A Stochastic Perturbed Parameterization Tendency Scheme for Diffusion (SPPTD) and Its Application to an Idealized Supercell Simulation

Abstract Diffusion plays an important role in supercell simulations. A stochastically perturbed parameterization tendency scheme for diffusion (SPPTD) is developed to incorporate diffusive uncertainties in ensemble forecasts. This scheme follows the same procedure as the previously published stochastically perturbed parameterization tendencies (SPPT) scheme but uses a recursive filter to generate smooth perturbations. It also employs horizontal and vertical localization to retain the impact of perturbation in areas with strong shear. Three additional restrictions are added for the sake of integration stability; these restrictions determine the area and amplitude of the perturbation and the situation to suspend SPPTD. The performance of this scheme is examined by using an idealized supercell storm. The model errors are simulated using different resolutions in the truth run (1 km) and ensemble forecasts (2 km). The results indicate that the ensemble forecasts using SPPTD encompass the intensity and displacement of maximum updraft helicity in the truth run. This scheme yields better results than can be obtained using initial perturbations or larger computational mixing coefficients. The sensitivity of SPPTD to each of its parameters is also examined. The results indicate that the optimal horizontal and temporal scales for SPPTD are 40 km and 30 min, respectively. Moderately adjusting the spatiotemporal scale by 10 km or 10 min does not significantly change the SPPTD performance. In this case study, an ensemble size of 20 is sufficient. Perturbing the diffusion terms of all variables using the same approach does not provide additional benefits other than that of selected variables and thus requires further study.

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The Impact of Stochastically Perturbed Parameterizations on Tornadic Supercell Cases in East China

Monthly Weather Review ◽

10.1175/mwr-d-18-0182.1 ◽

2018 ◽

Vol 147 (1) ◽

pp. 199-220 ◽

Cited By ~ 2

Author(s):

Shizhang Wang ◽

Xiaoshi Qiao ◽

Jinzhong Min ◽

Xiaoran Zhuang

Keyword(s):

Positive Impact ◽

Eastern China ◽

Three Dimensional ◽

Radar Data ◽

Ensemble Forecasts ◽

The Impact ◽

And Diffusion ◽

Temperature Tendency ◽

Vertical Localization ◽

Stochastically Perturbed

Abstract The impact of stochastically perturbed parameterizations on short-term tornadic supercell ensemble forecasts (EFs) was evaluated using two tornado cases that occurred in eastern China. The initial condition (IC) perturbations of EFs were generated by a three-dimensional variational data assimilation system with perturbed radar data. The parameterization perturbations of EFs were produced by a stochastic procedure that was applied to diffusion and microphysics parameterizations. This procedure perturbed tendencies from both parameterizations and intercept parameters (INTCPs) of the microphysics parameterizations. In addition to individually perturbing these quantities, a combination of perturbations of diffusion and INTCPs was also examined. A resampling method was proposed to handle perturbations that vary substantially, and a vertical localization was applied to the microphysics tendency perturbations. The results indicated that combining perturbations of diffusion and INTCPs produced the intensity and path forecasts of the low-level vortex (LLV) that better match observations for a weak tornado case; this combination also had a positive impact on the LLV intensity forecast for a much stronger tornado case. This combination outperformed the stochastic procedures that perturbed only diffusion or INTCPs, which indicated that it is better to use both error representations. The vertical localization prevented the temperature tendency perturbations of microphysics from always suppressing storms in negative perturbation (<0.0) areas. The negative INTCP and diffusion perturbations benefited the strong LLV, which is consistent with that of the idealized case. The current stochastic procedure could not address the LLV displacement error that is caused by the IC error.

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A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

Monthly Weather Review ◽

10.1175/mwr3357.1 ◽

2007 ◽

Vol 135 (4) ◽

pp. 1424-1438 ◽

Cited By ~ 7

Author(s):

Andrew R. Lawrence ◽

James A. Hansen

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Ensemble Forecast ◽

Ensemble Forecasting ◽

Limited Area ◽

Ensemble Size ◽

Probabilistic Forecasting ◽

Ensemble Forecasts ◽

Chaotic Model ◽

The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

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The Impact of a Stochastically Perturbing Microphysics Scheme on an Idealized Supercell Storm

Monthly Weather Review ◽

10.1175/mwr-d-17-0064.1 ◽

2017 ◽

Vol 146 (1) ◽

pp. 95-118 ◽

Cited By ~ 4

Author(s):

Xiaoshi Qiao ◽

Shizhang Wang ◽

Jinzhong Min

Keyword(s):

Stochastic Methods ◽

Microphysics Scheme ◽

Advanced Regional Prediction System ◽

Recursive Filters ◽

Supercell Storm ◽

Regional Prediction ◽

Double Moment ◽

The Impact ◽

Temperature Tendency ◽

Stochastically Perturbed

Abstract The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH.

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The Impact of Mesoscale Environmental Uncertainty on the Prediction of a Tornadic Supercell Storm Using Ensemble Data Assimilation Approach

Advances in Meteorology ◽

10.1155/2013/731647 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 17

Author(s):

Nusrat Yussouf ◽

Jidong Gao ◽

David J. Stensrud ◽

Guoqing Ge

Keyword(s):

Data Assimilation ◽

Environmental Variability ◽

Ensemble Forecasts ◽

Near Surface ◽

Model Based ◽

Supercell Storm ◽

Convective Scale ◽

Physics Ensemble ◽

Background Fields ◽

The Impact

Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative.

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Comparison of the Analyses and Forecasts of a Tornadic Supercell Storm from Assimilating Phased-Array Radar and WSR-88D Observations

Weather and Forecasting ◽

10.1175/waf-d-16-0159.1 ◽

2017 ◽

Vol 32 (4) ◽

pp. 1379-1401 ◽

Cited By ~ 10

Author(s):

Timothy A. Supinie ◽

Nusrat Yussouf ◽

Youngsun Jung ◽

Ming Xue ◽

Jing Cheng ◽

...

Keyword(s):

Phased Array ◽

Weather Forecasting ◽

Temporal Frequency ◽

Severe Weather ◽

Ensemble Forecasts ◽

Rapid Scan ◽

Supercell Storm ◽

Phased Array Radar ◽

Skill Scores ◽

The Impact

Abstract NOAA’s National Severe Storms Laboratory is actively developing phased-array radar (PAR) technology, a potential next-generation weather radar, to replace the current operational WSR-88D radars. One unique feature of PAR is its rapid scanning capability, which is at least 4–5 times faster than the scanning rate of WSR-88D. To explore the impact of such high-frequency PAR observations compared with traditional WSR-88D on severe weather forecasting, several storm-scale data assimilation and forecast experiments are conducted. Reflectivity and radial velocity observations from the 22 May 2011 Ada, Oklahoma, tornadic supercell storm are assimilated over a 45-min period using observations from the experimental PAR located in Norman, Oklahoma, and the operational WSR-88D radar at Oklahoma City, Oklahoma. The radar observations are assimilated into the ARPS model within a heterogeneous mesoscale environment and 1-h ensemble forecasts are generated from analyses every 15 min. With a 30-min assimilation period, the PAR experiment is able to analyze more realistic storm structures, resulting in higher skill scores and higher probabilities of low-level vorticity that align better with the locations of radar-derived rotation compared with the WSR-88D experiment. Assimilation of PAR observations for a longer 45-min time period generates similar forecasts compared to assimilating WSR-88D observations, indicating that the advantage of rapid-scan PAR is more noticeable over a shorter 30-min assimilation period. An additional experiment reveals that the improved accuracy from the PAR experiment over a shorter assimilation period is mainly due to its high-temporal-frequency sampling capability. These results highlight the benefit of PAR’s rapid-scan capability in storm-scale modeling that can potentially extend severe weather warning lead times.

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