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Abstract
The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology toward novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the nonresolved processes, i.e., usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parameterizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier–Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g., artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.
Abstract
The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology toward novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the nonresolved processes, i.e., usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parameterizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier–Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g., artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.
Abstract
Typhoon Morakot struck Taiwan during 7–9 August 2009 and became the deadliest tropical cyclone (TC) in five decades by producing up to 2635 mm of rain in 48 h, breaking the world record. The extreme rainfall of Morakot resulted from the strong interaction among several favorable factors that occurred simultaneously. These factors from large scale to small scale include the following: 1) weak environmental steering flow linked to the evolution of the monsoon gyre and consequently slow TC motion; 2) a strong moisture surge due to low-level southwesterly flow; 3) asymmetric rainfall and latent heating near southern Taiwan to further reduce the TC’s forward motion as its center began moving away from Taiwan; 4) enhanced rainfall due to steep topography; 5) atypical structure with a weak inner core, enhancing its susceptibility to the latent heating effect; and 6) cell merger and back building inside the rainbands associated with the interaction between the low-level jet and convective updrafts. From a forecasting standpoint, the present-day convective-permitting or cloud-resolving regional models are capable of short-range predictions of the Morakot event starting from 6 August. At longer ranges beyond 3 days, larger uncertainty exists in the track forecast and an ensemble approach is necessary. Due to the large computational demand at the required high resolution, the time-lagged strategy is shown to be a feasible option to produce useful information on rainfall probabilities of the event.
Abstract
Typhoon Morakot struck Taiwan during 7–9 August 2009 and became the deadliest tropical cyclone (TC) in five decades by producing up to 2635 mm of rain in 48 h, breaking the world record. The extreme rainfall of Morakot resulted from the strong interaction among several favorable factors that occurred simultaneously. These factors from large scale to small scale include the following: 1) weak environmental steering flow linked to the evolution of the monsoon gyre and consequently slow TC motion; 2) a strong moisture surge due to low-level southwesterly flow; 3) asymmetric rainfall and latent heating near southern Taiwan to further reduce the TC’s forward motion as its center began moving away from Taiwan; 4) enhanced rainfall due to steep topography; 5) atypical structure with a weak inner core, enhancing its susceptibility to the latent heating effect; and 6) cell merger and back building inside the rainbands associated with the interaction between the low-level jet and convective updrafts. From a forecasting standpoint, the present-day convective-permitting or cloud-resolving regional models are capable of short-range predictions of the Morakot event starting from 6 August. At longer ranges beyond 3 days, larger uncertainty exists in the track forecast and an ensemble approach is necessary. Due to the large computational demand at the required high resolution, the time-lagged strategy is shown to be a feasible option to produce useful information on rainfall probabilities of the event.
Abstract
The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.
Abstract
The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.
