scholarly journals Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments

2015 ◽  
Vol 30 (6) ◽  
pp. 1795-1817 ◽  
Author(s):  
Dustan M. Wheatley ◽  
Kent H. Knopfmeier ◽  
Thomas A. Jones ◽  
Gerald J. Creager
Keyword(s):  
Data Assimilation ◽  
Leading Edge ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
Low Level ◽  
The North ◽  
Weather Events ◽  
Probabilistic Forecasts ◽  
Bow Echo ◽  
System Part

Abstract This first part of a two-part study on storm-scale radar and satellite data assimilation provides an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) project. The NSSL Experimental WoF System for ensembles (NEWS-e) is used to produce storm-scale analyses and forecasts of six diverse severe weather events from spring 2013 and 2014. In this study, only Doppler reflectivity and radial velocity observations (and, when available, surface mesonet data) are assimilated into a 36-member, storm-scale ensemble using an ensemble Kalman filter (EnKF) approach. A series of 1-h ensemble forecasts are then initialized from storm-scale analyses during the 1-h period preceding the onset of storm reports. Of particular interest is the ability of these 0–1-h ensemble forecasts to reproduce the low-level rotational characteristics of supercell thunderstorms, as well as other convective hazards. For the tornado-producing thunderstorms considered in this study, ensemble probabilistic forecasts of low-level rotation generally indicated a rotating thunderstorm approximately 30 min before the time of first observed tornado. Displacement errors (often to the north of tornado-affected areas) associated with vorticity swaths were greatest in those forecasts launched 30–60 min before the time of first tornado. Similar forecasts were produced for a tornadic mesovortex along the leading edge of a bow echo and, again, highlighted a well-defined vorticity swath as much as 30 min prior to the first tornado.

Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data

2012 ◽  
Vol 140 (7) ◽  
pp. 2126-2146 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract This study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8–9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential). Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physics uncertainty and effectively plays a role of flow-dependent inflation (in precipitation regions) during EnKF data assimilation. The ensemble predicts with high probability (approximately 0.65) the near-surface mesovortex associated with the first of three reported tornadoes. Though a bias toward stronger precipitation is noted in the ensemble forecasts, all experiments produce skillful probabilistic forecasts of radar reflectivity on a 0–3-h time scale as evaluated by multiple probabilistic verification metrics. These results suggest that both the inclusion of CASA radar data and use of a mixed-microphysics ensemble during EnKF data assimilation positively impact the skill of 2–3-h ensemble forecasts of mesovortices, despite having little impact on the quality of precipitation forecasts (analyzed in terms of predicted radar reflectivity), and are important steps toward an operational EnKF-based ensemble analysis and probabilistic forecast system to support convective-scale warn-on-forecast operations.

scholarly journals Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones

2012 ◽  
Vol 140 (2) ◽  
pp. 696-716 ◽  
Author(s):  
Daniel T. Dawson II ◽  
Louis J. Wicker ◽  
Edward R. Mansell ◽  
Robin L. Tanamachi
Keyword(s):  
Wind Profile ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
Microphysics Scheme ◽  
Low Level ◽  
Severe Thunderstorms ◽  
Probabilistic Forecasts ◽  
Forecast Lead Time ◽  
Wind Profiles ◽  
The Impact

The early tornadic phase of the Greensburg, Kansas, supercell on the evening of 4 May 2007 is simulated using a set of storm-scale (1-km horizontal grid spacing) 30-member ensemble Kalman filter (EnKF) data assimilation and forecast experiments. The Next Generation Weather Radar (NEXRAD) level-II radar data from the Dodge City, Kansas (KDDC), Weather Surveillance Radar-1988 Doppler (WSR-88D) are assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (COMMAS). The initially horizontally homogeneous environments are initialized from one of three reconstructed soundings representative of the early tornadic phase of the storm, when a low-level jet (LLJ) was intensifying. To isolate the impact of the low-level wind profile, 0–3.5-km AGL wind profiles from Vance Air Force Base, Oklahoma (KVNX), WSR-88D velocity-azimuth display (VAD) analyses at 0130, 0200, and 0230 UTC are used. A sophisticated, double-moment bulk ice microphysics scheme is employed. For each of the three soundings, ensemble forecast experiments are initiated from EnKF analyses at various times prior to and shortly after the genesis of the Greensburg tornado (0200 UTC). Probabilistic forecasts of the mesocyclone-scale circulation(s) are generated and compared to the observed Greensburg tornado track. Probabilistic measures of significant rotation and observation-space diagnostic statistics are also calculated. It is shown that, in general, the track of the Greensburg tornado is well predicted, and forecasts improve as forecast lead time decreases. Significant variability is also seen across the experiments using different VAD wind profiles. Implications of these results regarding the choice of initial mesoscale environment, as well as for the “Warn-on-Forecast” paradigm for probabilistic numerical prediction of severe thunderstorms and tornadoes, are discussed.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

scholarly journals Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in Alabama

2015 ◽  
Vol 143 (8) ◽  
pp. 3044-3066 ◽  
Author(s):  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Louis J. Wicker ◽  
Kent H. Knopfmeier ◽  
Dustan M. Wheatley
Keyword(s):  
Short Range ◽  
Type System ◽  
Severe Weather ◽  
Forecast Errors ◽  
Model Errors ◽  
Ensemble Forecasts ◽  
Low Level ◽  
Design Storm ◽  
Probabilistic Forecasts ◽  
Arw Model

Abstract As part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h-long update system demonstrate the potential of a frequently updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF-type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.

scholarly journals The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jidong Gao ◽  
Ming Xue ◽  
David J. Stensrud
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
Advanced Regional Prediction System ◽  
Error Covariance ◽  
Variable Approach ◽  
Regional Prediction ◽  
Rms Errors

A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational analysis is performed using an equal weighting of static and flow-dependent error covariance as derived from ensemble forecasts. The method is first applied to the assimilation of simulated radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the EnKF method provides the best results for the model dynamic variables, while the hybrid method provides the best results for hydrometeor related variables in term of rms errors. Although storm structures can be established reasonably well using 3DVAR, the rms errors are generally worse than seen from the other two methods. With two radars, the results from 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the storm spin-up time because it fits the observations, especially the reflectivity observations, better than the EnKF and the 3DVAR at the beginning of the assimilation cycles.

The Impact of Initial Condition and Warm Conveyor Belt Forecast Uncertainty on Variability in the Downstream Waveguide in an ECWMF Case Study

2019 ◽  
Vol 147 (11) ◽  
pp. 4071-4089 ◽  
Author(s):  
Jeremy D. Berman ◽  
Ryan D. Torn
Keyword(s):  
Conveyor Belt ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Rossby Wave Breaking ◽  
Weather Forecasts ◽  
The North ◽  
Weather Events ◽  
High Impact Weather ◽  
Sensitivity Method ◽  
The Impact

Abstract Perturbations to the potential vorticity (PV) waveguide, which can result from latent heat release within the warm conveyor belt (WCB) of midlatitude cyclones, can lead to the downstream radiation of Rossby waves, and in turn high-impact weather events. Previous studies have hypothesized that forecast uncertainty associated with diabatic heating in WCBs can result in large downstream forecast variability; however, these studies have not established a direct connection between the two. This study evaluates the potential impact of latent heating variability in the WCB on subsequent downstream forecasts by applying the ensemble-based sensitivity method to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of a cyclogenesis event over the North Atlantic. For this case, ensemble members with a more amplified ridge are associated with greater negative PV advection by the irrotational wind, which is associated with stronger lower-tropospheric southerly moisture transport east of the upstream cyclone in the WCB. This transport is sensitive to the pressure trough to the south of the cyclone along the cold front, which in turn is modulated by earlier differences in the motion of the air masses on either side of the front. The position of the cold air behind the front is modulated by upstream tropopause-based PV anomalies, such that a deeper pressure trough is associated with a more progressive flow pattern, originating from Rossby wave breaking over the North Pacific. Overall, these results suggest that more accurate forecasts of upstream PV anomalies and WCBs may reduce forecast uncertainty in the downstream waveguide.

scholarly journals Analysis of the 13–14 July Gulf Surge Event during the 2004 North American Monsoon Experiment

2007 ◽  
Vol 135 (9) ◽  
pp. 3098-3117 ◽  
Author(s):  
Peter J. Rogers ◽  
Richard H. Johnson
Keyword(s):  
North American ◽  
Gulf Of California ◽  
Leading Edge ◽  
North American Monsoon ◽  
Strong Wind ◽  
Kelvin Wave ◽  
High Resolution Data ◽  
Low Level ◽  
Second Stage ◽  
The North

Abstract Gulf surges are disturbances that move northward along the Gulf of California (GOC), frequently advecting cool, moist air from the GOC or eastern tropical Pacific Ocean into the deserts of the southwest United States and northwest Mexico during the North American Monsoon (NAM). Little attention has been given to the dynamics of these disturbances because of the lack of reliable high-resolution data across the NAM region. High temporal and spatial observations collected during the 2004 North American Monsoon Experiment are used to investigate the structure and dynamical mechanisms of a significant gulf surge on 13–14 July 2004. Integrated Sounding Systems deployed along the east coast of the GOC and an enhanced network of rawinsonde sites across the NAM region are used in this study. Observations show that the 13–14 July gulf surge occurred in two primary stages. The first stage was preceded by anomalous low-level warming along the northern GOC on 13 July. Sharp cooling, moistening, and increased low-level south-southeasterly flow followed over a 12–18-h period. Over the northern gulf, the wind reached ∼20 m s−1 at 750 m AGL. Then there was a brief respite followed by the second stage—a similar, but deeper acceleration of the southerly flow associated with the passage of Tropical Storm (TS) Blas on 14 July. The initial surge disturbance traversed the GOC at a speed of ∼17–25 m s−1 and resulted in a deepening of the mixed layer along the northern gulf. Dramatic surface pressure rises also accompanied the surge. The weight of the evidence suggests that the first stage of the overall surge itself consisted of two parts. The initial part resembled borelike disturbances initiated by convective downdrafts impinging on the low-level stable layer over the region. The secondary part was characteristic of a Kelvin wave–type disturbance, as evident in the deeper layer of sharp cooling and strong wind that ensued. Another possible explanation for the first part is that the leading edge of this Kelvin wave steepened nonlinearly into a borelike disturbance. The second stage of the surge was associated with the increased circulation around TS Blas.

Bow Echo Mesovortices. Part II: Their Genesis

2009 ◽  
Vol 137 (5) ◽  
pp. 1514-1532 ◽  
Author(s):  
Nolan T. Atkins ◽  
Michael St. Laurent
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Convective System ◽  
Saint Louis ◽  
Low Level ◽  
Front Position ◽  
Bow Echo ◽  
Convective Scale ◽  
Mesoscale Convective ◽  
Mesoscale Convective Vortex

Abstract This two-part study examines the damaging potential and genesis of low-level, meso-γ-scale mesovortices formed within bow echoes. This was accomplished by analyzing quasi-idealized simulations of the 10 June 2003 Saint Louis bow echo event observed during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX). In Part II of this study, mesovortex genesis was investigated for vortices formed at different stages of convective system evolution. During the early “cellular” stage, cyclonic mesovortices were observed. The cyclonic mesovortices formed from the tilting of baroclinic horizontal vorticity acquired by downdraft parcels entering the mesovortex. As the convective system evolved into a bow echo, cyclonic–anticyclonic mesovortex pairs were also observed. The vortex couplet was produced by a local updraft maximum that tilted baroclinically generated vortex lines upward into arches. The local updraft maximum was created by a convective-scale downdraft that produced an outward bulge in the gust front position. Cyclonic-only mesovortices were predominantly observed as the convective system evolved into the mature bow echo stage. Similar to the early cellular stage, these mesovortices formed from the tilting of baroclinic horizontal vorticity acquired by downdraft parcels entering the mesovortex. The downdraft parcels descended within the rear-inflow jet. The generality of the mesovortex genesis mechanisms was assessed by examining the structure of observed mesovortices in Doppler radar data. The mesovortex genesis mechanisms were also compared to others reported in the literature and the genesis of low-level mesocyclones in supercell thunderstorms.

scholarly journals Radar Data Assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a Squall Line over the U.S. Great Plains

2013 ◽  
Vol 141 (7) ◽  
pp. 2245-2264 ◽  
Author(s):  
Juanzhen Sun ◽  
Hongli Wang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Great Plains ◽  
Radar Data ◽  
Variational Data Assimilation ◽  
Precipitation Forecast ◽  
Squall Line ◽  
Cold Pool ◽  
Low Level ◽  
Radar Data Assimilation

Abstract The Weather Research and Forecasting Model (WRF) four-dimensional variational data assimilation (4D-Var) system described in Part I of this study is compared with its corresponding three-dimensional variational data assimilation (3D-Var) system using a Great Plains squall line observed during the International H2O Project. Two 3D-Var schemes are used in the comparison: a standard 3D-Var radar data assimilation (DA) that is the same as the 4D-Var except for the exclusion of the constraining dynamical model and an enhanced 3D-Var that includes a scheme to assimilate an estimated in-cloud humidity field. The comparison is made by verifying their skills in 0–6-h quantitative precipitation forecast (QPF) against stage-IV analysis, as well as in wind forecasts against radial velocity observations. The relative impacts of assimilating radial velocity and reflectivity on QPF are also compared between the 4D-Var and 3D-Var by conducting data-denial experiments. The results indicate that 4D-Var substantially improves the QPF skill over the standard 3D-Var for the entire 6-h forecast range and over the enhanced 3D-Var for most forecast hours. Radial velocity has a larger impact relative to reflectivity in 4D-Var than in 3D-Var in the first 3 h because of a quicker precipitation spinup. The analyses and forecasts from the 4D-Var and 3D-Var schemes are further compared by examining the meridional wind, horizontal convergence, low-level cold pool, and midlevel temperature perturbation, using analyses from the Variational Doppler Radar Analysis System (VDRAS) as references. The diagnoses of these fields suggest that the 4D-Var analyzes the low-level cold pool, its leading edge convergence, and midlevel latent heating in closer resemblance to the VDRAS analyses than the 3D-Var schemes.

scholarly journals Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System

2016 ◽  
Vol 31 (3) ◽  
pp. 957-983 ◽  
Author(s):  
Nusrat Yussouf ◽  
John S. Kain ◽  
Adam J. Clark
Keyword(s):  
Flash Flood ◽  
Stage Iv ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Flood Event ◽  
Forecast Period ◽  
Low Level ◽  
Probabilistic Forecasts ◽  
The Impact

Abstract A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.

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