scholarly journals Impact of AERI Temperature and Moisture Retrievals on the Simulation of a Central Plains Severe Convective Weather Event

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 729
Author(s):  
William E. Lewis ◽  
Timothy J. Wagner ◽  
Jason A. Otkin ◽  
Thomas A. Jones
Keyword(s):  
Lower Atmosphere ◽  
Weather Research And Forecasting ◽  
Computational Domain ◽  
Central Plains ◽  
Ensemble Forecasts ◽  
Spatial Coverage ◽  
Skill Scores ◽  
Ensemble Adjustment Kalman Filter ◽  
Improved Performance ◽  
Convective Weather

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

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 Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

scholarly journals Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport Using the WRF Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Pedro Bolgiani ◽  
Daniel Santos-Muñoz ◽  
Ángel García Gago ◽  
...  
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Mountain Range ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Mountain Waves ◽  
Skill Scores ◽  
Stable Conditions ◽  
Pbl Scheme ◽  
Wind Velocities

Turbulence and aircraft icing associated with mountain waves are weather phenomena potentially affecting aviation safety. In this paper, these weather phenomena are analysed in the vicinity of the Adolfo Suárez Madrid-Barajas Airport (Spain). Mountain waves are formed in this area due to the proximity of the Guadarrama mountain range. Twenty different weather research and forecasting (WRF) model configurations are evaluated in an initial analysis. This shows the incompetence of some experiments to capture the phenomenon. The two experiments showing the best results are used to simulate thirteen episodes with observed mountain waves. Simulated pseudosatellite images are validated using satellite observations, and an analysis is performed through several skill scores applied to brightness temperature. Few differences are found among the different skill scores. Nevertheless, the Thompson microphysics scheme combined with the Yonsei university PBL scheme shows the best results. The simulations produced by this scheme are used to evaluate the characteristic variables of the mountain wave episodes at windward and leeward and over the mountain. The results show that north-northwest wind directions, moderate wind velocities, and neutral or slightly stable conditions are the main features for the episodes evaluated. In addition, a case study is analysed to evidence the WRF ability to properly detect turbulence and icing associated with mountain waves, even when there is no visual evidence available.

scholarly journals Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter Storms

2017 ◽  
Vol 32 (3) ◽  
pp. 1057-1078 ◽  
Author(s):  
Steven J. Greybush ◽  
Seth Saslo ◽  
Richard Grumm
Keyword(s):  
East Coast ◽  
Initial Conditions ◽  
Wave Pattern ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Winter Storms ◽  
Position Errors ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
East Coast Winter Storms

Abstract The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge’s sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.

scholarly journals Impact of a Stochastic Kinetic Energy Backscatter Scheme on Warm Season Convection-Allowing Ensemble Forecasts

2016 ◽  
Vol 144 (5) ◽  
pp. 1887-1908 ◽  
Author(s):  
Jeffrey D. Duda ◽  
Xuguang Wang ◽  
Fanyou Kong ◽  
Ming Xue ◽  
Judith Berner
Keyword(s):  
Kinetic Energy ◽  
Wrf Model ◽  
Warm Season ◽  
Weather Research And Forecasting ◽  
Potential Utility ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Central United States ◽  
Surface Observations

The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts.

scholarly journals Using Varied Microphysics to Account for Uncertainty in Warm-Season QPF in a Convection-Allowing Ensemble

2014 ◽  
Vol 142 (6) ◽  
pp. 2198-2219 ◽  
Author(s):  
Jeffrey D. Duda ◽  
Xuguang Wang ◽  
Fanyou Kong ◽  
Ming Xue
Keyword(s):  
Positive Impact ◽  
Warm Season ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Microphysics Scheme ◽  
Relative Operating Characteristic ◽  
Single Moment ◽  
Double Moment ◽  
Range Of Values

Abstract Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined. The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble. The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes.

Ensemble weather forecast of precipitation with a stochastic weather generator based on analogues circulation

2020 ◽  
Author(s):  
Meriem Krouma ◽  
Pascal Yiou ◽  
Céline Déandréis ◽  
Soulivanh Thao
Keyword(s):  
Model Development ◽  
Weather Generator ◽  
Lead Times ◽  
The European Union ◽  
Ensemble Forecasts ◽  
Analogue Model ◽  
Data Set ◽  
Stochastic Weather Generator ◽  
Horizon 2020 ◽  
Skill Scores

<p><strong>Abstract</strong></p><p>The aim of this study is to assess the skills of a stochastic weather generator (SWG) to forecast precipitation in Europe. The SWG is based on the random sampling of circulation analogues, which is a simple form of machine learning simulation. The SWG was developed and tested by Yiou and Déandréis (2019) to forecast daily average temperature and the NAO index. Ensemble forecasts with lead times from 5 to 80 days were evaluated with CRPSS scores against climatology and persistence forecasts. Reasonable scores were obtained up to 20 days.  In this study, we adapt the parameters of the analogue SWG to optimize the simulation of European precipitations. We then analyze the performance of this SWG for lead times of 2 to 20 days, with the forecast skill scores used by Yiou and Déandréis (2019). To achieve this objective, the SWG will use ECA&D precipitation data (Haylock. 2002), and the analogues of circulation will be computed from sea-level pressure (SLP) or geopotential heights (Z500) from the NCEP reanalysis. This provides 100-member ensemble forecasts on a daily time increment. We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence to weather regimes. Comparisons with “real” medium range forecasts from the ECMWF will be performed.</p><p><strong>References</strong></p><p>Yiou, P., and Céline D.. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development 12, 2 (2019): 723‑34.</p><p>Haylock, M. R. et al.. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. - Atmospheres 113, D20 (2008): doi:10.1029/2008JD010201.</p><p> </p><p><strong>A</strong><strong>cknowledge</strong></p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

2010 ◽  
Vol 49 (2) ◽  
pp. 268-287 ◽  
Author(s):  
Pedro A. Jiménez ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Juan P. Montávez ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Wrf Model ◽  
Spatial Dimension ◽  
Weather Research And Forecasting ◽  
Observational Period ◽  
Spatiotemporal Resolution ◽  
Wind Variability ◽  
Spatial Coverage

Abstract This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

scholarly journals Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions*

2015 ◽  
Vol 143 (11) ◽  
pp. 4578-4596 ◽  
Author(s):  
Michael Scheuerer ◽  
Thomas M. Hamill
Keyword(s):  
Probability Distributions ◽  
Precipitable Water ◽  
Training Dataset ◽  
Ensemble Forecasts ◽  
Gamma Distributions ◽  
Predictive Probability ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
The Mean ◽  
Set Up

Abstract A parametric statistical postprocessing method is presented that transforms raw (and frequently biased) ensemble forecasts from the Global Ensemble Forecast System (GEFS) into reliable predictive probability distributions for precipitation accumulations. Exploratory analysis based on 12 years of reforecast data and ⅛° climatology-calibrated precipitation analyses shows that censored, shifted gamma distributions can well approximate the conditional distribution of observed precipitation accumulations given the ensemble forecasts. A nonhomogeneous regression model is set up to link the parameters of this distribution to ensemble statistics that summarize the mean and spread of predicted precipitation amounts within a certain neighborhood of the location of interest, and in addition the predicted mean of precipitable water. The proposed method is demonstrated with precipitation reforecasts over the conterminous United States using common metrics such as Brier skill scores and reliability diagrams. It yields probabilistic forecasts that are reliable, highly skillful, and sharper than the previously demonstrated analog procedure. In situations with limited predictability, increasing the size of the neighborhood within which ensemble forecasts are considered as predictors can further improve forecast skill. It is found, however, that even a parametric postprocessing approach crucially relies on the availability of a sufficiently large training dataset.

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