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- Author or Editor: Henry E. Fuelberg x
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Abstract
Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm’s last flash. The most statistically significant predictors for lightning cessation are found to be the storm’s maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters.
Abstract
Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm’s last flash. The most statistically significant predictors for lightning cessation are found to be the storm’s maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters.
Abstract
Statistical structure functions are used to evaluate sounding data from the 6–7 March day of the 1982 AVE/VAS Ground Truth Field Experiment. Functional analyses are performed for five observation times starting at 1200 GMT 6 March and ending at 0000 GMT 7 March, and for the composite 12 h period. Data consist of mesoscale soundings from a special ground truth rawinsonde network and VAS-derived soundings from both a physical algorithm and a regression technique. The standard parameters of temperature, geopotential height, and mixing ratio are evaluated at the 850, 700, 500, 300 and 200 mb levels. Integrated parameters of thickness and precipitable water also are investigated.
Using structure function analyses, estimates of root-mean-square (rms) data uncertainty are obtained for the three data sources. Then, VAS soundings from the physical retrieval scheme are compared with those from the regression technique. Results indicate that both schemes have similar error characteristics and capabilities for determining gradients of mesoscale temperature and geopotential height. Signal-to-noise ratios for these parameters were quite favorable and greater than those of mixing ratio. Finally, sounding retrievals are evaluated against those from the ground-truth rawinsonde network. These results show that the VAS data generally describe weaker gradients than observed with the radiosondes. A notable exception is physically-derived mixing ratio at 850 mb.
Abstract
Statistical structure functions are used to evaluate sounding data from the 6–7 March day of the 1982 AVE/VAS Ground Truth Field Experiment. Functional analyses are performed for five observation times starting at 1200 GMT 6 March and ending at 0000 GMT 7 March, and for the composite 12 h period. Data consist of mesoscale soundings from a special ground truth rawinsonde network and VAS-derived soundings from both a physical algorithm and a regression technique. The standard parameters of temperature, geopotential height, and mixing ratio are evaluated at the 850, 700, 500, 300 and 200 mb levels. Integrated parameters of thickness and precipitable water also are investigated.
Using structure function analyses, estimates of root-mean-square (rms) data uncertainty are obtained for the three data sources. Then, VAS soundings from the physical retrieval scheme are compared with those from the regression technique. Results indicate that both schemes have similar error characteristics and capabilities for determining gradients of mesoscale temperature and geopotential height. Signal-to-noise ratios for these parameters were quite favorable and greater than those of mixing ratio. Finally, sounding retrievals are evaluated against those from the ground-truth rawinsonde network. These results show that the VAS data generally describe weaker gradients than observed with the radiosondes. A notable exception is physically-derived mixing ratio at 850 mb.
Abstract
Operational VAS satellite retrievals and derived parameters used in forecasting severe local storms were evaluated against corresponding radiosonde values. VAS products also were compared with the first-guess input to the retrieval algorithm. The evaluation methodology was to pair each radiosonde observation (RAOB) with the closest VAS retrieval within 50 km during a 4-month period in 1986.
VAS temperatures wore found to agree closely with radiosonde values; however, VAS dewpoints showed somewhat less agreement. VAS/RAOB sounding differences were poorly correlated with the number of pixels from which the retrievals were prepared. VAS discrepancies usually wore not well correlated with first-guess errors. A disappointing finding is that the retrievals degraded the first guess about as often as they improved it. VAS did tend to improve large first-guess errors, but not even this was guaranteed. Horizontal gradients of VAS products generally were stronger than those from radiosondes. VAS precipitable water agreed better with the ground truth than did dewpoints at individual levels, but VAS thicknesses were not much improved over the already accurate VAS temperatures.
Results for the severe storms forecasting parameters indicated that VAS/RAOB discrepancies increased with the amount of manipulation required during computation. VAS parameters incorporating observed surface data tended to give better results than those that did not. Of all the parameters examined, VAS-derived lifted index exhibited the best agreement with RAOB versions. VAS positive buoyant energy showed disappointingly poor comparisons. VAS retrievals provided poor measures of the low-level negative buoyant energy that must be overcome before convection can begin. Agreements between VAS/RAOB versions of the K index, total totals index, Showalter index, and several other parameters were intermediate to those of positive buoyant energy and lifted index.
Abstract
Operational VAS satellite retrievals and derived parameters used in forecasting severe local storms were evaluated against corresponding radiosonde values. VAS products also were compared with the first-guess input to the retrieval algorithm. The evaluation methodology was to pair each radiosonde observation (RAOB) with the closest VAS retrieval within 50 km during a 4-month period in 1986.
VAS temperatures wore found to agree closely with radiosonde values; however, VAS dewpoints showed somewhat less agreement. VAS/RAOB sounding differences were poorly correlated with the number of pixels from which the retrievals were prepared. VAS discrepancies usually wore not well correlated with first-guess errors. A disappointing finding is that the retrievals degraded the first guess about as often as they improved it. VAS did tend to improve large first-guess errors, but not even this was guaranteed. Horizontal gradients of VAS products generally were stronger than those from radiosondes. VAS precipitable water agreed better with the ground truth than did dewpoints at individual levels, but VAS thicknesses were not much improved over the already accurate VAS temperatures.
Results for the severe storms forecasting parameters indicated that VAS/RAOB discrepancies increased with the amount of manipulation required during computation. VAS parameters incorporating observed surface data tended to give better results than those that did not. Of all the parameters examined, VAS-derived lifted index exhibited the best agreement with RAOB versions. VAS positive buoyant energy showed disappointingly poor comparisons. VAS retrievals provided poor measures of the low-level negative buoyant energy that must be overcome before convection can begin. Agreements between VAS/RAOB versions of the K index, total totals index, Showalter index, and several other parameters were intermediate to those of positive buoyant energy and lifted index.
Abstract
Satellite-derived temperature profiles are used to determine if reliable estimates of synoptic-scale vertical motion can be obtained from the adiabatic, vorticity, and omega equation techniques. The period of study contains a short-wave trough over the Midwest and a convective outbreak over the middle Mississippi River Valley. Satellite soundings are available at 1–3 h intervals at five times. The emphasis is on assessing the strengths and weaknesses of the three vertical motion procedures and determining the effects of short-interval observations on the calculations.
Results show that the quasi-geostrophic omega equation provided patterns and magnitudes most consistent with observed weather events and 12 h radiosonde-derived motions. The vorticity method produced less satisfactory results, while adiabatic motions were unacceptable. The time derivative term dominated adiabatic motions and was a major influence in the vorticity method. Unrealistic temperature tendencies resulted from the retrieval algorithm; i.e., a diurnal temperature bias extended upwards to 500 mb, and there was a compensating effect at higher levels.
Abstract
Satellite-derived temperature profiles are used to determine if reliable estimates of synoptic-scale vertical motion can be obtained from the adiabatic, vorticity, and omega equation techniques. The period of study contains a short-wave trough over the Midwest and a convective outbreak over the middle Mississippi River Valley. Satellite soundings are available at 1–3 h intervals at five times. The emphasis is on assessing the strengths and weaknesses of the three vertical motion procedures and determining the effects of short-interval observations on the calculations.
Results show that the quasi-geostrophic omega equation provided patterns and magnitudes most consistent with observed weather events and 12 h radiosonde-derived motions. The vorticity method produced less satisfactory results, while adiabatic motions were unacceptable. The time derivative term dominated adiabatic motions and was a major influence in the vorticity method. Unrealistic temperature tendencies resulted from the retrieval algorithm; i.e., a diurnal temperature bias extended upwards to 500 mb, and there was a compensating effect at higher levels.
Abstract
Synoptic-scale kinetic energy budgets are computed using 3 and 6 h rawinsonde data during a period of intense convective activity. Modification by the storms of their surrounding synoptic-scale environments is determined by calculating budgets over limited volumes that just enclose two squall lines.
Large generation of kinetic energy is associated with areas of convection. Of particular interest is major transport of kinetic energy out of the volumes near the level of the jet stream. Kinetic energy generated in the lower levels of the storm environments is carried aloft by large-scale upward vertical motion. Transfer of kinetic energy from grid to subgrid scales of motion leads to a loss of energy in the storm environment.
Temporal variations in the generation, flux divergence and dissipation terms of the kinetic energy budget are related to the life cycles of the squall lines. Maximum energy conversion and transport occur near the time of maximum storm intensity while smaller values are observed during the development and decay stages. Spatial fields of the energy terms show that the most intense energy processes occurring during the period are associated with the squall lines. The energy fields move with the squall line and synoptic map features.
Abstract
Synoptic-scale kinetic energy budgets are computed using 3 and 6 h rawinsonde data during a period of intense convective activity. Modification by the storms of their surrounding synoptic-scale environments is determined by calculating budgets over limited volumes that just enclose two squall lines.
Large generation of kinetic energy is associated with areas of convection. Of particular interest is major transport of kinetic energy out of the volumes near the level of the jet stream. Kinetic energy generated in the lower levels of the storm environments is carried aloft by large-scale upward vertical motion. Transfer of kinetic energy from grid to subgrid scales of motion leads to a loss of energy in the storm environment.
Temporal variations in the generation, flux divergence and dissipation terms of the kinetic energy budget are related to the life cycles of the squall lines. Maximum energy conversion and transport occur near the time of maximum storm intensity while smaller values are observed during the development and decay stages. Spatial fields of the energy terms show that the most intense energy processes occurring during the period are associated with the squall lines. The energy fields move with the squall line and synoptic map features.
Abstract
An analysis is conducted to assess the sensitivity of kinematic parameters to random errors contained in rawinsonde data. Parameters considered are relative vorticity, vorticity advection, horizontal divergence, kinematic vertical motion and temperature advection. Input data are from the AYE-SESAME I experiment which coincides with the Red River Valley tornado outbreak (10–11 April 1979). National Weather Service rawinsonde data describe the effects of data errors on the synoptic scale, while the addition of 16 special sites permits a description of sensitivity on the meso-α scale.
Qualitative and quantitative analyses show that horizontal divergence is the most affected of the parameters studied, while vorticity advection ranks second. At 850, 500 and 300 mb, assumed data errors do not preclude detection of the major forcing processes associated with the tornado outbreak. Although the two scales of analysis respond somewhat differently to the data perturbations, fields from both sets of data usually show the same major features.
Abstract
An analysis is conducted to assess the sensitivity of kinematic parameters to random errors contained in rawinsonde data. Parameters considered are relative vorticity, vorticity advection, horizontal divergence, kinematic vertical motion and temperature advection. Input data are from the AYE-SESAME I experiment which coincides with the Red River Valley tornado outbreak (10–11 April 1979). National Weather Service rawinsonde data describe the effects of data errors on the synoptic scale, while the addition of 16 special sites permits a description of sensitivity on the meso-α scale.
Qualitative and quantitative analyses show that horizontal divergence is the most affected of the parameters studied, while vorticity advection ranks second. At 850, 500 and 300 mb, assumed data errors do not preclude detection of the major forcing processes associated with the tornado outbreak. Although the two scales of analysis respond somewhat differently to the data perturbations, fields from both sets of data usually show the same major features.
Abstract
Kinetic energy budgets are computed during a cold air outbreak in association with strong jet stream activity over the eastern United States. The period is characterized by large generation of kinetic energy due to cross-contour flow. Horizontal export and dissipation of energy to subgrid scales of motion constitute the important energy sinks.
Rawinsonde data at 3 and 6 h intervals during a 36 h period are used in the analysis and reveal that energy fluctuations on a time scale of less than 12 h are generally small even though the overall energy balance does change considerably during the period in conjunction with an upper level trough which moves through the region. An error analysis of the energy budget terms suggests that this major change in the budget is not due to random errors in the input data but is caused by the changing synoptic situation. The study illustrates the need to consider the time and space scales of associated weather phenomena in interpreting energy budgets obtained through use of higher frequency data.
Abstract
Kinetic energy budgets are computed during a cold air outbreak in association with strong jet stream activity over the eastern United States. The period is characterized by large generation of kinetic energy due to cross-contour flow. Horizontal export and dissipation of energy to subgrid scales of motion constitute the important energy sinks.
Rawinsonde data at 3 and 6 h intervals during a 36 h period are used in the analysis and reveal that energy fluctuations on a time scale of less than 12 h are generally small even though the overall energy balance does change considerably during the period in conjunction with an upper level trough which moves through the region. An error analysis of the energy budget terms suggests that this major change in the budget is not due to random errors in the input data but is caused by the changing synoptic situation. The study illustrates the need to consider the time and space scales of associated weather phenomena in interpreting energy budgets obtained through use of higher frequency data.
Abstract
This paper evaluates and intercompares three existing algorithms for calculating precipitable water (PW) using infrared radiances from the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS). The study exclusively uses simulated, rather than observed, VAS radiances in all retrievals. The National Environmental Satellite, Data, and Information Service simultaneous physical algorithm utilizes data from all 12 VAS channels and produces a vertical profile of temperature and dewpoint from which PW can be calculated. The Chesters technique and Jedlovec’s physical split-window technique retrieve PW from radiances in the two split window channels without first computing a dewpoint profile. All three algorithms also can be used with GOES-8 and GOES-9 data.
These algorithms have not been intercompared previously. Each is applied on case days having wide variations in temperature and moisture. The algorithms are supplied with first-guess information of varying accuracy to assess their sensitivity to the guess data. The performance of the techniques relative to one another is described, including important similarities and differences among them.
Results show that all three algorithms perform well within most temperature and moisture regimes. Each retrieves PW that is generally an improvement upon the first guess and is more accurate than PW predicted by surface temperature alone. However, each algorithm is somewhat dependent upon the first guess. Warm-biased first-guess surface temperatures are generally associated with moist-biased PW retrievals, while cold-biased first-guess surface temperatures are generally associated with dry-biased retrievals. The first-guess surface temperature errors reflect the presence, in either the first-guess or observed temperature profiles, of low-level inversions that cause the PW retrieval errors. Retrievals made where the observed contribution of low-level moisture to total column PW is small are usually moist biased, while those where the low-level contribution is large are usually dry biased. Both of these relationships exist irrespective of the sign of the first-guess PW error.
Abstract
This paper evaluates and intercompares three existing algorithms for calculating precipitable water (PW) using infrared radiances from the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS). The study exclusively uses simulated, rather than observed, VAS radiances in all retrievals. The National Environmental Satellite, Data, and Information Service simultaneous physical algorithm utilizes data from all 12 VAS channels and produces a vertical profile of temperature and dewpoint from which PW can be calculated. The Chesters technique and Jedlovec’s physical split-window technique retrieve PW from radiances in the two split window channels without first computing a dewpoint profile. All three algorithms also can be used with GOES-8 and GOES-9 data.
These algorithms have not been intercompared previously. Each is applied on case days having wide variations in temperature and moisture. The algorithms are supplied with first-guess information of varying accuracy to assess their sensitivity to the guess data. The performance of the techniques relative to one another is described, including important similarities and differences among them.
Results show that all three algorithms perform well within most temperature and moisture regimes. Each retrieves PW that is generally an improvement upon the first guess and is more accurate than PW predicted by surface temperature alone. However, each algorithm is somewhat dependent upon the first guess. Warm-biased first-guess surface temperatures are generally associated with moist-biased PW retrievals, while cold-biased first-guess surface temperatures are generally associated with dry-biased retrievals. The first-guess surface temperature errors reflect the presence, in either the first-guess or observed temperature profiles, of low-level inversions that cause the PW retrieval errors. Retrievals made where the observed contribution of low-level moisture to total column PW is small are usually moist biased, while those where the low-level contribution is large are usually dry biased. Both of these relationships exist irrespective of the sign of the first-guess PW error.
Abstract
The Geostationary Operational Environmental Satellite (GOES-8) temperature–moisture retrievals were compared with collocated National Weather Service radiosonde observations (RAOBs) to assess retrieval performance. Retrieved values of temperature and dewpoint were evaluated at individual levels. Precipitable water and thickness also were evaluated, and the GOES-8 retrievals were compared with the first-guess data used in the algorithm. The dataset consisted of 1113 RAOB–retrieval pairs (collocated to within 50 km) over the United States at 1200 UTC during August–November 1995.
GOES-8 temperature retrievals were found to agree better with their RAOB-derived counterparts than did the dewpoints. However, both temperatures and dewpoints were found to be highly dependent on their first-guess data from the Nested Grid Model. Retrievals generally were closer to the RAOBs than was the first guess. However, this was never guaranteed, even for large first-guess discrepancies. In fact, some retrievals did not agree as well with the RAOBs as did the first guess.
GOES-8 and RAOB-derived precipitable water (PW) and thickness showed closer agreement than the level-specific data. Both integrated parameters were dependent on their first guess. However, GOES-8 and RAOB PW agreed more often in layers above the surface where the guess was less accurate.
Comparison with a previous evaluation of retrievals from the Visible and Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) indicated that GOES-8 retrievals agreed better with RAOBs than did the VAS versions. This improvement is likely due to GOES-8’s increased number of channels and better signal-to-noise values, along with the assumed increase in quality of the first-guess data being used.
Abstract
The Geostationary Operational Environmental Satellite (GOES-8) temperature–moisture retrievals were compared with collocated National Weather Service radiosonde observations (RAOBs) to assess retrieval performance. Retrieved values of temperature and dewpoint were evaluated at individual levels. Precipitable water and thickness also were evaluated, and the GOES-8 retrievals were compared with the first-guess data used in the algorithm. The dataset consisted of 1113 RAOB–retrieval pairs (collocated to within 50 km) over the United States at 1200 UTC during August–November 1995.
GOES-8 temperature retrievals were found to agree better with their RAOB-derived counterparts than did the dewpoints. However, both temperatures and dewpoints were found to be highly dependent on their first-guess data from the Nested Grid Model. Retrievals generally were closer to the RAOBs than was the first guess. However, this was never guaranteed, even for large first-guess discrepancies. In fact, some retrievals did not agree as well with the RAOBs as did the first guess.
GOES-8 and RAOB-derived precipitable water (PW) and thickness showed closer agreement than the level-specific data. Both integrated parameters were dependent on their first guess. However, GOES-8 and RAOB PW agreed more often in layers above the surface where the guess was less accurate.
Comparison with a previous evaluation of retrievals from the Visible and Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) indicated that GOES-8 retrievals agreed better with RAOBs than did the VAS versions. This improvement is likely due to GOES-8’s increased number of channels and better signal-to-noise values, along with the assumed increase in quality of the first-guess data being used.
Abstract
Time tendencies of operationally prepared Visible-Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) retrievals and derived products are evaluated by comparing them against corresponding tendencies from radiosonde soundings. Temperature and dewpoint trends from the two sources are compared, as are trends of thickness and precipitable water. VAS retrieval tendencies also are compared with those of the first-guess limited-area fine-mesh model (LFM) input to determine relationships and/or improvements. Time intervals of 6 h receive the greatest attention; however, 3- and 9-h periods also are considered.
Agreements between VAS and radiosonde observation (RAOB) trends generally are found to be very poor, with correlations between two versions usually less than .5. VAS trends compare less favorably with the “ground truth” than do trends of LFM data, which served as first guess. VAS-RAOB trends of vertically integrated parameters, that is, thickness and precipitable water, agree somewhat better than those of temperature and dewpoint, but correlations still are very poor. In evaluating 3-, 6-, and 9-h intervals, statistical agreements between VAS and radiosonde trends are found to improve considerably with increasing time intervals. VAS trends are found to degrade the first-guess (LFM) trend about as often as they improve it.
Abstract
Time tendencies of operationally prepared Visible-Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) retrievals and derived products are evaluated by comparing them against corresponding tendencies from radiosonde soundings. Temperature and dewpoint trends from the two sources are compared, as are trends of thickness and precipitable water. VAS retrieval tendencies also are compared with those of the first-guess limited-area fine-mesh model (LFM) input to determine relationships and/or improvements. Time intervals of 6 h receive the greatest attention; however, 3- and 9-h periods also are considered.
Agreements between VAS and radiosonde observation (RAOB) trends generally are found to be very poor, with correlations between two versions usually less than .5. VAS trends compare less favorably with the “ground truth” than do trends of LFM data, which served as first guess. VAS-RAOB trends of vertically integrated parameters, that is, thickness and precipitable water, agree somewhat better than those of temperature and dewpoint, but correlations still are very poor. In evaluating 3-, 6-, and 9-h intervals, statistical agreements between VAS and radiosonde trends are found to improve considerably with increasing time intervals. VAS trends are found to degrade the first-guess (LFM) trend about as often as they improve it.
