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Abstract
A new version of a bin microphysical scheme implemented into the Weather Research and Forecasting (WRF) Model was used to study the effect of glaciogenic seeding on precipitation formation in orographic clouds. The tracking of silver iodide (AgI) particles inside of water drops allows the proper simulation of the immersion nucleation. The ice formations by deposition, condensational freezing, and contact nucleation of AgI particles are also simulated in the scheme. Cloud formation—both stably stratified and convective—and the spread of AgI particles were simulated by idealized flow over a two-dimensional (2D) bell-shaped mountain. The results of numerical experiments show the following: (i) Only the airborne seeding enhances precipitation in stably stratified layer clouds. Seeding can reduce or enhance precipitation in convective clouds. AgI seeding can significantly affect the spatial distribution of the surface precipitation in orographic clouds. (ii) The positive seeding effect is primarily due to additional diffusional growth of AgI-nucleated ice crystals in layer clouds. In convective clouds, seeding-induced changes of both diffusion and riming processes determine the seeding effect. (iii) The seeding effect is inversely related to the natural precipitation efficiency. (iv) Bulk seeding parameterization is adequate to simulate AgI seeding impacts on wintertime orographic clouds. More uncertainties of ground-seeding effects are found between bulk and bin simulations.
Abstract
A new version of a bin microphysical scheme implemented into the Weather Research and Forecasting (WRF) Model was used to study the effect of glaciogenic seeding on precipitation formation in orographic clouds. The tracking of silver iodide (AgI) particles inside of water drops allows the proper simulation of the immersion nucleation. The ice formations by deposition, condensational freezing, and contact nucleation of AgI particles are also simulated in the scheme. Cloud formation—both stably stratified and convective—and the spread of AgI particles were simulated by idealized flow over a two-dimensional (2D) bell-shaped mountain. The results of numerical experiments show the following: (i) Only the airborne seeding enhances precipitation in stably stratified layer clouds. Seeding can reduce or enhance precipitation in convective clouds. AgI seeding can significantly affect the spatial distribution of the surface precipitation in orographic clouds. (ii) The positive seeding effect is primarily due to additional diffusional growth of AgI-nucleated ice crystals in layer clouds. In convective clouds, seeding-induced changes of both diffusion and riming processes determine the seeding effect. (iii) The seeding effect is inversely related to the natural precipitation efficiency. (iv) Bulk seeding parameterization is adequate to simulate AgI seeding impacts on wintertime orographic clouds. More uncertainties of ground-seeding effects are found between bulk and bin simulations.
Abstract
This study evaluates an operational glaciogenic cloud-seeding program using ground-based generators of silver iodide (AgI), with a total of 190 seeded storms over 10 cold seasons, using the Weather Research and Forecasting Weather Modification (WRF-WxMod) scheme at 900-m grid spacing. This study examines both the quantitative change in precipitation and the ambient and cloud conditions impacting seeding efficacy. An ensemble approach is used, with differing model boundary conditions, ice nucleation physics, concentrations of cloud condensation nuclei, and boundary layer schemes. This is intended to provide an envelope of uncertainty of natural clouds and seeding impacts. The simulations are validated against radiosonde, snow gauge, and microwave radiometer observations, and the seeding impact is inferred from simulations with/without AgI seeding. The seeding-induced precipitation enhancement (“yield”) varies greatly between storms. A small portion of the cases produces the majority of the yield. Overall, the precipitation in the target area (the Wind River Range in Wyoming) increased by 1.10% ± 0.13% in the 10 years of operational seeding. This rather low fractional increase is related to the frequent seeding at unsuitable times, primarily because of low-level flow blocking. The flow and cloud structure for select cases are examined to provide better insight into the variability of yield. Cases with unblocked surface flow and abundant cloud liquid water tend to be the most productive. The technique presented here can be readily adapted to evaluate the seeding impact of other long-term glaciogenic seeding operations and to improve their operational efficiency.
Significance Statement
In the United States and elsewhere, there are several operational programs to enhance cold-season precipitation through glaciogenic seeding of orographic clouds. The impact of such activity on seasonal precipitation has always been difficult to quantify. Recent observational and numerical modeling studies indicate that orographic cloud seeding can increase precipitation, although the amounts and optimal seeding conditions remain uncertain. Operators lack guidance about the seeding efficacy and about the most suitable environmental conditions. In recent years a model parameterization, called Weather Research and Forecasting Weather Modification (WRF-WxMod), has been tested against detailed measurements. This sets the stage for our work, a well-designed numerical evaluation of 10 years of operational cloud seeding over the Wind River Range, a mountain range in Wyoming that feeds the Colorado River basin and other watersheds. The WRF-WxMod based simulation experiment presented here, one of the most computationally expensive numerical experiments on this subject to date, quantifies seeding impact and its uncertainty. It is demonstrated with a high degree of confidence that over this 10-yr period, suitable seeding conditions were rare over this mountain range, that most seeding events were unproductive, and that, as a result, the overall yield over 10 years was a mere 1.1%.
Abstract
This study evaluates an operational glaciogenic cloud-seeding program using ground-based generators of silver iodide (AgI), with a total of 190 seeded storms over 10 cold seasons, using the Weather Research and Forecasting Weather Modification (WRF-WxMod) scheme at 900-m grid spacing. This study examines both the quantitative change in precipitation and the ambient and cloud conditions impacting seeding efficacy. An ensemble approach is used, with differing model boundary conditions, ice nucleation physics, concentrations of cloud condensation nuclei, and boundary layer schemes. This is intended to provide an envelope of uncertainty of natural clouds and seeding impacts. The simulations are validated against radiosonde, snow gauge, and microwave radiometer observations, and the seeding impact is inferred from simulations with/without AgI seeding. The seeding-induced precipitation enhancement (“yield”) varies greatly between storms. A small portion of the cases produces the majority of the yield. Overall, the precipitation in the target area (the Wind River Range in Wyoming) increased by 1.10% ± 0.13% in the 10 years of operational seeding. This rather low fractional increase is related to the frequent seeding at unsuitable times, primarily because of low-level flow blocking. The flow and cloud structure for select cases are examined to provide better insight into the variability of yield. Cases with unblocked surface flow and abundant cloud liquid water tend to be the most productive. The technique presented here can be readily adapted to evaluate the seeding impact of other long-term glaciogenic seeding operations and to improve their operational efficiency.
Significance Statement
In the United States and elsewhere, there are several operational programs to enhance cold-season precipitation through glaciogenic seeding of orographic clouds. The impact of such activity on seasonal precipitation has always been difficult to quantify. Recent observational and numerical modeling studies indicate that orographic cloud seeding can increase precipitation, although the amounts and optimal seeding conditions remain uncertain. Operators lack guidance about the seeding efficacy and about the most suitable environmental conditions. In recent years a model parameterization, called Weather Research and Forecasting Weather Modification (WRF-WxMod), has been tested against detailed measurements. This sets the stage for our work, a well-designed numerical evaluation of 10 years of operational cloud seeding over the Wind River Range, a mountain range in Wyoming that feeds the Colorado River basin and other watersheds. The WRF-WxMod based simulation experiment presented here, one of the most computationally expensive numerical experiments on this subject to date, quantifies seeding impact and its uncertainty. It is demonstrated with a high degree of confidence that over this 10-yr period, suitable seeding conditions were rare over this mountain range, that most seeding events were unproductive, and that, as a result, the overall yield over 10 years was a mere 1.1%.
Abstract
This study uses the WRF large-eddy simulation model at 100-m resolution to examine the impact of ground-based glaciogenic seeding on shallow (~2 km deep), cold-based convection producing light snow showers over the Sierra Madre in southern Wyoming on 13 February 2012, as part of the AgI Seeding Cloud Impact Investigation (ASCII). Detailed observations confirm that simulation faithfully captures the orographic flow, convection, and natural snow production, especially on the upwind side. A comparison between treated and control simulations indicates that glaciogenic seeding effectively converts cloud water in convective updrafts to ice and snow in this case, resulting in increased surface precipitation. This comparison further shows that seeding enhances liquid water depletion by vapor deposition, and enhances buoyancy, updraft strength, and cloud-top height. This suggests that the dynamic seeding concept applies, notwithstanding the clouds’ low natural supercooled liquid water content. But the simulated cloud-top-height changes are benign (typically <100 m). This, combined with the fact that most natural and enhanced snow growth occurs in a temperature range in which the Bergeron diffusional growth process is effective, suggests that the modeled snowfall enhancement is largely due to static (microphysical) processes rather than dynamic ones.
Abstract
This study uses the WRF large-eddy simulation model at 100-m resolution to examine the impact of ground-based glaciogenic seeding on shallow (~2 km deep), cold-based convection producing light snow showers over the Sierra Madre in southern Wyoming on 13 February 2012, as part of the AgI Seeding Cloud Impact Investigation (ASCII). Detailed observations confirm that simulation faithfully captures the orographic flow, convection, and natural snow production, especially on the upwind side. A comparison between treated and control simulations indicates that glaciogenic seeding effectively converts cloud water in convective updrafts to ice and snow in this case, resulting in increased surface precipitation. This comparison further shows that seeding enhances liquid water depletion by vapor deposition, and enhances buoyancy, updraft strength, and cloud-top height. This suggests that the dynamic seeding concept applies, notwithstanding the clouds’ low natural supercooled liquid water content. But the simulated cloud-top-height changes are benign (typically <100 m). This, combined with the fact that most natural and enhanced snow growth occurs in a temperature range in which the Bergeron diffusional growth process is effective, suggests that the modeled snowfall enhancement is largely due to static (microphysical) processes rather than dynamic ones.
Abstract
The impact of glaciogenic seeding on precipitation remains uncertain, mainly because of the noisy nature of precipitation. Operational seeding programs often target cold-season orographic clouds because of their abundance of supercooled liquid water. Such clouds are complicated because of common natural seeding from above (seeder–feeder effect) or from below (blowing snow). Here, observations, mainly from a profiling airborne Doppler radar, and numerical simulations are used to examine the impact of glaciogenic seeding on a very shallow (<1 km), largely blocked cloud that is not naturally seeded from aloft or from below. This cloud has limited but persistent supercooled liquid water, a cloud-base (top) temperature of −12°C (−16°C), and produces only very light snowfall naturally. A Weather Research and Forecasting Model large-eddy simulation at 100-m resolution captures the observed upstream stability and wind profiles and reproduces the essential characteristics of the orographic flow, cloud, and precipitation. Both observations and simulations indicate that seeding locally increases radar (or computed) reflectivity in the target area, even after removal of the natural trend between these two periods in a nearby control region. A model sensitivity run suggests that seeding effectively glaciates the mostly liquid cloud and substantially increases snowfall within the seeding plume. This is due to a dramatic increase in the number of ice particles and not to their size. The increased ice particle concentration facilitates snow growth by vapor deposition in a cloud the temperature range of which is conducive to the Bergeron process.
Abstract
The impact of glaciogenic seeding on precipitation remains uncertain, mainly because of the noisy nature of precipitation. Operational seeding programs often target cold-season orographic clouds because of their abundance of supercooled liquid water. Such clouds are complicated because of common natural seeding from above (seeder–feeder effect) or from below (blowing snow). Here, observations, mainly from a profiling airborne Doppler radar, and numerical simulations are used to examine the impact of glaciogenic seeding on a very shallow (<1 km), largely blocked cloud that is not naturally seeded from aloft or from below. This cloud has limited but persistent supercooled liquid water, a cloud-base (top) temperature of −12°C (−16°C), and produces only very light snowfall naturally. A Weather Research and Forecasting Model large-eddy simulation at 100-m resolution captures the observed upstream stability and wind profiles and reproduces the essential characteristics of the orographic flow, cloud, and precipitation. Both observations and simulations indicate that seeding locally increases radar (or computed) reflectivity in the target area, even after removal of the natural trend between these two periods in a nearby control region. A model sensitivity run suggests that seeding effectively glaciates the mostly liquid cloud and substantially increases snowfall within the seeding plume. This is due to a dramatic increase in the number of ice particles and not to their size. The increased ice particle concentration facilitates snow growth by vapor deposition in a cloud the temperature range of which is conducive to the Bergeron process.
Abstract
The University of Pécs and NCAR Bin (UPNB) microphysical scheme was implemented into the mesoscale Weather Research and Forecast (WRF) Model that was used to study the impact of silver iodide (AgI) seeding on precipitation formation in winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect. The results of the numerical experiments show the following: (i) Comparisons with the soundings, snow gauges, and microwave radiometer data indicate that the three-dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective because of turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C), surface precipitation and precipitation efficiency show low susceptibility to the concentrations of cloud condensation nuclei and natural ice nucleating particles. (iv) If the available liquid water content promotes the enhancement of the number of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, horizontally integrated liquid water path (LWP), was evaluated to find the relation between seeding efficiency and liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm and is apparent if the horizontal LWP is larger than 1 mm, as based on the cases investigated in this study.
Abstract
The University of Pécs and NCAR Bin (UPNB) microphysical scheme was implemented into the mesoscale Weather Research and Forecast (WRF) Model that was used to study the impact of silver iodide (AgI) seeding on precipitation formation in winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect. The results of the numerical experiments show the following: (i) Comparisons with the soundings, snow gauges, and microwave radiometer data indicate that the three-dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective because of turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C), surface precipitation and precipitation efficiency show low susceptibility to the concentrations of cloud condensation nuclei and natural ice nucleating particles. (iv) If the available liquid water content promotes the enhancement of the number of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, horizontally integrated liquid water path (LWP), was evaluated to find the relation between seeding efficiency and liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm and is apparent if the horizontal LWP is larger than 1 mm, as based on the cases investigated in this study.
Abstract
This study evaluates the possible impact of aerosol solubility and regeneration on warm-phase orographic clouds and precipitation. The sensitivity evaluation is performed by simulating cloud formation over two identical 2D idealized mountains using a detailed bin microphysical scheme implemented into the Weather Research and Forecasting model (WRF) version 3. The dynamics, thermodynamics, topography, and microphysical pathways were designed to produce precipitating clouds in a linear hydrostatic mountain wave regime. The cloud over the second mountain is affected by regenerated aerosols advected from the cloud over the first mountain. Effects of aerosol solubility and regeneration were investigated with surface relative humidity of 95% and 85% for both clean and polluted background aerosol concentrations.
Among the findings are the following: 1) The total number of cloud drops decreases as the aerosol solubility decreases, and the impacts of aerosol solubility on cloud drops and precipitation are more significant in polluted clouds than in clean clouds. 2) Aerosol regeneration increases cloud drops and reduces the precipitation by 2%–80% in clouds over the second mountain. Regenerated aerosol particles replenish one-third to two-thirds of the missing particles when regeneration is not considered. 3) Different size distributions of regenerated aerosol particles have negligible effect on clouds and precipitation except for polluted clouds with high aerosol solubility. 4) When the solubility of initial aerosol particles decreases with an increasing size of aerosol particles, the modified solubility of regenerated aerosol particles increases precipitation over the second mountain.
Abstract
This study evaluates the possible impact of aerosol solubility and regeneration on warm-phase orographic clouds and precipitation. The sensitivity evaluation is performed by simulating cloud formation over two identical 2D idealized mountains using a detailed bin microphysical scheme implemented into the Weather Research and Forecasting model (WRF) version 3. The dynamics, thermodynamics, topography, and microphysical pathways were designed to produce precipitating clouds in a linear hydrostatic mountain wave regime. The cloud over the second mountain is affected by regenerated aerosols advected from the cloud over the first mountain. Effects of aerosol solubility and regeneration were investigated with surface relative humidity of 95% and 85% for both clean and polluted background aerosol concentrations.
Among the findings are the following: 1) The total number of cloud drops decreases as the aerosol solubility decreases, and the impacts of aerosol solubility on cloud drops and precipitation are more significant in polluted clouds than in clean clouds. 2) Aerosol regeneration increases cloud drops and reduces the precipitation by 2%–80% in clouds over the second mountain. Regenerated aerosol particles replenish one-third to two-thirds of the missing particles when regeneration is not considered. 3) Different size distributions of regenerated aerosol particles have negligible effect on clouds and precipitation except for polluted clouds with high aerosol solubility. 4) When the solubility of initial aerosol particles decreases with an increasing size of aerosol particles, the modified solubility of regenerated aerosol particles increases precipitation over the second mountain.
Abstract
It is still a daunting challenge for land surface models (LSMs) to correctly represent surface heat exchange for water-limited desert steppe ecosystems. This study aims to improve the ability of the Noah LSM to simulate surface heat fluxes through addressing uncertainties in precipitation forcing conditions, rapidly evolving vegetation properties, soil hydraulic properties (SHPs), and key parameterization schemes. Three years (2008–10) of observed surface heat fluxes and soil temperature over a desert steppe site in Inner Mongolia, China, are used to verify model simulations. The proper seasonal distribution of precipitation, along with more realistic vegetation parameters, can improve the simulation of sensible heat flux (SH) and the seasonal variability of latent heat flux. Correctly representing the low-surface exchange coefficient is crucial for improving SH for short vegetation like this desert steppe site. Relating C zil, the coefficient in the Noah surface exchange coefficient calculation, with canopy height h improves the simulated SH and the diurnal range of soil temperature over the simulation compared with using the default constant C zil. The exponential water stress formulation proposed here for the Jarvis scheme improves the partitioning between soil evaporation and transpiration. It is found that the surface energy fluxes are very sensitive to SHPs. This study highlights the important role of the proper parameter values and appropriate parameterizations for the surface exchange coefficient and water stress function in canopy resistance in capturing the observed surface energy fluxes and soil temperature variations for this desert steppe site.
Abstract
It is still a daunting challenge for land surface models (LSMs) to correctly represent surface heat exchange for water-limited desert steppe ecosystems. This study aims to improve the ability of the Noah LSM to simulate surface heat fluxes through addressing uncertainties in precipitation forcing conditions, rapidly evolving vegetation properties, soil hydraulic properties (SHPs), and key parameterization schemes. Three years (2008–10) of observed surface heat fluxes and soil temperature over a desert steppe site in Inner Mongolia, China, are used to verify model simulations. The proper seasonal distribution of precipitation, along with more realistic vegetation parameters, can improve the simulation of sensible heat flux (SH) and the seasonal variability of latent heat flux. Correctly representing the low-surface exchange coefficient is crucial for improving SH for short vegetation like this desert steppe site. Relating C zil, the coefficient in the Noah surface exchange coefficient calculation, with canopy height h improves the simulated SH and the diurnal range of soil temperature over the simulation compared with using the default constant C zil. The exponential water stress formulation proposed here for the Jarvis scheme improves the partitioning between soil evaporation and transpiration. It is found that the surface energy fluxes are very sensitive to SHPs. This study highlights the important role of the proper parameter values and appropriate parameterizations for the surface exchange coefficient and water stress function in canopy resistance in capturing the observed surface energy fluxes and soil temperature variations for this desert steppe site.
Abstract
Profiling airborne radar data and accompanying large-eddy-simulation (LES) modeling are used to examine the impact of ground-based glaciogenic seeding on cloud and precipitation in a shallow stratiform orographic winter storm. This storm occurred on 18 February 2009 over a mountain in Wyoming. The numerical simulations use the Weather Research and Forecasting (WRF) Model in LES mode with horizontal grid spacings of 300 and 100 m in a domain covering the entire mountain range, and a glaciogenic seeding parameterization coupled with the Thompson microphysics scheme. A series of non-LES simulations at 900-m resolution, each with different initial/boundary conditions, is validated against sounding, cloud, and precipitation data. The LES runs then are driven by the most representative 900-m non-LES simulation. The 100-m LES results compare reasonably well to the vertical-plane radar data. The modeled vertical-motion field reveals a turbulent boundary layer and gravity waves above this layer, as observed. The storm structure also validates well, but the model storm thins and weakens more rapidly than is observed. Radar reflectivity frequency-by-altitude diagrams suggest a positive seeding effect, but time- and space-matched model reflectivity diagrams only confirm this in a relative sense, in comparison with the trend in the control region upwind of seeding generators, and not in an absolute sense. A model sensitivity run shows that in this case natural storm weakening dwarfs the seeding effect, which does enhance snow mass and snowfall. Since the kinematic and microphysical structure of the storm is simulated well, future Part II of this study will examine how glaciogenic seeding impacts clouds and precipitation processes within the LES.
Abstract
Profiling airborne radar data and accompanying large-eddy-simulation (LES) modeling are used to examine the impact of ground-based glaciogenic seeding on cloud and precipitation in a shallow stratiform orographic winter storm. This storm occurred on 18 February 2009 over a mountain in Wyoming. The numerical simulations use the Weather Research and Forecasting (WRF) Model in LES mode with horizontal grid spacings of 300 and 100 m in a domain covering the entire mountain range, and a glaciogenic seeding parameterization coupled with the Thompson microphysics scheme. A series of non-LES simulations at 900-m resolution, each with different initial/boundary conditions, is validated against sounding, cloud, and precipitation data. The LES runs then are driven by the most representative 900-m non-LES simulation. The 100-m LES results compare reasonably well to the vertical-plane radar data. The modeled vertical-motion field reveals a turbulent boundary layer and gravity waves above this layer, as observed. The storm structure also validates well, but the model storm thins and weakens more rapidly than is observed. Radar reflectivity frequency-by-altitude diagrams suggest a positive seeding effect, but time- and space-matched model reflectivity diagrams only confirm this in a relative sense, in comparison with the trend in the control region upwind of seeding generators, and not in an absolute sense. A model sensitivity run shows that in this case natural storm weakening dwarfs the seeding effect, which does enhance snow mass and snowfall. Since the kinematic and microphysical structure of the storm is simulated well, future Part II of this study will examine how glaciogenic seeding impacts clouds and precipitation processes within the LES.
Abstract
Several Weather Research and Forecasting (WRF) Model simulations of natural and seeded clouds have been conducted in non-LES and LES (large-eddy simulation) modes to investigate the seeding impact on wintertime orographic clouds for an actual seeding case on 18 February 2009 in the Medicine Bow Mountains of Wyoming. Part I of this two-part series has shown the capability of WRF LES with 100-m grid spacing to capture the essential environmental conditions by comparing the model results with measurements from a variety of instruments. In this paper, the silver iodide (AgI) dispersion features, the AgI impacts on the turbulent kinetic energy (TKE), the microphysics, and the precipitation are examined in detail using the model data, which leads to five main results. 1) The vertical dispersion of AgI particles is more efficient in cloudy conditions than in clear conditions. 2) The wind shear and the buoyancy are both important TKE production mechanisms in the wintertime PBL over complex terrain in cloudy conditions. The buoyancy-induced eddies are more responsible for the AgI vertical dispersion than the shear-induced eddies are. 3) Seeding has insignificant effects on the cloud dynamics. 4) AgI particles released from the ground-based generators affect the cloud within the boundary layer below 1 km AGL through nucleating extra ice crystals, converting liquid water into ice, depleting more vapor, and generating more precipitation on the ground. The AgI nucleation rate is inversely related to the natural ice nucleation rate. 5) The seeding effects on the ground precipitation are confined within narrow areas. The relative seeding effect ranges between 5% and 20% for the simulations with different grid spacing.
Abstract
Several Weather Research and Forecasting (WRF) Model simulations of natural and seeded clouds have been conducted in non-LES and LES (large-eddy simulation) modes to investigate the seeding impact on wintertime orographic clouds for an actual seeding case on 18 February 2009 in the Medicine Bow Mountains of Wyoming. Part I of this two-part series has shown the capability of WRF LES with 100-m grid spacing to capture the essential environmental conditions by comparing the model results with measurements from a variety of instruments. In this paper, the silver iodide (AgI) dispersion features, the AgI impacts on the turbulent kinetic energy (TKE), the microphysics, and the precipitation are examined in detail using the model data, which leads to five main results. 1) The vertical dispersion of AgI particles is more efficient in cloudy conditions than in clear conditions. 2) The wind shear and the buoyancy are both important TKE production mechanisms in the wintertime PBL over complex terrain in cloudy conditions. The buoyancy-induced eddies are more responsible for the AgI vertical dispersion than the shear-induced eddies are. 3) Seeding has insignificant effects on the cloud dynamics. 4) AgI particles released from the ground-based generators affect the cloud within the boundary layer below 1 km AGL through nucleating extra ice crystals, converting liquid water into ice, depleting more vapor, and generating more precipitation on the ground. The AgI nucleation rate is inversely related to the natural ice nucleation rate. 5) The seeding effects on the ground precipitation are confined within narrow areas. The relative seeding effect ranges between 5% and 20% for the simulations with different grid spacing.
Abstract
The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.
Significance Statement
Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology.
Abstract
The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.
Significance Statement
Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology.
