The parameterization of slantwise convection in a numerical model

2021 ◽  
Author(s):  
Ting-Chen Chen ◽  
Man-Kong Yau ◽  
Daniel J. Kirshbaum
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Parameterization Scheme ◽  
Weather Research And Forecasting ◽  
Natural State ◽  
Convective Parameterization ◽  
Scale Models ◽  
Convective Parameterization Scheme ◽  
Slantwise Convection

<p>     Slantwise convection and the associated release of conditional symmetric instability (CSI) have been recognized as important baroclinic processes. Recent climatological studies have highlighted its significant association with midlatitude cyclone activities, raising questions about whether large-scale models can resolve slantwise convection and whether it should be parameterized.</p><p>     To address this issue, the present study simulates isolated free moist slantwise convection in an initially statically stable environment using the 2D idealized, non-hydrostatic Weather Research and Forecasting (WRF) Model. We first examined the sensitivity of the slantwise convection to the cross-band grid spacing (Δy; varied from 40 to 1 km) and found that experiments with ∆y> 5 km fail to capture the band dynamics and larger-scale feedbacks robustly and thus require parameterization. As most of the current convective parameterization schemes target upright convection in a local column, we implemented an additional 2D slantwise convective parameterization scheme and evaluated its impact for coarse-grid runs.</p><p>     The slantwise convective parameterization scheme operates along a sloped trajectory on a horizontally-variant cross section perpendicular to the local thermal wind, adjusting the environment toward a natural state to CSI within a given time scale. With the addition of the slantwise convective parameterization scheme, significant improvements are found in precipitation and the strength of the slantwise updraft, bringing the coarser-grid (∆y=40 km) simulation closer to the finer-grid (converged) results than its counterpart with only the upright convection scheme. After testing the slantwise convective parameterization scheme under idealized frameworks, we will further apply it to regional models to evaluate its benefit to the weather forecasting in real cases.</p>

scholarly journals Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

2012 ◽  
Vol 12 (5) ◽  
pp. 2409-2427 ◽  
Author(s):  
B. Yang ◽  
Y. Qian ◽  
G. Lin ◽  
R. Leung ◽  
Y. Zhang
Keyword(s):  
Uncertainty Quantification ◽  
Spatial Scales ◽  
Wrf Model ◽  
Parameter Tuning ◽  
Parameterization Scheme ◽  
Convective Precipitation ◽  
Optimal Parameters ◽  
Convective Parameterization ◽  
Input Parameters ◽  
Convective Parameterization Scheme

Abstract. The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

scholarly journals Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

2011 ◽  
Vol 11 (12) ◽  
pp. 31769-31817 ◽  
Author(s):  
B. Yang ◽  
Y. Qian ◽  
G. Lin ◽  
R. Leung ◽  
Y. Zhang
Keyword(s):  
Uncertainty Quantification ◽  
Spatial Scales ◽  
Wrf Model ◽  
Parameter Tuning ◽  
Parameterization Scheme ◽  
Convective Precipitation ◽  
Optimal Parameters ◽  
Convective Parameterization ◽  
Input Parameters ◽  
Convective Parameterization Scheme

Abstract. The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

scholarly journals Relationship between the Track and Structural Evolution of Hurricane Sandy (2012) Using a Regional Ensemble

2018 ◽  
Vol 146 (12) ◽  
pp. 4279-4302 ◽  
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Structural Evolution ◽  
Hurricane Sandy ◽  
Weather Research And Forecasting ◽  
Extratropical Transition ◽  
Ensemble Forecasts ◽  
High Predictability ◽  
Regression Mixture ◽  
The Relationship

Abstract An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

scholarly journals Estimating Daytime Planetary Boundary Layer Heights over a Valley from Rawinsonde Observations at a Nearby Airport: An Application to the Page Valley in Virginia, United States

2016 ◽  
Vol 55 (3) ◽  
pp. 791-809 ◽  
Author(s):  
Temple R. Lee ◽  
Stephan F. J. De Wekker
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Weather Research And Forecasting ◽  
Dispersion Modeling ◽  
Blue Ridge Mountains ◽  
Terrain Following ◽  
Regional Reanalysis

AbstractThe planetary boundary layer (PBL) height is an essential parameter required for many applications, including weather forecasting and dispersion modeling for air quality. Estimates of PBL height are not easily available and often come from twice-daily rawinsonde observations at airports, typically at 0000 and 1200 UTC. Questions often arise regarding the applicability of PBL heights retrieved from these twice-daily observations to surrounding locations. Obtaining this information requires knowledge of the spatial variability of PBL heights. This knowledge is particularly limited in regions with mountainous terrain. The goal of this study is to develop a method for estimating daytime PBL heights in the Page Valley, located in the Blue Ridge Mountains of Virginia. The approach includes using 1) rawinsonde observations from the nearest sounding station [Dulles Airport (IAD)], which is located 90 km northeast of the Page Valley, 2) North American Regional Reanalysis (NARR) output, and 3) simulations with the Weather Research and Forecasting (WRF) Model. When selecting days on which PBL heights from NARR compare well to PBL heights determined from the IAD soundings, it is found that PBL heights are higher (on the order of 200–400 m) over the Page Valley than at IAD and that these differences are typically larger in summer than in winter. WRF simulations indicate that larger sensible heat fluxes and terrain-following characteristics of PBL height both contribute to PBL heights being higher over the Page Valley than at IAD.

Convection Initiation Aided by Lake-Breeze Convergence over the Niagara Peninsula

2019 ◽  
Vol 147 (11) ◽  
pp. 3955-3979 ◽  
Author(s):  
Chun-Chih Wang ◽  
Daniel J. Kirshbaum ◽  
David M. L. Sills
Keyword(s):  
Large Scale ◽  
Inversion Layer ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Lake Breeze ◽  
Moist Convection ◽  
Warm Advection ◽  
American Science ◽  
Convection Initiation ◽  
Cloud Tops

Abstract Observations from the 2015 Environment and Climate Change Canada Pan/Parapan American Science Showcase (ECPASS) and real-case, cloud-resolving numerical simulations with the Weather Research and Forecasting (WRF) Model are used to investigate two cases of moist convection forced by lake-breeze convergence over southern Ontario (18 July and 15 August 2015). The two cases shared several characteristics, including high pressure conditions, similar morning soundings, and isolated afternoon convection along a line of lake-breeze convergence between Lakes Erie and Ontario. However, the convection was significantly stronger in the August case, with robustly deeper clouds and larger radar reflectivities than in the July case. Synoptic and mesoscale analyses of these events reveal that the key difference between them was their large-scale forcing. The July event exhibited a combination of strong warm advection and large-scale descent at midlevels (850–650 hPa), which created an inversion layer that capped cloud tops at 4–6 km. The August case exhibited similar features (large-scale descent and warm advection), but these were focused at higher levels (700–400 hPa) and weaker. As a consequence, the convection in the August case was less suppressed at midlevels and ascended deeper (reaching over 8 km). Although the subcloud updraft along the lake-breeze convergence zone was also found to be stronger in the August case, this difference was found to be an effect, rather than a cause, of stronger moist convection within the cloud layer.

scholarly journals Sudden Track Changes of Tropical Cyclones in Monsoon Gyres: Full-Physics, Idealized Numerical Experiments*

2015 ◽  
Vol 72 (4) ◽  
pp. 1307-1322 ◽  
Author(s):  
Jia Liang ◽  
Liguang Wu
Keyword(s):  
Tropical Cyclones ◽  
Numerical Experiments ◽  
Large Scale ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Coalescence Process ◽  
The West ◽  
Steering Flow ◽  
Sharp Turn ◽  
Wind Profiles

Abstract Tropical cyclones (TCs) in the eastern semicircle of large-scale monsoon gyres (MGs) were observed to take either a northward (sudden northward and northward without a sharp turn) or a westward TC turn, but only the northward turn was previously simulated in a barotropic model. To understand what controls TC track types in MGs, idealized numerical experiments are performed using the full-physics Weather Research and Forecasting (WRF) Model. These experiments indicate that TCs initially located in the eastern semicircle of MGs can generally take three types of tracks: a sudden northward track, a westward track, and a northward track without a sharp turn. The track types depend upon the TC movement relative to the MG center. In agreement with barotropic simulations, the WRF simulation confirms that approaching and being collocated with the MG center is crucial to the occurrence of sudden northward TC track changes and that sudden northward track changes can be generally accounted for by changes in the steering flow. TCs that take westward tracks and northward tracks without a sharp turn do not experience such a coalescence process. Westward TCs move faster than MGs and are then located to the west of the MG center, while TCs move more slowly than MGs and then take a northward track without a sharp turn. This study reveals that the specific TC track in the eastern semicircle of an MG is sensitive to the initial wind profiles of both MGs and TCs, suggesting that improvement in the observation of TC and MG structures is very important for predicting TC track types in MGs.

scholarly journals Using Betts-Miller-Janjic convective parameterization scheme in H14-31 model to forecast heavy rainfall in Vietnam

2007 ◽  
Vol 29 (2) ◽  
pp. 83-97
Author(s):  
Vu Thanh Hang ◽  
Kieu Thi Xin
Keyword(s):  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Parameterization Scheme ◽  
Model Resolution ◽  
Convective Parameterization ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Convective Adjustment ◽  
Convective Parameterization Scheme ◽  
The Tropics

According to Krishnamurti, improvements of physical parameterizations will mainly affect simulations for the tropics [10]. The study of William A. Gallus Jr. showed that the higher the model resolution and more detailed convective parameterizations, the better the skill in quantitative precipitation forecast (QPF) in general [16]. The quality of precipitation forecast is so sensitive to convective parameterization scheme (CPS) used in the model as well as model resolution. The fact shows that for high resolution regional model like H14-31 CPS based on low-level moisture convergence as Tiedtke did not give good heavy rainfall forecast in Vietnam. In this paper we used the scheme of Betts-Miller-Janjic (BMJ) based on the convective adjustment toward tropical observationally structures in reality instead of Tiedtke in Hl4-31. Statistical verification results and verification using CRA method of Hl4-31 of two CPSs for seperated cases and for three rain seasons (2003-2005) shows that heavy rainfall forecast of Hl4-31/BMJ is better than one of H14-31/TK for Vietnam-South China Sea. CRA verification also shows that it is possible to say that heavy rainfall forecast skill of l-I14-31/BMJ in tropics is nearly similar to the skill of LAPS of Australia.

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