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Abstract
Surface wind measurements taken during the summers of 1973 and 1975 in the Florida Area Cumulus Experiments (FACE) fine-mesh networks are used to calculate surface divergence on the convective scale and mesoscale.
Examination of the daily time series of divergence averaged over a 30 km × 25 km grid showed that on days with deep convective activity over and around the network, a definable sequence in the network-averaged surface wind divergence is observed. The sequence consists of five stages: persistent convergence, peak convergence, non-divergence, peak divergence and the return to a non-divergent state.
Use is made of observations of individual cases and of radar composites built around the above sequence in the network-averaged divergence fields to demonstrate that a convective-scale feedback mechanism consisting of outflows from previous storms triggering new growth is frequently observed during both experimental periods.
It is shown that the observations taken over the FACE 1975 network are consistent with the model results of Pielke (1974) and estimates of peninsular-scale convergence. Comparison of the convective-scale vertical transports over the network at the near surface (4 m) level with the peninsular-scale forcing reveals a relationship between the two. After the initiation of convective processes by the peninsular-scale forcing, downdraft-induced convergence maintains and intensifies the convective-scale activity long after the peninsular-scale forcing has passed its peak. The daily cycle of initiation, intensification, and the eventual decline of convective activity is related to the rate of change of the peninsular-scale divergence.
The links established between the various scales are of fundamental importance to the understanding of the initiation, maintenance, and decay of deep precipitating convection and to its theoretical parameterization.
Abstract
Surface wind measurements taken during the summers of 1973 and 1975 in the Florida Area Cumulus Experiments (FACE) fine-mesh networks are used to calculate surface divergence on the convective scale and mesoscale.
Examination of the daily time series of divergence averaged over a 30 km × 25 km grid showed that on days with deep convective activity over and around the network, a definable sequence in the network-averaged surface wind divergence is observed. The sequence consists of five stages: persistent convergence, peak convergence, non-divergence, peak divergence and the return to a non-divergent state.
Use is made of observations of individual cases and of radar composites built around the above sequence in the network-averaged divergence fields to demonstrate that a convective-scale feedback mechanism consisting of outflows from previous storms triggering new growth is frequently observed during both experimental periods.
It is shown that the observations taken over the FACE 1975 network are consistent with the model results of Pielke (1974) and estimates of peninsular-scale convergence. Comparison of the convective-scale vertical transports over the network at the near surface (4 m) level with the peninsular-scale forcing reveals a relationship between the two. After the initiation of convective processes by the peninsular-scale forcing, downdraft-induced convergence maintains and intensifies the convective-scale activity long after the peninsular-scale forcing has passed its peak. The daily cycle of initiation, intensification, and the eventual decline of convective activity is related to the rate of change of the peninsular-scale divergence.
The links established between the various scales are of fundamental importance to the understanding of the initiation, maintenance, and decay of deep precipitating convection and to its theoretical parameterization.
Abstract
It is shown that the magnitude of the flux adjustments required in a coupled model simulation to prevent climate drift does not reduce when a higher resolution, and more realistic, ocean component is used. This is because much of the flux adjustment field is dominated by regions of high horizontal SST gradient, where even small shifts in positioning lead to large SST errors and therefore large flux adjustments. A simple scheme is used to remove the effects of these regions in the calculation of flux adjustment and this results in adjustments considerably smaller than those usually obtained. Comparison of the resulting flux adjustment field with an estimate of the error in surface flux from the atmospheric model shows the remaining peak values are generally identifiable with known systematic errors in the atmospheric model.
Abstract
It is shown that the magnitude of the flux adjustments required in a coupled model simulation to prevent climate drift does not reduce when a higher resolution, and more realistic, ocean component is used. This is because much of the flux adjustment field is dominated by regions of high horizontal SST gradient, where even small shifts in positioning lead to large SST errors and therefore large flux adjustments. A simple scheme is used to remove the effects of these regions in the calculation of flux adjustment and this results in adjustments considerably smaller than those usually obtained. Comparison of the resulting flux adjustment field with an estimate of the error in surface flux from the atmospheric model shows the remaining peak values are generally identifiable with known systematic errors in the atmospheric model.
Abstract
Data gathered by the University of Wyoming King Air, the Atmospheric Environmental Services Twin otter and an NCAR Queen Air were used in thermodynamic analyses to determine the sources of environmental air entrained into cumulus clouds. The measurements were made in clouds ranging from small cumuli a few kilometers deep to a large supercell system. Previous results have indicated that the source of entrained air in continental cumuli is generally above the flight level, often near cloud top. The results reported here, however, suggest that the source of entrained air is close to, or slightly above, the observation level of the aircraft, even when the aircraft descends through different levels in the cloud. The results are consistent with the idea that cumulus clouds consist of thermal-like elements from which the least buoyant mixed parcels are shed off and the most buoyant mixed parcels may continue with the general ascent. A schematic model of cumulus convection is presented and supported by measurements of air motions in small cumulus clouds.
Abstract
Data gathered by the University of Wyoming King Air, the Atmospheric Environmental Services Twin otter and an NCAR Queen Air were used in thermodynamic analyses to determine the sources of environmental air entrained into cumulus clouds. The measurements were made in clouds ranging from small cumuli a few kilometers deep to a large supercell system. Previous results have indicated that the source of entrained air in continental cumuli is generally above the flight level, often near cloud top. The results reported here, however, suggest that the source of entrained air is close to, or slightly above, the observation level of the aircraft, even when the aircraft descends through different levels in the cloud. The results are consistent with the idea that cumulus clouds consist of thermal-like elements from which the least buoyant mixed parcels are shed off and the most buoyant mixed parcels may continue with the general ascent. A schematic model of cumulus convection is presented and supported by measurements of air motions in small cumulus clouds.
Abstract
Ultragiant aerosol particles (UGA) are potentially important for warm rain formation because of their ability to initiate coalescence immediately upon entering a cloud, so it is desirable to obtain local estimates during any field campaign that studies warm rain. Estimates of UGA in clear air from a one-dimensional optical array probe averaged over long time periods from the Small Cumulus Microphysics Study have been published in the literature, but further analysis and comparisons to other probes, presented here, show that the data on which these estimates were based were probably contaminated by noise. A possible explanation for the noise in the probe is given, as are new upper limits, based on few or no particles detected by a two-dimensional optical array probe.
Abstract
Ultragiant aerosol particles (UGA) are potentially important for warm rain formation because of their ability to initiate coalescence immediately upon entering a cloud, so it is desirable to obtain local estimates during any field campaign that studies warm rain. Estimates of UGA in clear air from a one-dimensional optical array probe averaged over long time periods from the Small Cumulus Microphysics Study have been published in the literature, but further analysis and comparisons to other probes, presented here, show that the data on which these estimates were based were probably contaminated by noise. A possible explanation for the noise in the probe is given, as are new upper limits, based on few or no particles detected by a two-dimensional optical array probe.
Abstract
The objective of this study is to address the problem of the production of rain in warm cumulus clouds that has been observed to occur within about 20 min. A hybrid model approach is used where a microphysical parcel model is run along trajectories produced by a 3D cloud model, with sufficiently high resolution to allow explicit representation of the effects of entrainment and mixing. The model calculations take the next step from the previous study, which showed that entrainment and mixing can accelerate the diffusional growth of cloud droplets to the production of raindrops by collision and coalescence. The mechanism depends on the variability in droplet trajectories arriving at a given location and time in a cumulus cloud. The resulting broadening favors collisions among droplets in the main peak of the droplet size distribution, which leads to the production of raindrop embryos. However, this production and the subsequent growth of the embryos to become raindrops only occur in regions of relatively high cloud water content. The modeling framework allows an objective test of this sequence of events that explain the seemingly contradictory notions of the enhancement of cloud droplet growth as a result of entrainment and mixing and the need for substantial cloud water content for collision and coalescence growth. The results show that raindrops can be produced within 20 min in warm cumulus clouds. The rain produced is sensitive to giant aerosols, but modification of the modeling framework is required to conduct a more robust test of their relative importance.
Abstract
The objective of this study is to address the problem of the production of rain in warm cumulus clouds that has been observed to occur within about 20 min. A hybrid model approach is used where a microphysical parcel model is run along trajectories produced by a 3D cloud model, with sufficiently high resolution to allow explicit representation of the effects of entrainment and mixing. The model calculations take the next step from the previous study, which showed that entrainment and mixing can accelerate the diffusional growth of cloud droplets to the production of raindrops by collision and coalescence. The mechanism depends on the variability in droplet trajectories arriving at a given location and time in a cumulus cloud. The resulting broadening favors collisions among droplets in the main peak of the droplet size distribution, which leads to the production of raindrop embryos. However, this production and the subsequent growth of the embryos to become raindrops only occur in regions of relatively high cloud water content. The modeling framework allows an objective test of this sequence of events that explain the seemingly contradictory notions of the enhancement of cloud droplet growth as a result of entrainment and mixing and the need for substantial cloud water content for collision and coalescence growth. The results show that raindrops can be produced within 20 min in warm cumulus clouds. The rain produced is sensitive to giant aerosols, but modification of the modeling framework is required to conduct a more robust test of their relative importance.
Abstract
Numerical experiments have been conducted to investigate the effect of updating models of the Indian Ocean using simulated temperature (mass) and velocity data. Two models are used: a linear reduced gravity model with one active layer, and a nonlinear 12-level general circulation model (GCM). In both cases an “identical twin” approach is adopted, in which the same model is used to generate the “observed” data in a “truth run”, as is used in the assimilation run.
Temperature data is found to be better than velocity data for initializing both models. However, further experiments with the layer model showed that increasing the model diffusion and decreasing the eddy viscosity results in velocity data being better for initializing. These results are ascribed to the energy distribution, with the proportion of kinetic energy being greater in the later experiments.
Simulated data from the proposed TOGA Indian Ocean XBT network were also assimilated into both models using a successive correction interpolation scheme. It is found that for the layer model, which had smooth horizontal variations in thermocline depth, the errors fall to zero within a couple of months. However, in the experiments with the GCM there is little reduction in the assimilation error after the first model update, due to the data analysis scheme not being able to resolve the horizontal temperature structure in the GCM.
Abstract
Numerical experiments have been conducted to investigate the effect of updating models of the Indian Ocean using simulated temperature (mass) and velocity data. Two models are used: a linear reduced gravity model with one active layer, and a nonlinear 12-level general circulation model (GCM). In both cases an “identical twin” approach is adopted, in which the same model is used to generate the “observed” data in a “truth run”, as is used in the assimilation run.
Temperature data is found to be better than velocity data for initializing both models. However, further experiments with the layer model showed that increasing the model diffusion and decreasing the eddy viscosity results in velocity data being better for initializing. These results are ascribed to the energy distribution, with the proportion of kinetic energy being greater in the later experiments.
Simulated data from the proposed TOGA Indian Ocean XBT network were also assimilated into both models using a successive correction interpolation scheme. It is found that for the layer model, which had smooth horizontal variations in thermocline depth, the errors fall to zero within a couple of months. However, in the experiments with the GCM there is little reduction in the assimilation error after the first model update, due to the data analysis scheme not being able to resolve the horizontal temperature structure in the GCM.
Abstract
Stochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the stochastically perturbed parameterization tendency (SPPT) scheme, which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely, the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good grounds for implementing stochastic schemes in ocean models, the results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.
Abstract
Stochastic parameterization provides a methodology for representing model uncertainty in ensemble forecasts. Here the impact on forecast reliability over seasonal time scales of three existing stochastic parameterizations in the ocean component of a coupled model is studied. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the stochastically perturbed parameterization tendency (SPPT) scheme, which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely, the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error on seasonal time scales. While there are good grounds for implementing stochastic schemes in ocean models, the results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.
Abstract
Surface, aircraft, and satellite observations are analyzed for the 21-day 1989 intensive field campaign of the First ISLSCP Field Experiment (FIFE) to determine the effect of precipitation, vegetation, and soil moisture distributions on the thermal properties of the surface including the heat and moisture fluxes, and the corresponding response in the boundary-layer circulation. Mean and variance properties of the surface variables are first documented at various time and space scales. These calculations are designed to set the stage for Part II, a modeling study that will focus on how time–space dependent rainfall distribution influences the intensity of the feedback between a vegetated surface and the atmospheric boundary layer. Further analysis shows strongly demarked vegetation and soil moisture gradients extending across the FIFE experimental site that were developed and maintained by the antecedent and ongoing spatial distribution of rainfall over the region. These gradients are shown to have a pronounced influence on the thermodynamic properties of the surface. Furthermore, perturbation surface wind analysis suggests for both short-term steady-state conditions and long-term averaged conditions that the gradient pattern maintained a diurnally oscillating local direct circulation with perturbation vertical velocities of the same order as developing cumulus clouds. Dynamical and scaling considerations suggest that the embedded perturbation circulation is driven by surface heating/cooling gradients and terrain effects rather than the manifestation of an inertial oscillation. The implication is that at even relatively small scales <30 km), the differential evolution in vegetation density and soil moisture distribution over a relatively homogenous ecotone can give rise to preferential boundary-layer circulations capable of modifying local-scale horizontal and vertical motions.
Abstract
Surface, aircraft, and satellite observations are analyzed for the 21-day 1989 intensive field campaign of the First ISLSCP Field Experiment (FIFE) to determine the effect of precipitation, vegetation, and soil moisture distributions on the thermal properties of the surface including the heat and moisture fluxes, and the corresponding response in the boundary-layer circulation. Mean and variance properties of the surface variables are first documented at various time and space scales. These calculations are designed to set the stage for Part II, a modeling study that will focus on how time–space dependent rainfall distribution influences the intensity of the feedback between a vegetated surface and the atmospheric boundary layer. Further analysis shows strongly demarked vegetation and soil moisture gradients extending across the FIFE experimental site that were developed and maintained by the antecedent and ongoing spatial distribution of rainfall over the region. These gradients are shown to have a pronounced influence on the thermodynamic properties of the surface. Furthermore, perturbation surface wind analysis suggests for both short-term steady-state conditions and long-term averaged conditions that the gradient pattern maintained a diurnally oscillating local direct circulation with perturbation vertical velocities of the same order as developing cumulus clouds. Dynamical and scaling considerations suggest that the embedded perturbation circulation is driven by surface heating/cooling gradients and terrain effects rather than the manifestation of an inertial oscillation. The implication is that at even relatively small scales <30 km), the differential evolution in vegetation density and soil moisture distribution over a relatively homogenous ecotone can give rise to preferential boundary-layer circulations capable of modifying local-scale horizontal and vertical motions.
Abstract
Observations of the formation of the first radar echoes in small cumulus clouds are compared with results of a stochastic coalescence model run in the framework of a closed parcel. The observations were made with an instrumented aircraft and a high-powered dual-wavelength radar during the Small Cumulus Microphysics Study (SCMS) in Florida. The principal conclusion is that coalescence growth on giant and ultragiant nuclei may be sufficient to explain observations.
The concentration of cloud droplets varied from under 300 cm−3 when surface winds were from the ocean, to over 1000 cm−3 when the wind direction was from the mainland. Although there is a slight tendency for the altitude of the first 0-dBZ echo to be lower on average in maritime than in continental clouds there were several cases where it was higher. The model results suggest that the lack of correlation is consistent with drops forming on giant and ultragiant nuclei. The first 0-dBZ echo was observed to form at higher altitudes in clouds with stronger updrafts.
Abstract
Observations of the formation of the first radar echoes in small cumulus clouds are compared with results of a stochastic coalescence model run in the framework of a closed parcel. The observations were made with an instrumented aircraft and a high-powered dual-wavelength radar during the Small Cumulus Microphysics Study (SCMS) in Florida. The principal conclusion is that coalescence growth on giant and ultragiant nuclei may be sufficient to explain observations.
The concentration of cloud droplets varied from under 300 cm−3 when surface winds were from the ocean, to over 1000 cm−3 when the wind direction was from the mainland. Although there is a slight tendency for the altitude of the first 0-dBZ echo to be lower on average in maritime than in continental clouds there were several cases where it was higher. The model results suggest that the lack of correlation is consistent with drops forming on giant and ultragiant nuclei. The first 0-dBZ echo was observed to form at higher altitudes in clouds with stronger updrafts.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.
Abstract
The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.
