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Abstract
Micro-oscillations in the atmosphere of the order of magnitude of minutes have been known and studied for many years. In 1936 Macelwane, and in 1939 Benioff, with their respective electromagnetic microbarographs showed that the spectrum of these micro-oscillations extends down into the order of seconds. The two different types of microbarographs respond to the same types of stimuli and low-level turbulence is an important source of the micro-oscillations. It is shown that fronts, as such, are not a source of these micro-oscillations, although micro-oscillations may accompany a front. The microbarograph has produced observational evidence supporting Haurwitz's theoretically derived conclusion that there is a similarity between internal wave patterns and convective patterns. It is shown that electromagnetic microbarographs are useful in studying cumulus activity.
Abstract
Micro-oscillations in the atmosphere of the order of magnitude of minutes have been known and studied for many years. In 1936 Macelwane, and in 1939 Benioff, with their respective electromagnetic microbarographs showed that the spectrum of these micro-oscillations extends down into the order of seconds. The two different types of microbarographs respond to the same types of stimuli and low-level turbulence is an important source of the micro-oscillations. It is shown that fronts, as such, are not a source of these micro-oscillations, although micro-oscillations may accompany a front. The microbarograph has produced observational evidence supporting Haurwitz's theoretically derived conclusion that there is a similarity between internal wave patterns and convective patterns. It is shown that electromagnetic microbarographs are useful in studying cumulus activity.
Abstract
An equation is developed, to show the magnitude of pressure change required to produce a given amplitude for a given period on the galvanometric record of the Macelwane electromagnetic microbarograph. The simultaneous occurrence of certain random oscillations recorded by the Macelwane microbarograph and various weather elements is investigated. It is found that there is a close analogy between these oscillations and atmospheric turbulence. With this analogy as a working hypothesis, a mathematical framework is developed which explains the observed relationships.
Abstract
An equation is developed, to show the magnitude of pressure change required to produce a given amplitude for a given period on the galvanometric record of the Macelwane electromagnetic microbarograph. The simultaneous occurrence of certain random oscillations recorded by the Macelwane microbarograph and various weather elements is investigated. It is found that there is a close analogy between these oscillations and atmospheric turbulence. With this analogy as a working hypothesis, a mathematical framework is developed which explains the observed relationships.
Abstract
The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.
Abstract
The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.
Abstract
The hypothesis that temporal averages of vertical motions over a single radar station are representative of weather systems large enough to be resolved by the radiosonde network is tested using data from the Flatland VHF radar, located in the very flat terrain of central Illinois. Six-hourly means of radar data were compared with four separate estimates of the synoptic or subsynoptic-scale vertical motions computed using the dynamical equations with unsmoothed rawinsonde data and with NMC gridded analyses. Spring and fall cases of large upward and downward vertical motions were selected for study. During the course of this study it was found that contamination of the Doppler radar spectra by heavy or moderate precipitation must be taken into account during analyses of VHF radar data in the troposphere.
The signs of the vertical-motion estimates from the indirect schemes in the extreme cases selected for study here nearly always agree, although the magnitudes often differ by a factor up to about 4. The adiabatic method was found to be unrepresentative due to the large time separation of radiosonde measurements. The 6-b average radar observations usually fall within the envelope of estimates from the various indirect methods. The major source of statistical uncertainty of the temporal means of the vertical motions seen by the radar is the mesoscale structure seen in shorter-period averages and not completely filtered out during averaging. Such structure is not resolved by the radiosonde network data and analyses.
Abstract
The hypothesis that temporal averages of vertical motions over a single radar station are representative of weather systems large enough to be resolved by the radiosonde network is tested using data from the Flatland VHF radar, located in the very flat terrain of central Illinois. Six-hourly means of radar data were compared with four separate estimates of the synoptic or subsynoptic-scale vertical motions computed using the dynamical equations with unsmoothed rawinsonde data and with NMC gridded analyses. Spring and fall cases of large upward and downward vertical motions were selected for study. During the course of this study it was found that contamination of the Doppler radar spectra by heavy or moderate precipitation must be taken into account during analyses of VHF radar data in the troposphere.
The signs of the vertical-motion estimates from the indirect schemes in the extreme cases selected for study here nearly always agree, although the magnitudes often differ by a factor up to about 4. The adiabatic method was found to be unrepresentative due to the large time separation of radiosonde measurements. The 6-b average radar observations usually fall within the envelope of estimates from the various indirect methods. The major source of statistical uncertainty of the temporal means of the vertical motions seen by the radar is the mesoscale structure seen in shorter-period averages and not completely filtered out during averaging. Such structure is not resolved by the radiosonde network data and analyses.
Abstract
On 30 May 2004, a supercell storm was sampled by a suite of instrumentation that had been deployed as part of the Thunderstorm Electrification and Lightning Experiment (TELEX). The instrumentation included the Oklahoma Lightning Mapping Array (OK-LMA), the National Severe Storms Laboratory S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) polarimetric radar at Norman, Oklahoma, and two mobile C-band, Shared Mobile Atmospheric Research and Teaching Radars (SMART-R). Combined, datasets collected by these instruments provided a unique opportunity to investigate the possible relationships among the supercell’s kinematic, microphysical, and electrical characteristics. This study focuses on the evolution of a ring of lightning activity that formed near the main updraft at approximately 0012 UTC, matured near 0039 UTC, and collapsed near 0050 UTC. During this time period, an F2-intensity tornado occurred near the lightning-ring region. Lightning density contours computed over 1-km layers are overlaid on polarimetric and dual-Doppler data to assess the low- and midlevel kinematic and microphysical characteristics within the lightning-ring region. Results indicate that the lightning ring begins in the middle and upper levels of the precipitation-cascade region, which is characterized by inferred graupel. The second time period shows that the lightning source densities take on a horizontal u-shaped pattern that is collocated with midlevel differential reflectivity and correlation coefficient rings and with the strong cyclonic vertical vorticity noted in the dual-Doppler data. The final time period shows dissipation of the u-shaped pattern and the polarimetric signatures as well as an increase in the lightning activity at the lower levels associated with the development of the rear-flank downdraft (RFD) and the envelopment of the vertical vorticity maximum by the RFD.
Abstract
On 30 May 2004, a supercell storm was sampled by a suite of instrumentation that had been deployed as part of the Thunderstorm Electrification and Lightning Experiment (TELEX). The instrumentation included the Oklahoma Lightning Mapping Array (OK-LMA), the National Severe Storms Laboratory S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) polarimetric radar at Norman, Oklahoma, and two mobile C-band, Shared Mobile Atmospheric Research and Teaching Radars (SMART-R). Combined, datasets collected by these instruments provided a unique opportunity to investigate the possible relationships among the supercell’s kinematic, microphysical, and electrical characteristics. This study focuses on the evolution of a ring of lightning activity that formed near the main updraft at approximately 0012 UTC, matured near 0039 UTC, and collapsed near 0050 UTC. During this time period, an F2-intensity tornado occurred near the lightning-ring region. Lightning density contours computed over 1-km layers are overlaid on polarimetric and dual-Doppler data to assess the low- and midlevel kinematic and microphysical characteristics within the lightning-ring region. Results indicate that the lightning ring begins in the middle and upper levels of the precipitation-cascade region, which is characterized by inferred graupel. The second time period shows that the lightning source densities take on a horizontal u-shaped pattern that is collocated with midlevel differential reflectivity and correlation coefficient rings and with the strong cyclonic vertical vorticity noted in the dual-Doppler data. The final time period shows dissipation of the u-shaped pattern and the polarimetric signatures as well as an increase in the lightning activity at the lower levels associated with the development of the rear-flank downdraft (RFD) and the envelopment of the vertical vorticity maximum by the RFD.
Abstract
Convection-permitting forecasts have improved the forecasts of flooding from intense rainfall. However, probabilistic forecasts, generally based upon ensemble methods, are essential to quantify forecast uncertainty. This leads to a need to understand how different aspects of the model system affect forecast behavior. We compare the uncertainty due to initial and boundary condition (IBC) perturbations and boundary layer turbulence using a superensemble (SE) created to determine the influence of 12 IBC perturbations versus 12 stochastic boundary layer (SBL) perturbations constructed using a physically based SBL scheme. We consider two mesoscale extreme precipitation events. For each, we run a 144-member SE. The SEs are analyzed to consider the growth of differences between the simulations, and the spatial structure and scales of those differences. The SBL perturbations rapidly spin up, typically within 12 h of precipitation commencing. The SBL perturbations eventually produce spread that is not statistically different from the spread produced by the IBC perturbations, though in one case there is initially increased spread from the IBC perturbations. Spatially, the growth from IBC occurs on larger scales than that produced by the SBL perturbations (typically by an order of magnitude). However, analysis across multiple scales shows that the SBL scheme produces a random relocation of precipitation up to the scale at which the ensemble members agree with each other. This implies that statistical postprocessing can be used instead of running larger ensembles. Use of these statistical postprocessing techniques could lead to more reliable probabilistic forecasts of convective events and their associated hazards.
Abstract
Convection-permitting forecasts have improved the forecasts of flooding from intense rainfall. However, probabilistic forecasts, generally based upon ensemble methods, are essential to quantify forecast uncertainty. This leads to a need to understand how different aspects of the model system affect forecast behavior. We compare the uncertainty due to initial and boundary condition (IBC) perturbations and boundary layer turbulence using a superensemble (SE) created to determine the influence of 12 IBC perturbations versus 12 stochastic boundary layer (SBL) perturbations constructed using a physically based SBL scheme. We consider two mesoscale extreme precipitation events. For each, we run a 144-member SE. The SEs are analyzed to consider the growth of differences between the simulations, and the spatial structure and scales of those differences. The SBL perturbations rapidly spin up, typically within 12 h of precipitation commencing. The SBL perturbations eventually produce spread that is not statistically different from the spread produced by the IBC perturbations, though in one case there is initially increased spread from the IBC perturbations. Spatially, the growth from IBC occurs on larger scales than that produced by the SBL perturbations (typically by an order of magnitude). However, analysis across multiple scales shows that the SBL scheme produces a random relocation of precipitation up to the scale at which the ensemble members agree with each other. This implies that statistical postprocessing can be used instead of running larger ensembles. Use of these statistical postprocessing techniques could lead to more reliable probabilistic forecasts of convective events and their associated hazards.
Abstract
Intense lake-effect snowstorms regularly develop over the eastern Great Lakes, resulting in extreme winter weather conditions with snowfalls sometimes exceeding 1 m. The Ontario Winter Lake-effect Systems (OWLeS) field campaign sought to obtain unprecedented observations of these highly complex winter storms.
OWLeS employed an extensive and diverse array of instrumentation, including the University of Wyoming King Air research aircraft, five university-owned upper-air sounding systems, three Center for Severe Weather Research Doppler on Wheels radars, a wind profiler, profiling cloud and precipitation radars, an airborne lidar, mobile mesonets, deployable weather Pods, and snowfall and particle measuring systems. Close collaborations with National Weather Service Forecast Offices during and following OWLeS have provided a direct pathway for results of observational and numerical modeling analyses to improve the prediction of severe lake-effect snowstorm evolution. The roles of atmospheric boundary layer processes over heterogeneous surfaces (water, ice, and land), mixed-phase microphysics within shallow convection, topography, and mesoscale convective structures are being explored.
More than 75 students representing nine institutions participated in a wide variety of data collection efforts, including the operation of radars, radiosonde systems, mobile mesonets, and snow observation equipment in challenging and severe winter weather environments.
Abstract
Intense lake-effect snowstorms regularly develop over the eastern Great Lakes, resulting in extreme winter weather conditions with snowfalls sometimes exceeding 1 m. The Ontario Winter Lake-effect Systems (OWLeS) field campaign sought to obtain unprecedented observations of these highly complex winter storms.
OWLeS employed an extensive and diverse array of instrumentation, including the University of Wyoming King Air research aircraft, five university-owned upper-air sounding systems, three Center for Severe Weather Research Doppler on Wheels radars, a wind profiler, profiling cloud and precipitation radars, an airborne lidar, mobile mesonets, deployable weather Pods, and snowfall and particle measuring systems. Close collaborations with National Weather Service Forecast Offices during and following OWLeS have provided a direct pathway for results of observational and numerical modeling analyses to improve the prediction of severe lake-effect snowstorm evolution. The roles of atmospheric boundary layer processes over heterogeneous surfaces (water, ice, and land), mixed-phase microphysics within shallow convection, topography, and mesoscale convective structures are being explored.
More than 75 students representing nine institutions participated in a wide variety of data collection efforts, including the operation of radars, radiosonde systems, mobile mesonets, and snow observation equipment in challenging and severe winter weather environments.
Abstract
Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.
Abstract
Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.
Abstract
The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.
Abstract
The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.
Climatologists from the climate centers of 12 states of the upper Midwest contributed temperature, precipitation, and related data for December 1982, January and February 1983. Analyses present the month-to-month spatial anomaly patterns of these parameters. Mean monthly temperatures were much above normal (30-year means) during the three months in virtually the entire region, with maximum magnitudes (+4 to +9°C) extending from the Dakotas to Iowa, and to Indiana (December) and Missouri (January and February).
December precipitation was also above normal with anomalies of + 100 mm in much of Missouri, Illinois, extreme southwest Michigan, and Indiana. The maximum anomaly was over +250 mm in southern Illinois. January and February precipitation anomalies showed only little deviation from normal.
Impacts of the mild winter were generally favorable to consumers in that heating demand was reduced from normal, and particularly reduced from that of the previous year. Costs for urban snow removal were much under budget, as well. The only potentially negative impact was a relatively high survival rate of insect larvae, which is usually controlled by normally colder winter temperatures.
The 1982 peach crop of southern Illinois was essentially lost during the 1981–82 winter due to record cold temperatures. The 1983 crop was also lost largely by a late spring frost, even though the winter was one of the warmest on record.
Climatologists from the climate centers of 12 states of the upper Midwest contributed temperature, precipitation, and related data for December 1982, January and February 1983. Analyses present the month-to-month spatial anomaly patterns of these parameters. Mean monthly temperatures were much above normal (30-year means) during the three months in virtually the entire region, with maximum magnitudes (+4 to +9°C) extending from the Dakotas to Iowa, and to Indiana (December) and Missouri (January and February).
December precipitation was also above normal with anomalies of + 100 mm in much of Missouri, Illinois, extreme southwest Michigan, and Indiana. The maximum anomaly was over +250 mm in southern Illinois. January and February precipitation anomalies showed only little deviation from normal.
Impacts of the mild winter were generally favorable to consumers in that heating demand was reduced from normal, and particularly reduced from that of the previous year. Costs for urban snow removal were much under budget, as well. The only potentially negative impact was a relatively high survival rate of insect larvae, which is usually controlled by normally colder winter temperatures.
The 1982 peach crop of southern Illinois was essentially lost during the 1981–82 winter due to record cold temperatures. The 1983 crop was also lost largely by a late spring frost, even though the winter was one of the warmest on record.
