Wind Gust Analysis in RTMA Yanqiu Zhu, Geoff DiMego, John Derber, Manuel Pondeca, Geoff Manikin, Russ Treadon, Dave Parrish, Jim Purser Environmental Modeling.

Slides:



Advertisements
Similar presentations
Ross Bannister Balance & Data Assimilation, ECMI, 30th June 2008 page 1 of 15 Balance and Data Assimilation Ross Bannister High Resolution Atmospheric.
Advertisements

Weather Station Data Quality and Interpolation Issues in Modeling Joe Russo International Workshop on Plant Epidemiology Surveillance for the Pest Forecasting.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Matthew Hendrickson, and Pascal Storck
© The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems.
A Global Daily Gauge-based Precipitation Analysis, Part I: Assessing Objective Techniques Mingyue Chen & CPC Precipitation Working Group CPC/NCEP/NOAA.
The Use of High Resolution Mesoscale Model Fields with the CALPUFF Dispersion Modelling System in Prince George BC Bryan McEwen Master’s project
Aspects of 6 June 2007: A Null “Moderate Risk” of Severe Weather Jonathan Kurtz Department of Geosciences University of Nebraska at Lincoln NOAA/NWS Omaha/Valley,
Rapid Refresh and RTMA. RUC: AKA-Rapid Refresh A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations.
NOAA/NWS Change to WRF 13 June What’s Happening? WRF replaces the eta as the NAM –NAM is the North American Mesoscale “timeslot” or “Model Run”
Recent performance statistics for AMPS real-time forecasts Kevin W. Manning – National Center for Atmospheric Research NCAR Earth System Laboratory Mesoscale.
RTMA (Real Time Mesoscale Analysis System) NWS New Mesoscale Analysis System for verifying model output and human forecasts.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
Daniel P. Tyndall and John D. Horel Department of Atmospheric Sciences, University of Utah Salt Lake City, Utah.
Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington
Weather Model Background ● The WRF (Weather Research and Forecasting) model had been developed by various research and governmental agencies became the.
Weather Research & Forecasting Model (WRF) Stacey Pensgen ESC 452 – Spring ’06.
Verification of Numerical Weather Prediction systems employed by the Australian Bureau of Meteorology over East Antarctica during the summer season.
Climatology and Predictability of Cool-Season High Wind Events in the New York City Metropolitan and Surrounding Area Michael Layer School of Marine and.
CARPE DIEM Centre for Water Resources Research NUID-UCD Contribution to Area-3 Dusseldorf meeting 26th to 28th May 2003.
Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.
Real Time Mesoscale Analysis John Horel Department of Meteorology University of Utah RTMA Temperature 1500 UTC 14 March 2008.
Space and Time Multiscale Analysis System A sequential variational approach Yuanfu Xie, Steven Koch Steve Albers and Huiling Yuan Global Systems Division.
Gridding Daily Climate Variables for use in ENSEMBLES Malcolm Haylock, Climatic Research Unit Nynke Hofstra, Mark New, Phil Jones.
On Improving GFS Forecast Skills in the Southern Hemisphere: Ideas and Preliminary Results Fanglin Yang Andrew Collard, Russ Treadon, John Derber NCEP-EMC.
June 19, 2007 GRIDDED MOS STARTS WITH POINT (STATION) MOS STARTS WITH POINT (STATION) MOS –Essentially the same MOS that is in text bulletins –Number and.
VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED STATES John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute.
Development of an EnKF/Hybrid Data Assimilation System for Mesoscale Application with the Rapid Refresh Ming Hu 1,2, Yujie Pan 3, Kefeng Zhu 3, Xuguang.
VERIFICATION OF NDFD GRIDDED FORECASTS USING ADAS John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute for Regional.
Space-Time Mesoscale Analysis System A sequential 3DVAR approach Yuanfu Xie, Steve Koch John McGinley and Steve Albers Global Systems Division Earth System.
AMB Verification and Quality Control monitoring Efforts involving RAOB, Profiler, Mesonets, Aircraft Bill Moninger, Xue Wei, Susan Sahm, Brian Jamison.
Combining CMORPH with Gauge Analysis over
P1.85 DEVELOPMENT OF SIMULATED GOES PRODUCTS FOR GFS AND NAM Hui-Ya Chuang and Brad Ferrier Environmental Modeling Center, NCEP, Washington DC Introduction.
The climate and climate variability of the wind power resource in the Great Lakes region of the United States Sharon Zhong 1 *, Xiuping Li 1, Xindi Bian.
Verification of Global Ensemble Forecasts Fanglin Yang Yuejian Zhu, Glenn White, John Derber Environmental Modeling Center National Centers for Environmental.
Assimilation of HF radar in the Ligurian Sea Spatial and Temporal scale considerations L. Vandenbulcke, A. Barth, J.-M. Beckers GHER/AGO, Université de.
Understanding hydrologic changes: application of the VIC model Vimal Mishra Assistant Professor Indian Institute of Technology (IIT), Gandhinagar
Part II  Access to Surface Weather Conditions:  MesoWest & ROMAN  Surface Data Assimilation:  ADAS.
1 Results from Winter Storm Reconnaissance Program 2008 Yucheng SongIMSG/EMC/NCEP Zoltan TothEMC/NCEP/NWS Sharan MajumdarUniv. of Miami Mark ShirleyNCO/NCEP/NWS.
1 Results from Winter Storm Reconnaissance Program 2007 Yucheng SongIMSG/EMC/NCEP Zoltan TothEMC/NCEP/NWS Sharan MajumdarUniv. of Miami Mark ShirleyNCO/NCEP/NWS.
P1.7 The Real-Time Mesoscale Analysis (RTMA) An operational objective surface analysis for the continental United States at 5-km resolution developed by.
Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan 1, Kefeng Zhu 1, Ming Xue 1,2, Xuguang.
1 Future NCEP Guidance Support for Surface Transportation Stephen Lord Director, NCEP Environmental Modeling Center 26 July 2007.
GSI applications within the Rapid Refresh and High Resolution Rapid Refresh 17 th IOAS-AOLS Conference 93 rd AMS Annual Meeting 9 January 2013 Patrick.
Boundary layer depth verification system at NCEP M. Tsidulko, C. M. Tassone, J. McQueen, G. DiMego, and M. Ek 15th International Symposium for the Advancement.
Analysis of Select Data Biases in North America Dr. Bradley Ballish NCEP/NCO/PMB October 2008 JAG/ODAA Meeting “Where America’s Climate and Weather Services.
The use of WSR-88D radar data at NCEP Shun Liu 1 David Parrish 2, John Derber 2, Geoff DiMego 2, Wan-shu Wu 2 Matthew Pyle 2, Brad Ferrier 1 1 IMSG/ National.
Rapid Update Cycle-RUC. RUC A major issue is how to assimilate and use the rapidly increasing array of offtime or continuous observations (not a 00.
MoPED temperature, pressure, and relative humidity observations at sub- minute intervals are accessed and bundled at the University of Utah into 5 minute.
Infrared Sounding Data in the GMAO Data Assimilation System JCSDA Infrared Sounding Working Group (ISWG) 30 January 2009.
Brody Sandel Aarhus University IntroductionResults Objectives  Perform the first global, high-resolution analysis of controls on tree cover  Analyze.
2. WRF model configuration and initial conditions  Three sets of initial and lateral boundary conditions for Katrina are used, including the output from.
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
Breakout Session 3: Analysis Strategies Charge(s): –Identify and evaluate the current capabilities to develop AORs –Recommendations on overcoming current.
Gridded analyses of near-surface ozone concentration, wind, temperature, and moisture at hourly intervals at 1 km horizontal resolution derived using the.
Jason Levit NOAA NextGen Weather Program June, 2013
Using RTMA Analysis to Substitute for Missing Airport Observations
Cloud & Visibility Improving Aviation Services Together
Moving from Empirical Estimation of Humidity to Observation: A Spatial and Temporal Evaluation of MTCLIM Assumptions Using Regional Networks Ruben Behnke.
Rapid Update Cycle-RUC
  Robert Gibson1, Douglas Drob2 and David Norris1 1BBN Technologies
Aircraft weather observations: Impacts for regional NWP models
Reinhold Steinacker Department of Meteorology and Geophysics
Naval Research Laboratory
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
CIMMSE Improving Inland Wind Forecasts October 2011 Project Update
Initialization of Numerical Forecast Models with Satellite data
NOAA Objective Sea Surface Salinity Analysis P. Xie, Y. Xue, and A
COAMPS Coupled Ocean Atmosphere Prediction System Developed by FNMOC and NRL (1996) Operational - MEL/FNMOC Experimental - NRL-MRY 27 km Spatial.
P2.5 Sensitivity of Surface Air Temperature Analyses to Background and Observation Errors Daniel Tyndall and John Horel Department.
Presentation transcript:

Wind Gust Analysis in RTMA Yanqiu Zhu, Geoff DiMego, John Derber, Manuel Pondeca, Geoff Manikin, Russ Treadon, Dave Parrish, Jim Purser Environmental Modeling Center National Centers for Environmental Prediction

Real-Time Mesoscale Analysis (RTMA)  RTMA is a NOAA-NWS gridded surface analysis system developed at EMC of NCEP in collaboration with the Global Systems Division (GSD)  One of its important applications is to provide a comprehensive set of high spatial and temporal resolution analyses that can be used to monitor potential severe weather events  2DVAR-version of Gridded Statistical Interpolation  Terrain-following anisotropic background error covariances  Background fields for CONUS is generated by downscaling RUC 1h forecasts

Generalizing GSI Control Variables RTMA analyses for surface pressure, 10 m wind, 2m temperature and moisture over CONUS on the 5-km NDFD grid Enhanced GSI flexibility to add/remove 2D and 3D control variables Wind gust was added as a new control variable A univariate analysis for wind gust

Main issues Compatibility among different data sources - Type 180 SFCSHP -- Surface marine - Type 181 ADPSFC -- Surface land (Synoptic, METAR) - Type 187 ADPSFC -- Surface land (METAR) - Type 188 MSONET – Surface mesonet Applicability of Mesonet use list and reject list Time period: Sept. 13 ~ 22, 2008

2D-pattern: Bias of O-F

Break-down of mesonet gust data after applying mesonet use & reject lists (Only a total of 43.4% of remained)

Gust data handling For the observations which were at the same location, the one that was closest to the analysis time was chosen Observation error was inflated based on the relative time to the analysis time. Less weight was given to the observations that were far away from the analysis time mesonet use list and reject list were applied Less weight was given to mesonet data that were less than 7.2m/s The discrepancy of observation station elevation and model surface was taken into account Gust background correlation length was chosen to be comparable to that of 10m wind

Bias and RMS of QC-ed O-F|O-A T180T181T187T188 Bias | | | | RMS 2.258| | | |2.640

Case study A high wind event on Dec. 31, 2008 Very strong wind across mid-Atlantic as low deepened off east coast 19m/s + gusts at DCA, IAD, BWI Tree and power line damage

1500Z gust observations

1500Z Guess and Analysis Guess over- forecasted in most of the area Analysis improved gust field, small scale features were evident

1800Z guess and analysis

Conclusions Wind gust speed was added as a new control variable Gust data from various data sources were examined and assimilated for an arbitrary time period Surface marine gust data showed reasonable good O-F bias METAR and strong mesonet gust data exhibited a pattern with negative bias over eastern regions except Florida and positive bias over western regions, while weak mesonet gust data had a significant negative bias over the CONUS The results implied the incompatibility between METAR gust data and weak mesonet gust data. The application of mesonet use and reject lists to mesonet gust data removed stations with very large O-F departures The use of gust data led to significant improvement of the gust field with detailed small scale features.

Future work Conduct routine gust analysis Further refine parameters used to construct gust background error covariance Gust data quality control - utilize wind data information - variational quality control Bias correction of gust background