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Abstract
The North American monsoon system is known to produce significant summertime precipitation on the west coast of Mexico and the southwestern United States, with some areas receiving greater than 50% of their yearly rainfall between the months of July and September. The onset of the monsoon is attributed to a shift in the large-scale upper-level anticyclonic flow over the central United States, and the associated increases in moisture flux and resulting precipitation are tied to the low-level jets from the Gulf of California and the Gulf of Mexico. Individual monsoon surge events vary in intensity, as does the magnitude of the diurnal cycle of the low-level jets and precipitation. Numerical modeling and forecasting of these interacting large-and mesoscale monsoon features is often difficult in terms of accurately recreating the varying flow regimes aloft and near the surface and over both the flat and steep terrain that are encompassed within the monsoon region of influence.
The Regional Atmospheric Modeling System (RAMS) at Colorado State University has been utilized to investigate seasonal monsoon simulations for the 1988 (United States drought), 1993 (Midwest flood), and 1997 (El Niño year) monsoon seasons. In Part I of this paper the credibility of RAMS, as far as its ability to reproduce observed features of the North American monsoon system, is evaluated. Part II provides interseasonal comparisons of model-simulated monsoon features from the three simulated extreme seasons and results of sensitivity studies to SSTs and soil moisture variability. Part III presents the development of potential vorticity anomalies associated with convection over Mexico and their downstream influence over the central United States.
Abstract
The North American monsoon system is known to produce significant summertime precipitation on the west coast of Mexico and the southwestern United States, with some areas receiving greater than 50% of their yearly rainfall between the months of July and September. The onset of the monsoon is attributed to a shift in the large-scale upper-level anticyclonic flow over the central United States, and the associated increases in moisture flux and resulting precipitation are tied to the low-level jets from the Gulf of California and the Gulf of Mexico. Individual monsoon surge events vary in intensity, as does the magnitude of the diurnal cycle of the low-level jets and precipitation. Numerical modeling and forecasting of these interacting large-and mesoscale monsoon features is often difficult in terms of accurately recreating the varying flow regimes aloft and near the surface and over both the flat and steep terrain that are encompassed within the monsoon region of influence.
The Regional Atmospheric Modeling System (RAMS) at Colorado State University has been utilized to investigate seasonal monsoon simulations for the 1988 (United States drought), 1993 (Midwest flood), and 1997 (El Niño year) monsoon seasons. In Part I of this paper the credibility of RAMS, as far as its ability to reproduce observed features of the North American monsoon system, is evaluated. Part II provides interseasonal comparisons of model-simulated monsoon features from the three simulated extreme seasons and results of sensitivity studies to SSTs and soil moisture variability. Part III presents the development of potential vorticity anomalies associated with convection over Mexico and their downstream influence over the central United States.
Abstract
The microphysics module of the version of the Regional Atmospheric Modeling System (RAMS) maintained at Colorado State University has undergone a series of improvements, including the addition of a large-cloud-droplet mode from 40 to 80 μm in diameter and the prognostic number concentration of cloud droplets through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was included to represent the dual modes of cloud droplets that often appear in nature. The activation of CCN is parameterized through the use of a Lagrangian parcel model that considers ambient cloud conditions for the nucleation of cloud droplets from aerosol. These new additions were tested in simulations of a supercell thunderstorm initiated from a warm, moist bubble. Model response was explored in regard to the microphysics sensitivity to the large-droplet mode, number concentrations of CCN and GCCN, size distributions of these nuclei, and the presence of nuclei sources and sinks.
Abstract
The microphysics module of the version of the Regional Atmospheric Modeling System (RAMS) maintained at Colorado State University has undergone a series of improvements, including the addition of a large-cloud-droplet mode from 40 to 80 μm in diameter and the prognostic number concentration of cloud droplets through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was included to represent the dual modes of cloud droplets that often appear in nature. The activation of CCN is parameterized through the use of a Lagrangian parcel model that considers ambient cloud conditions for the nucleation of cloud droplets from aerosol. These new additions were tested in simulations of a supercell thunderstorm initiated from a warm, moist bubble. Model response was explored in regard to the microphysics sensitivity to the large-droplet mode, number concentrations of CCN and GCCN, size distributions of these nuclei, and the presence of nuclei sources and sinks.
Abstract
This paper is the second in a two-part series describing recent additions to the microphysics module of the Regional Atmospheric Modeling System (RAMS) at Colorado State University. These changes include the addition of a large-cloud-droplet mode (40–80 μm in diameter) into the liquid-droplet spectrum and the parameterization of cloud-droplet nucleation through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was introduced to represent more precisely the natural dual mode of the cloud-droplet distribution. The parameterized droplet nucleation replaces the former estimation of cloud-droplet formation solely from supersaturation calculations. In Part I of this series, details of the improvements to the microphysics were presented, including the set of equations governing the development of cloud droplets in the Lagrangian parcel model that was employed to parameterize this complex process. Supercell simulations were examined with respect to the model sensitivity to the presence and concentration of large cloud droplets, CCN, and GCCN. Part II examines the sensitivity of the model microphysics to imposed aerosol variations in a wintertime snowfall event that occurred over Colorado on 28–29 February 2004. Model analyses and sensitivity are compared with the real-time forecast version 4.3 of RAMS as well as selected snowpack telemetry (SNOTEL) accumulated precipitation data and surface data from Storm Peak Laboratory in Steamboat Springs, Colorado.
Abstract
This paper is the second in a two-part series describing recent additions to the microphysics module of the Regional Atmospheric Modeling System (RAMS) at Colorado State University. These changes include the addition of a large-cloud-droplet mode (40–80 μm in diameter) into the liquid-droplet spectrum and the parameterization of cloud-droplet nucleation through activation of cloud condensation nuclei (CCN) and giant CCN (GCCN). The large-droplet mode was introduced to represent more precisely the natural dual mode of the cloud-droplet distribution. The parameterized droplet nucleation replaces the former estimation of cloud-droplet formation solely from supersaturation calculations. In Part I of this series, details of the improvements to the microphysics were presented, including the set of equations governing the development of cloud droplets in the Lagrangian parcel model that was employed to parameterize this complex process. Supercell simulations were examined with respect to the model sensitivity to the presence and concentration of large cloud droplets, CCN, and GCCN. Part II examines the sensitivity of the model microphysics to imposed aerosol variations in a wintertime snowfall event that occurred over Colorado on 28–29 February 2004. Model analyses and sensitivity are compared with the real-time forecast version 4.3 of RAMS as well as selected snowpack telemetry (SNOTEL) accumulated precipitation data and surface data from Storm Peak Laboratory in Steamboat Springs, Colorado.
Abstract
This paper presents the development and application of a binned approach to cloud-droplet riming within a bulk microphysics model. This approach provides a more realistic representation of collision–coalescence that occurs between ice and cloud particles of various sizes. The binned approach allows the application of specific collection efficiencies, within the stochastic collection equation, for individual size bins of droplets and ice particles; this is in sharp contrast to the bulk approach that uses a single collection efficiency to describe the growth of a distribution of an ice species by collecting cloud droplets. Simulations of a winter orographic cloud event reveal a reduction in riming when using the binned riming approach and, subsequently, larger amounts of supercooled liquid water within the orographic cloud.
Abstract
This paper presents the development and application of a binned approach to cloud-droplet riming within a bulk microphysics model. This approach provides a more realistic representation of collision–coalescence that occurs between ice and cloud particles of various sizes. The binned approach allows the application of specific collection efficiencies, within the stochastic collection equation, for individual size bins of droplets and ice particles; this is in sharp contrast to the bulk approach that uses a single collection efficiency to describe the growth of a distribution of an ice species by collecting cloud droplets. Simulations of a winter orographic cloud event reveal a reduction in riming when using the binned riming approach and, subsequently, larger amounts of supercooled liquid water within the orographic cloud.
Abstract
The Regional Atmospheric Modeling System was used to simulate four winter snowfall events over the Park Range of Colorado. For each event, three hygroscopic aerosol sensitivity simulations were performed with initial aerosol profiles representing clean, moderately polluted, and highly polluted scenarios. Previous work demonstrates that the addition of aerosols can produce a snowfall spillover effect, during events in which riming growth of snow is prevalent in the presence of supercooled liquid water, that is due to a modified orographic cloud containing more numerous but smaller cloud droplets. This study focuses on the detailed microphysical processes that lead to snow growth in each event and how these processes are modulated by the addition of hygroscopic aerosols. A conceptual model of hydrometeor growth processes is presented, along a vertical orographic transect, that reveals zones of vapor deposition of ice and liquid, riming growth, evaporation, sublimation, and regions in which the Wegener–Bergeron–Findeisen (WBF) snow growth process is active. While the aerosol-induced spillover effect is largely determined by the degree of reduction in ice particle riming, an enhancement in the WBF snow growth process under more polluted conditions largely offsets the loss of rime growth, thus leading to a minimal net change in the regional precipitation.
Abstract
The Regional Atmospheric Modeling System was used to simulate four winter snowfall events over the Park Range of Colorado. For each event, three hygroscopic aerosol sensitivity simulations were performed with initial aerosol profiles representing clean, moderately polluted, and highly polluted scenarios. Previous work demonstrates that the addition of aerosols can produce a snowfall spillover effect, during events in which riming growth of snow is prevalent in the presence of supercooled liquid water, that is due to a modified orographic cloud containing more numerous but smaller cloud droplets. This study focuses on the detailed microphysical processes that lead to snow growth in each event and how these processes are modulated by the addition of hygroscopic aerosols. A conceptual model of hydrometeor growth processes is presented, along a vertical orographic transect, that reveals zones of vapor deposition of ice and liquid, riming growth, evaporation, sublimation, and regions in which the Wegener–Bergeron–Findeisen (WBF) snow growth process is active. While the aerosol-induced spillover effect is largely determined by the degree of reduction in ice particle riming, an enhancement in the WBF snow growth process under more polluted conditions largely offsets the loss of rime growth, thus leading to a minimal net change in the regional precipitation.
Abstract
The Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) has undergone development focused on improving the treatment of aerosols in the microphysics model, with the goal of examining the impacts of aerosol characteristics, scavenging, and regeneration processes, among others, on precipitation processes in clouds ranging from stratocumulus to deep convection and mixed-phase orographic clouds. Improvements in the representation of aerosols allow for more comprehensive studies of aerosol effects on cloud systems across scales. In RAMS there are now sub- and supermicrometer modes of sulfate, mineral dust, sea salt, and regenerated aerosol. All aerosol species can compete for cloud droplet nucleation, and they are regenerated via hydrometeor evaporation. A newly applied heterogeneous ice nuclei parameterization accounts for deposition nucleation and condensation and immersion freezing of aerosols greater than 0.5-μm diameter. There are also schemes for trimodal sea salt emissions and bimodal dust lofting that are functions of wind speed and surface properties. Aerosol wet and dry deposition accounts for collection by falling hydrometeors as well as gravitational settling of aerosols on water, soil, and vegetation. Aerosol radiative effects are parameterized via the Mie theory. An examination of the simulated impact of aerosol characteristics, sources, and sinks reveals mixed sensitivity among cloud types. For example, reduced aerosol solubility has little impact on deep convection since supersaturations are large and nearly all accumulation-mode aerosols activate. In contrast, reduced solubility results in reduced aerosol activation in precipitating stratocumulus. This leads to lower cloud droplet concentration, larger droplet size, and more efficient warm rain processes.
Abstract
The Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) has undergone development focused on improving the treatment of aerosols in the microphysics model, with the goal of examining the impacts of aerosol characteristics, scavenging, and regeneration processes, among others, on precipitation processes in clouds ranging from stratocumulus to deep convection and mixed-phase orographic clouds. Improvements in the representation of aerosols allow for more comprehensive studies of aerosol effects on cloud systems across scales. In RAMS there are now sub- and supermicrometer modes of sulfate, mineral dust, sea salt, and regenerated aerosol. All aerosol species can compete for cloud droplet nucleation, and they are regenerated via hydrometeor evaporation. A newly applied heterogeneous ice nuclei parameterization accounts for deposition nucleation and condensation and immersion freezing of aerosols greater than 0.5-μm diameter. There are also schemes for trimodal sea salt emissions and bimodal dust lofting that are functions of wind speed and surface properties. Aerosol wet and dry deposition accounts for collection by falling hydrometeors as well as gravitational settling of aerosols on water, soil, and vegetation. Aerosol radiative effects are parameterized via the Mie theory. An examination of the simulated impact of aerosol characteristics, sources, and sinks reveals mixed sensitivity among cloud types. For example, reduced aerosol solubility has little impact on deep convection since supersaturations are large and nearly all accumulation-mode aerosols activate. In contrast, reduced solubility results in reduced aerosol activation in precipitating stratocumulus. This leads to lower cloud droplet concentration, larger droplet size, and more efficient warm rain processes.
Abstract
Hygroscopic pollution aerosols have the potential to alter winter orographic snowfall totals and spatial distributions by modification of high-elevation supercooled orographic clouds and the riming process. The authors investigate the cumulative effect of varying the concentrations of hygroscopic aerosols during January–February for four recent winter snowfall seasons over the high terrain of Colorado. Version 6.0 of the Regional Atmospheric Modeling System (RAMS) is used to determine the particular mountain ranges and seasonal conditions that are most susceptible. Multiple winter seasonal simulations are run at both 3- and 1-km horizontal grid spacing with varying aerosol vertical profiles. Model-predicted snowfall accumulation trends are compared with automated snow water equivalent observations at high-elevation sites. An increase in aerosol concentration leads to reduced riming of cloud water by ice particles within supercooled, liquid orographic clouds, thus leading to lighter rimed hydrometers with slower fall speeds and longer horizontal trajectories. This effect results in a spillover of snowfall from the windward slope to the leeward slope. A snowfall spillover effect is most evident in the southern and western regions of the San Juan Range where high-moisture-laden storms are more prevalent. The effect over the Park Range is also present in each simulated season, but with lower amplitudes and slightly varying magnitudes among seasons. Seasons with greater overall snowfall exhibit a greater response in magnitude and percentage change. The smallest spillover effect occurred downwind of the primary western slope mountain barriers. Although the aerosol effect on snowfall can be locally significant in particularly wet winter seasons, the interseasonal variability in synoptic conditions can impose much larger widespread changes in snowfall accumulation.
Abstract
Hygroscopic pollution aerosols have the potential to alter winter orographic snowfall totals and spatial distributions by modification of high-elevation supercooled orographic clouds and the riming process. The authors investigate the cumulative effect of varying the concentrations of hygroscopic aerosols during January–February for four recent winter snowfall seasons over the high terrain of Colorado. Version 6.0 of the Regional Atmospheric Modeling System (RAMS) is used to determine the particular mountain ranges and seasonal conditions that are most susceptible. Multiple winter seasonal simulations are run at both 3- and 1-km horizontal grid spacing with varying aerosol vertical profiles. Model-predicted snowfall accumulation trends are compared with automated snow water equivalent observations at high-elevation sites. An increase in aerosol concentration leads to reduced riming of cloud water by ice particles within supercooled, liquid orographic clouds, thus leading to lighter rimed hydrometers with slower fall speeds and longer horizontal trajectories. This effect results in a spillover of snowfall from the windward slope to the leeward slope. A snowfall spillover effect is most evident in the southern and western regions of the San Juan Range where high-moisture-laden storms are more prevalent. The effect over the Park Range is also present in each simulated season, but with lower amplitudes and slightly varying magnitudes among seasons. Seasons with greater overall snowfall exhibit a greater response in magnitude and percentage change. The smallest spillover effect occurred downwind of the primary western slope mountain barriers. Although the aerosol effect on snowfall can be locally significant in particularly wet winter seasons, the interseasonal variability in synoptic conditions can impose much larger widespread changes in snowfall accumulation.
Abstract
Low-level warm-phase clouds cover a substantial portion of Earth’s oceans and play an important role in the global water and energy budgets. The characteristics of these clouds are controlled by the large-scale environment, boundary layer conditions, and cloud microphysics. Variability in the concentration of aerosols can alter cloud microphysical and precipitation processes that subsequently impact the system dynamics and thermodynamics and thereby create aerosol–cloud dynamic–thermodynamic feedback effects. In this study, three distinct cloud regimes were simulated, including stratocumulus, low-level cumulus (cumulus under stratocumulus), and deeper cumulus clouds. The simulations were conducted without environmental large-scale forcing, thereby allowing all three cloud types to freely interact with the environmental state in an undamped fashion. Increases in aerosol concentration in these unforced, warm-phase, tropical cloud simulations lead to the production of fewer low-level cumuli; thinning and erosion of the widespread stratocumulus layer; and the development of deeper, inversion-penetrating cumuli. The mechanisms for these changes are explored. Despite the development of deeper, more heavily precipitating cumuli, the reduction of the widespread moderately precipitating stratocumulus clouds leads to an overall reduction in domainwide accumulated precipitation when aerosol concentrations are enhanced.
Abstract
Low-level warm-phase clouds cover a substantial portion of Earth’s oceans and play an important role in the global water and energy budgets. The characteristics of these clouds are controlled by the large-scale environment, boundary layer conditions, and cloud microphysics. Variability in the concentration of aerosols can alter cloud microphysical and precipitation processes that subsequently impact the system dynamics and thermodynamics and thereby create aerosol–cloud dynamic–thermodynamic feedback effects. In this study, three distinct cloud regimes were simulated, including stratocumulus, low-level cumulus (cumulus under stratocumulus), and deeper cumulus clouds. The simulations were conducted without environmental large-scale forcing, thereby allowing all three cloud types to freely interact with the environmental state in an undamped fashion. Increases in aerosol concentration in these unforced, warm-phase, tropical cloud simulations lead to the production of fewer low-level cumuli; thinning and erosion of the widespread stratocumulus layer; and the development of deeper, inversion-penetrating cumuli. The mechanisms for these changes are explored. Despite the development of deeper, more heavily precipitating cumuli, the reduction of the widespread moderately precipitating stratocumulus clouds leads to an overall reduction in domainwide accumulated precipitation when aerosol concentrations are enhanced.
Abstract
There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.
Abstract
There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.
Abstract
Radar data from the 2004 North American Monsoon Experiment (NAME) enhanced observing period were used to investigate diurnal trends and vertical structure of precipitating features relative to local terrain. Two-dimensional composites of reflectivity and rain rate, created from the two Servicio Meteorológico Nacional (SMN; Mexican Weather Service) C-band Doppler radars and NCAR’s S-band polarimetric Doppler radar (S-Pol), were divided into four elevation groups: over water, 0–1000 m (MSL), 1000–2000 m, and greater than 2000 m. Analysis of precipitation frequency and average rainfall intensity using these composites reveals a strong diurnal trend in precipitation similar to that observed by the NAME Event Rain Gauge Network. Precipitation occurs most frequently during the afternoon over the Sierra Madre Occidental (SMO), with the peak frequency moving over the lower elevations by evening. Also, the precipitation events over the lower elevations are less frequent but of greater intensity (rain rate) than those over the SMO. Precipitation echoes were partitioned into convective and stratiform components to allow for examination of vertical characteristics of convection using data from S-Pol. Analyses of reflectivity profiles and echo-top heights confirm that convection over the lower terrain is more intense and vertically developed than convection over the SMO. Warm-cloud depths, estimated from the Colorado State University–NAME upper-air and surface gridded analyses are, on average, 2 times as deep over the lower terrain as compared with over the SMO. Using a simplified stochastic model for drop growth, it is shown that these differences in warm-cloud depths could possibly explain the observed elevation-dependent trends in precipitation intensity.
Abstract
Radar data from the 2004 North American Monsoon Experiment (NAME) enhanced observing period were used to investigate diurnal trends and vertical structure of precipitating features relative to local terrain. Two-dimensional composites of reflectivity and rain rate, created from the two Servicio Meteorológico Nacional (SMN; Mexican Weather Service) C-band Doppler radars and NCAR’s S-band polarimetric Doppler radar (S-Pol), were divided into four elevation groups: over water, 0–1000 m (MSL), 1000–2000 m, and greater than 2000 m. Analysis of precipitation frequency and average rainfall intensity using these composites reveals a strong diurnal trend in precipitation similar to that observed by the NAME Event Rain Gauge Network. Precipitation occurs most frequently during the afternoon over the Sierra Madre Occidental (SMO), with the peak frequency moving over the lower elevations by evening. Also, the precipitation events over the lower elevations are less frequent but of greater intensity (rain rate) than those over the SMO. Precipitation echoes were partitioned into convective and stratiform components to allow for examination of vertical characteristics of convection using data from S-Pol. Analyses of reflectivity profiles and echo-top heights confirm that convection over the lower terrain is more intense and vertically developed than convection over the SMO. Warm-cloud depths, estimated from the Colorado State University–NAME upper-air and surface gridded analyses are, on average, 2 times as deep over the lower terrain as compared with over the SMO. Using a simplified stochastic model for drop growth, it is shown that these differences in warm-cloud depths could possibly explain the observed elevation-dependent trends in precipitation intensity.
