WRF Four-Dimensional Data Assimilation (FDDA) Jimy Dudhia.

Slides:



Advertisements
Similar presentations
UU Unitah Basin WRF Model Configuration (same as DEQ) See Alcott and Steenburgh 2013 for further details on most aspects of this numerical configuration:
Advertisements

An intraseasonal moisture nudging experiment in a tropical channel version of the WRF model: The model biases and the moisture nudging scale dependencies.
Günther Zängl, DWD1 Improvements for idealized simulations with the COSMO model Günther Zängl Deutscher Wetterdienst, Offenbach, Germany.
The Use of High Resolution Mesoscale Model Fields with the CALPUFF Dispersion Modelling System in Prince George BC Bryan McEwen Master’s project
Experiments with Assimilation of Fine Aerosols using GSI and EnKF with WRF-Chem (on the need of assimilating satellite observations) Mariusz Pagowski Georg.
GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.
Nesting. Eta Model Hybrid and Eta Coordinates ground MSL ground Pressure domain Sigma domain  = 0  = 1  = 1 Ptop  = 0.
The Effects of Grid Nudging on Polar WRF Forecasts in Antarctica Daniel F. Steinhoff 1 and David H. Bromwich 1 1 Polar Meteorology Group, Byrd Polar Research.
This is the footer WRF Basics Weather Research and Forecasting.
1 NGGPS Dynamic Core Requirements Workshop NCEP Future Global Model Requirements and Discussion Mark Iredell, Global Modeling and EMC August 4, 2014.
This is the footer Running WRF on HECToR Ralph Burton, NCAS (Leeds) Alan Gadian, NCAS (Leeds) With thanks to Paul Connolly, Hector.
Lili Lei1,2, David R. Stauffer1 and Aijun Deng1
Henry Fuelberg Nick Heath Sean Freeman FSU WRF-Chem During SEAC 4 RS.
Tanya L. Otte and Robert C. Gilliam NOAA Air Resources Laboratory, Research Triangle Park, NC (In partnership with U.S. EPA National Exposure Research.
Introduction to the WRF Modeling System Wei Wang NCAR/MMM.
Introduction to Running the WRF in LES Mode Jeff Massey April 1, 2014 University of Utah WRF Users Group.
ASSIMILATION OF GOES-DERIVED CLOUD PRODUCTS IN MM5.
WRF SLP Bias Issues Update from Santa Cruz meeting Matt Higgins John Cassano May 18,
The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.
The National Environmental Agency of Georgia L. Megrelidze, N. Kutaladze, Kh. Kokosadze NWP Local Area Models’ Failure in Simulation of Eastern Invasion.
Development of WRF-CMAQ Interface Processor (WCIP)
Mesoscale Modeling Review the tutorial at: –In class.
November 1, 2013 Bart Brashers, ENVIRON Jared Heath Bowden, UNC 3SAQS WRF Modeling Recommendations.
+ Best Practices in Regional Climate Modeling Dr. Michel d. S. Mesquita Bjerknes Centre for Climate Research, Uni Research
Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.
Russ Bullock 11 th Annual CMAS Conference October 17, 2012 Development of Methodology to Downscale Global Climate Fields to 12km Resolution.
Oceanic and Atmospheric Modeling of the Big Bend Region Steven L. Morey, Dmitry S. Dukhovksoy, Donald Van Dyke, and Eric P. Chassignet Center for Ocean.
Page1 PAGE 1 The influence of MM5 nudging schemes on CMAQ simulations of benzo(a)pyrene concentrations and depositions in Europe Volker Matthias, GKSS.
Comparison of Different Approaches NCAR Earth System Laboratory National Center for Atmospheric Research NCAR is Sponsored by NSF and this work is partially.
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.
Jonathan Pleim 1, Robert Gilliam 1, and Aijun Xiu 2 1 Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC (In partnership with the.
RAMS Input Data. Types of input data Meteorological data  First guess gridded data – NCEP, ECMWF, etc.  Surface observations (single level measurements.
Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss.
10/28/2014 Xiangshang Li, Yunsoo Choi, Beata Czader Earth and Atmospheric Sciences University of Houston The impact of the observational meteorological.
Higher Resolution Operational Models. Operational Mesoscale Model History Early: LFM, NGM (history) Eta (mainly history) MM5: Still used by some, but.
CCAM Regional climate modelling Dr Marcus Thatcher Research Scientist December 2007.
Accounting for Uncertainties in NWPs using the Ensemble Approach for Inputs to ATD Models Dave Stauffer The Pennsylvania State University Office of the.
Dispersion conditions in complex terrain - a case study of the January 2010 air pollution episode in Norway Viel Ødegaard Norwegian Meteorological.
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
For more information about this poster please contact Gerard Devine, School of Earth and Environment, Environment, University of Leeds, Leeds, LS2 9JT.
Lateral boundary conditions and forecast errors in the mid-latitudes – first results Luka Honzak, Nedjeljka Žagar, Gregor Skok CE Space-SI and University.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
AOS 100: Weather and Climate Instructor: Nick Bassill Class TA: Courtney Obergfell.
Photo image area measures 2” H x 6.93” W and can be masked by a collage strip of one, two or three images. The photo image area is located 3.19” from left.
Seasonal Modeling of the Export of Pollutants from North America using the Multiscale Air Quality Simulation Platform (MAQSIP) Adel Hanna, 1 Rohit Mathur,
Introduction to the WRF Modeling System
An Examination Of Interesting Properties Regarding A Physics Ensemble 2012 WRF Users’ Workshop Nick P. Bassill June 28 th, 2012.
Oct. 28 th th SRNWP, Bad Orb H.-S. Bauer, V. Wulfmeyer and F. Vandenberghe Comparison of different data assimilation techniques for a convective.
Page 1© Crown copyright 2005 DEVELOPMENT OF 1- 4KM RESOLUTION DATA ASSIMILATION FOR NOWCASTING AT THE MET OFFICE Sue Ballard, September 2005 Z. Li, M.
Mattias Mohr, Johan Arnqvist, Hans Bergström Uppsala University (Sweden) Simulating wind and turbulence profiles in and above a forest canopy using the.
Test Cases for the WRF Mass Coordinate Model 2D flow over a bell-shaped mountain WRFV1/test/em_hill2d_x 2D squall line (x, z ; y, z) WRFV1/test/em_squall2d_x.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Status of the COSMO-1 configuration at MeteoSwiss Guy.
Numerical Weather Prediction (NWP): The basics Mathematical computer models that predict the weather Contain the 7 fundamental equations of meteorology.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
Northwest Modeling Consortium
Numerical Weather Forecast Model (governing equations)
WRF Four-Dimensional Data Assimilation (FDDA)
Rapid Update Cycle-RUC
Meteorological Site Representativeness and AERSURFACE Issues
“Storm of the Century”: grid nudging and stochastic perturbatons
  Robert Gibson1, Douglas Drob2 and David Norris1 1BBN Technologies
University of Washington Ensemble Systems for Probabilistic Analysis and Forecasting Cliff Mass, Atmospheric Sciences University of Washington.
How do models work? METR 2021: Spring 2009 Lab 10.
Dynamical downscaling of ERA-40 with WRF in complex terrain in Norway – comparison with ENSEMBLES U. Heikkilä, A. D. Sandvik and A.
Winter storm forecast at 1-12 h range
Cliff Mass University of Washington
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
Lidia Cucurull, NCEP/JCSDA
Lightning Assimilation Techniques
REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5 METEOROLOGICAL FIELDS FOR VARIOUS AIR QUALITY MODELING APPLICATIONS Pat Dolwick*, U.S. EPA, RTP, NC, USA.
Presentation transcript:

WRF Four-Dimensional Data Assimilation (FDDA) Jimy Dudhia

FDDA Method of nudging model towards observations or analysis May be used for Dynamical initialization (pre-forecast period) Creating 4D meteorological datasets (e.g. for air quality model) Boundary conditions (outer domain nudged towards analysis) ARW only

Method Model is run with extra nudging terms for horizontal winds, temperature and water vapor In analysis nudging, these terms nudge point- by-point to a 3d space- and time-interpolated analysis field In obs-nudging, points near observations are nudged based on model error at obs site The nudging is a relaxation term with a user- defined time scale around an hour or more Nudging will work with nesting and restarts

Dynamic Initialization Model domains are nudged towards analysis in a pre- forecast period of 6-12 hours This has benefit of smooth start up at forecast time zero -6h 0h Forecast period Nudging period

Four-Dimensional Met Analysis Produces analyses between normal analysis times High-resolution balanced and mass-continuity winds can be output to drive off-line air quality models 0h Simulation period Analysis

Boundary Conditions Nudge an outer domain towards analysis through forecast This has benefit of providing smoother boundary conditions to domain of interest than if 15 km domain is the outer domain with interpolated-analysis boundary conditions Nudge 45 km domain only

FDDA Methods Two Methods Grid or analysis nudging (suitable for coarse resolution) Observation or station nudging (suitable for fine- scale or asynoptic obs) Nudging can be applied to winds, temperature, and water vapor Note: nudging terms are fake sources, so avoid FDDA use in dynamics or budget studies

Analysis Nudging (grid_fdda=1) Each grid-point is nudged towards a value that is time-interpolated from analyses In WRF p* is mu

Analysis Nudging G is nudging inverse time scale W is vertical weight (upper air and surface) e is a horizontal weight for obs density (not implemented yet)

Analysis Nudging 3d analysis nudging uses the WRF input fields at multiple times that are put in wrffdda_d01 file by program real when run with grid_fdda=1 With low time-resolution analyses, it is recommended not to use 3d grid-nudging in the boundary layer, especially for temperature Surface (2d) analysis nudging available in Version 3.1 Nudges surface and boundary layer only

Analysis-Nudging namelist options Can choose Frequency of nudging calculations (fgdt in minutes) Nudging time scale for each variable (guv, gt, gq in inverse seconds) Which variables not to nudge in the PBL (if_no_pbl_nudging_uv, etc.) Model level for each variable below which nudging is turned off (if_zfac_uv, k_zfac_uv, etc.) Ramping period over which nudging is turned off gradually (if_ramping, dt_ramp_min)

Surface Analysis Nudging In Version 3.1 added 2d (surface) nudging (grid_fdda=1 and grid_sfdda=1) for surface analyses wrfsfdda_d01 file created by obsgrid.exe Weights given by guv_sfc, gt_sfc, and gq_sfc Note: grid_fdda=1 must be used to activate this. If upper-air nudging not wanted, set upper weights guv, gt, gq =0.

Spectral Nudging In Version 3.1 added spectral nudging (grid_fdda=2) to do 3d nudging of only selected larger scales This may be useful for controlling longer wave phases for long simulations. Compensates for error due to low-frequency lateral boundaries. Top wavenumber nudged is selected in namelist (xwavenum, ywavenum, e.g. =3)  Typically choose so that (domain size)/(wavenumber)=~1000 km in each direction Nudges u, v, theta, geopotential (not q) Can nudge in all levels or use ramp above a specified model level (if_zfac_ph, k_zfac_ph, dk_zfac_ph, etc.)

Obs Nudging (obs_nudge_opt=1) Each grid point is nudged using a weighted average of differences from observations within a radius of influence and time window

Obs Nudging Grid point observation

Obs Nudging

R is radius of influence D is distance from ob modified by elevation difference

Obs Nudging t is the specified time window for the obs This is a function that ramps up and down

Obs Nudging  w s is the vertical weighting – usually the vertical influence is set small (0.005 sigma) so that data is only assimilated on its own sigma level  obs input file is a special ascii file ( OBS_DOMAIN101 ) with obs sorted in chronological order  each record is the obs (u, v, T, Q) at a given model position and time  Utility programs exist to convert data to this format from other common formats  In V3.1 obsgrid.exe can create this file from standard observations that are in little_r format

Obs-Nudging namelist options Can choose Frequency of nudging calculations (iobs_ionf) Nudging time scale for each variable (obs_coef_wind, etc.) Horizontal and vertical radius of influence (obs_rinxy, obs_rinsig) Time window (obs_twindo) Ramping period over which nudging is turned off gradually (obs_idynin, obs_dtramp)

Vertical weighting functions Added flexibility options for advanced usage of obs-nudging with surface observations (switches in run/README.namelist, e.g. obsnudgezfullr1_uv, etc.) These allow specifying how variables are nudged in a profile with their full weight and/or ramp down function relative to the surface or PBL top in different regimes (stable or unstable). Defaults are set to reasonable values, so these can be left out of namelist unless needed.

FDDA Summary FDDA grid nudging is suitable for coarser grid sizes where analysis can be better than model-produced fields Obs nudging can be used to assimilate asynoptic or high-frequency observations Grid and obs nudging can be combined FDDA has fake sources and sinks and so should not be used on the domain of interest and in the time period of interest for scientific studies and simulations

End