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- Author or Editor: Laura Bianco x
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Abstract
A new method for estimating the mixing depth of the atmosphere's convective boundary layer is developed for use with wind-profiling radars. This method applies “fuzzy logic” methods to give an improved determination of the atmospheric signal in radar spectra. The method then applies fuzzy logic again to calculate the depth of the convective boundary layer, using vertical profiles of both radar-derived signal-to-noise ratio and variance of vertical velocity. A comparison with independent boundary layer depth observations at two radar wind profiler sites shows that the new method gives significantly more accurate estimates of the boundary layer depth (correlation coefficients of 0.91 and 0.96) than does a standard method (correlation coefficients of 0.14 and 0.80). Also, the new method reduces the absolute error of the mixing-depth estimates to a level similar to the vertical range resolution of the profilers.
Abstract
A new method for estimating the mixing depth of the atmosphere's convective boundary layer is developed for use with wind-profiling radars. This method applies “fuzzy logic” methods to give an improved determination of the atmospheric signal in radar spectra. The method then applies fuzzy logic again to calculate the depth of the convective boundary layer, using vertical profiles of both radar-derived signal-to-noise ratio and variance of vertical velocity. A comparison with independent boundary layer depth observations at two radar wind profiler sites shows that the new method gives significantly more accurate estimates of the boundary layer depth (correlation coefficients of 0.91 and 0.96) than does a standard method (correlation coefficients of 0.14 and 0.80). Also, the new method reduces the absolute error of the mixing-depth estimates to a level similar to the vertical range resolution of the profilers.
Abstract
In this paper a Gabor transform–based algorithm is applied to identify and eliminate intermittent signal contamination in UHF wind profiling radars, such as that produced by migrating birds. The algorithm is applied in the time domain, and so it can be used to improve the accuracy of UHF radar wind profiler data in real time—an essential requirement if these wind profiler data are to be assimilated into operational weather forecast models. The added value of using a moment-level Weber–Wuertz pattern recognition scheme that follows the Gabor transform processing is demonstrated.
Abstract
In this paper a Gabor transform–based algorithm is applied to identify and eliminate intermittent signal contamination in UHF wind profiling radars, such as that produced by migrating birds. The algorithm is applied in the time domain, and so it can be used to improve the accuracy of UHF radar wind profiler data in real time—an essential requirement if these wind profiler data are to be assimilated into operational weather forecast models. The added value of using a moment-level Weber–Wuertz pattern recognition scheme that follows the Gabor transform processing is demonstrated.
Abstract
A self-consistent remote sensing physical method to retrieve atmospheric humidity high-resolution profiles by synergetic use of a microwave radiometer profiler (MWRP) and wind profiler radar (WPR) is illustrated. The proposed technique is based on the processing of WPR data for estimating the potential refractivity gradient profiles and their optimal combination with MWRP estimates of potential temperature profiles in order to fully retrieve humidity gradient profiles. The combined algorithm makes use of recent developments in WPR signal processing, computing the zeroth-, first-, and second-order moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data in the spectral domain. On the other hand, the application of neural network to brightness temperatures, measured by a multichannel MWRP, can provide continuous estimates of tropospheric temperature and humidity profiles. Performance of the combined algorithm in retrieving humidity profiles is compared with simultaneous in situ radiosonde observations (raob’s). The empirical sets of WPR and MWRP data were collected at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site. Combined microwave radiometer and wind profiler measurements show encouraging results and significantly improve the spatial vertical resolution of atmospheric humidity profiles. Finally, some of the limitations found in the use of this technique and possible future improvements are also discussed.
Abstract
A self-consistent remote sensing physical method to retrieve atmospheric humidity high-resolution profiles by synergetic use of a microwave radiometer profiler (MWRP) and wind profiler radar (WPR) is illustrated. The proposed technique is based on the processing of WPR data for estimating the potential refractivity gradient profiles and their optimal combination with MWRP estimates of potential temperature profiles in order to fully retrieve humidity gradient profiles. The combined algorithm makes use of recent developments in WPR signal processing, computing the zeroth-, first-, and second-order moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data in the spectral domain. On the other hand, the application of neural network to brightness temperatures, measured by a multichannel MWRP, can provide continuous estimates of tropospheric temperature and humidity profiles. Performance of the combined algorithm in retrieving humidity profiles is compared with simultaneous in situ radiosonde observations (raob’s). The empirical sets of WPR and MWRP data were collected at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site. Combined microwave radiometer and wind profiler measurements show encouraging results and significantly improve the spatial vertical resolution of atmospheric humidity profiles. Finally, some of the limitations found in the use of this technique and possible future improvements are also discussed.
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.
Abstract
The performance of mesoscale meteorological models is evaluated for the coastal zone and Los Angeles area of Southern California, and for the San Joaquin Valley. Several configurations of the Weather Research and Forecasting Model (WRF) with differing grid spacing, initialization, planetary boundary layer (PBL) physics, and land surface models are compared. One configuration of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model is also included, providing results from an independent development and process flow. Specific phenomena of interest for air quality studies are examined. All model configurations are biased toward higher wind speeds than observed. The diurnal cycle of wind direction and speed (land–sea-breeze cycle) as modeled and observed by a wind profiler at Los Angeles International Airport is examined. Each of the models shows different flaws in the cycle. Soundings from San Nicolas Island, a case study involving the Research Vessel (R/V) Atlantis and the NOAA P3 aircraft, and satellite images are used to evaluate simulation performance for cloudy boundary layers. In a case study, the boundary layer structure over the water is poorly simulated by all of the WRF configurations except one with the total energy–mass flux boundary layer scheme and ECMWF reanalysis. The original WRF configuration had a substantial bias toward low PBL heights in the San Joaquin Valley, which are improved in the final configuration. WRF runs with 12-km grids have larger errors in wind speed and direction than those present in the 4-km grid runs.
Abstract
The performance of mesoscale meteorological models is evaluated for the coastal zone and Los Angeles area of Southern California, and for the San Joaquin Valley. Several configurations of the Weather Research and Forecasting Model (WRF) with differing grid spacing, initialization, planetary boundary layer (PBL) physics, and land surface models are compared. One configuration of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model is also included, providing results from an independent development and process flow. Specific phenomena of interest for air quality studies are examined. All model configurations are biased toward higher wind speeds than observed. The diurnal cycle of wind direction and speed (land–sea-breeze cycle) as modeled and observed by a wind profiler at Los Angeles International Airport is examined. Each of the models shows different flaws in the cycle. Soundings from San Nicolas Island, a case study involving the Research Vessel (R/V) Atlantis and the NOAA P3 aircraft, and satellite images are used to evaluate simulation performance for cloudy boundary layers. In a case study, the boundary layer structure over the water is poorly simulated by all of the WRF configurations except one with the total energy–mass flux boundary layer scheme and ECMWF reanalysis. The original WRF configuration had a substantial bias toward low PBL heights in the San Joaquin Valley, which are improved in the final configuration. WRF runs with 12-km grids have larger errors in wind speed and direction than those present in the 4-km grid runs.
Abstract
Persistent cold pools form as layers of cold stagnant air within topographical depressions mainly during wintertime, when the near-surface air cools and/or the air aloft warms and daytime surface heating is insufficient to mix out the stable layer. An area often affected by persistent cold pools is the Columbia River basin in the Pacific Northwest, when a high pressure system east of the Cascade Range promotes radiative cooling and easterly flow. The only major outflow for the easterly flow is through the narrow Columbia River Gorge that cuts through the north–south-oriented Cascade Range and often experiences very strong gap flows. Observations collected during the Second Wind Forecast Improvement Project (WFIP2) are used to study a persistent cold pool in the Columbia River basin between 10 and 19 January 2017 that was associated with a strong gap flow. We used data from various remote sensing and in situ instruments and an optimal estimation physical retrieval to obtain thermodynamic profiles to address the temporal and spatial characteristics of the cold pool and gap flow and to investigate the physical processes involved during formation, maintenance, and decay. While large-scale temperature advection occurred during all phases, we found that the cold-pool vertical structure was modulated by the existence of low-level clouds and that turbulent shear-induced mixing and downslope wind storms likely played a role during its decay.
Abstract
Persistent cold pools form as layers of cold stagnant air within topographical depressions mainly during wintertime, when the near-surface air cools and/or the air aloft warms and daytime surface heating is insufficient to mix out the stable layer. An area often affected by persistent cold pools is the Columbia River basin in the Pacific Northwest, when a high pressure system east of the Cascade Range promotes radiative cooling and easterly flow. The only major outflow for the easterly flow is through the narrow Columbia River Gorge that cuts through the north–south-oriented Cascade Range and often experiences very strong gap flows. Observations collected during the Second Wind Forecast Improvement Project (WFIP2) are used to study a persistent cold pool in the Columbia River basin between 10 and 19 January 2017 that was associated with a strong gap flow. We used data from various remote sensing and in situ instruments and an optimal estimation physical retrieval to obtain thermodynamic profiles to address the temporal and spatial characteristics of the cold pool and gap flow and to investigate the physical processes involved during formation, maintenance, and decay. While large-scale temperature advection occurred during all phases, we found that the cold-pool vertical structure was modulated by the existence of low-level clouds and that turbulent shear-induced mixing and downslope wind storms likely played a role during its decay.
Abstract
Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and especially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer profilers, six radar wind profilers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical profiles of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the field project. Characteristics of these events are described, including statistics of the wind speed distributions and profiles, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.
Abstract
Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and especially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer profilers, six radar wind profilers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical profiles of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the field project. Characteristics of these events are described, including statistics of the wind speed distributions and profiles, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.
Abstract
During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.
Abstract
During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.
Abstract
A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power 
Abstract
A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power
Abstract
The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.
Abstract
The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.
