Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.

Slides:



Advertisements
Similar presentations
VDRAS and 0-6 Hour NWP - Recent activities
Advertisements

Chapter 13 – Weather Analysis and Forecasting
Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y.
Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting Juanzhen Sun NCAR, Boulder, Colorado Oct 25, 2011.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
1 October 26, th COPS Workshop, Cambridge, UK COPS (Convective and Orographically-induced Precipitation Study) Goal: Advance the quality of forecasts.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,
Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Institut für Physik der Atmosphäre On the Value of.
GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.
Daily runs and real time assimilation during the COPS campaign with AROME Pierre Brousseau, Y. Seity, G. Hello, S. Malardel, C. Fisher, L. Berre, T. Montemerle,
Initializing a Hurricane Vortex with an EnKF Yongsheng Chen Chris Snyder MMM / NCAR.
An Efficient Ensemble Data Assimilation Approach and Tests with Doppler Radar Data Jidong Gao Ming Xue Center for Analysis and Prediction of Storms, University.
January 24, 2005 The LAPS “hot start” Initializing mesoscale forecast models with active cloud and precipitation processes Paul Schultz NOAA Forecast Systems.
ASSIMILATION of RADAR DATA at CONVECTIVE SCALES with the EnKF: PERFECT-MODEL EXPERIMENTS USING WRF / DART Altuğ Aksoy National Center for Atmospheric Research.
Weather Research & Forecasting Model (WRF) Stacey Pensgen ESC 452 – Spring ’06.
Impact of the 4D-Var Assimilation of Airborne Doppler Radar Data on Numerical Simulations of the Genesis of Typhoon Nuri (2008) Zhan Li and Zhaoxia Pu.
Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM Xiwu Zhan 1, Paul Houser 2, Sujay Kumar 1 Kristi Arsenault 1, Brian Cosgrove 3 1 UMBC-GEST/NASA-GSFC;
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.
ASSIMILATION OF GOES-DERIVED CLOUD PRODUCTS IN MM5.
Warn on Forecast Briefing September 2014 Warn on Forecast Brief for NCEP planning NSSL and GSD September 2014.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
COSMO General Meeting, Offenbach, 7 – 11 Sept Dependance of bias on initial time of forecasts 1 WG1 Overview
IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and.
5/31/2016 Y.-R.Guo 1, X. X. Ma 1, H.-C. Lin 2, C.-T. Terng 2, Y.-H. Kuo 1 1 National Center for Atmospheric Research (NCAR) 2 Central Weather Bureau, 64.
Assimilating Reflectivity Observations of Convective Storms into Convection-Permitting NWP Models David Dowell 1, Chris Snyder 2, Bill Skamarock 2 1 Cooperative.
MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.
Potential Benefits of Multiple-Doppler Radar Data to Quantitative Precipitation Forecasting: Assimilation of Simulated Data Using WRF-3DVAR System Soichiro.
WSN05 6 Sep 2005 Toulouse, France Efficient Assimilation of Radar Data at High Resolution for Short-Range Numerical Weather Prediction Keith Brewster,
Challenges and practical applications of data assimilation in numerical weather prediction Data Assimilation Education Forum Part I: Overview of Data Assimilation.
Data assimilation, short-term forecast, and forecasting error
The Impact of FORMOSAT-3/COSMIC GPS RO Data on Typhoon Prediction
Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.
DATA ASSIMILATION FOR HURRICANE PREDICTION Experimental system and results of semi-operational implementation during the 2010 Atlantic Hurricane Season.
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.
© Crown copyright Met Office Data Assimilation Developments at the Met Office Recent operational changes, and plans Andrew Lorenc, DAOS, Montreal, August.
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
Oct WRF 4D-Var System Xiang-Yu Huang, Xin Zhang Qingnong Xiao, Zaizhong Ma, John Michalakes, Tom henderson and Wei Huang MMM Division National.
5 th ICMCSDong-Kyou Lee Seoul National University Dong-Kyou Lee, Hyun-Ha Lee, Jo-Han Lee, Joo-Wan Kim Radar Data Assimilation in the Simulation of Mesoscale.
1985 Hurricane Elenna taken from the Space Shuttle Hurricane/Typhoon Data Assimilation using Space-Time Multi-scale Analysis System (STMAS) Koch S., Y.
Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen 6th EnKF Workshop York University.
Data Assimilation for High Impact Weather Forecast Yuanfu Xie NOAA/OAR/ESRL OAR/ESRL/GSD/Forecast Applications Branch.
Implementation and Testing of 3DEnVAR and 4DEnVAR Algorithms within the ARPS Data Assimilation Framework Chengsi Liu, Ming Xue, and Rong Kong Center for.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
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.
Radar Data Assimilation for Explicit Forecasting of Storms Juanzhen Sun National Center for Atmospheric Research.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
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.
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.
Low-level Wind Analysis and Prediction During B08FDP 2006 Juanzhen Sun and Mingxuan Chen Other contributors: Jim Wilson Rita Roberts Sue Dettling Yingchun.
Progress in Radar Assimilation at MeteoSwiss Daniel Leuenberger 1, Marco Stoll 2 and Andrea Rossa 3 1 MeteoSwiss 2 Geographisches Institut, University.
CWB Midterm Review 2011 Forecast Applications Branch NOAA ESRL/GSD.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
RUC Convective Probability Forecasts using Ensembles and Hourly Assimilation Steve Weygandt Stan Benjamin Forecast Systems Laboratory NOAA.
Implementation of Terrain Resolving Capability for The Variational Doppler Radar Analysis System (VDRAS) Tai, Sheng-Lun 1, Yu-Chieng Liou 1,3, Juanzhen.
CARPE DIEM 4 th meeting Critical Assessment of available Radar Precipitation Estimation techniques and Development of Innovative approaches for Environmental.
© Crown copyright Met Office Mismatching Perturbations at the Lateral Boundaries in Limited-Area Ensemble Forecasting Jean-François Caron … or why limited-area.
Hybrid Data Assimilation
Xuexing Qiu and Fuqing Dec. 2014
Tadashi Fujita (NPD JMA)
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.
Arpae Hydro-Meteo-Climate Service, Bologna, Italy
Yuanfu Xie, Steve Albers, Hongli Jiang Paul Schultz and ZoltanToth
Radar Data Assimilation
Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting JuanzhenSun NCAR, Boulder, Colorado Oct 25, 2011.
Daniel Leuenberger1, Christian Keil2 and George Craig2
Winter storm forecast at 1-12 h range
CAPS Real-time Storm-Scale EnKF Data Assimilation and Forecasts for the NOAA Hazardous Weather Testbed Spring Forecasting Experiments: Towards the Goal.
Local Analysis and Prediction System (LAPS)
FSOI adapted for used with 4D-EnVar
Presentation transcript:

Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks

Convective-scale DA: a brief look back 1990: Lilly’s motivational publication - NWP of thunderstorm – has its time come? : studies on single Doppler retrievals early 2000’s: studies on assimilation of radar observations into NWP models Late 2000’s - now: preliminary use of radar data in operational NWP systems

Radar data assimilation methods Warm-start algorithms using information derived from reflectivity - Cloud analysis - Latent heat nudging - DDFI – diabatic digital filter initialization - Assimilate estimated in-cloud humidity or vertical velocity Variational data assimilation Assimilating both radial velocity and reflectivity - 3D-Var (operational or semi-operational) - 4D-Var (semi-operational) Ensemble Kalman Filter (EnKF) Assimilating both radial velocity and reflectivity - Relatively new to radar data assimilation - Mostly used for research

High-resolution (<4km) RU (<3H) systems (as of 2012) SystemOrganizationRU cycleDA methodModel HRRRNOAA/GSDhourlyDDFIARW-WRF BJRUCNCAR/BMB3-hourly3DVarARW-WRF RTFDDANCARhourlyNewtonian nudging ARW-WRF LAPSNOAA/GSDhourlySuccessive correction ARW,MM5,or RAMS ADASCAPSN/A3DVarARPS AROME- France Meteo France3-hourly3DVarAROME COSMO-DEDWD3-hourlyNewtonian nudging COSMO UKVMet Office3-hourly3DVarUnified Model

What are the critical observations? Data impact study by Meteo France (Brousseau et al. 2014) Below 200km wave length, the reduction of error is only provided by radar, ground-based GPS, and aircraft Below 200km wave length, the reduction of error is only 10% - where most improvement is needed for heavy precipitation forecast

NCAR’s efforts on convective-scale DA: Overview 1992 – 2002: pioneer research and development work on radar DA using the 4DVar technique and a cloud- scale model - Sun et al. (1992), Sun and Crook (1994, 1997, 1998) 2003 – 2014: development of radar DA for WRF 3DVar - Xiao et al. ( 2005, 2007, 2008), Xiao and Sun (2008), Wang et al. (2012) 2010 – 2014: development of radar DA for WRF 4DVar - Wang et al. (2013), Sun and Wang (2013)

NCAR’s efforts on convective-scale DA : Current capability WRFDA 3DVar - 1h update cycles assimilating radial velocity and reflectivity in addition to all conventional observations - Indirect assimilation of reflectivity including an scheme for humidity adjustment - Options for choice of momentum control variables - Operational capability WRFDA 4DVar - One of a few for convective-scale - Use WRF tangent linear model as constraint - Can be run with multi-incremental option to save computation cost - Adjoint of Kessler warm rain microphysics and a simple cumulus scheme - Active research is being conducted to improve efficiency

Average score of 4 convective cases during summer mm hourly precipitation An example with WRF 3D-Var 23 July 2009 t = 3ht = 4h OBS No Radar Radar NCAR/BMB WRF 3DVar Pre-operational testing Assimilate both radial wind and reflectivity OPER (no radar) With radar

Recent improvement of WRF 3DVar OBS Old New t=1h t=6h New Old

Hourly Precipitation forecasts Obs WRF 3DVarWRF 4DVar 4DVar 3DVar w/o DI 3DVar with DI 6h fcst of 1h accum. rainfall 5mm Radius of Influence = 24km Comparison of WRF 3DVar with 4DVar  Diabatic initialization significantly improves QPF  Compared with 4DVar, 3DVar produces a slower squall line and the precipitation amount is ove-predicted with DI FSS

Hourly Precipitation forecasts 4DVar radar DA for Typhoon Fanapi Hourly precipitation at t = 6h OBS GTS3DVarRDR4DVar Surface analysis 4DVar successfully predicts the two low pressure centers with similar magnitudes as in the surface analysis

Hourly Precipitation forecasts If NCAR collaborate with CWB for the next fiscal 5 years, what will be a feasible operational goal?  An fully operational hybrid-3DVar convective-scale data assimilation system with hourly update cycles assimilating operational radar reflectivity, radial velocity, surface precipitation, and ground-based GPS  A semi-operational multi-incremental 4DVar system assimilating the same types of observations as 3DVar

Hourly Precipitation forecasts Year 1 Pre-operational testing of a radar QC system capable of detecting typhoon centers and performing accurate velocity dealiasing Pre-operational testing of a radar QC system capable of detecting typhoon centers and performing accurate velocity dealiasing Operation R & D Add a typhoon QC scheme to existing radar QC package 3DVar: combine DA of radar, surface, and ground GPS 4DVar: test the performance of physics Add a typhoon QC scheme to existing radar QC package 3DVar: combine DA of radar, surface, and ground GPS 4DVar: test the performance of physics

Hourly Precipitation forecasts Year 2 Pre-operational testing of 3DVar with 3-hourly cycles and radar data assimilation Pre-operational testing of 3DVar with 3-hourly cycles and radar data assimilation Operation R & D 3DVar: multiple case testing for operational configuration 4DVar: design optimal multiple-incremental scheme for convective-scale DA over Taiwan 3DVar: multiple case testing for operational configuration 4DVar: design optimal multiple-incremental scheme for convective-scale DA over Taiwan

Hourly Precipitation forecasts Year 3 Operational implementation of 3DVar with 3-hourly cycles and radar data assimilation Operational implementation of 3DVar with 3-hourly cycles and radar data assimilation Operation R & D 3DVar: testing of convective- scale hybrid-3DVar with hourly update cycles 4DVar: improve efficiency by coding improvement and model simplification 3DVar: testing of convective- scale hybrid-3DVar with hourly update cycles 4DVar: improve efficiency by coding improvement and model simplification

Hourly Precipitation forecasts Year 4 Pre-operational testing of hybrid 3DVar with hourly cycles and radar data assimilation Pre-operational testing of hybrid 3DVar with hourly cycles and radar data assimilation Operation R & D 3DVar: Pre-operation evaluation of the hybrid-3DVar 4DVar: comparison with the hybrid-3DVar over multiple cases 3DVar: Pre-operation evaluation of the hybrid-3DVar 4DVar: comparison with the hybrid-3DVar over multiple cases

Hourly Precipitation forecasts Year 5 Operational implementation of hybrid 3DVar with hourly cycles and radar data assimilation Semi-operational testing of 4DVar and comparison with the operational 3DVar Operational implementation of hybrid 3DVar with hourly cycles and radar data assimilation Semi-operational testing of 4DVar and comparison with the operational 3DVar Operation R & D 3DVar: evaluation of the performance of the operational hybrid-3DVar 4DVar: testing of the hybrid-4DVar 3DVar: evaluation of the performance of the operational hybrid-3DVar 4DVar: testing of the hybrid-4DVar

Hourly Precipitation forecasts Final Remarks  Improving convective-scale DA and forecast has a high society and economy impact and hence a priority for weather service centers worldwide  It is challenging and therefore a long term effort  Applying NCAR’s R&D experience in the convective-scale DA to CWB’s forecast operation will not only benefit CWB but also the overall advancement of high impact weather prediction  One of the most important aspects of the collaboration is to train the young meteorologists who will carry on the weather research and forecast to next generation