GFS and Global Models.

Slides:



Advertisements
Similar presentations
Rossana Dragani Using and evaluating PROMOTE services at ECMWF PROMOTE User Meeting Nice, 16 March 2009.
Advertisements

Slide 1ECMWF forecast User Meeting -- Reading, June 2006 Verification of weather parameters Anna Ghelli, ECMWF.
NEMS/GFS Overview Mark Iredell, Software Team Lead.
“Where America’s Climate, Weather and Ocean Services Begin” NCEP CONDUIT UPDATE Brent A Gordon NCEP Central Operations January 31, 2006.
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.
Observing System Simulation Experiments to Evaluate the Potential Impact of Proposed Observing Systems on Hurricane Prediction: R. Atlas, T. Vukicevic,
Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.
2012: Hurricane Sandy 125 dead, 60+ billion dollars damage.
Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems Craig Schwartz and.
Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Global model and 64 levels Relatively primitive.
Slide 1 Bilateral meeting 2011Slide 1, ©ECMWF Status and plans for the ECMWF forecasting System.
Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.
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.
A Sequential Hybrid 4DVAR System Implemented Using a Multi-Grid Technique Yuanfu Xie 1, Steven E. Koch 1, and Steve Albers 1,2 1 NOAA Earth System Research.
Course Evaluation Closes June 8th.
Higher Resolution Operational Models. Major U.S. High-Resolution Mesoscale Models (all non-hydrostatic ) WRF-ARW (developed at NCAR) NMM-B (developed.
A JHT FUNDED PROJECT GFDL PERFORMANCE AND TRANSITION TO HWRF Morris Bender, Timothy Marchok (GFDL) Isaac Ginis, Biju Thomas (URI)
Suru Saha and Hua-Lu Pan, EMC/NCEP With Input from Stephen Lord, Mark Iredell, Shrinivas Moorthi, David Behringer, Ken Mitchell, Bob Kistler, Jack Woollen,
NCEP Models and Ensembles By Richard H. Grumm National Weather Service State College PA and Robert Hart The Pennsylvania State University.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
Lecture 5 Weather Maps and Models Chapters 5 and Chapter 6 Homework Due Friday, October 3, 2014 TYU Ch 6: 1,2,5,7,11,14,17,18,20; TYPSS Ch 6: 2 TYU Ch.
Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan.
CTB computer resources / CFSRR project Hua-Lu Pan.
Oct. 28 th th SRNWP, Bad Orb H.-S. Bauer, V. Wulfmeyer and F. Vandenberghe Comparison of different data assimilation techniques for a convective.
452 NWP Major Steps in the Forecast Process Data Collection Quality Control Data Assimilation Model Integration Post Processing of Model Forecasts.
MPO 674 Lecture 2 1/20/15. Timeline (continued from Class 1) 1960s: Lorenz papers: finite limit of predictability? 1966: First primitive equations model.
Vincent N. Sakwa RSMC, Nairobi
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.
Higher Resolution Operational Models
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.
452 NWP Major Steps in the Forecast Process Data Collection Quality Control Data Assimilation Model Integration Post Processing of Model Forecasts.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course Mar 2016.
452 NWP 2015.
SEASONAL PREDICTION OVER EAST ASIA FOR JUNE-JULY-AUGUST 2017
Numerical Weather Forecast Model (governing equations)
Predictability: How can we predict the climate decades into the future when we can’t even predict the weather for next week? Predictability of the first.
452 NWP 2017.
452 NWP 2016.
Rapid Update Cycle-RUC
Grid Point Models Surface Data.
452 NWP 2015.
Update on the Northwest Regional Modeling System 2013
Course Evaluation Now online You should have gotten an with link.
Overview of Deterministic Computer Models
Course Evaluation Now online You should have gotten an with link.
HWRF Initialization Overview
The art of weather forecasting
Better Forecasting Bureau
David Salstein, Edward Lorenz, Alan Robock, and John Roads
Aviation Forecast Guidance from the RUC
Course Evaluation Now online You should have gotten an with link.
Rapid Update Cycle-RUC Rapid Refresh-RR High Resolution Rapid Refresh-HRRR RTMA.
Global Forecast System (GFS) Model
Lidia Cucurull, NCEP/JCSDA
Integration of NCAR DART-EnKF to NCAR-ATEC multi-model, multi-physics and mutli- perturbantion ensemble-RTFDDA (E-4DWX) forecasting system Linlin Pan 1,
AGREPS – ACCESS Global and Regional Ensemble Prediction System
Comparison of different combinations of ensemble-based and variational data assimilation approaches for deterministic NWP Mark Buehner Data Assimilation.
Tornado Outbreak Modeling
NWP Strategy of DWD after 2006 GF XY DWD Feb-19.
The Technology and Future of Weather Forecasting ATMS 490
Vertical Coordinates and Nesting
Global Forecast System (GFS) Model
Global Forecast System (GFS) Model
Cliff Mass and David Ovens University of Washington
Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer
A Comparison of In-Situ Data with Meso-Scale Forecasts
Global Forecast System (GFS) Model
452 NWP 2019.
AGREPS – ACCESS Global and Regional EPS
Global Forecast System (GFS) Model
Presentation transcript:

GFS and Global Models

Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Spectral global model and 64 levels Relatively primitive microphysics. Sophisticated surface physics and radiation Run four times a day to 384 hr (16 days!). Major increase in skill during past decades derived from using direct satellite radiance in the 3DVAR analysis scheme and other satellite assets. T574 (~27 km) over the first 192 hours (8 days) of the model forecast and T190 (70 km) for 180 through 384 hours--major implications for resolution change!

GFS Vertical coordinates are hybrid sigma/pressure… sigma at low levels to pressure aloft.

Vertical coordinate comparison across North America

GFS Data Assimilation (GDAS) Has a later data cut-off time than the mesoscale models…and thus can get a higher percentage of data. Uses much more satellite assets..thus improve global analysis and forecasts. Major gains in southern hemisphere Hybrid Data assimilation based on 3DVAR (they call it GSI) and GFE ensemble (next slide) Every 6 hr.

GFS Hybrid Data Assimilation

GFS is not the only global model and is not even the best

Next Generation Global Models Under Development! Will use different geometries

MPAS: Hexagonal Shapes

NOAA FIM Model