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- Author or Editor: John F. Dostalek x
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Abstract
Statistics are compiled comparing calculations of total precipitable water (TPW) as given by GOES sounder derived product imagery (DPI) to that computed from radiosonde data for the 12-month period March 1998–February 1999. In order to investigate the impact of the GOES sounder data, these results are evaluated against statistics generated from the comparison between the first guess fields used by the DPI (essentially Eta Model forecasts) and the radiosonde data. It is found that GOES data produce both positive and negative results. Biases in the first guess are reduced for moist atmospheres, but are increased in dry atmospheres. Time tendencies in TPW as measured by the DPI show a higher correlation to radiosonde data than does the first guess. Two specific examples demonstrating differences between the DPI and Eta Model forecasts are given.
Abstract
Statistics are compiled comparing calculations of total precipitable water (TPW) as given by GOES sounder derived product imagery (DPI) to that computed from radiosonde data for the 12-month period March 1998–February 1999. In order to investigate the impact of the GOES sounder data, these results are evaluated against statistics generated from the comparison between the first guess fields used by the DPI (essentially Eta Model forecasts) and the radiosonde data. It is found that GOES data produce both positive and negative results. Biases in the first guess are reduced for moist atmospheres, but are increased in dry atmospheres. Time tendencies in TPW as measured by the DPI show a higher correlation to radiosonde data than does the first guess. Two specific examples demonstrating differences between the DPI and Eta Model forecasts are given.
Abstract
The depth of boundary layer water vapor plays a critical role in convective cloud formation in the warm season, but numerical models often struggle with accurate predictions of above-surface moisture. Satellite retrievals of water vapor have been developed, but they are limited by the use of a model’s first guess, instrument spectral resolution, horizontal footprint size, and vertical resolution. In 2016, Geostationary Operational Environmental Satellite-R (GOES-R), the first in a series of new-generation geostationary satellites, will be launched. Its Advanced Baseline Imager will provide unprecedented spectral, spatial, and temporal resolution. Among the bands are two centered at 10.35 and 12.3 μm. The brightness temperature difference between these bands is referred to as the split-window difference, and has been shown to provide information about atmospheric column water vapor. In this paper, the split-window difference is reexamined from the perspective of GOES-R and radiative transfer model simulations are used to better understand the factors controlling its value. It is shown that the simple split-window difference can provide useful information for forecasters about deepening low-level water vapor in a cloud-free environment.
Abstract
The depth of boundary layer water vapor plays a critical role in convective cloud formation in the warm season, but numerical models often struggle with accurate predictions of above-surface moisture. Satellite retrievals of water vapor have been developed, but they are limited by the use of a model’s first guess, instrument spectral resolution, horizontal footprint size, and vertical resolution. In 2016, Geostationary Operational Environmental Satellite-R (GOES-R), the first in a series of new-generation geostationary satellites, will be launched. Its Advanced Baseline Imager will provide unprecedented spectral, spatial, and temporal resolution. Among the bands are two centered at 10.35 and 12.3 μm. The brightness temperature difference between these bands is referred to as the split-window difference, and has been shown to provide information about atmospheric column water vapor. In this paper, the split-window difference is reexamined from the perspective of GOES-R and radiative transfer model simulations are used to better understand the factors controlling its value. It is shown that the simple split-window difference can provide useful information for forecasters about deepening low-level water vapor in a cloud-free environment.
Abstract
By combining observations from the Geostationary Operational Environmental Satellite (GOES) 3.9- and 10.7-ÎĽm channels, the reflected component of the 3.9-ÎĽm radiance can be isolated. In this paper, these 3.9-ÎĽm reflectivity measurements of thunderstorm tops are studied in terms of their climatological values and their utility in diagnosing cloud-top microphysical structure. These measurements provide information about internal thunderstorm processes, including updraft strength, and may be useful for severe weather nowcasting. Three years of summertime thunderstorm-top 3.9-ÎĽm reflectivity values are analyzed to produce maps of climatological means across the United States. Maxima occur in the high plains and Rocky Mountain regions, while lower values are observed over much of the eastern United States. A simple model is used to establish a relationship between 3.9-ÎĽm reflectivity and ice crystal size at cloud top. As the mean diameter of a cloud-top ice crystal distribution decreases, more solar radiation near 3.9 ÎĽm is reflected. Using the North American Regional Reanalysis dataset, the thermodynamic environment that favors thunderstorms with large 3.9-ÎĽm reflectivity values is identified. In the high plains and mountains, environments with relatively dry boundary layers, steep lapse rates, and large vertical shear values favor thunderstorms with enhanced 3.9-ÎĽm reflectivity. Thunderstorm processes that lead to small ice crystals at cloud top are discussed, and a possible relationship between updraft strength and 3.9-ÎĽm reflectivity is presented.
Abstract
By combining observations from the Geostationary Operational Environmental Satellite (GOES) 3.9- and 10.7-ÎĽm channels, the reflected component of the 3.9-ÎĽm radiance can be isolated. In this paper, these 3.9-ÎĽm reflectivity measurements of thunderstorm tops are studied in terms of their climatological values and their utility in diagnosing cloud-top microphysical structure. These measurements provide information about internal thunderstorm processes, including updraft strength, and may be useful for severe weather nowcasting. Three years of summertime thunderstorm-top 3.9-ÎĽm reflectivity values are analyzed to produce maps of climatological means across the United States. Maxima occur in the high plains and Rocky Mountain regions, while lower values are observed over much of the eastern United States. A simple model is used to establish a relationship between 3.9-ÎĽm reflectivity and ice crystal size at cloud top. As the mean diameter of a cloud-top ice crystal distribution decreases, more solar radiation near 3.9 ÎĽm is reflected. Using the North American Regional Reanalysis dataset, the thermodynamic environment that favors thunderstorms with large 3.9-ÎĽm reflectivity values is identified. In the high plains and mountains, environments with relatively dry boundary layers, steep lapse rates, and large vertical shear values favor thunderstorms with enhanced 3.9-ÎĽm reflectivity. Thunderstorm processes that lead to small ice crystals at cloud top are discussed, and a possible relationship between updraft strength and 3.9-ÎĽm reflectivity is presented.
Abstract
New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.
Abstract
New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.
