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- Author or Editor: Francis J. Merceret x
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Abstract
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Abstract
Electric-field measurements made in and near clouds during two airborne field programs are presented. Aircraft equipped with multiple electric-field mills and cloud physics sensors were flown near active convection and into thunderstorm anvil and debris clouds. The magnitude of the electric field was measured as a function of position with respect to the cloud edge to provide an observational basis for modifications to the lightning launch commit criteria (LLCC) used by the U.S. space program. These LLCC are used to reduce the risk that an ascending launch vehicle will trigger a lightning strike that could cause the loss of the mission or vehicle. Even with fields of tens of kV m−1 inside electrically active convective clouds, the fields external to these clouds decay to less than 3 kV m−1 within 15 km of cloud edge. Fields that exceed 3 kV m−1 were not found external to anvil and debris clouds.
Abstract
Electric-field measurements made in and near clouds during two airborne field programs are presented. Aircraft equipped with multiple electric-field mills and cloud physics sensors were flown near active convection and into thunderstorm anvil and debris clouds. The magnitude of the electric field was measured as a function of position with respect to the cloud edge to provide an observational basis for modifications to the lightning launch commit criteria (LLCC) used by the U.S. space program. These LLCC are used to reduce the risk that an ascending launch vehicle will trigger a lightning strike that could cause the loss of the mission or vehicle. Even with fields of tens of kV m−1 inside electrically active convective clouds, the fields external to these clouds decay to less than 3 kV m−1 within 15 km of cloud edge. Fields that exceed 3 kV m−1 were not found external to anvil and debris clouds.
Abstract
The accuracy and availability of data from a network of 915-MHz boundary layer wind profilers operated by the U.S. Air Force on the Eastern Range are assessed using an automated quality control (QC) algorithm developed by the authors. The accuracy and reliability of the automated algorithm is assessed using the results of an extensive manual examination of the same data used for the assessment of the instruments. The details of the automated algorithm and the manual screening process are provided.
Data were collected over a 647-day period from five profilers configured to produce one profile every 15 min, resulting in about 200 000 measurements. The results indicate that the instruments provide reliable, accurate data except when maintenance problems or heavy precipitation are present. Precipitation affected as much as 25% of the measurements in the dataset. The automated QC algorithm proved extremely effective in identifying unacceptable data. Only 0.03% of the data passing automated QC were identified as bad by manual review. While some valid data were identified as bad, the automated algorithm appears to provide exceptional performance for use in automated operational assimilation of boundary profiler data for model initialization and data visualization.
Abstract
The accuracy and availability of data from a network of 915-MHz boundary layer wind profilers operated by the U.S. Air Force on the Eastern Range are assessed using an automated quality control (QC) algorithm developed by the authors. The accuracy and reliability of the automated algorithm is assessed using the results of an extensive manual examination of the same data used for the assessment of the instruments. The details of the automated algorithm and the manual screening process are provided.
Data were collected over a 647-day period from five profilers configured to produce one profile every 15 min, resulting in about 200 000 measurements. The results indicate that the instruments provide reliable, accurate data except when maintenance problems or heavy precipitation are present. Precipitation affected as much as 25% of the measurements in the dataset. The automated QC algorithm proved extremely effective in identifying unacceptable data. Only 0.03% of the data passing automated QC were identified as bad by manual review. While some valid data were identified as bad, the automated algorithm appears to provide exceptional performance for use in automated operational assimilation of boundary profiler data for model initialization and data visualization.
Abstract
The performance of an improved signal-processing algorithm implemented on the NASA 50-MHz radar wind profiler at Kennedy Space Center is analyzed. In 1990, NASA began using a 50-MHz Doppler radar wind profiler to demonstrate the applicability of the technology to assessing launch wind conditions at Kennedy Space Center. To produce critical wind profiles in minimal time, NASA replaced the conventional signal-processing system delivered by the manufacturer with a more robust system. The new signal-processing system uses a median filter to remove spurious Doppler spectral data and constrains the search for the atmospheric signal by a first guess. The new system has been in nearly continuous operation since mid-1994. Over this period, the system performance was evaluated in varied weather conditions, and numerous comparisons with wind profiles from radar-tracked jimspheres were accomplished. The system is now integrated into the prelaunch wind evaluation structure. This paper discusses the details of the new signal-processing system and presents the results of the performance analysis.
Abstract
The performance of an improved signal-processing algorithm implemented on the NASA 50-MHz radar wind profiler at Kennedy Space Center is analyzed. In 1990, NASA began using a 50-MHz Doppler radar wind profiler to demonstrate the applicability of the technology to assessing launch wind conditions at Kennedy Space Center. To produce critical wind profiles in minimal time, NASA replaced the conventional signal-processing system delivered by the manufacturer with a more robust system. The new signal-processing system uses a median filter to remove spurious Doppler spectral data and constrains the search for the atmospheric signal by a first guess. The new system has been in nearly continuous operation since mid-1994. Over this period, the system performance was evaluated in varied weather conditions, and numerous comparisons with wind profiles from radar-tracked jimspheres were accomplished. The system is now integrated into the prelaunch wind evaluation structure. This paper discusses the details of the new signal-processing system and presents the results of the performance analysis.
Abstract
An ongoing challenge in mesoscale numerical weather prediction (NWP) is to determine the ideal method for verifying the performance of high-resolution, detailed forecasts. Traditional objective techniques that evaluate NWP model performance based on point error statistics may not be positively correlated with the value of forecast information for certain applications of mesoscale NWP, and subjective evaluation techniques are often costly and time consuming. As a result, objective event-based verification methodologies are required in order to determine the added value of high-resolution NWP models.
This paper presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) NWP model. The contour error map (CEM) technique identifies sea-breeze transition times in objectively analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea-breeze wind direction and wind speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique improves upon traditional objective verification techniques and previously used subjective verification methodologies because it is automated, accounts for both spatial and temporal variations, correctly identifies and verifies the sea-breeze transition times, and provides verification contour maps and simple statistical parameters for easy interpretation. The CEM algorithm details are presented and validated against independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation.
Abstract
An ongoing challenge in mesoscale numerical weather prediction (NWP) is to determine the ideal method for verifying the performance of high-resolution, detailed forecasts. Traditional objective techniques that evaluate NWP model performance based on point error statistics may not be positively correlated with the value of forecast information for certain applications of mesoscale NWP, and subjective evaluation techniques are often costly and time consuming. As a result, objective event-based verification methodologies are required in order to determine the added value of high-resolution NWP models.
This paper presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) NWP model. The contour error map (CEM) technique identifies sea-breeze transition times in objectively analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea-breeze wind direction and wind speed to compare the observed and forecast winds behind the sea-breeze front. The CEM technique improves upon traditional objective verification techniques and previously used subjective verification methodologies because it is automated, accounts for both spatial and temporal variations, correctly identifies and verifies the sea-breeze transition times, and provides verification contour maps and simple statistical parameters for easy interpretation. The CEM algorithm details are presented and validated against independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation.
