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Abstract
Previous work found that cold pools in ordinary convection are more sensitive to the microphysics scheme when the lifting condensation level (LCL) is higher owing to a greater evaporation potential, which magnifies microphysical uncertainties. In the current study, we explore whether the same reasoning can be applied to supercellular cold pools. To do this, four perturbed-microphysics ensembles are run, with each using an environment with a different LCL. Similar to ordinary convection, the sensitivity of supercellular cold pools to the microphysics increases with higher LCLs, though the physical reasoning for this increase in sensitivity differs from a previous study. Using buoyancy budgets along parcel trajectories that terminate in the cold pool, we find that negative buoyancy generated by microphysical cooling is partially countered by a decrease in environmental potential temperatures as the parcel descends. This partial erosion of negative buoyancy as parcels descend is most pronounced in the low-LCL storms, which have steeper vertical profiles of environmental potential temperature in the lower atmosphere. When this erosion is accounted for, the strength of the strongest cold pools in the low-LCL ensemble is reduced, resulting in a narrower distribution of cold pool strengths. This narrower distribution is indicative of reduced sensitivity to the microphysics. These results suggest that supercell behavior and supercell hazards (e.g., tornadoes) may be more predictable in low-LCL environments.
Significance Statement
Thunderstorms typically produce “pools” of cold air beneath them owing in part to the evaporation of rain and melting of ice produced by the storm. Past work has found that in computer simulations of thunderstorms, the cold pools that form beneath thunderstorms are sensitive to how rain and ice are modeled in the simulation. In this study, we show that in the strongest thunderstorms that are capable of producing tornadoes, this sensitivity is reduced when the humidity in the lowest few kilometers above the surface is increased. Exploring why the sensitivity is reduced when the humidity increases provides a deeper understanding of the relationship between humidity and cold pool strength, which is important for severe storm forecasting.
Abstract
Previous work found that cold pools in ordinary convection are more sensitive to the microphysics scheme when the lifting condensation level (LCL) is higher owing to a greater evaporation potential, which magnifies microphysical uncertainties. In the current study, we explore whether the same reasoning can be applied to supercellular cold pools. To do this, four perturbed-microphysics ensembles are run, with each using an environment with a different LCL. Similar to ordinary convection, the sensitivity of supercellular cold pools to the microphysics increases with higher LCLs, though the physical reasoning for this increase in sensitivity differs from a previous study. Using buoyancy budgets along parcel trajectories that terminate in the cold pool, we find that negative buoyancy generated by microphysical cooling is partially countered by a decrease in environmental potential temperatures as the parcel descends. This partial erosion of negative buoyancy as parcels descend is most pronounced in the low-LCL storms, which have steeper vertical profiles of environmental potential temperature in the lower atmosphere. When this erosion is accounted for, the strength of the strongest cold pools in the low-LCL ensemble is reduced, resulting in a narrower distribution of cold pool strengths. This narrower distribution is indicative of reduced sensitivity to the microphysics. These results suggest that supercell behavior and supercell hazards (e.g., tornadoes) may be more predictable in low-LCL environments.
Significance Statement
Thunderstorms typically produce “pools” of cold air beneath them owing in part to the evaporation of rain and melting of ice produced by the storm. Past work has found that in computer simulations of thunderstorms, the cold pools that form beneath thunderstorms are sensitive to how rain and ice are modeled in the simulation. In this study, we show that in the strongest thunderstorms that are capable of producing tornadoes, this sensitivity is reduced when the humidity in the lowest few kilometers above the surface is increased. Exploring why the sensitivity is reduced when the humidity increases provides a deeper understanding of the relationship between humidity and cold pool strength, which is important for severe storm forecasting.
Abstract
The effects of observation errors on rank histograms and reliability diagrams are investigated using a perfect model approach. The three-variable Lorenz-63 model was used to simulate an idealized ensemble prediction system (EPS) with 50 perturbed ensemble members and one control forecast. Observation errors at verification time were introduced by adding normally distributed noise to the true state at verification time. Besides these simulations, a theoretical analysis was also performed. One of the major findings was that rank histograms are very sensitive to the presence of observation errors, leading to overpopulated upper- and lowermost ranks. This sensitivity was shown to grow for larger ensemble sizes. Reliability diagrams were far less sensitive in this respect. The resulting u-shaped rank histograms can easily be misinterpreted as indicating too little spread in the ensemble prediction system. To account for this effect when real observations are used to assess an ensemble prediction system, normally distributed noise based on the verifying observation error can be added to the ensemble members before the statistics are calculated. The method has been tested for the ECMWF ensemble forecasts of ocean waves and forecasts of the geopotential at 500 hPa. The EPS waves were compared with buoy observations from the Global Telecommunication System (GTS) for a period of almost 3 yr. When the buoy observations were taken as the true value, more than 25% of the observations appeared in the two extreme ranks for the day 3 forecast range. This number was reduced to less than 10% when observation errors were added to the ensemble members. Ensemble forecasts of the 500-hPa geopotential were verified against the ECMWF analysis. When analysis errors were neglected, the maximum number of outliers was more than 10% for most areas except for Europe, where the analysis errors are relatively smaller. Introducing noise to the ensemble members, based on estimates of analysis errors, reduced the number of outliers, particularly in the short range, where a peak around day 1 more or less vanished.
Abstract
The effects of observation errors on rank histograms and reliability diagrams are investigated using a perfect model approach. The three-variable Lorenz-63 model was used to simulate an idealized ensemble prediction system (EPS) with 50 perturbed ensemble members and one control forecast. Observation errors at verification time were introduced by adding normally distributed noise to the true state at verification time. Besides these simulations, a theoretical analysis was also performed. One of the major findings was that rank histograms are very sensitive to the presence of observation errors, leading to overpopulated upper- and lowermost ranks. This sensitivity was shown to grow for larger ensemble sizes. Reliability diagrams were far less sensitive in this respect. The resulting u-shaped rank histograms can easily be misinterpreted as indicating too little spread in the ensemble prediction system. To account for this effect when real observations are used to assess an ensemble prediction system, normally distributed noise based on the verifying observation error can be added to the ensemble members before the statistics are calculated. The method has been tested for the ECMWF ensemble forecasts of ocean waves and forecasts of the geopotential at 500 hPa. The EPS waves were compared with buoy observations from the Global Telecommunication System (GTS) for a period of almost 3 yr. When the buoy observations were taken as the true value, more than 25% of the observations appeared in the two extreme ranks for the day 3 forecast range. This number was reduced to less than 10% when observation errors were added to the ensemble members. Ensemble forecasts of the 500-hPa geopotential were verified against the ECMWF analysis. When analysis errors were neglected, the maximum number of outliers was more than 10% for most areas except for Europe, where the analysis errors are relatively smaller. Introducing noise to the ensemble members, based on estimates of analysis errors, reduced the number of outliers, particularly in the short range, where a peak around day 1 more or less vanished.
Abstract
Recent high-resolution numerical simulations of supercells have identified a feature referred to as the streamwise vorticity current (SVC). Some have presumed the SVC to play a role in tornadogenesis and maintenance, though observations of such a feature have been limited. To this end, 125-m dual-Doppler wind syntheses and mobile mesonet observations are used to examine three observed supercells for evidence of an SVC. Two of the three supercells are found to contain a feature similar to an SVC, while the other supercell contains an antistreamwise vorticity ribbon on the southern fringe of the forward flank. A closer examination of the two supercells with SVCs reveals that the SVCs are located on the cool side of boundaries within the forward flank that separate colder, more turbulent flow from warmer, more laminar flow, similar to numerical simulations. Furthermore, the observed SVCs are similar to those in simulations in that they appear to be associated with baroclinic vorticity generation and have similar appearances in vertical cross sections. Aside from some apparent differences in the location of the maximum streamwise vorticity between simulated and observed SVCs, the SVCs seen in numerical simulations are indeed similar to reality. The SVC, however, may not be essential for tornadogenesis, at least for weak tornadoes, because the supercell that did not have a well-defined SVC produced at least one brief, weak tornado during the analysis period.
Abstract
Recent high-resolution numerical simulations of supercells have identified a feature referred to as the streamwise vorticity current (SVC). Some have presumed the SVC to play a role in tornadogenesis and maintenance, though observations of such a feature have been limited. To this end, 125-m dual-Doppler wind syntheses and mobile mesonet observations are used to examine three observed supercells for evidence of an SVC. Two of the three supercells are found to contain a feature similar to an SVC, while the other supercell contains an antistreamwise vorticity ribbon on the southern fringe of the forward flank. A closer examination of the two supercells with SVCs reveals that the SVCs are located on the cool side of boundaries within the forward flank that separate colder, more turbulent flow from warmer, more laminar flow, similar to numerical simulations. Furthermore, the observed SVCs are similar to those in simulations in that they appear to be associated with baroclinic vorticity generation and have similar appearances in vertical cross sections. Aside from some apparent differences in the location of the maximum streamwise vorticity between simulated and observed SVCs, the SVCs seen in numerical simulations are indeed similar to reality. The SVC, however, may not be essential for tornadogenesis, at least for weak tornadoes, because the supercell that did not have a well-defined SVC produced at least one brief, weak tornado during the analysis period.
Abstract
From time to time atmospheric flows become organized and form coherent long-lived structures. Such structures could be propagating, quasi-stationary, or recur in place. We investigate the ability of principal components analysis (PCA) and archetypal analysis (AA) to identify long-lived events, excluding propagating forms. Our analysis is carried out on the Southern Hemisphere midtropospheric flow represented by geopotential height at 500 hPa (Z 500). The leading basis patterns of Z 500 for PCA and AA are similar and describe structures representing (or similar to) the southern annular mode (SAM) and Pacific–South American (PSA) pattern. Long-lived events are identified here from sequences of 8 days or longer where the same basis pattern dominates for PCA or AA. AA identifies more long-lived events than PCA using this approach. The most commonly occurring long-lived event for both AA and PCA is the annular SAM-like pattern. The second most commonly occurring event is the PSA-like Pacific wave train for both AA and PCA. For AA the flow at any given time is approximated as weighted contributions from each basis pattern, which lends itself to metrics for discriminating among basis patterns. These show that the longest long-lived events are in general better expressed than shorter events. Case studies of long-lived events featuring a blocking structure and an annular structure show that both PCA and AA can identify and discriminate the dominant basis pattern that most closely resembles the flow event.
Abstract
From time to time atmospheric flows become organized and form coherent long-lived structures. Such structures could be propagating, quasi-stationary, or recur in place. We investigate the ability of principal components analysis (PCA) and archetypal analysis (AA) to identify long-lived events, excluding propagating forms. Our analysis is carried out on the Southern Hemisphere midtropospheric flow represented by geopotential height at 500 hPa (Z 500). The leading basis patterns of Z 500 for PCA and AA are similar and describe structures representing (or similar to) the southern annular mode (SAM) and Pacific–South American (PSA) pattern. Long-lived events are identified here from sequences of 8 days or longer where the same basis pattern dominates for PCA or AA. AA identifies more long-lived events than PCA using this approach. The most commonly occurring long-lived event for both AA and PCA is the annular SAM-like pattern. The second most commonly occurring event is the PSA-like Pacific wave train for both AA and PCA. For AA the flow at any given time is approximated as weighted contributions from each basis pattern, which lends itself to metrics for discriminating among basis patterns. These show that the longest long-lived events are in general better expressed than shorter events. Case studies of long-lived events featuring a blocking structure and an annular structure show that both PCA and AA can identify and discriminate the dominant basis pattern that most closely resembles the flow event.
Abstract
On the afternoon and evening of 22 May 2002, high-resolution observations of the boundary layer (BL) and a dryline were obtained in the eastern Oklahoma and Texas panhandles during the International H2O Project. Using overdetermined multiple-Doppler radar syntheses in concert with a Lagrangian analysis of water vapor and temperature fields, the 3D kinematic and thermodynamic structure of the dryline and surrounding BL have been analyzed over a nearly 2-h period. The dryline is resolved as a strong (2–4 g kg−1 km−1) gradient of water vapor mixing ratio that resides in a nearly north–south-oriented zone of convergence. Maintained through frontogenesis, the dryline is also located within a gradient of virtual potential temperature, which induces a persistent, solenoidally forced secondary circulation. Initially quasi-stationary, the dryline retrogrades to the west during early evening and displays complicated substructures including small wavelike perturbations that travel from south to north at nearly the speed of the mean BL flow. A second, minor dryline has similar characteristics to the first, but has weaker gradients and circulations. The BL adjacent to the dryline exhibits complicated structures, consisting of combinations of open cells, horizontal convective rolls, and transverse rolls. Strong convergence and vertical motion at the dryline act to lift moisture, and high-based cumulus clouds are observed in the analysis domain. Although the top of the analysis domain is below the lifted condensation level height, vertical extrapolation of the moisture fields generally agrees with cloud locations. Mesoscale vortices that move along the dryline induce a transient eastward dryline motion due to the eastward advection of dry air following misocyclone passage. Refractivity-based moisture and differential reflectivity analyses are used to help interpret the Lagrangian analyses.
Abstract
On the afternoon and evening of 22 May 2002, high-resolution observations of the boundary layer (BL) and a dryline were obtained in the eastern Oklahoma and Texas panhandles during the International H2O Project. Using overdetermined multiple-Doppler radar syntheses in concert with a Lagrangian analysis of water vapor and temperature fields, the 3D kinematic and thermodynamic structure of the dryline and surrounding BL have been analyzed over a nearly 2-h period. The dryline is resolved as a strong (2–4 g kg−1 km−1) gradient of water vapor mixing ratio that resides in a nearly north–south-oriented zone of convergence. Maintained through frontogenesis, the dryline is also located within a gradient of virtual potential temperature, which induces a persistent, solenoidally forced secondary circulation. Initially quasi-stationary, the dryline retrogrades to the west during early evening and displays complicated substructures including small wavelike perturbations that travel from south to north at nearly the speed of the mean BL flow. A second, minor dryline has similar characteristics to the first, but has weaker gradients and circulations. The BL adjacent to the dryline exhibits complicated structures, consisting of combinations of open cells, horizontal convective rolls, and transverse rolls. Strong convergence and vertical motion at the dryline act to lift moisture, and high-based cumulus clouds are observed in the analysis domain. Although the top of the analysis domain is below the lifted condensation level height, vertical extrapolation of the moisture fields generally agrees with cloud locations. Mesoscale vortices that move along the dryline induce a transient eastward dryline motion due to the eastward advection of dry air following misocyclone passage. Refractivity-based moisture and differential reflectivity analyses are used to help interpret the Lagrangian analyses.
Abstract
A dryline and misocyclones have been simulated in a cloud-resolving model by applying specified initial and time-dependent lateral boundary conditions obtained from analyses of the 22 May 2002 International H2O Project (IHOP_2002) dataset. The initial and lateral boundary conditions were obtained from a combination of the time–spaced Lagrangian analyses for temperature and moisture with horizontal velocities from multiple-Doppler wind syntheses. The simulated dryline, horizontal dry-convective rolls (HCRs) and open cells (OCCs), misocyclones, and cumulus clouds are similar to the corresponding observed features. The misocyclones move northward at nearly the mean boundary layer (BL) wind speed, rotate dryline gradients owing to their circulations, and move the local dryline eastward via their passage. Cumuli develop along a secondary dryline, along HCR and OCC segments between the primary and secondary drylines, along HCR and OCC segments that have moved over the dryline, and within the dryline updraft. After the initial shearing instability develops, misocyclogenesis proceeds from tilting and stretching of vorticity by the persistent secondary dryline circulation. The resulting misocyclone evolution is discussed.
Abstract
A dryline and misocyclones have been simulated in a cloud-resolving model by applying specified initial and time-dependent lateral boundary conditions obtained from analyses of the 22 May 2002 International H2O Project (IHOP_2002) dataset. The initial and lateral boundary conditions were obtained from a combination of the time–spaced Lagrangian analyses for temperature and moisture with horizontal velocities from multiple-Doppler wind syntheses. The simulated dryline, horizontal dry-convective rolls (HCRs) and open cells (OCCs), misocyclones, and cumulus clouds are similar to the corresponding observed features. The misocyclones move northward at nearly the mean boundary layer (BL) wind speed, rotate dryline gradients owing to their circulations, and move the local dryline eastward via their passage. Cumuli develop along a secondary dryline, along HCR and OCC segments between the primary and secondary drylines, along HCR and OCC segments that have moved over the dryline, and within the dryline updraft. After the initial shearing instability develops, misocyclogenesis proceeds from tilting and stretching of vorticity by the persistent secondary dryline circulation. The resulting misocyclone evolution is discussed.
Abstract
Precipitation forecasts from five global numerical weather prediction (NWP) models are verified against rain gauge observations using the new stable equitable error in probability space (SEEPS) score. It is based on a 3 × 3 contingency table and measures the ability of a forecast to discriminate between “dry,” “light precipitation,” and “heavy precipitation.” In SEEPS, the threshold defining the boundary between the light and heavy categories varies systematically with precipitation climate. Results obtained for SEEPS are compared to those of more well-known scores, and are broken down with regard to individual contributions from the contingency table. It is found that differences in skill between the models are consistent for different scores, but are small compared to seasonal and geographical variations, which themselves can be largely ascribed to the varying prevalence of deep convection. Differences between the tropics and extratropics are quite pronounced. SEEPS scores at forecast day 1 in the tropics are similar to those at day 6 in the extratropics. It is found that the model ranking is robust with respect to choices in the score computation. The issue of observation representativeness is addressed using a “quasi-perfect model” approach. Results suggest that just under one-half of the current forecast error at day 1 in the extratropics can be attributed to the fact that gridbox values are verified against point observations.
Abstract
Precipitation forecasts from five global numerical weather prediction (NWP) models are verified against rain gauge observations using the new stable equitable error in probability space (SEEPS) score. It is based on a 3 × 3 contingency table and measures the ability of a forecast to discriminate between “dry,” “light precipitation,” and “heavy precipitation.” In SEEPS, the threshold defining the boundary between the light and heavy categories varies systematically with precipitation climate. Results obtained for SEEPS are compared to those of more well-known scores, and are broken down with regard to individual contributions from the contingency table. It is found that differences in skill between the models are consistent for different scores, but are small compared to seasonal and geographical variations, which themselves can be largely ascribed to the varying prevalence of deep convection. Differences between the tropics and extratropics are quite pronounced. SEEPS scores at forecast day 1 in the tropics are similar to those at day 6 in the extratropics. It is found that the model ranking is robust with respect to choices in the score computation. The issue of observation representativeness is addressed using a “quasi-perfect model” approach. Results suggest that just under one-half of the current forecast error at day 1 in the extratropics can be attributed to the fact that gridbox values are verified against point observations.
Abstract
A supercell produced a nearly tornadic vortex during an intercept by the Second Verification of the Origins of Rotation in Tornadoes Experiment on 26 May 2010. Using observations from two mobile radars performing dual-Doppler scans, a five-probe mobile mesonet, and a proximity sounding, factors that prevented this vortex from strengthening into a significant tornado are examined. Mobile mesonet observations indicate that portions of the supercell outflow possessed excessive negative buoyancy, likely owing in part to low boundary layer relative humidity, as indicated by a high environmental lifted condensation level. Comparisons to a tornadic supercell suggest that the Prospect Valley storm had enough far-field circulation to produce a significant tornado, but was unable to converge this circulation to a sufficiently small radius. Trajectories suggest that the weak convergence might be due to the low-level mesocyclone ingesting parcels with considerable crosswise vorticity from the near-storm environment, which has been found to contribute to less steady and weaker low-level updrafts in supercell simulations. Yet another factor that likely contributed to the weak low-level circulation was the inability of parcels rich in streamwise vorticity from the forward-flank precipitation region to reach the low-level mesocyclone, likely owing to an unfavorable pressure gradient force field. In light of these results, we suggest that future research should continue focusing on the role of internal, storm-scale processes in tornadogenesis, especially in marginal environments.
Abstract
A supercell produced a nearly tornadic vortex during an intercept by the Second Verification of the Origins of Rotation in Tornadoes Experiment on 26 May 2010. Using observations from two mobile radars performing dual-Doppler scans, a five-probe mobile mesonet, and a proximity sounding, factors that prevented this vortex from strengthening into a significant tornado are examined. Mobile mesonet observations indicate that portions of the supercell outflow possessed excessive negative buoyancy, likely owing in part to low boundary layer relative humidity, as indicated by a high environmental lifted condensation level. Comparisons to a tornadic supercell suggest that the Prospect Valley storm had enough far-field circulation to produce a significant tornado, but was unable to converge this circulation to a sufficiently small radius. Trajectories suggest that the weak convergence might be due to the low-level mesocyclone ingesting parcels with considerable crosswise vorticity from the near-storm environment, which has been found to contribute to less steady and weaker low-level updrafts in supercell simulations. Yet another factor that likely contributed to the weak low-level circulation was the inability of parcels rich in streamwise vorticity from the forward-flank precipitation region to reach the low-level mesocyclone, likely owing to an unfavorable pressure gradient force field. In light of these results, we suggest that future research should continue focusing on the role of internal, storm-scale processes in tornadogenesis, especially in marginal environments.
Abstract
Spatial variability of precipitation is analyzed to characterize to what extent precipitation observed at a single location is representative of precipitation over a larger area. Characterization of precipitation representativeness is made in probabilistic terms using a parametric approach, namely, by fitting a censored shifted gamma distribution to observation measurements. Parameters are estimated and analyzed for independent precipitation datasets, among which one is based on high-density gauge measurements. The results of this analysis serve as a basis for accounting for representativeness error in an ensemble verification process. Uncertainty associated with the scale mismatch between forecast and observation is accounted for by applying a perturbed-ensemble approach before the computation of scores. Verification results reveal a large impact of representativeness error on precipitation forecast reliability and skill estimates. The parametric model and estimated coefficients presented in this study could be used directly for forecast postprocessing to partly compensate for the limitation of any modeling system in terms of precipitation subgrid-scale variability.
Abstract
Spatial variability of precipitation is analyzed to characterize to what extent precipitation observed at a single location is representative of precipitation over a larger area. Characterization of precipitation representativeness is made in probabilistic terms using a parametric approach, namely, by fitting a censored shifted gamma distribution to observation measurements. Parameters are estimated and analyzed for independent precipitation datasets, among which one is based on high-density gauge measurements. The results of this analysis serve as a basis for accounting for representativeness error in an ensemble verification process. Uncertainty associated with the scale mismatch between forecast and observation is accounted for by applying a perturbed-ensemble approach before the computation of scores. Verification results reveal a large impact of representativeness error on precipitation forecast reliability and skill estimates. The parametric model and estimated coefficients presented in this study could be used directly for forecast postprocessing to partly compensate for the limitation of any modeling system in terms of precipitation subgrid-scale variability.
Abstract
Several studies have documented the sensitivity of convective storm simulations to the microphysics parameterization, but there is less research documenting how these sensitivities change with environmental conditions. In this study, the influence of the lifting condensation level (LCL) on the sensitivity of simulated ordinary convective storm cold pools to the microphysics parameterization is examined. To do this, seven perturbed-microphysics ensembles with nine members each are used, where each ensemble uses a different base state with a surface-based LCL between 500 and 2000 m. A comparison of ensemble standard deviations of cold-pool properties shows a clear trend of increasing sensitivity to the microphysics as the LCL is raised. In physical terms, this trend is the result of lower relative humidities in high-LCL environments that increase low-level rain evaporational cooling rates, which magnifies differences in evaporation already present among the members of a given ensemble owing to the microphysics variations. Omitting supersaturation from the calculation of rain evaporation so that only the raindrop size distribution influences evaporation leads to more evaporation in the low-LCL simulations (owing to more drops), as well as a slightly larger spread in evaporational cooling amounts between members in the low-LCL ensembles. Cold pools in the low-LCL environments are also found to develop earlier and are initially more sensitive to raindrop breakup owing to a larger warm-cloud depth. Altogether, these results suggest that convective storms may be more predictable in low-LCL environments, and forecasts of convection in high-LCL environments may benefit the most from microphysics perturbations within an ensemble forecasting system.
Significance Statement
Computer simulations of thunderstorms can have grid spacings ranging from tens to thousands of meters. Because individual precipitation particles form on scales smaller than these grid spacings, the bulk effects of precipitation processes in models must be approximated. Past studies have found that models are sensitive to these approximations. In this study, we test whether the sensitivity to these approximations changes with the relative humidity in the lowest 1–2 km of the atmosphere. We found that increasing the relative humidity decreases the sensitivity of simulations to the precipitation process approximations. These results can inform meteorologists about the uncertainties surrounding computer-generated thunderstorm forecasts and suggest environmental conditions where using several computer models with different precipitation process approximations may be beneficial.
Abstract
Several studies have documented the sensitivity of convective storm simulations to the microphysics parameterization, but there is less research documenting how these sensitivities change with environmental conditions. In this study, the influence of the lifting condensation level (LCL) on the sensitivity of simulated ordinary convective storm cold pools to the microphysics parameterization is examined. To do this, seven perturbed-microphysics ensembles with nine members each are used, where each ensemble uses a different base state with a surface-based LCL between 500 and 2000 m. A comparison of ensemble standard deviations of cold-pool properties shows a clear trend of increasing sensitivity to the microphysics as the LCL is raised. In physical terms, this trend is the result of lower relative humidities in high-LCL environments that increase low-level rain evaporational cooling rates, which magnifies differences in evaporation already present among the members of a given ensemble owing to the microphysics variations. Omitting supersaturation from the calculation of rain evaporation so that only the raindrop size distribution influences evaporation leads to more evaporation in the low-LCL simulations (owing to more drops), as well as a slightly larger spread in evaporational cooling amounts between members in the low-LCL ensembles. Cold pools in the low-LCL environments are also found to develop earlier and are initially more sensitive to raindrop breakup owing to a larger warm-cloud depth. Altogether, these results suggest that convective storms may be more predictable in low-LCL environments, and forecasts of convection in high-LCL environments may benefit the most from microphysics perturbations within an ensemble forecasting system.
Significance Statement
Computer simulations of thunderstorms can have grid spacings ranging from tens to thousands of meters. Because individual precipitation particles form on scales smaller than these grid spacings, the bulk effects of precipitation processes in models must be approximated. Past studies have found that models are sensitive to these approximations. In this study, we test whether the sensitivity to these approximations changes with the relative humidity in the lowest 1–2 km of the atmosphere. We found that increasing the relative humidity decreases the sensitivity of simulations to the precipitation process approximations. These results can inform meteorologists about the uncertainties surrounding computer-generated thunderstorm forecasts and suggest environmental conditions where using several computer models with different precipitation process approximations may be beneficial.
