Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Whitaker, T. Hamill (2002)
Ensemble Data Assimilation without Perturbed ObservationsMonthly Weather Review, 130
J. Kain, J. Fritsch (1993)
Convective parameterization for mesoscale models : The Kain-Fritsch Scheme
P. Heinselman, D. Priegnitz, K. Manross, Travis Smith, Ric Adams (2008)
Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array RadarWeather and Forecasting, 23
S. Mason, N. Graham (1999)
Conditional Probabilities, Relative Operating Characteristics, and Relative Operating LevelsWeather and Forecasting, 14
F. Brock, K. Crawford, R. Elliott, G. Cuperus, S. Stadler, H. Johnson, M. Eilts (1995)
The Oklahoma Mesonet: A Technical OverviewJournal of Atmospheric and Oceanic Technology, 12
A. Aksoy, D. Dowell, C. Snyder (2010)
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble ForecastsMonthly Weather Review, 138
M. Xue, Mingjing Tong, K. Droegemeier (2006)
An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and ForecastingJournal of Atmospheric and Oceanic Technology, 23
Nathan Snook, Ming Xue, Youngsun Jung (2012)
Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses using WSR-88D and CASA Radar DataMonthly Weather Review, 140
Jian Zhang, K. Howard, C. Langston, S. Vasiloff, Brian Kaney, A. Arthur, S. Cooten, K. Kelleher, D. Kitzmiller, F. Ding, D. Seo, Ernest Wells, Charles Dempsey (2011)
National mosaic and multi-sensor QPE (NMQ) system description, results, and future plansBulletin of the American Meteorological Society, 92
M. Chou (1992)
A Solar Radiation Model for Use in Climate StudiesJournal of the Atmospheric Sciences, 49
P. Sakov, G. Evensen, Laurent Bertino (2010)
Asynchronous data assimilation with the EnKFTellus A: Dynamic Meteorology and Oceanography, 62
(2005)
Efficient assimilation of radar data at high resolution for short-range numerical weather prediction
Yuh-Lang Lin, R. Farley, H. Orville (1983)
Bulk Parameterization of the Snow Field in a Cloud ModelJournal of Applied Meteorology, 22
M. Xue, K. Droegemeier, V. Wong, A. Shapiro, K. Brewster, F. Carr, D. Weber, Y. Liu, D. Wang (2001)
The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applicationsMeteorology and Atmospheric Physics, 76
Mingjing Tong (2007)
Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Radar Data and Ensemble Square-root Kalman Filter . Part I : Sensitivity Analysis and Parameter
N. Yussouf, E. Mansell, Louis Wicker, Dustan Wheatley, D. Stensrud (2013)
The Ensemble Kalman Filter Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Using Single- and Double-Moment Microphysics SchemesMonthly Weather Review, 141
V. Lakshmanan, Travis Smith, G. Stumpf, K. Hondl (2007)
The Warning Decision Support System–Integrated InformationWeather and Forecasting, 22
(1996)
Parameterization of PBL turbulence in a multi-scale non-hydrostatic model
A. Aksoy, D. Dowell, C. Snyder (2009)
A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale AnalysesMonthly Weather Review, 137
E. Fiori, A. Parodi, F. Siccardi (2010)
Turbulence Closure Parameterization and Grid Spacing Effects in Simulated Supercell StormsJournal of the Atmospheric Sciences, 67
R. Tanamachi, P. Heinselman, Louis Wicker (2015)
Impacts of a Storm Merger on the 24 May 2011 El Reno, Oklahoma, Tornadic SupercellWeather and Forecasting, 30
M. Xue, Ming Hu, Alexander Schenkman (2014)
Numerical Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell and Embedded Tornado Using ARPS with the Assimilation of WSR-88D DataWeather and Forecasting, 29
Nathan Snook, M. Xue, Youngsun Jung (2011)
Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar DataMonthly Weather Review, 139
D. Dowell, Fuqing Zhang, Louis Wicker, C. Snyder, N. Crook (2004)
Wind and Temperature Retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: Ensemble Kalman Filter ExperimentsMonthly Weather Review, 132
Anderson (2001)
An ensemble adjustment Kalman filter for data assimilationMon. Wea. Rev., 129
C. Kuster, P. Heinselman, Marcus Austin (2015)
31 May 2013 El Reno Tornadoes: Advantages of Rapid-Scan Phased-Array Radar Data from a Warning Forecaster’s Perspective*Weather and Forecasting, 30
C. Potvin, Louis Wicker (2013)
Assessing Ensemble Forecasts of Low-Level Supercell Rotation within an OSSE FrameworkWeather and Forecasting, 28
J. Milbrandt, M. Yau (2006)
A Multimoment Bulk Microphysics Parameterization. Part III: Control Simulation of a HailstormJournal of the Atmospheric Sciences, 63
M. Xue, Donghai Wang, Jidong Gao, K. Brewster, K. Droegemeier (2003)
The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilationMeteorology and Atmospheric Physics, 82
J. Kain, J. Fritsch (1990)
A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective ParameterizationJournal of the Atmospheric Sciences, 47
D. Dowell, Louis Wicker, C. Snyder (2011)
Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale AnalysesMonthly Weather Review, 139
D. Forsyth (2005)
The National Weather Radar Testbed (Phased-Array)
Shizhang Wang, Ming Xue, Jinzhong Min (2013)
A four‐dimensional asynchronous ensemble square‐root filter (4DEnSRF) algorithm and tests with simulated radar dataQuarterly Journal of the Royal Meteorological Society, 139
G. Evensen (1994)
Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statisticsJournal of Geophysical Research, 99
H. Lange, G. Craig (2014)
The Impact of Data Assimilation Length Scales on Analysis and Prediction of Convective StormsMonthly Weather Review, 142
Dustan Wheatley, N. Yussouf, D. Stensrud (2014)
Ensemble Kalman Filter Analyses and Forecasts of a Severe Mesoscale Convective System Using Different Choices of Microphysics SchemesMonthly Weather Review, 142
D. Dowell, G. Romine, M. Snyder (2010)
Ensemble storm-scale data assimilation and prediction for severe convective storms [presentation]
J. Milbrandt, M. Yau (2006)
A Multimoment Bulk Microphysics Parameterization. Part IV: Sensitivity ExperimentsJournal of the Atmospheric Sciences, 63
W. Briggs (2007)
Statistical Methods in the Atmospheric SciencesJournal of the American Statistical Association, 102
(2006)
New dimension of NCEP ShortRange Ensemble Forecasting (SREF) system: Inclusion of WRF members
Mingjing Tong, M. Xue (2005)
Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experimentsMonthly Weather Review, 133
Jeffrey Anderson (2001)
An Ensemble Adjustment Kalman Filter for Data AssimilationMonthly Weather Review, 129
N. Yussouf, J. Kain, A. Clark (2016)
Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble SystemWeather and Forecasting, 31
T. Lei, M. Xue, Tianyou Yu (2003)
Multi-scale Analysis and Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Assimilating Radar and Surface Network Data using EnKF
N. Yussouf, D. Stensrud (2010)
Impact of Phased-Array Radar Observations over a Short Assimilation Period: Observing System Simulation Experiments Using an Ensemble Kalman FilterMonthly Weather Review, 138
M. Xue, K. Droegemeier, V. Wong (2000)
The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verificationMeteorology and Atmospheric Physics, 75
Song‐You Hong, H. Pan (1996)
Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast ModelMonthly Weather Review, 124
Max Suarex, Ming-Dah Chou (1994)
Technical report series on global modeling and data assimilation. Volume 3: An efficient thermal infrared radiation parameterization for use in general circulation models
N. Yussouf, D. Dowell, Louis Wicker, K. Knopfmeier, Dustan Wheatley (2015)
Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in AlabamaMonthly Weather Review, 143
C. Snyder, Fuqing Zhang (2003)
Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman FilterMonthly Weather Review, 131
Yunheng Wang, Youngsun Jung, Timothy Supinie, M. Xue
A Hybrid Mpi/openmp Parallel Algorithm and Performance Analysis for an Ensemble 4 Square Root Filter Designed for Multi-scale Observations 5 6
D. Dawson, Louis Wicker, E. Mansell, R. Tanamachi (2012)
Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated MesocyclonesMonthly Weather Review, 140
D. Dawson, Ii And, M. Xue, J. Milbrandt, M. Yau (2010)
Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic ThunderstormsMonthly Weather Review, 138
Xuan Xin (2015)
Mesovortices within the 8 May 2009 Bow Echo over the Central United States : Analyses of the Characteristics and Evolution Based on Doppler Radar Observations and a High-Resolution Model Simulation
A. Betts (1973)
A Composite Mesoscale Cumulonimbus BudgetJournal of the Atmospheric Sciences, 30
Daniel Ii, Ming Xue, J. Milbrandt, M. Yau (2009)
Comparison of Evaporation and Cold Pool Development between Single-moment and Multi-moment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms
Mingjing Tong, M. Xue (2008)
Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter IdentifiabilityMonthly Weather Review, 136
M. Weber, John Cho, J. Herd, J. Flavin, W. Benner, G. Torok (2007)
The Next-Generation Multimission U.S. Surveillance Radar Network
N. Yussouf, D. Stensrud (2012)
Comparison of Single-Parameter and Multiparameter Ensembles for Assimilation of Radar Observations Using the Ensemble Kalman FilterMonthly Weather Review, 140
Katie Bowden, P. Heinselman, D. Kingfield, Rick Thomas (2015)
Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind EventsWeather and Forecasting, 30
D. Stensrud, M. Xue, Louis Wicker, K. Kelleher, Michael Foster, J. Schaefer, Russell Schneider, S. Benjamin, S. Weygandt, John Ferree, Jason Tuell (2009)
CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020Bulletin of the American Meteorological Society, 90
Yunheng Wang, Youngsun Jung, Timothy Supinie, Ming Xue (2013)
A Hybrid MPI–OpenMP Parallel Algorithm and Performance Analysis for an Ensemble Square Root Filter Designed for Multiscale ObservationsJournal of Atmospheric and Oceanic Technology, 30
P. Heinselman, S. Torres (2011)
High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array RadarJournal of Applied Meteorology and Climatology, 50
M. Chou (1990)
Parameterizations for the Absorption of Solar Radiation by O2 and CO2 with Application to Climate StudiesJournal of Climate, 3
Xin Xu, M. Xue, Y. Wang (2013)
Mesovortices within the 8 May 2009 Bow Echo over the Central United States: Analyses of the Characteristics and Evolution Based on Doppler Radar Observations and a High-Resolution Model SimulationMonthly Weather Review, 143
Betts (1973)
A composite mesoscale cumulonimbus budgetJ. Atmos. Sci., 30
Youngsun Jung, M. Xue, Mingjing Tong (2012)
Ensemble Kalman Filter Analyses of the 29–30 May 2004 Oklahoma Tornadic Thunderstorm Using One- and Two-Moment Bulk Microphysics Schemes, with Verification against Polarimetric Radar DataMonthly Weather Review, 140
(2011)
El Reno, Oklahoma, tornadic supercell. Wea. Forecasting
G. Gaspari, S. Cohn (1999)
Construction of correlation functions in two and three dimensionsQuarterly Journal of the Royal Meteorological Society, 125
D. Zrnic, J. Kimpel, D. Forsyth, A. Shapiro, G. Crain, R. Ferek, J. Heimmer, W. Benner, T. McNellis, R. Vogt (2007)
Agile-Beam Phased Array Radar for Weather ObservationsBulletin of the American Meteorological Society, 88
D. Dawson, M. Xue, J. Milbrandt, A. Shapiro (2015)
Sensitivity of Real-Data Simulations of the 3 May 1999 Oklahoma City Tornadic Supercell and Associated Tornadoes to Multimoment Microphysics. Part I: Storm- and Tornado-Scale Numerical ForecastsMonthly Weather Review, 143
P. Heinselman, Daphne LaDue, H. Lazrus (2012)
Exploring Impacts of Rapid-Scan Radar Data on NWS Warning DecisionsWeather and Forecasting, 27
C. Curtis, S. Torres (2011)
Adaptive Range Oversampling to Achieve Faster Scanning on the National Weather Radar Testbed Phased-Array RadarJournal of Atmospheric and Oceanic Technology, 28
Renée, A., McPherson, Christopher, Fiebrich, Kenneth, C., Crawford, Ronald, L., Elliott, James, R., Kilby, David, Grimsley, Janet, E., Martínez, Jeffrey, B., Basara, Bradley, G., Illston, Dale, Morris, Kevin, Kloesel, Stephen, J., Stadler, Andrea, D., Melvin, Albert, Sutherland, H. Shrivastava, D. J., Carlson, Michael Wolfinbarger, Jared, P., Bostic, Demkó (2007)
Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma MesonetJournal of Atmospheric and Oceanic Technology, 24
C. Emersic, P. Heinselman, D. MacGorman, E. Bruning (2011)
Lightning Activity in a Hail-Producing Storm Observed with Phased-Array RadarMonthly Weather Review, 139
Madison Miller, V. Lakshmanan, Travis Smith (2013)
An Automated Method for Depicting Mesocyclone Paths and IntensitiesWeather and Forecasting, 28
J. Newman, P. Heinselman (2012)
Evolution of a Quasi-Linear Convective System Sampled by Phased Array RadarMonthly Weather Review, 140
Nathan Snook, M. Xue, Youngsun Jung (2015)
Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective SystemMonthly Weather Review, 143
Bryan Putnam, M. Xue, Youngsun Jung, Nathan Snook, Guifu Zhang (2014)
The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman FilterMonthly Weather Review, 142
R. Sobash, D. Stensrud (2015)
Assimilating Surface Mesonet Observations with the EnKF to Improve Ensemble Forecasts of Convection Initiation on 29 May 2012Monthly Weather Review, 143
D. Stensrud, Jidong Gao (2010)
Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range ForecastsMonthly Weather Review, 138
P. Heinselman, Daphne LaDue, D. Kingfield, R. Hoffman (2015)
Tornado Warning Decisions Using Phased-Array Radar DataWeather and Forecasting, 30
D. Stensrud, Louis Wicker, M. Xue, D. Dawson, N. Yussouf, Dustan Wheatley, T. Thompson, Nathan Snook, Travis Smith, Alexander Schenkman, C. Potvin, E. Mansell, T. Lei, K. Kuhlman, Youngsun Jung, T. Jones, Jidong Gao, M. Coniglio, H. Brooks, K. Brewster (2012)
Progress and challenges with Warn-on-ForecastAtmospheric Research, 123
D. Dowell, Louis Wicker (2009)
Additive Noise for Storm-Scale Ensemble Data AssimilationJournal of Atmospheric and Oceanic Technology, 26
Wen-Yih Sun, Chiao-zen Chang (1986)
Diffusion Model for a Convective Layer. Part I: Numerical Simulation of Convective Boundary Layer, 25
Nathan Snook, M. Xue, Youngsun Jung
Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using Wsr-88d and Casa Radar Data
J. Whitaker, T. Hamill (2012)
Evaluating Methods to Account for System Errors in Ensemble Data AssimilationMonthly Weather Review, 140
(1982)
A model for the assessment of weather forecasts
AbstractNOAA’s National Severe Storms Laboratory is actively developing phased-array radar (PAR) technology, a potential next-generation weather radar, to replace the current operational WSR-88D radars. One unique feature of PAR is its rapid scanning capability, which is at least 4–5 times faster than the scanning rate of WSR-88D. To explore the impact of such high-frequency PAR observations compared with traditional WSR-88D on severe weather forecasting, several storm-scale data assimilation and forecast experiments are conducted. Reflectivity and radial velocity observations from the 22 May 2011 Ada, Oklahoma, tornadic supercell storm are assimilated over a 45-min period using observations from the experimental PAR located in Norman, Oklahoma, and the operational WSR-88D radar at Oklahoma City, Oklahoma. The radar observations are assimilated into the ARPS model within a heterogeneous mesoscale environment and 1-h ensemble forecasts are generated from analyses every 15 min. With a 30-min assimilation period, the PAR experiment is able to analyze more realistic storm structures, resulting in higher skill scores and higher probabilities of low-level vorticity that align better with the locations of radar-derived rotation compared with the WSR-88D experiment. Assimilation of PAR observations for a longer 45-min time period generates similar forecasts compared to assimilating WSR-88D observations, indicating that the advantage of rapid-scan PAR is more noticeable over a shorter 30-min assimilation period. An additional experiment reveals that the improved accuracy from the PAR experiment over a shorter assimilation period is mainly due to its high-temporal-frequency sampling capability. These results highlight the benefit of PAR’s rapid-scan capability in storm-scale modeling that can potentially extend severe weather warning lead times.
Weather and Forecasting – American Meteorological Society
Published: Aug 1, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.