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Abstract
A practical approach is recommended for identifying and archiving tornado events, based on the use of definitions that label all vortices as either type I, II, or III tornadoes. This methodology will provide a more meaningful tornado climatology in Storm Data, which separates and classifies all vortices associated in any manner with cumuliform clouds. Tornadoes produced within the mesocyclone of discrete supercell storms, with strong local updrafts (SLUs), will be classified as type I tornadoes. Frequently, these type I tornadoes result from the interaction of the SLU with strong rear-flank downdrafts (RFDs), or with shear vortices in the PBL. Tornadoes produced in association with quasi-linear convective systems (QLCS) will be classified as type II tornadoes (including cold pool, rear-inflow jets, bookend, and mesovortex events along the line). All other vortex types (including landspouts, waterspouts, gustnadoes, cold air vortices, and tornadoes not associated with mesocyclones or QLCS) will be labeled as type III tornadoes. A general discussion is provided that further clarifies the differences and categorization of these three classifications (which encompass 15 tornado species), along with a recommendation that NOAA adopt this taxonomy in operational and data archiving practices. Radar analysis and field observations, combined with storm-scale meteorological expertise, should allow for the official “typing” of tornado reports by NOAA personnel. Establishment of such a climatological database in Storm Data may be of value in assessing the effects (if any) of twenty-first-century global warming on U.S. tornado trends.
Abstract
A practical approach is recommended for identifying and archiving tornado events, based on the use of definitions that label all vortices as either type I, II, or III tornadoes. This methodology will provide a more meaningful tornado climatology in Storm Data, which separates and classifies all vortices associated in any manner with cumuliform clouds. Tornadoes produced within the mesocyclone of discrete supercell storms, with strong local updrafts (SLUs), will be classified as type I tornadoes. Frequently, these type I tornadoes result from the interaction of the SLU with strong rear-flank downdrafts (RFDs), or with shear vortices in the PBL. Tornadoes produced in association with quasi-linear convective systems (QLCS) will be classified as type II tornadoes (including cold pool, rear-inflow jets, bookend, and mesovortex events along the line). All other vortex types (including landspouts, waterspouts, gustnadoes, cold air vortices, and tornadoes not associated with mesocyclones or QLCS) will be labeled as type III tornadoes. A general discussion is provided that further clarifies the differences and categorization of these three classifications (which encompass 15 tornado species), along with a recommendation that NOAA adopt this taxonomy in operational and data archiving practices. Radar analysis and field observations, combined with storm-scale meteorological expertise, should allow for the official “typing” of tornado reports by NOAA personnel. Establishment of such a climatological database in Storm Data may be of value in assessing the effects (if any) of twenty-first-century global warming on U.S. tornado trends.
Abstract
The current global operational four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system at NCEP adopts a background ensemble at a reduced resolution, which restricts the range of spatial scales that the ensemble background error covariance can resolve. A prior study developed a multiresolution ensemble 4DEnVar method and determined that this approach can provide a comparable forecast to an approach using solely high-resolution members, while substantially reducing the computational cost. This study further develops the multiresolution ensemble 4DEnVar approach to allow for a flexible number of low- and high-resolution ensemble members as well as varying localization length scales between the high- and low-resolution ensembles. Three 4DEnVar experiments with the same computational costs are compared. The first experiment has an 80-member high-resolution background ensemble with single-scale optimally tuned localization (SR-High). The second and third experiments utilize the multiresolution background ensembles. One has 130 low-resolution and 40 high-resolution members (MR170) while the other has 180 low-resolution members and 24 high-resolution members (MR204). Both multiresolution ensemble experiments utilize differing localization radii with ensemble resolution. Despite having the same costs, both MR170 and MR204 improves global forecasts and decreases tropical cyclone track errors for up to 5 days’ lead time compared to SR-High. Improvements are most apparent in larger-scale features, such as jet streams and the environmental steering flow of tropical cyclones. Additionally, MR170 outperforms MR204 in terms of global and tropical cyclone track forecasts, demonstrating the value of both increasing sampling at large scales and retaining substantial information at small scales.
Abstract
The current global operational four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system at NCEP adopts a background ensemble at a reduced resolution, which restricts the range of spatial scales that the ensemble background error covariance can resolve. A prior study developed a multiresolution ensemble 4DEnVar method and determined that this approach can provide a comparable forecast to an approach using solely high-resolution members, while substantially reducing the computational cost. This study further develops the multiresolution ensemble 4DEnVar approach to allow for a flexible number of low- and high-resolution ensemble members as well as varying localization length scales between the high- and low-resolution ensembles. Three 4DEnVar experiments with the same computational costs are compared. The first experiment has an 80-member high-resolution background ensemble with single-scale optimally tuned localization (SR-High). The second and third experiments utilize the multiresolution background ensembles. One has 130 low-resolution and 40 high-resolution members (MR170) while the other has 180 low-resolution members and 24 high-resolution members (MR204). Both multiresolution ensemble experiments utilize differing localization radii with ensemble resolution. Despite having the same costs, both MR170 and MR204 improves global forecasts and decreases tropical cyclone track errors for up to 5 days’ lead time compared to SR-High. Improvements are most apparent in larger-scale features, such as jet streams and the environmental steering flow of tropical cyclones. Additionally, MR170 outperforms MR204 in terms of global and tropical cyclone track forecasts, demonstrating the value of both increasing sampling at large scales and retaining substantial information at small scales.
Abstract
Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.
Abstract
Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.
Abstract
The National Oceanic and Atmospheric Administration (NOAA) Global Data Assimilation System/Global Forecast System (GDAS/GFS) was extended to assimilate brightness temperatures from the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over ocean surfaces, and to characterize observation biases and errors. A 6-week impact experiment was performed using the GDAS/GFS data assimilation system. The addition of SAPHIR observations on top of the current global observing system improved analysis and forecast humidity root-mean-square error (RMSE) results at the upper levels of the troposphere by about 6%, mostly at 100 hPa, when verified against European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, though some degradation to the forecast humidity was seen at 150–200 hPa. The forecast impacts were predominant at earlier lead times between 24 and 96 h. Verification using global radiosonde observations also showed a reduction of the humidity RMSE from 4% to 6% between 500 hPa and the surface when assimilating SAPHIR, while temperature and wind speed RMSEs were reduced by up to 9% and 7% near the tropical tropopause, respectively. Other conventional forecast skill parameters including the 500-hPa geopotential height anomaly correlation showed neutral impact when assimilating SAPHIR.
Abstract
The National Oceanic and Atmospheric Administration (NOAA) Global Data Assimilation System/Global Forecast System (GDAS/GFS) was extended to assimilate brightness temperatures from the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over ocean surfaces, and to characterize observation biases and errors. A 6-week impact experiment was performed using the GDAS/GFS data assimilation system. The addition of SAPHIR observations on top of the current global observing system improved analysis and forecast humidity root-mean-square error (RMSE) results at the upper levels of the troposphere by about 6%, mostly at 100 hPa, when verified against European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, though some degradation to the forecast humidity was seen at 150–200 hPa. The forecast impacts were predominant at earlier lead times between 24 and 96 h. Verification using global radiosonde observations also showed a reduction of the humidity RMSE from 4% to 6% between 500 hPa and the surface when assimilating SAPHIR, while temperature and wind speed RMSEs were reduced by up to 9% and 7% near the tropical tropopause, respectively. Other conventional forecast skill parameters including the 500-hPa geopotential height anomaly correlation showed neutral impact when assimilating SAPHIR.
The growth of the wind industry in recent years has motivated investigation into wind farm interference with the operation of the nationwide Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Observations of a wind farm were taken with a Doppler on Wheels (DOW) during the DOW Radar Observations at Purdue Study (DROPS), a largely studentled field program that took place in the fall of 2009. The DOW sampled clear-air weather and precipitation at locations within 5 km of the Benton County, Indiana, wind farm to determine the wind turbines' effect on Doppler velocity and ref lectivity data. These data were analyzed and compared with data from the Indianapolis WSR-88D (KIND) and a local television station (WLFI) radar. In precipitation, the DOW data show velocity couplets that have the appearance of isolated tornadic vortices. Under clear-air sampling, significant multipath scattering is evident, but no velocity couplets would meet the DOW-equivalent tornado detection algorithm criteria. Broader impacts of these findings are discussed, and suggestions are made for additional studies that would explore how to mitigate these impacts.
The growth of the wind industry in recent years has motivated investigation into wind farm interference with the operation of the nationwide Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Observations of a wind farm were taken with a Doppler on Wheels (DOW) during the DOW Radar Observations at Purdue Study (DROPS), a largely studentled field program that took place in the fall of 2009. The DOW sampled clear-air weather and precipitation at locations within 5 km of the Benton County, Indiana, wind farm to determine the wind turbines' effect on Doppler velocity and ref lectivity data. These data were analyzed and compared with data from the Indianapolis WSR-88D (KIND) and a local television station (WLFI) radar. In precipitation, the DOW data show velocity couplets that have the appearance of isolated tornadic vortices. Under clear-air sampling, significant multipath scattering is evident, but no velocity couplets would meet the DOW-equivalent tornado detection algorithm criteria. Broader impacts of these findings are discussed, and suggestions are made for additional studies that would explore how to mitigate these impacts.
Abstract
Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.
Abstract
Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.
Abstract
Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.
Abstract
Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.
