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- Author or Editor: Allen B. White x
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Abstract
Humidity variability at the top of the marine atmospheric boundary layer and in the overlying free troposphere was examined using data collected during the marine stratocumulus phase of the First Regional Experiment (FIRE) of the International Satellite Cloud Climatology Program. A time series of the humidity structure-function parameter C q 2 derived from Doppler wind profiler reflectivity data is compared to a concurrent time series of specific humidity q. Both q and its vertical gradient were calculated from rawinsonde data obtained from sondes launched within 500 m of the profiler. Time-height correlation analysis between log(C q 2) and log(∂q/∂z)2 shows that the two time series are highly correlated at and just above the inversion base, with r approximately equal to 0.7. The correlation is slightly lower in the free troposphere where r is about 0.5 (a value of r greater than 0.2 is significant at the 95% confidence level). There is also correlation between log(C q 2) and log(q), which is maximized at an offset in height between the two instruments.
Closer analysis of a short-lived clearing event shows locally reduced values of C q 2 in a region of enhanced ∂q/∂z. This apparent paradox can be explained by noting the absence of enhanced entrainment associated with cloud-top radiative cooling. The combined wind profiler-rawinsonde datasets were also used to estimate the entrainment velocity w e for clear and cloudy conditions. An average value of w e equal to 0.38 cm s−1 was obtained for cloudy conditions; for the clear case a value of 0.13 cm s−1 was obtained.
Abstract
Humidity variability at the top of the marine atmospheric boundary layer and in the overlying free troposphere was examined using data collected during the marine stratocumulus phase of the First Regional Experiment (FIRE) of the International Satellite Cloud Climatology Program. A time series of the humidity structure-function parameter C q 2 derived from Doppler wind profiler reflectivity data is compared to a concurrent time series of specific humidity q. Both q and its vertical gradient were calculated from rawinsonde data obtained from sondes launched within 500 m of the profiler. Time-height correlation analysis between log(C q 2) and log(∂q/∂z)2 shows that the two time series are highly correlated at and just above the inversion base, with r approximately equal to 0.7. The correlation is slightly lower in the free troposphere where r is about 0.5 (a value of r greater than 0.2 is significant at the 95% confidence level). There is also correlation between log(C q 2) and log(q), which is maximized at an offset in height between the two instruments.
Closer analysis of a short-lived clearing event shows locally reduced values of C q 2 in a region of enhanced ∂q/∂z. This apparent paradox can be explained by noting the absence of enhanced entrainment associated with cloud-top radiative cooling. The combined wind profiler-rawinsonde datasets were also used to estimate the entrainment velocity w e for clear and cloudy conditions. An average value of w e equal to 0.38 cm s−1 was obtained for cloudy conditions; for the clear case a value of 0.13 cm s−1 was obtained.
Rain Drop Size Distributions Estimated from NOAA Snow-Level Radar DataAbstract
Using NOAA’s S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter Dm from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.
Abstract
Using NOAA’s S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter Dm from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.
Abstract
A previous study showed success in determining the convective boundary layer depth with radar wind-profiling radars using fuzzy logic methods, and improvements to the earlier work are discussed. The improved method uses the Vaisala multipeak picking (MPP) procedure to identify the atmospheric signal in radar spectra in place of a fuzzy logic peak picking procedure that was previously used. The method then applies fuzzy logic techniques to calculate the depth of the convective boundary layer. The planetary boundary layer depth algorithm is improved with respect to the one used in the previous study in that it adds information obtained from the small-scale turbulence (vertical profiles of the spectral width of the vertical velocity), while also still using vertical profiles of the radar-derived refractive index structure parameter C 2 n and the variance of vertical velocity. Modifications to the fuzzy logic rules (especially to those using vertical velocity data) that improve the algorithm’s accuracy in cloudy boundary layers are incorporated. In addition, a reliability threshold value to the fuzzy logic–derived score is applied to eliminate PBL depth data values with low score values. These low score values correspond to periods when the PBL structure does not match the conceptual model of the convective PBL built into the algorithm. Also, as a final step, an optional temporal continuity test on boundary layer depth has been developed that helps improve the algorithm’s skill. A comparison with independent boundary layer depth estimations made “by eye” by meteorologists at two radar wind-profiler sites, significantly different in their characteristics, shows that the new improved method gives significantly more accurate estimates of the boundary layer depth than does the previous method, and also much better estimates than the simpler “standard” method of selecting the peak of C 2 n . The new method produces an absolute error of the mixing-depth estimates comparable to the vertical range resolution of the profilers.
Abstract
A previous study showed success in determining the convective boundary layer depth with radar wind-profiling radars using fuzzy logic methods, and improvements to the earlier work are discussed. The improved method uses the Vaisala multipeak picking (MPP) procedure to identify the atmospheric signal in radar spectra in place of a fuzzy logic peak picking procedure that was previously used. The method then applies fuzzy logic techniques to calculate the depth of the convective boundary layer. The planetary boundary layer depth algorithm is improved with respect to the one used in the previous study in that it adds information obtained from the small-scale turbulence (vertical profiles of the spectral width of the vertical velocity), while also still using vertical profiles of the radar-derived refractive index structure parameter C 2 n and the variance of vertical velocity. Modifications to the fuzzy logic rules (especially to those using vertical velocity data) that improve the algorithm’s accuracy in cloudy boundary layers are incorporated. In addition, a reliability threshold value to the fuzzy logic–derived score is applied to eliminate PBL depth data values with low score values. These low score values correspond to periods when the PBL structure does not match the conceptual model of the convective PBL built into the algorithm. Also, as a final step, an optional temporal continuity test on boundary layer depth has been developed that helps improve the algorithm’s skill. A comparison with independent boundary layer depth estimations made “by eye” by meteorologists at two radar wind-profiler sites, significantly different in their characteristics, shows that the new improved method gives significantly more accurate estimates of the boundary layer depth than does the previous method, and also much better estimates than the simpler “standard” method of selecting the peak of C 2 n . The new method produces an absolute error of the mixing-depth estimates comparable to the vertical range resolution of the profilers.
A Real-Time Online Data Product that Automatically Detects Easterly Gap-Flow Events and Precipitation Type in the Columbia River GorgeAbstract
A real-time, hourly updated, online graphical data product that displays the depth and strength of easterly gap flow in the Columbia River Gorge using a 915-MHz Doppler wind profiler is presented. During precipitation events, this data product also displays observed precipitation accumulation and diagnosed precipitation type using measurements provided by a collocated heated tipping-bucket rain gauge, an optical disdrometer, and temperature and relative humidity sensors. Automated algorithms that determine the existence and depth of the gap flow, as well as precipitation type, are described. The Columbia River Gorge is the only major gap in the Cascade Mountains of Oregon and Washington. Consequently, both easterly and westerly directed gap-flow events are common in this region. Especially during late autumn and winter, easterly gap flow can cause hazardous and damaging weather (e.g., snow, freezing rain, and strong winds) in the Portland, Oregon–Vancouver, Washington metropolitan area. The product described here was developed to help forecasters at the Portland National Weather Service Forecast Office monitor cool-season easterly gap-flow events in order to provide situational awareness and guide warnings to the public about potential weather-related hazards.
Abstract
A real-time, hourly updated, online graphical data product that displays the depth and strength of easterly gap flow in the Columbia River Gorge using a 915-MHz Doppler wind profiler is presented. During precipitation events, this data product also displays observed precipitation accumulation and diagnosed precipitation type using measurements provided by a collocated heated tipping-bucket rain gauge, an optical disdrometer, and temperature and relative humidity sensors. Automated algorithms that determine the existence and depth of the gap flow, as well as precipitation type, are described. The Columbia River Gorge is the only major gap in the Cascade Mountains of Oregon and Washington. Consequently, both easterly and westerly directed gap-flow events are common in this region. Especially during late autumn and winter, easterly gap flow can cause hazardous and damaging weather (e.g., snow, freezing rain, and strong winds) in the Portland, Oregon–Vancouver, Washington metropolitan area. The product described here was developed to help forecasters at the Portland National Weather Service Forecast Office monitor cool-season easterly gap-flow events in order to provide situational awareness and guide warnings to the public about potential weather-related hazards.
Abstract
Because knowledge of the melting level is critical to river forecasters and other users, an objective algorithm to detect the brightband height from profiles of radar reflectivity and Doppler vertical velocity collected with a Doppler wind profiling radar is presented. The algorithm uses vertical profiles to detect the bottom portion of the bright band, where vertical gradients of radar reflectivity and Doppler vertical velocity are negatively correlated. A search is then performed to find the peak radar reflectivity above this feature, and the brightband height is assigned to the altitude of the peak. Reflectivity profiles from the off-vertical beams produced when the radar is in the Doppler beam swinging mode provide additional brightband measurements. A consensus test is applied to subhourly values to produce a quality-controlled, hourly averaged brightband height. A comparison of radar-deduced brightband heights with melting levels derived from temperature profiles measured with rawinsondes launched from the same radar site shows that the brightband height is, on average, 192 m lower than the melting level. A method for implementing the algorithm and making the results available to the public in near–real time via the Internet is described. The importance of melting level information in hydrological prediction is illustrated using the NWS operational river forecast model applied to mountainous watersheds in California. It is shown that a 2000-ft increase in the melting level can triple run off during a modest 24-h rainfall event. The ability to monitor the brightband height is likely to aid in melting-level forecasting and verification.
Abstract
Because knowledge of the melting level is critical to river forecasters and other users, an objective algorithm to detect the brightband height from profiles of radar reflectivity and Doppler vertical velocity collected with a Doppler wind profiling radar is presented. The algorithm uses vertical profiles to detect the bottom portion of the bright band, where vertical gradients of radar reflectivity and Doppler vertical velocity are negatively correlated. A search is then performed to find the peak radar reflectivity above this feature, and the brightband height is assigned to the altitude of the peak. Reflectivity profiles from the off-vertical beams produced when the radar is in the Doppler beam swinging mode provide additional brightband measurements. A consensus test is applied to subhourly values to produce a quality-controlled, hourly averaged brightband height. A comparison of radar-deduced brightband heights with melting levels derived from temperature profiles measured with rawinsondes launched from the same radar site shows that the brightband height is, on average, 192 m lower than the melting level. A method for implementing the algorithm and making the results available to the public in near–real time via the Internet is described. The importance of melting level information in hydrological prediction is illustrated using the NWS operational river forecast model applied to mountainous watersheds in California. It is shown that a 2000-ft increase in the melting level can triple run off during a modest 24-h rainfall event. The ability to monitor the brightband height is likely to aid in melting-level forecasting and verification.
Abstract
A new S-band vertical profiler with a coupler option for extending the dynamic range of the radar’s receiver is discussed. The added dynamic range allows the profiler to record radar reflectivity measurements in moderate to heavy precipitation that otherwise would not have been possible with this system because of receiver saturation. The radar hardware, signal processor, and operating software are based on existing S-band and UHF profiler technology. Results from a side-by-side comparison with a calibrated Ka-band radar are used to determine the calibration and sensitivity of the S-band profiler. In a typical cloud profiling mode of operation, the sensitivity is −14 dBZ at 10 km. Examples taken from a recent field campaign are shown to illustrate the profiler’s ability to measure vertical velocity and radar reflectivity profiles in clouds and precipitation, with particular emphasis on the benefit provided by the coupler technology.
Abstract
A new S-band vertical profiler with a coupler option for extending the dynamic range of the radar’s receiver is discussed. The added dynamic range allows the profiler to record radar reflectivity measurements in moderate to heavy precipitation that otherwise would not have been possible with this system because of receiver saturation. The radar hardware, signal processor, and operating software are based on existing S-band and UHF profiler technology. Results from a side-by-side comparison with a calibrated Ka-band radar are used to determine the calibration and sensitivity of the S-band profiler. In a typical cloud profiling mode of operation, the sensitivity is −14 dBZ at 10 km. Examples taken from a recent field campaign are shown to illustrate the profiler’s ability to measure vertical velocity and radar reflectivity profiles in clouds and precipitation, with particular emphasis on the benefit provided by the coupler technology.
Abstract
An algorithm to compute the magnitude of humidity gradient profiles from the measurements of the zeroth, first, and second moments of wind profiling radar (WPR) Doppler spectra was developed and tested. The algorithm extends the National Oceanic and Atmospheric Administration (NOAA)/Environmental Technology Laboratory (ETL) Advanced Signal Processing System (SPS), which provides quality control of radar data in the spectral domain for wind profile retrievals, to include the retrieval of humidity gradient profiles. The algorithm uses a recently developed approximate formula for correcting Doppler spectral widths for the spatial and temporal filtering effects. Data collected by a 3-beam 915-MHz WPR onboard the NOAA research vessel Ronald H. Brown (RHB) and a 5-beam 449-MHz WPR developed at the ETL were used in this study. The two datasets cover vastly different atmospheric conditions, with the 915-MHz shipborne system probing the tropical ocean atmosphere and the 449-MHz WPR probing continental winter upslope icing storm in the Colorado Front Range. Vaisala radiosonde measurements of humidity and temperature profiles on board the RHB and the standard National Weather Service (NWS) radiosonde measurements at Stapleton, Colorado, were used for comparisons. The cases chosen represent typical atmospheric conditions and not special atmospheric situations. Results show that using SPS-obtained measurements of the zeroth, first, and second spectral moments provide radar-obtained humidity gradient profiles up to 3 km AGL.
Abstract
An algorithm to compute the magnitude of humidity gradient profiles from the measurements of the zeroth, first, and second moments of wind profiling radar (WPR) Doppler spectra was developed and tested. The algorithm extends the National Oceanic and Atmospheric Administration (NOAA)/Environmental Technology Laboratory (ETL) Advanced Signal Processing System (SPS), which provides quality control of radar data in the spectral domain for wind profile retrievals, to include the retrieval of humidity gradient profiles. The algorithm uses a recently developed approximate formula for correcting Doppler spectral widths for the spatial and temporal filtering effects. Data collected by a 3-beam 915-MHz WPR onboard the NOAA research vessel Ronald H. Brown (RHB) and a 5-beam 449-MHz WPR developed at the ETL were used in this study. The two datasets cover vastly different atmospheric conditions, with the 915-MHz shipborne system probing the tropical ocean atmosphere and the 449-MHz WPR probing continental winter upslope icing storm in the Colorado Front Range. Vaisala radiosonde measurements of humidity and temperature profiles on board the RHB and the standard National Weather Service (NWS) radiosonde measurements at Stapleton, Colorado, were used for comparisons. The cases chosen represent typical atmospheric conditions and not special atmospheric situations. Results show that using SPS-obtained measurements of the zeroth, first, and second spectral moments provide radar-obtained humidity gradient profiles up to 3 km AGL.
The NOAA FM-CW Snow-Level RadarAbstract
A vertically pointing radar for monitoring radar brightband height (BBH) has been developed. This new radar utilizes frequency-modulated continuous wave (FM-CW) techniques to provide high-resolution data at a fraction of the cost of comparable pulsed radars. This S-band radar provides details of the vertical structure of precipitating clouds, with full Doppler information. Details of the radar design are presented along with observations from one storm. Results from a calibration using these storm data show the radar meets the design goals. Eleven of these radars have been deployed and provide BBH data in near–real time.
Abstract
A vertically pointing radar for monitoring radar brightband height (BBH) has been developed. This new radar utilizes frequency-modulated continuous wave (FM-CW) techniques to provide high-resolution data at a fraction of the cost of comparable pulsed radars. This S-band radar provides details of the vertical structure of precipitating clouds, with full Doppler information. Details of the radar design are presented along with observations from one storm. Results from a calibration using these storm data show the radar meets the design goals. Eleven of these radars have been deployed and provide BBH data in near–real time.
