Convection-Resolving NWP with WRF. Section coordinator Ming Xue University of Oklahoma
|
|
- Ellen Jennings
- 5 years ago
- Views:
Transcription
1 Convection-Resolving NWP with WRF Section coordinator Ming Xue University of Oklahoma
2 Convection-resolving NWP Is NWP that explicitly treats moist convective systems ranging from organized MCSs to individual convective cells; For strongly fored and organized convective systems, skillful forecasts can often be obtained as far as 36 hours using 2-4 km grid resolutions; Less organized and/or weakly forced systems require higher resolutions ( 1 km) and predictability range is much shorter; Continental-scale NWP at 1-km resolution routinely possible within a few years; Explicit prediction of convective cells important for themselves and for proper feedback to large scales
3 Research Priorities Data assimilation for convection-resolving NWP. Physics improvements for convectionresolving NWP. Model numerics and computational infrastructure. Convective-scale predictability study and probabilistic forecasting
4 Data Assimilation for Convection- Resolving NWP Need good estimate of both convective storms and their environment; Remote sensing platforms crucial; including radar, wind profilers, GPS, clear-air radar winds and refractivity, polarimetric radar parameters, and high-resolution satellite data; High-res conventional data, including mesonets; Advanced DA methods, such as 4DVAR and EnKF essential; properly designed 3DVAR and other physics-based analysis methods can still be useful. Need multi-scale DA capabilities.
5 Better understand the obs needs at this resolution detailed data impact studies and identify deficiencies; Proper handling of error characteristics and spatial representativeness of observations;
6 Physics Improvements for Convection- Resolving NWP Physics appear to be the largest source of error at cloudresolving scale; Microphysics and SGS turbulence seem to have the largest impact on resolved convective cells and QPF, especially at shorter ranges and when storms initialized well; LSM, sfc physics and PBL have largest impact on convective initiation and longer range storm evolution; Cloud-radiation interaction modulates convective system dynamics and evolution and affects surface energy balance; Need careful detailed diagnostic analyses of existing schemes in controlled settings and verifications against observations too many cheap scheme inter-comparisons not enough understanding! Need to build and evaluate consistent suites of physics packages, not in random combinations in the name of diversity!
7
8 Model Numerics and Computational Infrastructure. Highly accurate numerical schemes with minimum damping, e.g., conservative and monotonic schemes, are strongly desirable for small-scale often discontinuous flows; Efficient high-order schemes preferred; Also need highly scalable pre- and post-processing software; including that for DA. Codes, including those for physics packages, need to be readable! Monotonicity v.s. lower-order enstrophy conserving.
9 Convective-scale Predictability Study and Probabilistic Forecasting Convective-scale predictability poorly understood, and varies significantly with type of convective systems; Better understanding of the error growth dynamics, in the presence of model error, is important for DA as well as for model improvement; Probability information even more important at this scale because of generally high forecast uncertainty; Ensemble-based DA and forecast systems should be developed that should include model uncertainties too; Need to support multiple well tuned physics options and perhaps dynamic cores to better capture the uncertainty range.
10 Model evalution Identify sources of errors, failures, in wholistic way need understanding of both physics and statistics physical scientists and statisticians working together to come up with most useful scores;
11 Verification of at convective-resolving scales Non-traditional verifications! Quantify and standardize things that are seen subjectively; Assess features, errors in both phase and amplitude, modes of convection, etc. Verification scores that reveal the handling of physical processes; Direction verifications against indirect observations; Close link with data assimilation systems;
12 Proposed Action Items Promote and seek community and funding agency support, through workshops, conferences, and publications, for more in-depth analysis and diagnostic studies of state of the art physics packages, and the development of more advanced physical parameterization schemes designed specifically for the convection-resolving scales. Promote the training of next-generation scientists specialized in atmospheric physics, and in advanced data assimilation, and in effectively applying statistical theories and methods to atmospheric data assimilation, verification and probabilistic prediction. Working closely with statisticians. Provide an efficient and flexible modeling and data assimilation framework that facilitates rapid experimentation.
13 Comments Need to evaluate over extended periods Issue of the suppression of convection by cumulus scheme Need for cumulus scheme at intermediate (~few km) resolutions; B.C. effect? Solution: large domain, twoway nesting?
14 GSI Analyses of Radar Data and Impact on forecasts of WRF-ARW and WRF-NMM Ming Hu, Shun Liu and Ming Xue CAPS, University of Oklahoma
15 May 23, 2005 Test Case
16 May 23, 2005 Case Impact of GSI + ARPS cloud analysis on WRF-ARW forecast 6-hour forecast starting at 0600 UTC 9-km resolution grid Working on merging and improving RUC and ARPS cloud analysis for more general applications (e.g., dx~10 km) within GSI framework, and using additional satellite and sfc cloud obs in NCEP data stream
17 Observation GSI+Cloud Analysis GSI Interpolated NAM 0600 UTC t=0.0h
18 Observation GSI+Cloud Analysis GSI Interpolated NAM 0700 UTC t=1.0h
19 Observation GSI+Cloud Analysis GSI Interpolated NAM 0800 UTC t=2.0h
20 Observation GSI+Cloud Analysis GSI Interpolated NAM 0900 UTC t=3.0h
21 Observation GSI+Cloud Analysis GSI Interpolated NAM 1000 UTC t=4.0h
22 Observation GSI+Cloud Analysis GSI Interpolated NAM 1100 UTC t=5.0h
23 Observation GSI+Cloud Analysis GSI Interpolated NAM 1200 UTC t=6.0h
24 GSI-analyzed winds and increments with superobbed radial velocity data NAM Background GSI analysis 1x filter scale, 0.5 superobbing GSI analysis 1/8 x filter scale, 0.1 superobbing full winds increments
25 Composite reflectivity at 11UTC from 6 radars and 6-h WRF-NMM forecast valid at 12 UTC Observed reflectivity at 1100 UTC Predicted reflectivity at 1200 UTC NAM Background GSI analysis 1x filter scale, 0.5 superobbing GSI analysis 1/8 x filter scale, 0.1 superobbing
26 Forecast Examples: May 8 th, 2003 OKC tornado OKC tornado UTC 30 km long path KTLX F4 Tornado # UTC UTC
27 Prediction using 100 m resolution grid sfc winds pert. pressure obs. tornado track (over 22 minutes)
28 Assimilation and Prediction of May 29-30, 2004 North OKC Tornado case using EnKF X KVNX 1 km Analysis and Prediction Grid F1 F3 F0 F2 F2 OKC X KTLX 1h EnKF Assimilation 40 members 1 h Forecast 00 UTC 01 UTC 02 UTC
29 Animation of 1-h Forecast Initialized with EnKF Reflectivity at 1.2 Elevation Forecast Observation 0100 UTC 0200 UTC
30 Wakimoto et al.(2006 MWR). Surface analysis + satellite images Dryline Convective Initiation Study of Xue and Martin (2006a,b MWR) May 24, 2002 IHOP Case
31 t=3h, 2100 UTC sfc. winds, qv, and composite reflectivity
32 t=4h, 2200 UTC
33 t=5h, 2300 UTC
34 2000 UTC 2015 UTC 2030 UTC 2045 UTC t=2h t=2h 15min t=2h 30min t=2h 45min C C B B B A A A C B A
35 June 12, 2002 IHOP CI Case 2140 UTC Courtesy of Wilson and Roberts (2006)
36 June 12, 2002 IHOP CI Case 21:20Z Wed 12 Jun 2002 T= s (3:20:00) FIRST LEVEL ABOVE GROUND (SURFACE) (km) (km) Sat. obs 2120 UTC Ref (dbz, shaded) qv (g/kg, contour) U-V (m/s, vector) MIN=0.000 MAX=56.1 MIN=2.491 MAX=21.41 inc=1.000 Umin= Umax=15.08 Vmin= Vmax=15.23 ARPS 3km Starting from 18 UTC IC 6 h assimilation at 1 h intervals
Implementation and Evaluation of WSR-88D Reflectivity Data Assimilation for WRF-ARW via GSI and Cloud Analysis. Ming Hu University of Oklahoma
Implementation and Evaluation of WSR-88D Reflectivity Data Assimilation for WRF-ARW via GSI and Cloud Analysis Ming Hu University of Oklahoma 1. Previous work and Goal of visiting Previous work: Radar
More informationSome Applications of WRF/DART
Some Applications of WRF/DART Chris Snyder, National Center for Atmospheric Research Mesoscale and Microscale Meteorology Division (MMM), and Institue for Mathematics Applied to Geoscience (IMAGe) WRF/DART
More informationCAPS Storm-Scale Ensemble Forecasting (SSEF) System
CAPS Storm-Scale Ensemble Forecasting (SSEF) System Fanyou Kong, Ming Xue, Xuguang Wang, Keith Brewster Center for Analysis and Prediction of Storms University of Oklahoma (collaborated with NSSL, SPC,
More information13A. 4 Analysis and Impact of Super-obbed Doppler Radial Velocity in the NCEP Grid-point Statistical Interpolation (GSI) Analysis System
13A. 4 Analysis and Impact of Super-obbed Doppler Radial Velocity in the NCEP Grid-point Statistical Interpolation (GSI) Analysis System Shun Liu 1, Ming Xue 1,2, Jidong Gao 1,2 and David Parrish 3 1 Center
More informationNumerical Weather Prediction: Data assimilation. Steven Cavallo
Numerical Weather Prediction: Data assimilation Steven Cavallo Data assimilation (DA) is the process estimating the true state of a system given observations of the system and a background estimate. Observations
More informationDevelopment and Research of GSI- based Var/EnKF/EnVar/ Hybrid System to Assimilate Radar ObservaBons for ConvecBve Scale NWP
Development and Research of GSI- based Var/EnKF/EnVar/ Hybrid System to Assimilate Radar ObservaBons for ConvecBve Scale NWP Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK D Acknowledgement
More informationEnsemble Kalman Filter Assimilation of Radar Data for a Convective Storm using a Two-moment Microphysics Scheme 04/09/10
Ensemble Kalman Filter Assimilation of Radar Data for a Convective Storm using a Two-moment Microphysics Scheme 04/09/10 Youngsun Jung 1, Ming Xue 1,2, and Mingjing Tong 3 CAPS 1 and School of Meteorology
More informationMotivation & Goal. We investigate a way to generate PDFs from a single deterministic run
Motivation & Goal Numerical weather prediction is limited by errors in initial conditions, model imperfections, and nonlinearity. Ensembles of an NWP model provide forecast probability density functions
More informationThunderstorm-Scale EnKF Analyses Verified with Dual-Polarization, Dual-Doppler Radar Data
Thunderstorm-Scale EnKF Analyses Verified with Dual-Polarization, Dual-Doppler Radar Data David Dowell and Wiebke Deierling National Center for Atmospheric Research, Boulder, CO Ensemble Data Assimilation
More informationAssimilation of Airborne Doppler Radar Observations Using the Unified GSI based Hybrid Ensemble Variational Data Assimilation System for HWRF
Assimilation of Airborne Doppler Radar Observations Using the Unified GSI based Hybrid Ensemble Variational Data Assimilation System for HWRF Xuguang Wang xuguang.wang@ou.edu University of Oklahoma, Norman,
More informationChengsi Liu 1, Ming Xue 1, 2, Youngsun Jung 1, Lianglv Chen 3, Rong Kong 1 and Jingyao Luo 3 ISDA 2019
Development of Optimized Radar Data Assimilation Capability within the Fully Coupled EnKF EnVar Hybrid System for Convective Permitting Ensemble Forecasting and Testing via NOAA Hazardous Weather Testbed
More informationConvective-scale Warn-on-Forecast The Future of Severe Weather Warnings in the USA?
Convective-scale Warn-on-Forecast The Future of Severe Weather Warnings in the USA? David J. Stensrud Department of Meteorology The Pennsylvania State University Present Warning System: 2 March 2012 Warning
More informationNew Development in CAPS Storm-Scale Ensemble Forecasting System for NOAA 2012 HWT Spring Experiment
New Development in CAPS Storm-Scale Ensemble Forecasting System for NOAA 2012 HWT Spring Experiment Fanyou Kong, Ming Xue, Kevin W. Thomas, Yunheng Wang, Keith Brewster, Xuguang Wang (Center for Analysis
More information2012 and changes to the Rapid Refresh and HRRR weather forecast models
2012 and 2013-15 changes to the Rapid Refresh and HRRR weather forecast models 31 October 2012 Stan Benjamin Steve Weygandt Curtis Alexander NOAA Earth System Research Laboratory Boulder, CO FPAW - 2012
More informationCAPS Storm-Scale Ensemble Forecast for the NOAA Hazardous Weather Testbed (HWT) Spring Experiment
CAPS Storm-Scale Ensemble Forecast for the NOAA Hazardous Weather Testbed (HWT) Spring Experiment Fanyou Kong, Ming Xue, Kevin W. Thomas, Yunheng Wang, Keith Brewster, Xuguang Wang (Center for Analysis
More informationDevelopment and Research of GSI-based EnVar System to Assimilate Radar Observations for Convective Scale Analysis and Forecast
Development and Research of GSI-based EnVar System to Assimilate Radar Observations for Convective Scale Analysis and Forecast Xuguang Wang, Yongming Wang School of Meteorology University of Oklahoma,
More informationImplementation and evaluation of cloud analysis with WSR-88D reflectivity data for GSI and WRF-ARW
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L07808, doi:10.1029/2006gl028847, 2007 Implementation and evaluation of cloud analysis with WSR-88D reflectivity data for GSI and WRF-ARW
More informationA Cycled GSI+EnKF and Storm-Scale Ensemble Forecasting (SSEF) Experiment
A Cycled GSI+EnKF and Storm-Scale Ensemble Forecasting (SSEF) Experiment Fanyou Kong, Ming Xue, Youngsun Jung, Keith A. Brewster, Gang Zhao Center for Analysis and Prediction of Storms University of Oklahoma
More informationAssimilation of cloud/precipitation data at regional scales
Assimilation of cloud/precipitation data at regional scales Thomas Auligné National Center for Atmospheric Research auligne@ucar.edu Acknowledgments to: Steven Cavallo, David Dowell, Aimé Fournier, Hans
More informationJidong Gao and David Stensrud. NOAA/National Severe Storm Laboratory Norman, Oklahoma
Assimilation of Reflectivity and Radial Velocity in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification Jidong Gao and David Stensrud NOAA/National Severe Storm Laboratory Norman,
More informationToward improved initial conditions for NCAR s real-time convection-allowing ensemble. Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell
Toward improved initial conditions for NCAR s real-time convection-allowing ensemble Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell Storm-scale ensemble design Can an EnKF be used to initialize
More informationDeterministic and Ensemble Storm scale Lightning Data Assimilation
LI Mission Advisory Group & GOES-R Science Team Workshop 27-29 May 2015 Deterministic and Ensemble Storm scale Lightning Data Assimilation Don MacGorman, Ted Mansell (NOAA/National Severe Storms Lab) Alex
More informationImplementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI
Implementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI Shun Liu 1, Ming Xue 1,2 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology University
More informationAssimilate W88D Doppler Vr for Humberto 05
Assimilate W88D Doppler Vr for Humberto 05 WRF/EnKF Forecast vs. Observations vs. 3DVAR Min SLP Max wind The WRF/3DVAR (as a surrogate of operational algorithm) assimilates the same radar data but without
More informationJ8.2 INITIAL CONDITION SENSITIVITY ANALYSIS OF A MESOSCALE FORECAST USING VERY LARGE ENSEMBLES. University of Oklahoma Norman, Oklahoma 73019
J8.2 INITIAL CONDITION SENSITIVITY ANALYSIS OF A MESOSCALE FORECAST USING VERY LARGE ENSEMBLES William J. Martin 1, * and Ming Xue 1,2 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology
More informationVariational data assimilation of lightning with WRFDA system using nonlinear observation operators
Variational data assimilation of lightning with WRFDA system using nonlinear observation operators Virginia Tech, Blacksburg, Virginia Florida State University, Tallahassee, Florida rstefane@vt.edu, inavon@fsu.edu
More informationTHE DECORRELATION SCALE: METHODOLOGY AND APPLICATION FOR PRECIPITATION FORECASTS
THE DECORRELATION SCALE: METHODOLOGY AND APPLICATION FOR PRECIPITATION FORECASTS Madalina Surcel, Isztar Zawadzki and M. K. Yau Thanking Adam Clark (NSSL), Ming Xue (OU, CAPS) and Fanyou Kong (CAPS) for
More informationConvection-Permitting Ensemble Forecasts at CAPS for Hazardous Weather Testbed (HWT)
Convection-Permitting Ensemble Forecasts at CAPS for Hazardous Weather Testbed (HWT) Ming Xue Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma mxue@ou.edu August,
More informationAssimilation of Satellite Infrared Brightness Temperatures and Doppler Radar Observations in a High-Resolution OSSE
Assimilation of Satellite Infrared Brightness Temperatures and Doppler Radar Observations in a High-Resolution OSSE Jason Otkin and Becky Cintineo University of Wisconsin-Madison, Cooperative Institute
More informationVerifying Ensemble Forecasts Using A Neighborhood Approach
Verifying Ensemble Forecasts Using A Neighborhood Approach Craig Schwartz NCAR/MMM schwartz@ucar.edu Thanks to: Jack Kain, Ming Xue, Steve Weiss Theory, Motivation, and Review Traditional Deterministic
More information2B.6 LATEST DEVELOPMENT OF 3DVAR SYSTEM FOR ARPS AND ITS APPLICATION TO A TORNADIC SUPERCELL STORM. Guoqing Ge * and Jidong Gao
2B.6 LATEST DEVELOPMENT OF 3DVAR SYSTEM FOR ARPS AND ITS APPLICATION TO A TORNADIC SUPERCELL STORM Guoqing Ge * and Jidong Gao Center for Analysis and Prediction of Storms and school of meteorology, University
More informationDevelopment of an Hourly- Updated NAM Forecast System
Development of an Hourly- Updated NAM Forecast System Jacob Carley ab, Eric Rogers b, Shun Liu ab, Brad Ferrier ab, Eric Aligo ab, Matthew Pyle b, and Geoff DiMego b a IMSG, b NOAA/NWS/NCEP/EMC jacob.carley@noaa.gov
More informationDi Wu, Xiquan Dong, Baike Xi, Zhe Feng, Aaron Kennedy, and Gretchen Mullendore. University of North Dakota
Di Wu, Xiquan Dong, Baike Xi, Zhe Feng, Aaron Kennedy, and Gretchen Mullendore University of North Dakota Objectives 3 case studies to evaluate WRF and NAM performance in Oklahoma (OK) during summer 2007,
More informationRetrospective and near real-time tests of GSIbased EnKF-Var hybrid data assimilation system for HWRF with airborne radar data
Retrospective and near real-time tests of GSIbased EnKF-Var hybrid data assimilation system for HWRF with airborne radar data Xuguang Wang, Xu Lu, Yongzuo Li University of Oklahoma, Norman, OK In collaboration
More informationThe Developmental Testbed Center: Update on Data Assimilation System Testing and Community Support
93rd AMS Annual Meeting/17th IOAS-AOLS/3rd Conference on Transition of Research to Operations, Austin, TX, Jan 6-10, 2013 The Developmental Testbed Center: Update on Data Assimilation System Testing and
More information5.3 TESTING AND EVALUATION OF THE GSI DATA ASSIMILATION SYSTEM
5.3 TESTING AND EVALUATION OF THE GSI DATA ASSIMILATION SYSTEM Kathryn M Newman*, C. Zhou, H. Shao, X.Y. Huang, M. Hu National Center for Atmospheric Research, Boulder, CO Developmental Testbed Center
More informationThe GNSS-RO Data Impact on the Typhoon Predictions by MPAS-GSI Model
The GNSS-RO Data Impact on the Typhoon Predictions by MPAS-GSI Model 1 Shu-Ya Chen, 1,2 Cheng-Peng Shih, 2,3 Ching-Yuang Huang, 2 Wen-Hsin Teng, and 1 Yang-Cheng Huang 1 GPS Science and Application Research
More informationThe Nowcasting Demonstration Project for London 2012
The Nowcasting Demonstration Project for London 2012 Susan Ballard, Zhihong Li, David Simonin, Jean-Francois Caron, Brian Golding, Met Office, UK Introduction The success of convective-scale NWP is largely
More informationNumerical Prediction of 8 May 2003 Oklahoma City Supercell Tornado with ARPS and Radar Data Assimilation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Numerical Prediction of 8 May 2003 Oklahoma City Supercell Tornado with ARPS and Radar Data Assimilation
More informationMesoscale Ensemble Data Assimilation: Opportunities and Challenges. Fuqing Zhang Penn State University
Mesoscale Ensemble Data Assimilation: Opportunities and Challenges Fuqing Zhang Penn State University Mesoscale EnKF: some incomplete background 1 st proposed by Evensen (1994); Houtekamer and Micthell
More informationFinal Report for NSF Award ATM Optimal Utilization and Impact of Water Vapor and Other High Resolution Observations in Storm-Scale QPF
Final Report for NSF Award ATM- 0129892 Optimal Utilization and Impact of Water Vapor and Other High Resolution Observations in Storm-Scale QPF Prof. Ming Xue 1,2, Principal Investigator Prof. Frederick
More informationEnhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation
Enhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation Weiguang Chang and Isztar Zawadzki Department of Atmospheric and Oceanic Sciences Faculty
More informationCanadian Meteorological and Oceanographic Society and American Meteorological Society 21 st Conference on Numerical Weather Prediction 31 May 2012
Canadian Meteorological and Oceanographic Society and American Meteorological Society 21 st Conference on Numerical Weather Prediction 31 May 2012 The High Resolution Rapid Refresh (): An hourly updating
More informationPreprint, 18 th Conference on Numerical Weather Prediction American Meteorological Society, Park City, UT
Preprint, 1 th Conference on Numerical Weather Prediction American Meteorological Society, Park City, UT 1B.4 Analysis and Prediction of May 3 Oklahoma City Tornadic Thunderstorm and Embedded Tornado using
More informationABSTRACT 3 RADIAL VELOCITY ASSIMILATION IN BJRUC 3.1 ASSIMILATION STRATEGY OF RADIAL
REAL-TIME RADAR RADIAL VELOCITY ASSIMILATION EXPERIMENTS IN A PRE-OPERATIONAL FRAMEWORK IN NORTH CHINA Min Chen 1 Ming-xuan Chen 1 Shui-yong Fan 1 Hong-li Wang 2 Jenny Sun 2 1 Institute of Urban Meteorology,
More informationA pseudo-ensemble hybrid data assimilation system for HWRF
A pseudo-ensemble hybrid data assimilation system for HWRF Xuyang Ge UCAR visiting postdoctoral scientist at PSU/NCEP Contributors: Fuqing Zhang and Yonghui Weng (PSU) Mingjing Tong and Vijay Tallapragada
More informationSingle-Doppler EnKF assimilation of high-resolution data from the 29 May 2004 OKC supercell: Comparisons with dual-doppler analyses.
Single-Doppler assimilation of high-resolution data from the 29 May 2004 OKC supercell: Comparisons with dual-doppler analyses. Lou Wicker NOAA/National Severe Storms Lab, Norman OK With lots of help from:
More informationReport on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE)
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE) Motivation As an expansion of computing resources for operations at EMC is becoming available
More informationHMON (HNMMB): Development of a new Hurricane model for NWS/NCEP operations
1 HMON (HNMMB): Development of a new Hurricane model for NWS/NCEP operations Avichal Mehra, EMC Hurricane and Mesoscale Teams Environmental Modeling Center NOAA / NWS / NCEP HMON: A New Operational Hurricane
More informationReal case simulations using spectral bin cloud microphysics: Remarks on precedence research and future activity
Real case simulations using spectral bin cloud microphysics: Remarks on precedence research and future activity Takamichi Iguchi 1,2 (takamichi.iguchi@nasa.gov) 1 Earth System Science Interdisciplinary
More informationComparing Variational, Ensemble-based and Hybrid Data Assimilations at Regional Scales
Comparing Variational, Ensemble-based and Hybrid Data Assimilations at Regional Scales Meng Zhang and Fuqing Zhang Penn State University Xiang-Yu Huang and Xin Zhang NCAR 4 th EnDA Workshop, Albany, NY
More informationAssimilation of radar reflectivity
Assimilation of radar reflectivity Axel Seifert Deutscher Wetterdienst, Offenbach, Germany Convective-scale NWP at DWD: Plans for 2020 Storm-scale ICON-RUC-EPS: hourly 12h ensemble forecasts based on short
More informationRadiance Data Assimilation with an EnKF
Radiance Data Assimilation with an EnKF Zhiquan Liu, Craig Schwartz, Xiangyu Huang (NCAR/MMM) Yongsheng Chen (York University) 4/7/2010 4th EnKF Workshop 1 Outline Radiance Assimilation Methodology Apply
More informationImplementation and evaluation of a regional data assimilation system based on WRF-LETKF
Implementation and evaluation of a regional data assimilation system based on WRF-LETKF Juan José Ruiz Centro de Investigaciones del Mar y la Atmosfera (CONICET University of Buenos Aires) With many thanks
More informationThe Impacts of GPS Radio Occultation Data on the Analysis and Prediction of Tropical Cyclones. Bill Kuo, Xingqin Fang, and Hui Liu UCAR COSMIC
The Impacts of GPS Radio Occultation Data on the Analysis and Prediction of Tropical Cyclones Bill Kuo, Xingqin Fang, and Hui Liu UCAR COSMIC GPS Radio Occultation α GPS RO observations advantages for
More informationMultiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002
OCTOBER 2007 X I A O A N D S U N 3381 Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 QINGNONG XIAO AND JUANZHEN SUN Mesoscale
More informationAMPS Update June 2016
AMPS Update June 2016 Kevin W. Manning Jordan G. Powers Mesoscale and Microscale Meteorology Laboratory National Center for Atmospheric Research Boulder, CO 11 th Antarctic Meteorological Observation,
More informationDevelopment and research of GSI based hybrid EnKF Var data assimilation for HWRF to improve hurricane prediction
Development and research of GSI based hybrid EnKF Var data assimilation for HWRF to improve hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li School of Meteorology University of Oklahoma, Norman, OK,
More informationWeather Research and Forecasting Model
Weather Research and Forecasting Model Goals: Develop an advanced mesoscale forecast and assimilation system, and accelerate research advances into operations 36h WRF Precip Forecast Collaborative partnership,
More informationWRF Modeling System Overview
WRF Modeling System Overview Louisa Nance National Center for Atmospheric Research (NCAR) Developmental Testbed Center (DTC) 27 February 2007 1 Outline What is WRF? WRF Modeling System WRF Software Design
More informationCOSMIC GPS Radio Occultation and
An Impact Study of FORMOSAT-3/ COSMIC GPS Radio Occultation and Dropsonde Data on WRF Simulations 27 Mei-yu season Fang-Ching g Chien Department of Earth Sciences Chien National and Taiwan Kuo (29), Normal
More informationTransitioning Physics Advancements into the Operational Hurricane WRF Model
Transitioning Physics Advancements into the Operational Hurricane WRF Model KATHRYN NEWMAN, MRINAL BISWAS, LAURIE CARSON N OA A / ESR L T EA M M E M B E RS: E. K ALINA, J. F RIMEL, E. GRELL, AND L. B ERNARDET
More informationHybrid Variational Ensemble Data Assimilation for Tropical Cyclone
Hybrid Variational Ensemble Data Assimilation for Tropical Cyclone Forecasts Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK Acknowledgement: OU: Ting Lei, Yongzuo Li, Kefeng Zhu,
More information13.2 IMPACT OF CONFIGURATIONS OF RAPID INTERMITTENT ASSIMILATION OF WSR-88D RADAR DATA FOR THE 8 MAY 2003 OKLAHOMA CITY TORNADIC THUNDERSTORM CASE
13.2 IMPACT OF CONFIGURATIONS OF RAPID INTERMITTENT ASSIMILATION OF WSR-D RADAR DATA FOR THE MAY 23 OKLAHOMA CITY TORNADIC THUNDERSTORM CASE Ming Hu and Ming Xue* Center for Analysis and Prediction of
More informationDoppler radial wind spatially correlated observation error: operational implementation and initial results
Doppler radial wind spatially correlated observation error: operational implementation and initial results D. Simonin, J. Waller, G. Kelly, S. Ballard,, S. Dance, N. Nichols (Met Office, University of
More informationImpact of Configurations of Rapid Intermittent Assimilation of WSR-88D Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case
FEBRUARY 2007 H U A N D X U E 507 Impact of Configurations of Rapid Intermittent Assimilation of WSR-88D Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case MING HU AND MING XUE Center
More informationSeoul National University. Ji-Hyun Ha, Gyu-Ho Lim and Dong-Kyou Lee
Numerical simulation with radar data assimilation over the Korean Peninsula Seoul National University Ji-Hyun Ha, Gyu-Ho Lim and Dong-Kyou Lee Introduction The forecast skill associated with warm season
More informationComparison of Convection-permitting and Convection-parameterizing Ensembles
Comparison of Convection-permitting and Convection-parameterizing Ensembles Adam J. Clark NOAA/NSSL 18 August 2010 DTC Ensemble Testbed (DET) Workshop Introduction/Motivation CAMs could lead to big improvements
More informationThe EMC Mission.. In response to operational requirements:
The EMC Mission.. In response to operational requirements: Maintain operational model suite The scientific correctness and integrity of operational forecast modeling systems Modify current operational
More informationGSI Data Assimilation System Support and Testing Activities: 2013 Annual Update
14Th Annual WRF Users Workshop, Boulder, CO, June 24-28, 2013 GSI Data Assimilation System Support and Testing Activities: 2013 Annual Update Hui Shao1, Ming Hu2, Chunhua Zhou1, Kathryn Newman1, Mrinal
More informationQuantifying Uncertainty through Global and Mesoscale Ensembles
Quantifying Uncertainty through Global and Mesoscale Ensembles Teddy R. Holt Naval Research Laboratory Monterey CA 93943-5502 phone: (831) 656-4740 fax: (831) 656-4769 e-mail: holt@nrlmry.navy.mil Award
More informationARW/EnKF performance for the 2009 Hurricane Season
ARW/EnKF performance for the 2009 Hurricane Season Ryan D. Torn, Univ. at Albany, SUNY Chris Davis, Steven Cavallo, Chris Snyder, Wei Wang, James Done, NCAR/MMM 4 th EnKF Workshop 8 April 2010, Rensselaerville,
More informationCAPS STORMSCALE ENSEMBLE FORECASTING SYSTEM: IMPACT OF IC AND LBC PERTURBATIONS
$%& ' ( ) %$* +, % - '.&/! CAPS STORMSCALE ENSEMBLE FORECASTING SYSTEM: IMPACT OF IC AND LBC PERTURBATIONS Fanyou Kong 1 *, Ming Xue 1,2, Kevin W. Thomas 1, Yunheng Wang 1, Keith A. Brewster 1, Youngsun
More informationA Study of Convective Initiation Failure on 22 Oct 2004
A Study of Convective Initiation Failure on 22 Oct 2004 Jennifer M. Laflin Philip N. Schumacher NWS Sioux Falls, SD August 6 th, 2011 Introduction Forecasting challenge: strong forcing for ascent and large
More informationNear-surface weather prediction and surface data assimilation: challenges, development, and potential data needs
Near-surface weather prediction and surface data assimilation: challenges, development, and potential data needs Zhaoxia Pu Department of Atmospheric Sciences University of Utah, Salt Lake City, Utah,
More informationHow do Spectrally Vast AR Thwart Attempts to Skillfully Forecast their Continental Precipitation?
How do Spectrally Vast AR Thwart Attempts to Skillfully Forecast their Continental Precipitation? International Atmospheric Rivers Conference Modeling and Methods Session 1 August 9, 2016 Andrew Martin
More information1. Current atmospheric DA systems 2. Coupling surface/atmospheric DA 3. Trends & ideas
1 Current issues in atmospheric data assimilation and its relationship with surfaces François Bouttier GAME/CNRM Météo-France 2nd workshop on remote sensing and modeling of surface properties, Toulouse,
More informationLogistics. Goof up P? R? Can you log in? Requests for: Teragrid yes? NCSA no? Anders Colberg Syrowski Curtis Rastogi Yang Chiu
Logistics Goof up P? R? Can you log in? Teragrid yes? NCSA no? Requests for: Anders Colberg Syrowski Curtis Rastogi Yang Chiu Introduction to Numerical Weather Prediction Thanks: Tom Warner, NCAR A bit
More informationEnsemble Kalman Filters for WRF-ARW. Chris Snyder MMM and IMAGe National Center for Atmospheric Research
Ensemble Kalman Filters for WRF-ARW Chris Snyder MMM and IMAGe National Center for Atmospheric Research Preliminaries Notation: x = modelʼs state w.r.t. some discrete basis, e.g. grid-pt values y = Hx
More informationConvective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter
Convective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter Jon Poterjoy, Ryan Sobash, and Jeffrey Anderson National Center for Atmospheric Research
More informationExtracting probabilistic severe weather guidance from convection-allowing model forecasts. Ryan Sobash 4 December 2009 Convection/NWP Seminar Series
Extracting probabilistic severe weather guidance from convection-allowing model forecasts Ryan Sobash 4 December 2009 Convection/NWP Seminar Series Identification of severe convection in high-resolution
More informationDaniel T. Dawson II* 1,2, Ming Xue 1,2, Jason A. Milbrandt 2, M. K. Yau 3, and Guifu Zhang 2
Preprints, 22th Conf. on Weather Analysis and Forecasting and 18th Conf. on Numerical Weather Prediction Amer. Meteor. Soc., Park City, UT, 25-29 June 2007 10B.2 IMPACT OF MULTI-MOMENT MICROPHYSICS AND
More informationPSU HFIP 2010 Summary: Performance of the ARW-EnKF Real-time Cloud-resolving TC Ensemble Analysis and Forecasting System.
PSU HFIP 2010 Summary: Performance of the ARW-EnKF Real-time Cloud-resolving TC Ensemble Analysis and Forecasting System Fuqing Zhang Penn State University Contributors: Yonghui Weng, John Gamache and
More informationA GSI-based convection-allowing EnKF and ensemble forecast system for PECAN
A GSI-based convection-allowing EnKF and ensemble forecast system for PECAN Aaron Johnson, Xuguang Wang, Samuel Degelia University of Oklahoma, Norman, OK 26 May 2016 7 th EnKF Data Assimilation Workshop,
More informationAssimilation of Mesoscale Observations for use in Numerical Weather Prediction
Assimilation of Mesoscale Observations for use in Numerical Weather Prediction Steve Koch Thermodynamic Profiling Technologies Workshop Boulder 12 14 April 2011 Important Questions (from 2003 USWRP Mesoscale
More informationMESO-NH cloud forecast verification with satellite observation
MESO-NH cloud forecast verification with satellite observation Jean-Pierre CHABOUREAU Laboratoire d Aérologie, University of Toulouse and CNRS, France http://mesonh.aero.obs-mip.fr/chaboureau/ DTC Verification
More informationThe Developmental Testbed Center (DTC) Steve Koch, NOAA/FSL
The Developmental Testbed Center (DTC) Steve Koch, NOAA/FSL A facility where the NWP research and operational communities interact to accelerate testing and evaluation of new models and techniques for
More informationP15.13 DETECTION OF HAZARDOUS WEATHER PHENOMENA USING DATA ASSIMILATION TECHNIQUES
P15.13 DETECTION OF HAZARDOUS WEATHER PHENOMENA USING DATA ASSIMILATION TECHNIQUES 1. INTRODUCTION Robert Fritchie*, Kelvin Droegemeier, Ming Xue, Mingjing Tong, Elaine Godfrey School of Meteorology and
More information2010 CAPS Spring Forecast Experiment Program Plan (A Brief Version)
21 CAPS Spring Forecast Experiment Program Plan (A Brief Version) May 17, 21 Table of Content Table of Content... 2 1. Overview of New Features for 21 Season... 3 2. Program Duration... 4 3. Forecast System
More informationPlans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation
NOAA Earth System Research Laboratory Plans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation Tom Hamill (substituting for Stan Benjamin and team) NOAA Earth System
More informationOverview of HFIP FY10 activities and results
Overview of HFIP FY10 activities and results Bob Gall HFIP Annual Review Meeting Miami Nov 9, 2010 Outline In this presentation I will show a few preliminary results from the summer program. More detail
More informationIntroduction to NCEP's time lagged North American Rapid Refresh Ensemble Forecast System (NARRE-TL)
Introduction to NCEP's time lagged North American Rapid Refresh Ensemble Forecast System (NARRE-TL) Binbin Zhou 1,2, Jun Du 2, Geoff Manikin 2 & Geoff DiMego 2 1. I.M. System Group 2. EMC/NCEP/NWS/NOAA
More informationImpact of GPS RO Data on the Prediction of Tropical Cyclones
Impact of GPS RO Data on the Prediction of Tropical Cyclones Ying-Hwa Kuo, Hui Liu, UCAR Ching-Yuang Huang, Shu-Ya Chen, NCU Ling-Feng Hsiao, Ming-En Shieh, Yu-Chun Chen, TTFRI Outline Tropical cyclone
More informationDynamic Ensemble Model Evaluation of Elevated Thunderstorms sampled by PRECIP
Dynamic Ensemble Model Evaluation of Elevated Thunderstorms sampled by PRECIP Joshua S. Kastman, Patrick S. Market, and Neil Fox, University of Missouri, Columbia, MO Session 8B - Numerical Weather Prediction
More informationImportance of Numerical Weather Prediction in Variable Renewable Energy Forecast
Importance of Numerical Weather Prediction in Variable Renewable Energy Forecast Dr. Abhijit Basu (Integrated Research & Action for Development) Arideep Halder (Thinkthrough Consulting Pvt. Ltd.) September
More informationPreliminary results. Leonardo Calvetti, Rafael Toshio, Flávio Deppe and Cesar Beneti. Technological Institute SIMEPAR, Curitiba, Paraná, Brazil
HIGH RESOLUTION WRF SIMULATIONS FOR WIND GUST EVENTS Preliminary results Leonardo Calvetti, Rafael Toshio, Flávio Deppe and Cesar Beneti Technological Institute SIMEPAR, Curitiba, Paraná, Brazil 3 rd WMO/WWRP
More informationFAA Weather Research Plans
FAA Weather Research Plans Presented to: Friends /Partners in Aviation Weather Vision Forum By: Ray Moy FAA Aviation Weather Office Date: Aviation Weather Research Program (AWRP) Purpose: Applied Research
More informationEnKF Review. P.L. Houtekamer 7th EnKF workshop Introduction to the EnKF. Challenges. The ultimate global EnKF algorithm
Overview 1 2 3 Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation 6th EnKF Purpose EnKF equations localization After the 6th EnKF (2014), I decided with Prof. Zhang to summarize progress
More informationWRF Modeling System Overview
WRF Modeling System Overview Jimy Dudhia What is WRF? WRF: Weather Research and Forecasting Model Used for both research and operational forecasting It is a supported community model, i.e. a free and shared
More informationFrancis O. 1, David H. Bromwich 1,2
Impact of assimilating COSMIC GPS RO moisture and temperature profiles on Polar WRF simulations of West Antarctic cyclones Francis O. O@eno 1, David H. Bromwich 1,2 1 Polar Meteorology Group BPRC 2 Atmospheric
More information