scholarly journals Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

2018 ◽  
Vol 146 (8) ◽  
pp. 2361-2379 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Object Oriented ◽  
Comprehensive Analysis ◽  
Initial Condition ◽  
Forecast System ◽  
Model Assumption ◽  
Ensemble Forecasts ◽  
Severe Thunderstorms ◽  
Perfect Model

Abstract As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

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

2016 ◽  
Vol 144 (7) ◽  
pp. 2579-2604 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Lead Time ◽  
Direct Result ◽  
Small Scale ◽  
Lead Times ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
Perfect Model ◽  
Perturbation Energy ◽  
Data Assimilation System

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.

scholarly journals Prediction of Monthly-Mean Temperature: The Roles of Atmospheric and Land Initial Conditions and Sea Surface Temperature

2010 ◽  
Vol 23 (3) ◽  
pp. 717-725 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Lower Boundary ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Lower Boundary Condition ◽  
Asymptotic Value ◽  
Mean Temperature

Abstract Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.

Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands

2008 ◽  
Vol 23 (6) ◽  
pp. 1253-1267 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang ◽  
Rudolf M. van Westrhenen
Keyword(s):  
The Netherlands ◽  
Weather Prediction ◽  
Limited Area ◽  
Regression Equations ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorms ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Total Lightning

Abstract The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km × 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

scholarly journals Short-Range Ensemble Forecasts of Precipitation Type

2005 ◽  
Vol 20 (4) ◽  
pp. 609-626 ◽  
Author(s):  
Matthew S. Wandishin ◽  
Michael E. Baldwin ◽  
Steven L. Mullen ◽  
John V. Cortinas
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Precipitation Forecast ◽  
Initial Condition ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Precipitation Type ◽  
Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

scholarly journals The similarity-based method: a new object detection method for deterministic and ensemble weather forecasts

2019 ◽  
Vol 16 ◽  
pp. 209-213
Author(s):  
Lucie Rottner ◽  
Philippe Arbogast ◽  
Mayeul Destouches ◽  
Yamina Hamidi ◽  
Laure Raynaud
Keyword(s):  
Detection Method ◽  
Weather Prediction ◽  
Object Oriented ◽  
Small Scale ◽  
Object Oriented Method ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Mesoscale Structures ◽  
Ensemble Weather Forecasts ◽  
Meteorological Models

Abstract. A new object-oriented method has been developed to detect hazardous phenomena predicted by Numerical Weather Prediction (NWP) models. This method, called similarity-based method, is looking for specific meteorological objects in the forecasts, which are defined by a reference histogram representing the meteorological phenomena to be detected. The similarity-based method enables to cope with small scale unpredictable details of mesoscale structures in meteorological models and to quantify the uncertainties on the location of the predicted phenomena. Applied to ensemble forecasts, the similarity-based method can be viewed as a particular case of neighborhood processing, allowing spatialized probabilities to be computed. An application to rainfall detection using forecasts from the AROME deterministic and ensemble models is presented.

scholarly journals Global Ensemble Predictions of 2009’s Tropical Cyclones Initialized with an Ensemble Kalman Filter

2011 ◽  
Vol 139 (2) ◽  
pp. 668-688 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclones ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Central Pressure ◽  
Ensemble Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Track Error

Abstract Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO). The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias. Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

scholarly journals Simultaneous Ensemble Postprocessing for Multiple Lead Times with Standardized Anomalies

2017 ◽  
Vol 145 (7) ◽  
pp. 2523-2531 ◽  
Author(s):  
Markus Dabernig ◽  
Georg J. Mayr ◽  
Jakob W. Messner ◽  
Achim Zeileis
Keyword(s):  
Standard Deviation ◽  
Conventional Method ◽  
Numerical Weather Prediction ◽  
Statistical Models ◽  
Lead Time ◽  
Observation Interval ◽  
Weather Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Time Specific

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

scholarly journals Evaluation and Benchmarking of Operational Short-Range Ensemble Mean and Median Streamflow Forecasts for the Ohio River Basin

2018 ◽  
Vol 19 (10) ◽  
pp. 1689-1706 ◽  
Author(s):  
Thomas E. Adams III ◽  
Randel Dymond
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Geographic Location ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Ohio River ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Meteorological Model ◽  
Ensemble Mean

Abstract This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified.

scholarly journals Predictability of Sudden Stratospheric Warmings in the ECMWF Extended-Range Forecast System

2018 ◽  
Vol 146 (4) ◽  
pp. 1063-1075 ◽  
Author(s):  
Alexey Yu. Karpechko
Keyword(s):  
Lead Time ◽  
The Arctic ◽  
Lead Times ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Extended Range ◽  
Medium Range ◽  
Tropospheric Circulation ◽  
Mean Square Errors

The skill of the Arctic stratospheric retrospective ensemble forecasts (hindcasts) of the European Centre for Medium-Range Weather Forecasts extended-range system is analyzed with a focus on the predictability of the major sudden stratospheric warmings (SSWs) during the period 1993–2016. Thirteen SSWs took place during this period. It is found that forecasts initialized 10–15 days before the SSWs show worse skill in the stratosphere than forecasts initialized during normal conditions in terms of root-mean-square errors but not in terms of anomaly correlation. Using the spread of ensemble members to estimate forecasted SSW probability, it is shown that some SSWs can be predicted with high (>0.9) probability at lead times of 12–13 days if a difference of 3 days between actual and forecasted SSW is allowed. Focusing on SSWs with significant impacts on the tropospheric circulation, on average, the forecasted SSW probability is found to increase from nearly 0 at 1-month lead time to 0.3 at day 13 before SSW, and then rapidly increases to nearly 1 at day 7. The period between days 8 and 12 is when most of the SSWs are predicted, with a probability of 0.5–0.9, which is considerably larger than the observed SSW occurrence frequency. Therefore, this period can be thought of as an estimate of the SSW predictability limit in this system. Indications that the predictability limit for some SSWs may be longer than 2 weeks are also found; however, this result is inconclusive and more studies are needed to understand when and why such long predictability is possible.

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