains poorly dated, limiting our understanding of its role in Holocene climate change and sea level rise. Here new 10Be cosmogenic exposure ages on glacially deposited boulders are used to date the final disappearance of the Labrador sector of the LIS (LS-LIS). These data suggest that following the deglaciation of the southeastern Hudson Bay coastline at 8.0 ± 0.2 cal ka BP, the southwestern margin of the LS-LIS rapidly retreated ∼600 km in 140 yr and most likely in ∼600 yr at a rate of ∼900 m yr−1, with final deglaciation by 6.8 ± 0.2 10Be ka. The disappearance of the LS-LIS ∼6.8 10Be ka and attendant reduction in freshwater runoff may have induced the formation of Labrador Deep Seawater, while the loss of the high albedo surface may have initiated the Holocene Thermal Maximum in eastern Canada and southern Greenland. Moreover, the rapid melting just prior to ∼6.8 10Be ka indicates that the remnant LIS may be the primary source of a postulated rapid rise in global sea level of ∼5 m that occurred sometime between 7.6 and 6.5 cal ka BP.
Abstract
Retreat of the Laurentide Ice Sheet (LIS) following the Last Glacial Maximum 21 000 yr BP affected regional to global climate and accounted for the largest proportion of sea level rise. Although the late Pleistocene LIS retreat chronology is relatively well constrained, its Holocene chronology remains poorly dated, limiting our understanding of its role in Holocene climate change and sea level rise. Here new 10Be cosmogenic exposure ages on glacially deposited boulders are used to date the final disappearance of the Labrador sector of the LIS (LS-LIS). These data suggest that following the deglaciation of the southeastern Hudson Bay coastline at 8.0 ± 0.2 cal ka BP, the southwestern margin of the LS-LIS rapidly retreated ∼600 km in 140 yr and most likely in ∼600 yr at a rate of ∼900 m yr−1, with final deglaciation by 6.8 ± 0.2 10Be ka. The disappearance of the LS-LIS ∼6.8 10Be ka and attendant reduction in freshwater runoff may have induced the formation of Labrador Deep Seawater, while the loss of the high albedo surface may have initiated the Holocene Thermal Maximum in eastern Canada and southern Greenland. Moreover, the rapid melting just prior to ∼6.8 10Be ka indicates that the remnant LIS may be the primary source of a postulated rapid rise in global sea level of ∼5 m that occurred sometime between 7.6 and 6.5 cal ka BP.
Abstract
Numerical forecasts from a pilot program on short-range ensemble forecasting at the National Centers for Environmental Prediction are examined. The ensemble consists of 10 forecasts made using the 80-km Eta Model and 5 forecasts from the regional spectral model. Results indicate that the accuracy of the ensemble mean is comparable to that from the 29-km Meso Eta Model for both mandatory level data and the 36-h forecast cyclone position. Calculations of spread indicate that at 36 and 48 h the spread from initial conditions created using the breeding of growing modes technique is larger than the spread from initial conditions created using different analyses. However, the accuracy of the forecast cyclone position from these two initialization techniques is nearly identical. Results further indicate that using two different numerical models assists in increasing the ensemble spread significantly.
There is little correlation between the spread in the ensemble members and the accuracy of the ensemble mean for the prediction of cyclone location. Since information on forecast uncertainty is needed in many applications, and is one of the reasons to use an ensemble approach, the lack of a correlation between spread and forecast uncertainty presents a challenge to the production of short-range ensemble forecasts.
Even though the ensemble dispersion is not found to be an indication of forecast uncertainty, significant spread can occur within the forecasts over a relatively short time period. Examples are shown to illustrate how small uncertainties in the model initial conditions can lead to large differences in numerical forecasts from an identical numerical model.
Abstract
Numerical forecasts from a pilot program on short-range ensemble forecasting at the National Centers for Environmental Prediction are examined. The ensemble consists of 10 forecasts made using the 80-km Eta Model and 5 forecasts from the regional spectral model. Results indicate that the accuracy of the ensemble mean is comparable to that from the 29-km Meso Eta Model for both mandatory level data and the 36-h forecast cyclone position. Calculations of spread indicate that at 36 and 48 h the spread from initial conditions created using the breeding of growing modes technique is larger than the spread from initial conditions created using different analyses. However, the accuracy of the forecast cyclone position from these two initialization techniques is nearly identical. Results further indicate that using two different numerical models assists in increasing the ensemble spread significantly.
There is little correlation between the spread in the ensemble members and the accuracy of the ensemble mean for the prediction of cyclone location. Since information on forecast uncertainty is needed in many applications, and is one of the reasons to use an ensemble approach, the lack of a correlation between spread and forecast uncertainty presents a challenge to the production of short-range ensemble forecasts.
Even though the ensemble dispersion is not found to be an indication of forecast uncertainty, significant spread can occur within the forecasts over a relatively short time period. Examples are shown to illustrate how small uncertainties in the model initial conditions can lead to large differences in numerical forecasts from an identical numerical model.
Abstract
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.
Abstract
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.
Abstract
The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.
Abstract
The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.
Abstract
The first phase of an atmospheric tracer experiment program, designated Project Sagebrush, was conducted at the Idaho National Laboratory in October 2013. The purpose was to reevaluate the results of classical field experiments in short-range plume dispersion (e.g., Project Prairie Grass) using the newer technologies that are available for measuring both turbulence levels and tracer concentrations. All releases were conducted during the daytime with atmospheric conditions ranging from neutral to unstable. The key finding was that the values of the horizontal plume spread parameter σ y tended to be larger, by up to a factor of ~2, than those measured in many previous field studies. The discrepancies tended to increase with downwind distance. The values of the ratio σ y /σ θ , where σ θ is the standard deviation of the horizontal wind direction, also trend near the upper limit or above the range of values determined in earlier studies. There was also evidence to suggest that the value of σ y began to be independent of σ θ for σ θ greater than 18°. It was also found that the commonly accepted range of values for σ θ in different stability conditions might be limiting, at best, and might possibly be unrealistically low, especially at night in low wind speeds. The results raise questions about the commonly accepted magnitudes of σ y derived from older studies. These values are used in the parameterization and validation of both older stability-class dispersion models as well as newer models that are based on Taylor’s equation and modern PBL theory.
Abstract
The first phase of an atmospheric tracer experiment program, designated Project Sagebrush, was conducted at the Idaho National Laboratory in October 2013. The purpose was to reevaluate the results of classical field experiments in short-range plume dispersion (e.g., Project Prairie Grass) using the newer technologies that are available for measuring both turbulence levels and tracer concentrations. All releases were conducted during the daytime with atmospheric conditions ranging from neutral to unstable. The key finding was that the values of the horizontal plume spread parameter σ y tended to be larger, by up to a factor of ~2, than those measured in many previous field studies. The discrepancies tended to increase with downwind distance. The values of the ratio σ y /σ θ , where σ θ is the standard deviation of the horizontal wind direction, also trend near the upper limit or above the range of values determined in earlier studies. There was also evidence to suggest that the value of σ y began to be independent of σ θ for σ θ greater than 18°. It was also found that the commonly accepted range of values for σ θ in different stability conditions might be limiting, at best, and might possibly be unrealistically low, especially at night in low wind speeds. The results raise questions about the commonly accepted magnitudes of σ y derived from older studies. These values are used in the parameterization and validation of both older stability-class dispersion models as well as newer models that are based on Taylor’s equation and modern PBL theory.
