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Abstract
Climatological data in recent years have become sufficient for the further study of tornadoes which occur in hurricane systems. Several characteristics of the hurricane tornado are determined from data for an 8-yr. period by plotting the center positions of each hurricane and its associated tornadoes. The data show: (1) a comparison between hurricane and non-hurricane tornadoes; (2) a “Significant Sector” for tornado-genesis; (3) a “Preferred Quadrant” of the hurricane for tornado-genesis; (4) the most favorable time of day for tornado occurrence; (5) tornado frequencies with respect to various speeds and distances of the hurricane on and off shore; (6) a tenative hurricane model.
Abstract
Climatological data in recent years have become sufficient for the further study of tornadoes which occur in hurricane systems. Several characteristics of the hurricane tornado are determined from data for an 8-yr. period by plotting the center positions of each hurricane and its associated tornadoes. The data show: (1) a comparison between hurricane and non-hurricane tornadoes; (2) a “Significant Sector” for tornado-genesis; (3) a “Preferred Quadrant” of the hurricane for tornado-genesis; (4) the most favorable time of day for tornado occurrence; (5) tornado frequencies with respect to various speeds and distances of the hurricane on and off shore; (6) a tenative hurricane model.
Abstract
This paper introduces a conceptual framework for comparing methods that isolate important coupled modes of variability between time series of two fields. Four specific methods are compared: principal component analysis with the fields combined (CPCA), canonical correlation analysis (CCA) and a variant of CCA proposed by Barnett and Preisendorfer (BP), principal component analysis of one single field followed by correlation of its component amplitudes with the second field (SFPCA), and singular value decomposition of the covariance matrix between the two fields (SVD). SVD and CPCA are easier to implement than BP, and do not involve user-chosen parameters. All methods are applied to a simple but geophysically relevant model system and their ability to detect a coupled signal is compared as parameters such as the number of points in each field, the number of samples in the time domain, and the signal-to-noise ratio are varied.
In datasets involving geophysical fields, the number of sampling times may not be much larger than the number of observing locations or grid points for each field. In a model system with these characteristics, CPCA usually extracted the coupled pattern somewhat more accurately than SVD, BP, and SFPCA, since the patterns it yielded exhibit smaller sampling variability; SVD and BP gave quite similar results; and CCA was uncompetitive due to a high sampling variability unless the coupled signal was highly localized. The coupled modes derived from CPCA and SFPCA exhibit an undesirable mean bias toward the leading EOFs of the individual fields; in fact, for small signal-to-noise ratios these methods may identify a coupled signal that is similar to a dominant EOF of one of the fields but is completely orthogonal to the true coupled signal within that field. For longer time series, or in situations where the coupled signal does not resemble the EOFs of the individual fields, these biases can make SVD and BP substantially superior to CPCA.
Abstract
This paper introduces a conceptual framework for comparing methods that isolate important coupled modes of variability between time series of two fields. Four specific methods are compared: principal component analysis with the fields combined (CPCA), canonical correlation analysis (CCA) and a variant of CCA proposed by Barnett and Preisendorfer (BP), principal component analysis of one single field followed by correlation of its component amplitudes with the second field (SFPCA), and singular value decomposition of the covariance matrix between the two fields (SVD). SVD and CPCA are easier to implement than BP, and do not involve user-chosen parameters. All methods are applied to a simple but geophysically relevant model system and their ability to detect a coupled signal is compared as parameters such as the number of points in each field, the number of samples in the time domain, and the signal-to-noise ratio are varied.
In datasets involving geophysical fields, the number of sampling times may not be much larger than the number of observing locations or grid points for each field. In a model system with these characteristics, CPCA usually extracted the coupled pattern somewhat more accurately than SVD, BP, and SFPCA, since the patterns it yielded exhibit smaller sampling variability; SVD and BP gave quite similar results; and CCA was uncompetitive due to a high sampling variability unless the coupled signal was highly localized. The coupled modes derived from CPCA and SFPCA exhibit an undesirable mean bias toward the leading EOFs of the individual fields; in fact, for small signal-to-noise ratios these methods may identify a coupled signal that is similar to a dominant EOF of one of the fields but is completely orthogonal to the true coupled signal within that field. For longer time series, or in situations where the coupled signal does not resemble the EOFs of the individual fields, these biases can make SVD and BP substantially superior to CPCA.
Abstract
Single field principal component analysis (PCA), direct singular value decomposition (SVD), canonical correlation analysis (CCA), and combined principal component analysis (CPCA) of two fields are applied to a 39-winter dataset consisting of normalized seasonal mean sea surface temperature anomalies over the North Pacific and concurrent 500-mb height anomalies over the same region. The CCA solutions are obtained by linear transformations of the SVD solutions. Spatial patterns and various measures of the variances and covariances explained by the modes derived from the different types of expansions are compared, with emphasis on the relative merits of SVD versus CCA. Results for two different analysis domains (i.e., the Pacific sector versus a full hemispheric domain for the 500-mb height field) are also compared in order to assess the domain dependence of the two techniques. The SVD solution is also compared with the results of 28 Monte Carlo simulations in which the temporal order of the SST grids is randomized and found to be highly significant.
As expected, the leading SVD modes explain substantially more of the squared covariance between the two fields than any of the CCA modes, while the paired expansion coefficients of the leading CCA modes are more strongly correlated than any of the SVD modes. The expansion coefficient for the leading SVD mode is almost identical to the leading principal component of the SST field, regardless of whether the 500-mb height field is hemispheric or restricted to the Pacific sector. SST patterns strongly resembling the second and third EOFs are also recovered among the three leading SVD modes.
The leading CCA mode in the expansion based on the three leading singular value vectors for the Pacific sector resembles the pattern of anomalies observed in association with ENSO. The other modes more closely resemble the patterns derived from PCA of the 500-mb height field than those for the SVD modes on which they are based. The SVD and CPCA solutions for the first three modes proved to be quite similar.
The SVD and CCA solutions based on the hemispheric 500-mb height field are indicative of a coupling between the interannual variability of North Pacific and North Atlantic SST by virtue of their mutual relationship to one of the atmosphere's most prominent planetary wave patterns.
Abstract
Single field principal component analysis (PCA), direct singular value decomposition (SVD), canonical correlation analysis (CCA), and combined principal component analysis (CPCA) of two fields are applied to a 39-winter dataset consisting of normalized seasonal mean sea surface temperature anomalies over the North Pacific and concurrent 500-mb height anomalies over the same region. The CCA solutions are obtained by linear transformations of the SVD solutions. Spatial patterns and various measures of the variances and covariances explained by the modes derived from the different types of expansions are compared, with emphasis on the relative merits of SVD versus CCA. Results for two different analysis domains (i.e., the Pacific sector versus a full hemispheric domain for the 500-mb height field) are also compared in order to assess the domain dependence of the two techniques. The SVD solution is also compared with the results of 28 Monte Carlo simulations in which the temporal order of the SST grids is randomized and found to be highly significant.
As expected, the leading SVD modes explain substantially more of the squared covariance between the two fields than any of the CCA modes, while the paired expansion coefficients of the leading CCA modes are more strongly correlated than any of the SVD modes. The expansion coefficient for the leading SVD mode is almost identical to the leading principal component of the SST field, regardless of whether the 500-mb height field is hemispheric or restricted to the Pacific sector. SST patterns strongly resembling the second and third EOFs are also recovered among the three leading SVD modes.
The leading CCA mode in the expansion based on the three leading singular value vectors for the Pacific sector resembles the pattern of anomalies observed in association with ENSO. The other modes more closely resemble the patterns derived from PCA of the 500-mb height field than those for the SVD modes on which they are based. The SVD and CPCA solutions for the first three modes proved to be quite similar.
The SVD and CCA solutions based on the hemispheric 500-mb height field are indicative of a coupling between the interannual variability of North Pacific and North Atlantic SST by virtue of their mutual relationship to one of the atmosphere's most prominent planetary wave patterns.
Abstract
A 5-km horizontal resolution regional ocean–sea ice–ice shelf model of the Ross Sea is used to examine the effects of changes in wind strength, air temperature, and increased meltwater input on the formation of high-salinity shelf water (HSSW), on-shelf transport and vertical mixing of Circumpolar Deep Water (CDW) and its transformation into modified CDW (MCDW), and basal melt of the Ross Ice Shelf (RIS). A 20% increase in wind speed, with no other atmospheric changes, reduced summer sea ice minimum area by 20%, opposite the observed trend of the past three decades. Increased winds with spatially uniform, reduced atmospheric temperatures increased summer sea ice concentrations, on-shelf transport of CDW, vertical mixing of MCDW, HSSW volume, and (albeit small) RIS basal melt. Winds and atmospheric temperatures from the SRES A1B scenario forcing of the MPI ECHAM5 model decreased on-shelf transport of CDW and vertical mixing of MCDW for 2046–61 and 2085–2100 relative to the end of the twentieth century. The RIS basal melt increased slightly by 2046–61 (9%) and 2085–2100 (13%). Advection of lower-salinity water onto the continental shelf did not significantly affect sea ice extent for the 2046–61 or 2085–2100 simulations. However, freshening reduces on-shelf transport of CDW, vertical mixing of MCDW, and the volume of HSSW produced. The reduced vertical mixing of MCDW, while partially balanced by the reduced on-shelf transport of CDW, enhances the RIS basal melt rate relative to the twentieth-century simulation for 2046–61 (13%) and 2085–2100 (17%).
Abstract
A 5-km horizontal resolution regional ocean–sea ice–ice shelf model of the Ross Sea is used to examine the effects of changes in wind strength, air temperature, and increased meltwater input on the formation of high-salinity shelf water (HSSW), on-shelf transport and vertical mixing of Circumpolar Deep Water (CDW) and its transformation into modified CDW (MCDW), and basal melt of the Ross Ice Shelf (RIS). A 20% increase in wind speed, with no other atmospheric changes, reduced summer sea ice minimum area by 20%, opposite the observed trend of the past three decades. Increased winds with spatially uniform, reduced atmospheric temperatures increased summer sea ice concentrations, on-shelf transport of CDW, vertical mixing of MCDW, HSSW volume, and (albeit small) RIS basal melt. Winds and atmospheric temperatures from the SRES A1B scenario forcing of the MPI ECHAM5 model decreased on-shelf transport of CDW and vertical mixing of MCDW for 2046–61 and 2085–2100 relative to the end of the twentieth century. The RIS basal melt increased slightly by 2046–61 (9%) and 2085–2100 (13%). Advection of lower-salinity water onto the continental shelf did not significantly affect sea ice extent for the 2046–61 or 2085–2100 simulations. However, freshening reduces on-shelf transport of CDW, vertical mixing of MCDW, and the volume of HSSW produced. The reduced vertical mixing of MCDW, while partially balanced by the reduced on-shelf transport of CDW, enhances the RIS basal melt rate relative to the twentieth-century simulation for 2046–61 (13%) and 2085–2100 (17%).
During the summer of 1993, Project ATMOSPHERE, in cooperation with the University of Oklahoma School of Meteorology, conducted a workshop to enhance both the meteorological background and leadership skills of AMS Atmospheric Education Resource Agents (AERAs). Fifty-eight teachers representing 39 states and the District of Columbia attended this workshop, which focused on atmospheric water processes and severe local storms. In addition to lectures and laboratory activities, AERAs also visited a variety of research and operational support facilities in the Norman area. This workshop was the third phase of training for AERAs, who represent the AMS in their local areas, providing instructional guidance for teachers and curricular input on the atmospheric sciences to their respective local and state educational agencies.
During the summer of 1993, Project ATMOSPHERE, in cooperation with the University of Oklahoma School of Meteorology, conducted a workshop to enhance both the meteorological background and leadership skills of AMS Atmospheric Education Resource Agents (AERAs). Fifty-eight teachers representing 39 states and the District of Columbia attended this workshop, which focused on atmospheric water processes and severe local storms. In addition to lectures and laboratory activities, AERAs also visited a variety of research and operational support facilities in the Norman area. This workshop was the third phase of training for AERAs, who represent the AMS in their local areas, providing instructional guidance for teachers and curricular input on the atmospheric sciences to their respective local and state educational agencies.
A technique for interactively producing sea-surface temperatures (SST) from VAS multispectral radiance observations and displaying the derived field is outlined. High-resolution composite images using data from several times per day and over a several-day period are shown to illustrate how the technique is applied. The cloud-screening procedures are interactive so that they can be optimized to eliminate the effects of small clouds while still retrieving a sufficient number of SSTs to permit analysis of mesoscale flow features. SST-image products have been produced in real time at the University of Wisconsin as part of the genesis of Atlantic lows experiment (GALE) and as part of the NOAA operational VAS assessment (NOVA) program.
A technique for interactively producing sea-surface temperatures (SST) from VAS multispectral radiance observations and displaying the derived field is outlined. High-resolution composite images using data from several times per day and over a several-day period are shown to illustrate how the technique is applied. The cloud-screening procedures are interactive so that they can be optimized to eliminate the effects of small clouds while still retrieving a sufficient number of SSTs to permit analysis of mesoscale flow features. SST-image products have been produced in real time at the University of Wisconsin as part of the genesis of Atlantic lows experiment (GALE) and as part of the NOAA operational VAS assessment (NOVA) program.
Abstract
Examining forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment, recent research diagnosed a strong relationship between the cumulative pathlengths of simulated rotating storms (measured using a three-dimensional object identification algorithm applied to forecast updraft helicity) and the cumulative pathlengths of tornadoes. This paper updates those results by including data from the 2011 SSEF system, and illustrates forecast examples from three major 2011 tornado outbreaks—16 and 27 April, and 24 May—as well as two forecast failure cases from June 2010. Finally, analysis updraft helicity (UH) from 27 April 2011 is computed using a three-dimensional variational data assimilation system to obtain 1.25-km grid-spacing analyses at 5-min intervals and compared to forecast UH from individual SSEF members.
Abstract
Examining forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment, recent research diagnosed a strong relationship between the cumulative pathlengths of simulated rotating storms (measured using a three-dimensional object identification algorithm applied to forecast updraft helicity) and the cumulative pathlengths of tornadoes. This paper updates those results by including data from the 2011 SSEF system, and illustrates forecast examples from three major 2011 tornado outbreaks—16 and 27 April, and 24 May—as well as two forecast failure cases from June 2010. Finally, analysis updraft helicity (UH) from 27 April 2011 is computed using a three-dimensional variational data assimilation system to obtain 1.25-km grid-spacing analyses at 5-min intervals and compared to forecast UH from individual SSEF members.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
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
Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.
Abstract
Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.