Slides:



Advertisements
Similar presentations
Assimilation of radar data - research plan
Advertisements

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
30 th September 2010 Bannister & Migliorini Slide 1 of 9 High-resolution assimilation and weather forecasting Ross Bannister and Stefano Migliorini (NCEO,
GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.
Hierarchical models STAT 518 Sp 08. Rainfall measurement Rain gauge (1 hr) High wind, low rain rate (evaporation) Spatially localized, temporally moderate.
Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter April 2006, EnKF Wildflower Meeting Greg Hakim & Ryan Torn University of Washington.
Update on the Regional Modeling System NASA Roses Meeting April 13, 2007.
Probabilistic Mesoscale Analyses & Forecasts Progress & Ideas Greg Hakim University of Washington Brian Ancell, Bonnie.
WWRP/THORPEX WORKSHOP on 4D-VAR and ENSEMBLE KALMAN FILTER INTER-COMPARISONS BUENOS AIRES - ARGENTINA, NOVEMBER 2008 Topics for Discussion in Session.
Advanced data assimilation methods- EKF and EnKF Hong Li and Eugenia Kalnay University of Maryland July 2006.
Experimenting with the LETKF in a dispersion model coupled with the Lorenz 96 model Author: Félix Carrasco, PhD Student at University of Buenos Aires,
Models for model error –Additive noise. What is Q(x 1, x 2, t 1, t 2 )? –Covariance inflation –Multiplicative noise? –Parameter uncertainty –“Structural”
Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.
Francesca Marcucci, Lucio Torrisi with the contribution of Valeria Montesarchio, ISMAR-CNR CNMCA, National Meteorological Center,Italy First experiments.
“1995 Sunrise Fire – Long Island” Using an Ensemble Kalman Filter to Explore Model Performance on Northeast U.S. Fire Weather Days Michael Erickson and.
Warn-on-Forecast Capabilities and Possible Contributions by CAPS By Ming Xue Center for Analysis and Prediction of Storms and School of Meteorology University.
A Radar Data Assimilation Experiment for COPS IOP 10 with the WRF 3DVAR System in a Rapid Update Cycle Configuration. Thomas Schwitalla Institute of Physics.
ESA DA Projects Progress Meeting 2University of Reading Advanced Data Assimilation Methods WP2.1 Perform (ensemble) experiments to quantify model errors.
Numerical Weather Prediction at DWD COSMO-EU Grid spacing: 7 km Layers: 40 Forecast range: 78 h at 00 and 12 UTC 48 h at 06 and 18 UTC 1 grid element:
Space and Time Multiscale Analysis System A sequential variational approach Yuanfu Xie, Steven Koch Steve Albers and Huiling Yuan Global Systems Division.
STEPS: An empirical treatment of forecast uncertainty Alan Seed BMRC Weather Forecasting Group.
Assimilation of HF Radar Data into Coastal Wave Models NERC-funded PhD work also supervised by Clive W Anderson (University of Sheffield) Judith Wolf (Proudman.
Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.
COSMO General Meeting, Offenbach, 7 – 11 Sept Dependance of bias on initial time of forecasts 1 WG1 Overview
2004 SIAM Annual Meeting Minisymposium on Data Assimilation and Predictability for Atmospheric and Oceanographic Modeling July 15, 2004, Portland, Oregon.
The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 GV for ECMWF's Data Assimilation Research Peter
Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.
WSN05 6 Sep 2005 Toulouse, France Efficient Assimilation of Radar Data at High Resolution for Short-Range Numerical Weather Prediction Keith Brewster,
Use of radar data in ALADIN Marián Jurašek Slovak Hydrometeorological Institute.
Prediction of Tropical Cyclones Chapter 9. Tropical weather data from traditional sources (surface and radiosonde) is scarce, so remote sensing via other.
Weather forecasting by computer Michael Revell NIWA
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
Deutscher Wetterdienst Vertical localization issues in LETKF Breogan Gomez, Andreas Rhodin, Hendrik Reich.
Development of ATOVS Data Assimilation for Regional Forecast System Eunjoo Lee NWPD, KMA.
Bioscience – Aarhus University Integrating monitoring data across different data types, locations and habitat types Christian Damgaard Bioscience Aarhus.
T. Bergot - Météo-France CNRM/GMME 1) Methodology 2) Results for Paris-CdG airport Improved site-specific numerical model of fog and low clouds -dedicated.
DRAFT – Page 1 – January 14, 2016 Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion 1, Kao-Shen Chung 1, Monique.
Challenges and Strategies for Combined Active/Passive Precipitation Retrievals S. Joseph Munchak 1, W. S. Olson 1,2, M. Grecu 1,3 1: NASA Goddard Space.
MIIDAPS Application to GSI for QC and Dynamic Emissivity in Passive Microwave Data Assimilation.
Cloudy GOES-Imager assimilation with hydrometeor control variable Byoung-Joo JUNG NCAR/MMM Student presentation at JCSDA Summer Colloquium on Satellite.
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.
Incrementing moisture fields with satellite observations
Michael Coniglio NSSL Stacey Hitchcock CSU Kent Knopfmeier CIMMS/NSSL 10/20/2015 IMPACT OF ASSIMILATING MPEX MOBILE UPSONDE OBSERVATIONS ON SHORT- TERM.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
Kalman Filter and Data Streaming Presented By :- Ankur Jain Department of Computer Science 7/21/03.
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
Example Radar Data Assimilation Direct analysis of radar radial velocity data using enhanced NCEP GSI-3DVAR Analysis of radar reflectivity via ARPS complex.
The Water Cycle - Kickoff by Kevin Trenberth -Wide Ranging Discussion -Vapor -Precip/Clouds -Surface Hydrology (Land and Ocean) -Observations and scales.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course Mar 2016.
STMAS/LAPS for Convective Weather Analysis and Short-range Forecast
Space and Time Mesoscale Analysis System — Theory and Application 2007
Xuexing Qiu and Fuqing Dec. 2014
Steve Albers, Kirk Holub, Yuanfu Xie (CIRA & NOAA/ESRL/GSD)
Summer 2014 Group Meeting August 14, 2014 Scott Sieron
Plans and Progress for Ensemble Data Assimilation, Modeling, and Predictability for Lake Effect Snow Steven J. Greybush Matthew Kumjian, Fuqing Zhang,
A few examples of heavy precipitation forecast Ming Xue Director
Update on the Northwest Regional Modeling System 2013
Center for Analysis and Prediction of Storms (CAPS) Briefing by Ming Xue, Director CAPS is one of the 1st NSF Science and Technology Centers established.
University of Washington Modeling Infrastructure Available for Olympex
Radar Data Assimilation
EG2234 Earth Observation Weather Forecasting.
Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting JuanzhenSun NCAR, Boulder, Colorado Oct 25, 2011.
Vertical localization issues in LETKF
Challenge: High resolution models need high resolution observations
CAPS Real-time Storm-Scale EnKF Data Assimilation and Forecasts for the NOAA Hazardous Weather Testbed Spring Forecasting Experiments: Towards the Goal.
New Approaches to Data Assimilation
WMO NWP Wokshop: Blending Breakout
Massimo FERRI 9th COSMO General Meeting
Evaluate the integral {image}
Presentation transcript:

3DVar EnKF

Radar reflectivity (composite), 2113 UTC 25 August www.rap.ucar.edu/weather/radar

Future directions for DA Maturing and continuing: use of remote sensors Still in research and development: adaptive, integrated forecast systems The frontier: small scales and short range Other physical systems

Use of remote sensing Not my expertise! Maturing: “great leap forward” for radiances Dealing with future observations High density: correlations, thinning, compression “Moist” variables, particularly cloud and precip Obs not yet used effectively (imagers) Network design: What obs do we need?

Adaptive, integrated forecast systems Unite probabilistic forecasting and DA Adaptive: system can change based on need E.g., change obs network to improve specified forecast E.g., select “most important” obs to assimilate Self-regulating: system diagnoses errors (at least) and corrects E.g., evaluation of model-error hypotheses E.g., parameter estimation

Small scales & short range Models will soon resolve convection Usage of high-density observations (space and ground-based sensors) Fundamentally different from large scales Different dynamics Models untested Predictability different