Convection-permitting Ensemble Research Articles

Dynamics and Predictability of the Intensification of Hurricane Edouard (2014)

Abstract The dynamics and predictability of the intensification of Hurricane Edouard (2014) are explored through a 60-member convection-permitting ensemble initialized with an ensemble Kalman filter that assimilates dropsondes collected during NASA’s Hurricane and Severe Storm Sentinel (HS3) investigation. The 126-h forecasts are initialized when Edouard was designated as a tropical depression and include Edouard’s near–rapid intensification (RI) from a tropical storm to a strong category-2 hurricane. Although the deterministic forecast was very successful and many members correctly forecasted Edouard’s intensification, there was significant spread in the timing of intensification among the members of the ensemble. Utilizing composite groups created according to the near-RI-onset times of the members, it is shown that, for increasing magnitudes of deep-layer shear, RI onset is increasingly delayed; intensification will not occur once a critical shear threshold is exceeded. Although the timing of intensification varies by as much as 48 h, a decrease in shear is observed across the intensifying composite groups ~6–12 h prior to RI. This decrease in shear is accompanied by a reduction in vortex tilt, as the precession and subsequent alignment process begins ~24–48 h prior to RI. Sensitivity experiments reveal that some of the variation in RI timing can be attributed to differences in initial intensity, as the earliest-developing members have the strongest initial vortices regardless of their environment. Significant sensitivity and limited predictability exists for members with weaker initial vortices and/or that are embedded in less conducive environments, under which the randomness of moist convective processes and minute initial differences distant from the surface center can produce divergent forecasts.

On the forecast skill of a convection-permitting ensemble

Abstract. The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale – Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational – Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

Impact of Assimilating Dropsonde Observations from MPEX on Ensemble Forecasts of Severe Weather Events

AbstractOver the central Great Plains, mid- to upper-tropospheric weather disturbances often modulate severe storm development. These disturbances frequently pass over the Intermountain West region of the United States during the early morning hours preceding severe weather events. This region has fewer in situ observations of the atmospheric state compared with most other areas of the United States, contributing toward greater uncertainty in forecast initial conditions. Assimilation of supplemental observations is hypothesized to reduce initial condition uncertainty and improve forecasts of high-impact weather.During the spring of 2013, the Mesoscale Predictability Experiment (MPEX) leveraged ensemble-based targeting methods to key in on regions where enhanced observations might reduce mesoscale forecast uncertainty. Observations were obtained with dropsondes released from the NSF/NCAR Gulfstream-V aircraft during the early morning hours preceding 15 severe weather events over areas upstream from anticipated convection. Retrospective data-denial experiments are conducted to evaluate the value of dropsonde observations in improving convection-permitting ensemble forecasts. Results show considerable variation in forecast performance from assimilating dropsonde observations, with a modest but statistically significant improvement, akin to prior targeted observation studies that focused on synoptic-scale prediction. The change in forecast skill with dropsonde information was not sensitive to the skill of the control forecast. Events with large positive impact sampled both the disturbance and adjacent flow, akin to results from past synoptic-scale targeting studies, suggesting that sampling both the disturbance and adjacent flow is necessary regardless of the horizontal scale of the feature of interest.

A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

Abstract The impacts of multiscale flow-dependent initial condition (IC) perturbations for storm-scale ensemble forecasts of midlatitude convection are investigated using perfect-model observing system simulation experiments. Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill. Scale dependence of the results is assessed by evaluating 2-h storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts. Forecasts are initialized with different IC ensembles, including an ensemble of multiscale perturbations produced by a multiscale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multiscale perturbations. Mesoscale precipitation forecasts initialized with the multiscale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times. This multiscale advantage is due to greater consistency between the IC perturbations and IC uncertainty. This advantage also affects the short-term, smaller-scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multiscale perturbations as a direct result of the smaller-scale IC perturbation energy. The small-scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ~5-h lead time. Altogether, these results suggest that the multiscale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm-scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser-resolution ensemble.

MOGREPS-UK Convection-Permitting Ensemble Products for Surface Water Flood Forecasting: Rationale and First Results

ABSTRACT Flooding is one of the costliest hazards in the United Kingdom. A large part of the annual flood damage is caused by surface water flooding that is a direct result of intense rainfall. Traditional catchment-based approaches to flood prediction are not applicable for surface water floods. However, given sufficiently accurate forecasts of rainfall intensity, with sufficient lead time, actions can be taken to reduce their impact. These actions require reliable information about severity and areas at risk that is clear and easily interpreted. The accuracy requirements, in particular, are very challenging, as they relate to prediction of intensities that occur only infrequently and that typically affect only small areas. In this paper, forecasts of intense rainfall from a new convection-permitting ensemble prediction system are evaluated using radar observations of intense rain and surface water flooding reports. An urban flooding case that occurred in Edinburgh in 2011 is first investigated and then a broader look is taken at performance through a 3-month period during the London Olympic and Paralympic Games in 2012. Conclusions are drawn about the value of the ensemble and the particular means of presenting the forecasts, and areas requiring further work are highlighted.

The Utility of Convection-Permitting Ensembles for the Prediction of Stationary Convective Bands

Abstract This study examines convection-permitting numerical simulations of four cases of terrain-locked quasi-stationary convective bands over the United Kingdom. For each case, a 2.2-km-grid-length, 12-member ensemble and a 1.5-km-grid-length deterministic forecast are analyzed, each with two different initialization times. Object-based verification is applied to determine whether the simulations capture the structure, location, timing, intensity, and duration of the observed precipitation. These verification diagnostics reveal that the forecast skill varies greatly between the four cases. Although the deterministic and ensemble simulations captured some aspects of the precipitation correctly in each case, they never simultaneously captured all of them satisfactorily. In general, the models predicted banded precipitation accumulations at approximately the correct time and location, but the precipitating structures were more cellular and less persistent than the coherent quasi-stationary bands that were observed. Ensemble simulations from the two different initialization times were not significantly different, which suggests a potential benefit of time-lagging subsequent ensembles to increase ensemble size. The predictive skill of the upstream larger-scale flow conditions and the simulated precipitation on the convection-permitting grids were strongly correlated, which suggests that more accurate forecasts from the parent ensemble should improve the performance of the convection-permitting ensemble nested within it.

Forecasting Tornadoes Using Convection-Permitting Ensembles

Abstract Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

Prediction and Predictability of High-Impact Western Pacific Landfalling Tropical Cyclone Vicente (2012) through Convection-Permitting Ensemble Assimilation of Doppler Radar Velocity

Abstract The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44 m s−1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente’s intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow.

Dynamics and Predictability of Hurricane Nadine (2012) Evaluated through Convection-Permitting Ensemble Analysis and Forecasts

Abstract The governing dynamics and uncertainties of an ensemble simulation of Hurricane Nadine (2012) are assessed through the use of a regional-scale convection-permitting analysis and forecast system based on the Weather Research and Forecasting (WRF) Model and an ensemble Kalman filter (EnKF). For this case, the data that are utilized were collected during the 2012 phase of the National Aeronautics and Space Administration’s (NASA) Hurricane and Severe Storm Sentinel (HS3) experiment. The majority of the tracks of this ensemble were successful, correctly predicting Nadine’s turn toward the southwest ahead of an approaching midlatitude trough, though 10 members forecasted Nadine to be carried eastward by the trough. Ensemble composite and sensitivity analyses reveal the track divergence to be caused by differences in the environmental steering flow that resulted from uncertainties associated with the position and subsequent strength of a midlatitude trough. Despite the general success of the ensemble track forecasts, the intensity forecasts indicated that Nadine would strengthen, which did not happen. A sensitivity experiment performed with the inclusion of sea surface temperature (SST) updates significantly reduced the intensity errors associated with the simulation. This weakening occurred as a result of cooling of the SST field in the vicinity of Nadine, which led to weaker surface sensible and latent heat fluxes at the air–sea interface. A comparison of environmental variables, including relative humidity, temperature, and shear yielded no obvious differences between the WRF-EnKF simulations and the HS3 observations. However, an initial intensity bias in which the WRF-EnKF vortices are stronger than the observed vortex appears to be the most likely cause of the final intensity errors.

Mesoscale Thermodynamic Influences on Convection Initiation near a Surface Dryline in a Convection-Permitting Ensemble

Abstract In this study, the authors examine initiation of severe convection along a daytime surface dryline in a 10-member ensemble of convection-permitting simulations. Results indicate that the minimum buoyancy Bmin of PBL air parcels must be small (Bmin > −0.5°C) for successful deep convection initiation (CI) to occur along the dryline. Comparing different ensemble members reveals that CAPE magnitudes (allowing for entrainment) and the width of the zone of negligible Bmin extending eastward from the dryline act together to influence CI. Since PBL updrafts that initiate along the dryline move rapidly northeast in the vertically sheared flow as they grow into the free troposphere, a wider zone of negligible Bmin helps ensure adequate time for incipient storms to mature, which, itself, is hastened by larger CAPE. Local Bmin budget calculations and trajectory analysis are used to quantify physical processes responsible for the reduction of negative buoyancy prior to CI. Here, the grid-resolved forcing and forcing from temperature and moisture tendencies in the PBL scheme (arising from surface fluxes) contribute about equally in ensemble composites. However, greater spatial variability in grid-resolved forcing focuses the location of the greatest net forcing along the dryline. The grid-resolved forcing is influenced by a thermally direct vertical circulation, where time-averaged ascent at the east edge of the dryline results in locally deeper moisture and cooler conditions near the PBL top. Horizontal temperature advection spreads the cooler air eastward above higher equivalent potential temperature air at source levels of convecting air parcels, resulting in a wider zone of negligible Bmin that facilitates sustained CI.

Representing Forecast Error in a Convection-Permitting Ensemble System

Abstract Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established. Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.

Characterizing and Optimizing Precipitation Forecasts from a Convection-Permitting Ensemble Initialized by a Mesoscale Ensemble Kalman Filter

Abstract Convection-permitting Weather Research and Forecasting (WRF) Model forecasts with 3-km horizontal grid spacing were produced for a 50-member ensemble over a domain spanning three-quarters of the contiguous United States between 25 May and 25 June 2012. Initial conditions for the 3-km forecasts were provided by a continuously cycling ensemble Kalman filter (EnKF) analysis–forecast system with 15-km horizontal grid length. The 3-km forecasts were evaluated using both probabilistic and deterministic techniques with a focus on hourly precipitation. All 3-km ensemble members overpredicted rainfall and there was insufficient forecast precipitation spread. However, the ensemble demonstrated skill at discriminating between both light and heavy rainfall events, as measured by the area under the relative operating characteristic curve. Subensembles composed of 20–30 members usually demonstrated comparable resolution, reliability, and skill as the full 50-member ensemble. On average, deterministic forecasts initialized from mean EnKF analyses were at least as or more skillful than forecasts initialized from individual ensemble members “closest” to the mean EnKF analyses, and “patched together” forecasts composed of members closest to the ensemble mean during each forecast interval were skillful but came with caveats. The collective results underscore the need to improve convection-permitting ensemble spread and have important implications for optimizing EnKF-initialized forecasts.

How Do Forecasters Utilize Output from a Convection-Permitting Ensemble Forecast System? Case Study of a High-Impact Precipitation Event

Abstract The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.

Diurnal Radiation Cycle Impact on the Pregenesis Environment of Hurricane Karl (2010)

AbstractThrough convection-permitting ensemble and sensitivity experiments, this study examines the impact of the diurnal radiation cycle on the pregenesis environment of Hurricane Karl (2010). It is found that the pregenesis environmental stability and the intensity of deep moist convection can be considerably modulated by the diurnal extremes in radiation. Nighttime destabilization of the local and large-scale environment through radiative cooling may promote deep moist convection and increase the genesis potential, likely enhancing the intensity of the resultant tropical cyclones. Modified longwave and shortwave radiation experiments found tropical cyclone development to be highly sensitive to the periodic cycle of heating and cooling, with suppressed formation in the daytime-only and no-radiation experiments and quicker intensification compared with the control for nighttime-only experiments.

Provision of boundary conditions for a convection-permitting ensemble: comparison of two different approaches

Abstract. The current resolution of the operational global models favours the possibility of driving convection-permitting limited-area model (LAM) simulations directly, sparing the necessity for an intermediate step with a coarser-resolution LAM. Though the resolution of global ensemble systems is generally lower than that of deterministic ones, it is also possible to consider this opportunity in the field of ensemble forecasting. The aim of this paper is to investigate the effect of this choice for driving a convection-permitting ensemble based on the COSMO model, for a specific application, namely the forecast of intense autumn precipitation events over Italy. The impact of the direct nesting in the ECMWF global ensemble is compared to a two-step nesting, which makes use of a LAM ensemble system with parametrised convection. Results show that the variability introduced in the geopotential field by the direct nesting is usually contained within the uncertainty described by the standard ensemble, and differences between pairs of members following different nesting approaches are generally smaller than the ensemble error, computed with respect to analysis. The relation between spread and error is even improved by the direct nesting approach. In terms of precipitation, it is found that the forecasts issued by members with different nesting approaches generally have differences at spatial scales between 16 and 180 km, depending on the case, hence not negligible. Nevertheless, the skill of the LAM ensemble precipitation forecasts, evaluated by means of an objective verification, is comparable. Therefore, the overall quality of the 2.8 km ensemble for the specific application is not deteriorated by the provision of lower resolution lateral boundary conditions directly from the global ensemble.

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Abstract In this study, the ability of several cloud microphysical and planetary boundary layer parameterization schemes to accurately simulate cloud characteristics within 4-km grid-spacing ensemble forecasts over the contiguous United States was evaluated through comparison of synthetic Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures with observations. Four double-moment microphysics schemes and five planetary boundary layer (PBL) schemes were evaluated. Large differences were found in the simulated cloud cover, especially in the upper troposphere, when using different microphysics schemes. Overall, the results revealed that the Milbrandt–Yau and Morrison microphysics schemes tended to produce too much upper-level cloud cover, whereas the Thompson and the Weather Research and Forecasting Model (WRF) double-moment 6-class (WDM6) microphysics schemes did not contain enough high clouds. Smaller differences occurred in the cloud fields when using different PBL schemes, with the greatest spread in the ensemble statistics occurring during and after daily peak heating hours. Results varied somewhat depending upon the verification method employed, which indicates the importance of using a suite of verification tools when evaluating high-resolution model performance. Finally, large differences between the various microphysics and PBL schemes indicate that large uncertainties remain in how these schemes represent subgrid-scale processes.

Predictability and Dynamics of a Nonintensifying Tropical Storm: Erika (2009)

Abstract In this study, the predictability of Tropical Storm Erika (2009) is evaluated by analyzing a 60-member convection-permitting ensemble initialized with perturbations from a real-time ensemble Kalman filter (EnKF) system. Erika was forecast to intensify into a hurricane by most operational numerical models, but in reality it never exceeded 50 kt (1 kt = 0.51 m s−1). There is a fairly large spread in the final intensities of the 60 ensemble members indicating large uncertainty in the deterministic prediction of Erika's intensity at 36–48-h lead times. An investigation into which factors prevented intensification of the weaker ensemble members provides insight that may aid in the forecasting of the intensity of future tropical cyclones under similar conditions. A variety of environmental and storm-related factors are examined, and the parameters that have the greatest relation to future intensity are determined based on ensemble sensitivity and correlation analysis. It appears that midlevel relative humidity, absolute vorticity, and the distribution of convection relative to the storm center all play a role in determining whether a given ensemble member intensifies or not. In addition, although differences in deep-layer shear among ensemble members are difficult to discern, many of the ensemble members that do not intensify fail to do so because of apparent dry air intrusions that wrap around the centers of the storms, particularly in the 700–500-hPa layer. In the presence of moderate shear, this dry air is able to penetrate the cores of the cyclones, thereby preventing further development.

Observing Strategy and Observation Targeting for Tropical Cyclones Using Ensemble-Based Sensitivity Analysis and Data Assimilation

Abstract An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting Model is used with ensemble-based sensitivity analysis to explore observing strategies and observation targeting for tropical cyclones. The case selected for this study is Typhoon Morakot (2009), a western Pacific storm that brought record-breaking rainfall to Taiwan. Forty-eight hours prior to making landfall, ensemble sensitivity analysis using a 50-member convection-permitting ensemble predicts that dropsonde observations located in the southwest quadrant of the typhoon will have the highest impact on reducing the forecast uncertainty of the track, intensity, and rainfall of Morakot. A series of observing system simulation experiments (OSSEs) demonstrate that assimilating synthetic dropsonde observations located in regions with higher predicted observation impacts will, on average, lead to a better rainfall forecast than in regions with smaller predicted impacts. However, these OSSEs also suggest that the effectiveness of the current-generation ensemble-based tropical cyclone targeting strategies may be limited. The limitations may be due to strong nonlinearity in the governing dynamics of the typhoon (e.g., moist convection), the accuracy of the ensemble background covariance, and the projection of individual dropsonde observations to the complicated targeted sensitivity vectors from the ensemble.

Impact of Stochastic Physics in a Convection-Permitting Ensemble

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

Practical and Intrinsic Predictability of Severe and Convective Weather at the Mesoscales

Abstract This study explores both the practical and intrinsic predictability of severe convective weather at the mesoscales using convection-permitting ensemble simulations of a squall line and bow echo event during the Bow Echo and Mesoscale Convective Vortex (MCV) Experiment (BAMEX) on 9–10 June 2003. Although most ensemble members—initialized with realistic initial condition uncertainties smaller than the NCEP Global Forecast System Final Analysis (GFS FNL) using an ensemble Kalman filter—forecast broad areas of severe convection, there is a large variability of forecast performance among different members, highlighting the limit of practical predictability. In general, the best-performing members tend to have a stronger upper-level trough and associated surface low, producing a more conducive environment for strong long-lived squall lines and bow echoes, once triggered. The divergence in development is a combination of a dislocation of the upper-level trough, surface low with corresponding marginal environmental differences between developing and nondeveloping members, and cold pool evolution by deep convection prior to squall line formation. To further explore the intrinsic predictability of the storm, a sequence of sensitivity experiments was performed with the initial condition differences decreased to nearly an order of magnitude smaller than typical analysis and observation errors. The ensemble forecast and additional sensitivity experiments demonstrate that this storm has a limited practical predictability, which may be further improved with more accurate initial conditions. However, it is possible that the true storm could be near the point of bifurcation, where predictability is intrinsically limited. The limits of both practical and intrinsic predictability highlight the need for probabilistic and ensemble forecasts for severe weather prediction.