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Abstract
The ability of a stochastically perturbed parameterization (SPP) approach to represent uncertainties in the model component of the Canadian Global Ensemble Prediction System was demonstrated in Part I of this investigation. The goal of this second step in SPP evaluation is to determine whether the scheme represents a viable alternative to the current operational combination of a multiphysics configuration and stochastically perturbed parameterization tendencies (SPPT). An assessment of the impact of each model uncertainty estimate in isolation reveals that, although the multiphysics configuration is highly effective at generating ensemble spread, it is often the result of differing biases rather than a reflection of flow-dependent error growth. Moreover, some of the members of the multiphysics ensemble suffer from large errors on regional scales as a result of suboptimal configurations. The SPP scheme generates a greater diversity of member solutions than the SPPT scheme in isolation, and it has an impact on forecast performance that is similar to that of current operational uncertainty estimates. When the SPP framework is combined with recent upgrades to the model physics suite that are only applicable in the stochastic perturbation context, the quality of global ensemble guidance is significantly improved.
Significance Statement
The stochastically perturbed parameterization (SPP) technique was introduced in Part I to represent model uncertainties in forecasts generated by an operational global ensemble prediction system. We focus here on the viability of this technique as a replacement for the system’s current uncertainty estimates: multiphysics and stochastic perturbations of physics tendencies. Despite the practical success of this combination, it suffers from physical inconsistencies and poor conservation properties. The adoption of SPP allows the ensemble to benefit from a recent set of model updates that couple with this new representation of model uncertainty to yield significant improvements in the quality of forecasts generated by the system.
Abstract
The ability of a stochastically perturbed parameterization (SPP) approach to represent uncertainties in the model component of the Canadian Global Ensemble Prediction System was demonstrated in Part I of this investigation. The goal of this second step in SPP evaluation is to determine whether the scheme represents a viable alternative to the current operational combination of a multiphysics configuration and stochastically perturbed parameterization tendencies (SPPT). An assessment of the impact of each model uncertainty estimate in isolation reveals that, although the multiphysics configuration is highly effective at generating ensemble spread, it is often the result of differing biases rather than a reflection of flow-dependent error growth. Moreover, some of the members of the multiphysics ensemble suffer from large errors on regional scales as a result of suboptimal configurations. The SPP scheme generates a greater diversity of member solutions than the SPPT scheme in isolation, and it has an impact on forecast performance that is similar to that of current operational uncertainty estimates. When the SPP framework is combined with recent upgrades to the model physics suite that are only applicable in the stochastic perturbation context, the quality of global ensemble guidance is significantly improved.
Significance Statement
The stochastically perturbed parameterization (SPP) technique was introduced in Part I to represent model uncertainties in forecasts generated by an operational global ensemble prediction system. We focus here on the viability of this technique as a replacement for the system’s current uncertainty estimates: multiphysics and stochastic perturbations of physics tendencies. Despite the practical success of this combination, it suffers from physical inconsistencies and poor conservation properties. The adoption of SPP allows the ensemble to benefit from a recent set of model updates that couple with this new representation of model uncertainty to yield significant improvements in the quality of forecasts generated by the system.
Abstract
Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
Abstract
Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
Abstract
This study investigates the preservation of tracer interrelationships during advection in large-eddy simulations of an idealized deep convective cloud, which is particularly relevant to chemistry, aerosol, and cloud microphysics models. Employing the Cloud Model 1, advection is represented using third-, fifth-, and seventh-order weighted essentially non-oscillatory schemes. As a simplified analogy for cloud hydrometeors and aerosols, several inert passive tracers following linear and nonlinear relationships are initialized after the cloud reaches ∼6-km depth. Numerical mixing in the simulated turbulent convective clouds leads to significant deviations from the initial nonlinear relationships between tracers. In these simulations, a considerable fraction of the grid points where the tracers’ nonlinear relationships are altered from advection are classified as unrealistic (e.g., ∼13% for the environmental tracers on average), including errors from range-preserving unmixing and overshooting. Errors in the sum of three tracers are also relatively large, ranging between ∼1% and 16% for 5% of the grid points in and near the cloud. The magnitude of unrealistic mixing and errors in the sum of three tracers generally increase with the order of accuracy of the advection scheme. These results are consistent across model grid spacings ranging from 50 to 200 m, and across three different flow realizations for each combination of grid spacing and advection scheme tested. Tests employing a previously proposed scalar normalization procedure show substantially reduced errors in the sum of three tracers with a relatively small negative impact on other tracer relationships. This analysis, therefore, suggests efficacy of the normalization procedure when applied to turbulent three-dimensional cloud simulations.
Significance Statement
In nature, transporting several quantities through bulk motions of a fluid does not affect preexisting relationships between them. However, this is not always accomplished in numerical models of the atmosphere, because of intrinsic limitations in the transport algorithms employed. We aim to investigate how these errors behave in 3D realistic simulations of a cumulus cloud, where the turbulent flow constitutes a particular challenge. We show that relationships between quantities are significantly and frequently perturbed during bulk transport in the model. Moreover, our results suggest that increasing complexity of the bulk-transport algorithms (in a way that is conventionally employed for improving the representation of individual quantities) tends to worsen the representation of relationships between two or three quantities.
Abstract
This study investigates the preservation of tracer interrelationships during advection in large-eddy simulations of an idealized deep convective cloud, which is particularly relevant to chemistry, aerosol, and cloud microphysics models. Employing the Cloud Model 1, advection is represented using third-, fifth-, and seventh-order weighted essentially non-oscillatory schemes. As a simplified analogy for cloud hydrometeors and aerosols, several inert passive tracers following linear and nonlinear relationships are initialized after the cloud reaches ∼6-km depth. Numerical mixing in the simulated turbulent convective clouds leads to significant deviations from the initial nonlinear relationships between tracers. In these simulations, a considerable fraction of the grid points where the tracers’ nonlinear relationships are altered from advection are classified as unrealistic (e.g., ∼13% for the environmental tracers on average), including errors from range-preserving unmixing and overshooting. Errors in the sum of three tracers are also relatively large, ranging between ∼1% and 16% for 5% of the grid points in and near the cloud. The magnitude of unrealistic mixing and errors in the sum of three tracers generally increase with the order of accuracy of the advection scheme. These results are consistent across model grid spacings ranging from 50 to 200 m, and across three different flow realizations for each combination of grid spacing and advection scheme tested. Tests employing a previously proposed scalar normalization procedure show substantially reduced errors in the sum of three tracers with a relatively small negative impact on other tracer relationships. This analysis, therefore, suggests efficacy of the normalization procedure when applied to turbulent three-dimensional cloud simulations.
Significance Statement
In nature, transporting several quantities through bulk motions of a fluid does not affect preexisting relationships between them. However, this is not always accomplished in numerical models of the atmosphere, because of intrinsic limitations in the transport algorithms employed. We aim to investigate how these errors behave in 3D realistic simulations of a cumulus cloud, where the turbulent flow constitutes a particular challenge. We show that relationships between quantities are significantly and frequently perturbed during bulk transport in the model. Moreover, our results suggest that increasing complexity of the bulk-transport algorithms (in a way that is conventionally employed for improving the representation of individual quantities) tends to worsen the representation of relationships between two or three quantities.
Abstract
The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.
Abstract
The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.
Abstract
Isolated warm-rain cells are an important feature over the tropical oceans. Although warm rain is typically associated with relatively small raindrops, large raindrops (>4.5 mm in diameter) have been observed in some cases. Previous studies have examined warm rain cells with large drops on a case-study basis, but they have yet to be investigated in a broader, statistical sense. During the recent Propagation of Intraseasonal Oscillations (PISTON) field campaign, a C-band polarimetric radar routinely measured extreme values of differential reflectivity in small, isolated convection, indicating the presence of large drops. Using an objective feature identification and tracking algorithm, this study offers new insights to the structure and frequency of cells containing large drops. Cells with high differential reflectivity (>3.5 dB) were present in 24% of all radar scans. The cells were typically small (8-km2 mean area), short lived (usually <10 min), and shallow (3.7-km mean height). High differential reflectivity was more often found on the upwind side of the cells, suggesting a size sorting mechanism was operating establishing a low concentration of large drops on the upwind side. Differential reflectivity also tended to increase at lower altitudes, which is hypothesized to be due to continued drop growth and increasing temperature (increasing the dielectric constant of water). Rapid vertical cross-section radar scans, as well as transects made by a Learjet aircraft with onboard particle probes, are also used to analyze these cells, and support the conclusions drawn from statistical analysis.
Abstract
Isolated warm-rain cells are an important feature over the tropical oceans. Although warm rain is typically associated with relatively small raindrops, large raindrops (>4.5 mm in diameter) have been observed in some cases. Previous studies have examined warm rain cells with large drops on a case-study basis, but they have yet to be investigated in a broader, statistical sense. During the recent Propagation of Intraseasonal Oscillations (PISTON) field campaign, a C-band polarimetric radar routinely measured extreme values of differential reflectivity in small, isolated convection, indicating the presence of large drops. Using an objective feature identification and tracking algorithm, this study offers new insights to the structure and frequency of cells containing large drops. Cells with high differential reflectivity (>3.5 dB) were present in 24% of all radar scans. The cells were typically small (8-km2 mean area), short lived (usually <10 min), and shallow (3.7-km mean height). High differential reflectivity was more often found on the upwind side of the cells, suggesting a size sorting mechanism was operating establishing a low concentration of large drops on the upwind side. Differential reflectivity also tended to increase at lower altitudes, which is hypothesized to be due to continued drop growth and increasing temperature (increasing the dielectric constant of water). Rapid vertical cross-section radar scans, as well as transects made by a Learjet aircraft with onboard particle probes, are also used to analyze these cells, and support the conclusions drawn from statistical analysis.
Abstract
This study investigates the characteristics of mesoscale convective systems (MCSs) as a function of MCS organizational mode over China, using long-term precipitation radar observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). The spaceborne radar-based MCS climatology shows maximum population over low-elevation regions, marked decrease over the foothills, and minimum frequency over the Tibetan Plateau. Linear and nonlinear MCSs account for 17% and 83% of the MCSs over China, respectively. Linear MCSs have much stronger convective intensity and heavier precipitation than nonlinear MCSs, as indicated by TRMM convective proxies. Interestingly, though broad-stratiform MCSs have the weakest convection, they produce the heaviest (maximum) rain rate and the largest amount of heavy rainfall among nonlinear MCSs. Among various types of linear MCSs, bow echoes (BEs) and no-stratiform (NS) systems exhibit the strongest convective intensity, embedded lines exhibit the weakest, and convective lines with trailing/leading stratiform in between. BEs and NSs share the most vertically extended structure, strongest microwave ice scattering, and highest lightning flash rates, but NS systems have a much lower surface rain rate likely due to a drier environment. Vertical radar reflectivity profiles suggest that both ice-based and warm-rain processes play an important role in the precipitation processes of linear MCSs over China, including the most intense BE storms. In short, this study helps to better understand the convective organization, precipitation structure, and ensemble microphysical properties of MCSs over China, and potentially provides guidelines for evaluating high-resolution model simulations and satellite rainfall retrievals for monsoonal MCSs.
Abstract
This study investigates the characteristics of mesoscale convective systems (MCSs) as a function of MCS organizational mode over China, using long-term precipitation radar observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). The spaceborne radar-based MCS climatology shows maximum population over low-elevation regions, marked decrease over the foothills, and minimum frequency over the Tibetan Plateau. Linear and nonlinear MCSs account for 17% and 83% of the MCSs over China, respectively. Linear MCSs have much stronger convective intensity and heavier precipitation than nonlinear MCSs, as indicated by TRMM convective proxies. Interestingly, though broad-stratiform MCSs have the weakest convection, they produce the heaviest (maximum) rain rate and the largest amount of heavy rainfall among nonlinear MCSs. Among various types of linear MCSs, bow echoes (BEs) and no-stratiform (NS) systems exhibit the strongest convective intensity, embedded lines exhibit the weakest, and convective lines with trailing/leading stratiform in between. BEs and NSs share the most vertically extended structure, strongest microwave ice scattering, and highest lightning flash rates, but NS systems have a much lower surface rain rate likely due to a drier environment. Vertical radar reflectivity profiles suggest that both ice-based and warm-rain processes play an important role in the precipitation processes of linear MCSs over China, including the most intense BE storms. In short, this study helps to better understand the convective organization, precipitation structure, and ensemble microphysical properties of MCSs over China, and potentially provides guidelines for evaluating high-resolution model simulations and satellite rainfall retrievals for monsoonal MCSs.
Abstract
Hailstones have large damage potential; however, their explicit prediction remains quite challenging. The uncertainty in a model’s initial condition and microphysics are two of the significant contributors to the challenge. This two-part study aims to investigate the impacts of improved initial condition and microphysics on hail prediction for a moderate hailstorm that occurred in Beijing on 10 June 2016. In the first part, the role of initial conditions on hail prediction is explored by assimilating high-density observations into a numerical model with a recently developed explicit hail microphysics scheme. High-resolution and high-frequency observations from radar and surface networks are assimilated using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. The role of the initial conditions in improving explicit hail prediction with two different planetary boundary layer (PBL) schemes, the Yonsei University (YSU) scheme and the Mellor–Yamada–Janjić (MYJ) scheme, is then examined. Results indicate that the data assimilation significantly improves the hail size and location prediction for both PBL schemes by reducing errors in surface wind, temperature, and moisture fields. It is also shown that the improved analyses of low-level and midlevel vertical wind shear, resulting mainly from radar data assimilation, are pivotal to the improvement of hailstorm prediction with the YSU scheme, while the improved analysis of thermodynamic field resulting from the assimilation of both radar and surface data plays a more important role with the MYJ scheme. The results of this work shed light on the influence of data assimilation and provide insights on explicit hail predictability with respect to model initial conditions.
Abstract
Hailstones have large damage potential; however, their explicit prediction remains quite challenging. The uncertainty in a model’s initial condition and microphysics are two of the significant contributors to the challenge. This two-part study aims to investigate the impacts of improved initial condition and microphysics on hail prediction for a moderate hailstorm that occurred in Beijing on 10 June 2016. In the first part, the role of initial conditions on hail prediction is explored by assimilating high-density observations into a numerical model with a recently developed explicit hail microphysics scheme. High-resolution and high-frequency observations from radar and surface networks are assimilated using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. The role of the initial conditions in improving explicit hail prediction with two different planetary boundary layer (PBL) schemes, the Yonsei University (YSU) scheme and the Mellor–Yamada–Janjić (MYJ) scheme, is then examined. Results indicate that the data assimilation significantly improves the hail size and location prediction for both PBL schemes by reducing errors in surface wind, temperature, and moisture fields. It is also shown that the improved analyses of low-level and midlevel vertical wind shear, resulting mainly from radar data assimilation, are pivotal to the improvement of hailstorm prediction with the YSU scheme, while the improved analysis of thermodynamic field resulting from the assimilation of both radar and surface data plays a more important role with the MYJ scheme. The results of this work shed light on the influence of data assimilation and provide insights on explicit hail predictability with respect to model initial conditions.
Abstract
Influences of cloud liquid water, cloud ice, rain, snow, and graupel on all-sky simulations of the Cross-track Infrared Sounder (CrIS) brightness temperature (TB) are assessed for the 399 data assimilation (DA) channels. The analyses generated by the Gridpoint Statistical Interpolation (GSI) 3D-Var system assimilating conventional and clear-sky satellite radiance observations are used as initial conditions for the Weather Research and Forecasting Model to generate 6-h forecasts with three different microphysics schemes (WSM6, Thompson, and Morrison), which are then used as input to the Community Radiative Transfer Model for all-sky TB simulations. Under all-sky conditions, biases with the WSM6 scheme are negative for all channels and greater in magnitude than −3.5 K. The biases with the Thompson and Morrison schemes vary between −1 and 1 K for all channels. Bias differences among three MP schemes are small in stratus, altocumulus, and cumulus clouds, but large in cirrus and cirrocumulus clouds. The TB simulations in stratus, altocumulus, and cumulus clouds are mostly influenced by the cloud top pressure, while that in cirrus and cirrocumulus clouds depends strongly on cloud optical depth. All-sky TB simulations in cirrus conditions are more positively biased than those under cirrocumulus conditions, probably due to the microphysics schemes producing too thick cirrus clouds. Sensitivity experiments suggest that the TB discrepancies among DA experiments with three MP schemes are mostly caused by the ice or snow type rather than the effective radius of hydrometeor in the upper troposphere. Finally, we propose to combine a cloud-effect parameter with cloud types for modeling observation error characteristics in all-sky DA.
Abstract
Influences of cloud liquid water, cloud ice, rain, snow, and graupel on all-sky simulations of the Cross-track Infrared Sounder (CrIS) brightness temperature (TB) are assessed for the 399 data assimilation (DA) channels. The analyses generated by the Gridpoint Statistical Interpolation (GSI) 3D-Var system assimilating conventional and clear-sky satellite radiance observations are used as initial conditions for the Weather Research and Forecasting Model to generate 6-h forecasts with three different microphysics schemes (WSM6, Thompson, and Morrison), which are then used as input to the Community Radiative Transfer Model for all-sky TB simulations. Under all-sky conditions, biases with the WSM6 scheme are negative for all channels and greater in magnitude than −3.5 K. The biases with the Thompson and Morrison schemes vary between −1 and 1 K for all channels. Bias differences among three MP schemes are small in stratus, altocumulus, and cumulus clouds, but large in cirrus and cirrocumulus clouds. The TB simulations in stratus, altocumulus, and cumulus clouds are mostly influenced by the cloud top pressure, while that in cirrus and cirrocumulus clouds depends strongly on cloud optical depth. All-sky TB simulations in cirrus conditions are more positively biased than those under cirrocumulus conditions, probably due to the microphysics schemes producing too thick cirrus clouds. Sensitivity experiments suggest that the TB discrepancies among DA experiments with three MP schemes are mostly caused by the ice or snow type rather than the effective radius of hydrometeor in the upper troposphere. Finally, we propose to combine a cloud-effect parameter with cloud types for modeling observation error characteristics in all-sky DA.
Abstract
Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso- and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017 to 2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation.
Abstract
Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso- and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017 to 2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation.
Abstract
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
Abstract
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
