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- Author or Editor: Michael P. Meyers x
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Abstract
A prolonged orographic precipitation event occurred over the Sierra Nevada in central California on 12–13 February 1986. This well-documented case was investigated via the nonhydrostatic version of the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The two-dimensional, cross-barrier simulations produced flow fields and microphysical structure, which compared well with observations. The feasibility of producing quantitative precipitation forecasts (QPF) with an explicit cloud model was also demonstrated.
The experiments exhibited a profound sensitivity to the input sounding. Initializing with a sounding, which is representative of the upstream environment, was the most critical factor to the success of the simulation. The QPF was also quite sensitive to input graupel density. Decreasing the density of graupel led to increases in the overall precipitation. Sensitivities to other microphysical parameters as well as orography and dynamics were also examined.
Abstract
A prolonged orographic precipitation event occurred over the Sierra Nevada in central California on 12–13 February 1986. This well-documented case was investigated via the nonhydrostatic version of the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The two-dimensional, cross-barrier simulations produced flow fields and microphysical structure, which compared well with observations. The feasibility of producing quantitative precipitation forecasts (QPF) with an explicit cloud model was also demonstrated.
The experiments exhibited a profound sensitivity to the input sounding. Initializing with a sounding, which is representative of the upstream environment, was the most critical factor to the success of the simulation. The QPF was also quite sensitive to input graupel density. Decreasing the density of graupel led to increases in the overall precipitation. Sensitivities to other microphysical parameters as well as orography and dynamics were also examined.
Abstract
Two new primary ice-nucleation parameterizations are examined in the Regional Atmospheric Modeling System (RAMS) cloud model via sensitivity tests on a wintertime precipitation event in the Sierra Nevada region. A model combining the effects of deposition and condensation-freezing nucleation is formulated based on data obtained from continuous-flow diffusion chambers. The data indicate an exponential variation of ice-nuclei concentrations with ice supersaturation reasonably independent of temperatures between −7° and −20°C. Predicted ice concentrations from these measurements exceed values predicted by the widely used temperatures dependent Fletcher approximation by as much as one order of magnitude at temperatures warmer than −20°C. A contact-freezing nucleation model is also formulated based on laboratory data gathered by various authors using techniques that isolated this nucleation mode. Predicted contact nuclei concentrations based on the newer measurements are as much as three orders of magnitude less than values estimated by Young's model, which has been widely used for predicted schemes.
Simulations of the orographic precipitation event over the Sierra Nevada indicate that the pristine ice fields are very sensitive to the changes in the ice-nucleation formulation, with the pristine ice field resulting from the new formulation comparing much better to the observed magnitudes and structure from the case study. Deposition-condensation-freezing nucleation dominates contact-freezing nucleation in the new scheme, except in the downward branch of the mountain wave, where contact freezing dominates in the evaporating cloud. Secondary ice production is more dominant at warm temperatures in the new scheme, producing more pristine ice crystals over the barrier. The old contact-freezing nucleation scheme overpredicts pristine ice-crystal concentrations, which depletes cloud water available for secondary ice production. The effect of the new parameterizations on the precipitating hydrometeors is substantial with nearly a 10% increase in precipitation across the domain. Graupel precipitation increased dramatically due to more cloud water available with the new scheme.
Abstract
Two new primary ice-nucleation parameterizations are examined in the Regional Atmospheric Modeling System (RAMS) cloud model via sensitivity tests on a wintertime precipitation event in the Sierra Nevada region. A model combining the effects of deposition and condensation-freezing nucleation is formulated based on data obtained from continuous-flow diffusion chambers. The data indicate an exponential variation of ice-nuclei concentrations with ice supersaturation reasonably independent of temperatures between −7° and −20°C. Predicted ice concentrations from these measurements exceed values predicted by the widely used temperatures dependent Fletcher approximation by as much as one order of magnitude at temperatures warmer than −20°C. A contact-freezing nucleation model is also formulated based on laboratory data gathered by various authors using techniques that isolated this nucleation mode. Predicted contact nuclei concentrations based on the newer measurements are as much as three orders of magnitude less than values estimated by Young's model, which has been widely used for predicted schemes.
Simulations of the orographic precipitation event over the Sierra Nevada indicate that the pristine ice fields are very sensitive to the changes in the ice-nucleation formulation, with the pristine ice field resulting from the new formulation comparing much better to the observed magnitudes and structure from the case study. Deposition-condensation-freezing nucleation dominates contact-freezing nucleation in the new scheme, except in the downward branch of the mountain wave, where contact freezing dominates in the evaporating cloud. Secondary ice production is more dominant at warm temperatures in the new scheme, producing more pristine ice crystals over the barrier. The old contact-freezing nucleation scheme overpredicts pristine ice-crystal concentrations, which depletes cloud water available for secondary ice production. The effect of the new parameterizations on the precipitating hydrometeors is substantial with nearly a 10% increase in precipitation across the domain. Graupel precipitation increased dramatically due to more cloud water available with the new scheme.
Abstract
Ice initiation by specific cloud seeding aerosols, quantified in laboratory studies, has been formulated for use in mesoscale numerical cloud models. This detailed approach, which explicitly represents artificial ice nuclei activation, is unique for mesoscale simulators of cloud seeding. This new scheme was applied in the simulation of an orographic precipitation event seeded with the specific aerosols on 18 December 1986 from the Sierra Cooperative Pilot Project using the Regional Atmospheric Modeling System (RAMS). Total ice concentrations formed following seeding agreed well with observations. RAMS's three-dimensional results showed that the new seeding parameterization impacted the microphysical fields producing increased pristine ice crystal, aggregate, and graupel mass downstream of the seeded regions. Pristine ice concentration also increased as much as an order of magnitude in some locations due to seeding. Precipitation augmentation due to the seeding was 0.1–0.7 mm, similar to values inferred from the observations. Simulated precipitation enhancement occurred due to increased precipitation efficiency since no large precipitation deficits occurred in the simulation. These maxima were collocated with regions of supercooled liquid water where nucleation by man-made ice nucleus aerosols was optimized.
Abstract
Ice initiation by specific cloud seeding aerosols, quantified in laboratory studies, has been formulated for use in mesoscale numerical cloud models. This detailed approach, which explicitly represents artificial ice nuclei activation, is unique for mesoscale simulators of cloud seeding. This new scheme was applied in the simulation of an orographic precipitation event seeded with the specific aerosols on 18 December 1986 from the Sierra Cooperative Pilot Project using the Regional Atmospheric Modeling System (RAMS). Total ice concentrations formed following seeding agreed well with observations. RAMS's three-dimensional results showed that the new seeding parameterization impacted the microphysical fields producing increased pristine ice crystal, aggregate, and graupel mass downstream of the seeded regions. Pristine ice concentration also increased as much as an order of magnitude in some locations due to seeding. Precipitation augmentation due to the seeding was 0.1–0.7 mm, similar to values inferred from the observations. Simulated precipitation enhancement occurred due to increased precipitation efficiency since no large precipitation deficits occurred in the simulation. These maxima were collocated with regions of supercooled liquid water where nucleation by man-made ice nucleus aerosols was optimized.
Abstract
An effort to improve descriptions of ice initiation processes of relevance to cirrus clouds for use in regional-scale numerical cloud models with bulk microphysical schemes is described. This is approached by deriving practical parameterizations of the process of ice initiation by homogeneous freezing of cloud and haze (CCN) particles in the atmosphere. The homogeneous freezing formulations may be used with generalized distributions of cloud water and CCN (pure ammonium sulfate assumed). Numerical cloud model sensitivity experiments were made using a microphysical parcel model and a mososcale cloud model to investigate the impact of the homogeneous freezing process and heterogeneous ice nucleation processes on the formation and makeup of cirrus clouds. These studies point out the critical nature of assumptions made regarding the abundance and character of heterogeneous ice nuclei (IN) present in the upper troposphere. Conclusions regarding the sources of ice crystals in cirrus clouds and the potential impact of human activities on these populations must await further measurements of CCN and particularly IN in upper-tropospheric and lower-stratospheric regions.
Abstract
An effort to improve descriptions of ice initiation processes of relevance to cirrus clouds for use in regional-scale numerical cloud models with bulk microphysical schemes is described. This is approached by deriving practical parameterizations of the process of ice initiation by homogeneous freezing of cloud and haze (CCN) particles in the atmosphere. The homogeneous freezing formulations may be used with generalized distributions of cloud water and CCN (pure ammonium sulfate assumed). Numerical cloud model sensitivity experiments were made using a microphysical parcel model and a mososcale cloud model to investigate the impact of the homogeneous freezing process and heterogeneous ice nucleation processes on the formation and makeup of cirrus clouds. These studies point out the critical nature of assumptions made regarding the abundance and character of heterogeneous ice nuclei (IN) present in the upper troposphere. Conclusions regarding the sources of ice crystals in cirrus clouds and the potential impact of human activities on these populations must await further measurements of CCN and particularly IN in upper-tropospheric and lower-stratospheric regions.
Abstract
Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Ri b ) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Ri b parameterizations to stable regimes and developed parameterizations for the friction velocity (u *), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Ri b parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Ri b parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Ri b parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.
Abstract
Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Ri b ) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Ri b parameterizations to stable regimes and developed parameterizations for the friction velocity (u *), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Ri b parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Ri b parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Ri b parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.
Abstract
Observational data collected during the FIRE II experiment showing the existence of bimodal ice spectra along with experimental evidence of the size dependence of riming are utilized in the development of a bimodal ice spectrum parameterization for use in the RAMS model. Two ice classes are defined: pristine ice and snow, each described by a separate, complete gamma distribution function. Pristine ice is small ice consisting of particles with mean sizes less than 125 µm, while snow is the large class consisting of particles greater than 125 µm. Analytical equations are formulated for the conversion between the ice classes by vapor depositional growth (sublimation). During ice subsaturated conditions, a number concentration sink is parameterized for all ice species. The performance of the parameterizations in a simple parcel model is discussed and evaluated against an explicit Lagrangian parcel microphysical model.
Abstract
Observational data collected during the FIRE II experiment showing the existence of bimodal ice spectra along with experimental evidence of the size dependence of riming are utilized in the development of a bimodal ice spectrum parameterization for use in the RAMS model. Two ice classes are defined: pristine ice and snow, each described by a separate, complete gamma distribution function. Pristine ice is small ice consisting of particles with mean sizes less than 125 µm, while snow is the large class consisting of particles greater than 125 µm. Analytical equations are formulated for the conversion between the ice classes by vapor depositional growth (sublimation). During ice subsaturated conditions, a number concentration sink is parameterized for all ice species. The performance of the parameterizations in a simple parcel model is discussed and evaluated against an explicit Lagrangian parcel microphysical model.
Abstract
The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR’s performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR’s initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R 2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R 2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.
Abstract
The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR’s performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR’s initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R 2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R 2 ≈ 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.
Abstract
Over the 3-day period of 24–26 October 1997, a powerful winter storm was the cause of two exceptional weather phenomena: 1) blizzard conditions from Wyoming to southern New Mexico along the Front Range of the Rocky Mountains and 2) hurricane-force winds at the surface near Steamboat Springs, Colorado, with the destruction of about 5300 ha of old-growth forest. This rare event was caused by a deep, cutoff low pressure system that provided unusually strong, deep easterly flow over the Front Range for an extended period. The event was characterized by highly variable snowfall and some very large snowfall totals; over a horizontal distance of 15 km, in some cases, snowfall varied by as much as 1.0 m, with maximum total snowfall depths near 1.5 m. Because this variability was caused, in part, by terrain effects, this work investigates the capability of a mesoscale model constructed in terrain-following coordinates (the Regional Atmospheric Modeling System: RAMS) to forecast small-scale (meso γ), orographically forced spatial variability of the snowfall. There are few investigations of model-forecast liquid precipitation versus observations at meso-γ-scale horizontal grid spacing. Using a limited observational dataset, mean absolute percent errors of precipitation (liquid equivalent) of 41% and 9% were obtained at horizontal grid spacings of 5.00 and 1.67 km, respectively. A detailed, high-temporal-resolution (30-min intervals) comparison of modeled versus actual snowfall rates at a fully instrumented snow measurement testing site shows significant model skill. A companion paper, Part II, will use the same RAMS simulations to describe the observations and modeling of the simultaneous mountain-windstorm-induced forest blowdown event.
Abstract
Over the 3-day period of 24–26 October 1997, a powerful winter storm was the cause of two exceptional weather phenomena: 1) blizzard conditions from Wyoming to southern New Mexico along the Front Range of the Rocky Mountains and 2) hurricane-force winds at the surface near Steamboat Springs, Colorado, with the destruction of about 5300 ha of old-growth forest. This rare event was caused by a deep, cutoff low pressure system that provided unusually strong, deep easterly flow over the Front Range for an extended period. The event was characterized by highly variable snowfall and some very large snowfall totals; over a horizontal distance of 15 km, in some cases, snowfall varied by as much as 1.0 m, with maximum total snowfall depths near 1.5 m. Because this variability was caused, in part, by terrain effects, this work investigates the capability of a mesoscale model constructed in terrain-following coordinates (the Regional Atmospheric Modeling System: RAMS) to forecast small-scale (meso γ), orographically forced spatial variability of the snowfall. There are few investigations of model-forecast liquid precipitation versus observations at meso-γ-scale horizontal grid spacing. Using a limited observational dataset, mean absolute percent errors of precipitation (liquid equivalent) of 41% and 9% were obtained at horizontal grid spacings of 5.00 and 1.67 km, respectively. A detailed, high-temporal-resolution (30-min intervals) comparison of modeled versus actual snowfall rates at a fully instrumented snow measurement testing site shows significant model skill. A companion paper, Part II, will use the same RAMS simulations to describe the observations and modeling of the simultaneous mountain-windstorm-induced forest blowdown event.
Abstract
A devastating winter storm affected the Rocky Mountain states over the 3-day period of 24–26 October 1997. Blizzard conditions persisted over the foothills and adjoining plains from Wyoming to southern New Mexico, with maximum total snowfall amounts near 1.5 m. ( of this two-part paper describes the observations and modeling of this blizzard event.) During the morning of 25 October 1997, wind gusts in excess of 50 m s−1 were estimated west of the Continental Divide near Steamboat Springs in northern Colorado. These winds flattened approximately 5300 ha (13 000 acres) of old-growth forest in the Routt National Forest and Mount Zirkel Wilderness. Observations, analysis, and numerical modeling were used to examine the kinematics of this extreme event. A high-resolution, local-area model (the Regional Atmospheric Modeling System) was used to investigate the ability of a local model to capture the timing and strength of the windstorm and the aforementioned blizzard. Results indicated that a synergistic combination of strong cross-barrier easterly flow; very cold lower-tropospheric air over Colorado, which modified the stability profile; and the presence of a critical layer led to devastating downslope winds. The high-resolution simulations demonstrated the potential for accurately capturing mesoscale spatial and temporal features of a downslope windstorm more than 1 day in advance. These simulations were quasi forecast in nature, because a combination of two 48-h Eta Model forecasts were used to specify the lateral boundary conditions. Increased predictive detail of the windstorm was also found by decreasing the horizontal grid spacing from 5 to 1.67 km in the local-area model simulations.
Abstract
A devastating winter storm affected the Rocky Mountain states over the 3-day period of 24–26 October 1997. Blizzard conditions persisted over the foothills and adjoining plains from Wyoming to southern New Mexico, with maximum total snowfall amounts near 1.5 m. ( of this two-part paper describes the observations and modeling of this blizzard event.) During the morning of 25 October 1997, wind gusts in excess of 50 m s−1 were estimated west of the Continental Divide near Steamboat Springs in northern Colorado. These winds flattened approximately 5300 ha (13 000 acres) of old-growth forest in the Routt National Forest and Mount Zirkel Wilderness. Observations, analysis, and numerical modeling were used to examine the kinematics of this extreme event. A high-resolution, local-area model (the Regional Atmospheric Modeling System) was used to investigate the ability of a local model to capture the timing and strength of the windstorm and the aforementioned blizzard. Results indicated that a synergistic combination of strong cross-barrier easterly flow; very cold lower-tropospheric air over Colorado, which modified the stability profile; and the presence of a critical layer led to devastating downslope winds. The high-resolution simulations demonstrated the potential for accurately capturing mesoscale spatial and temporal features of a downslope windstorm more than 1 day in advance. These simulations were quasi forecast in nature, because a combination of two 48-h Eta Model forecasts were used to specify the lateral boundary conditions. Increased predictive detail of the windstorm was also found by decreasing the horizontal grid spacing from 5 to 1.67 km in the local-area model simulations.
Abstract
The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.
Abstract
The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.
