ANALYSIS & FORECAST DATA FROM NCEP. Zoltan Toth NCEP. Acknowledgements: Malaquias Pena, Yuejian Zhu, Glenn Rutledge, John Ward
|
|
|
- Phoebe Pierce
- 6 years ago
- Views:
Transcription
1 ANALYSIS & FORECAST DATA FROM NCEP Zoltan Toth NCEP Acknowledgements: Malaquias Pena, Yuejian Zhu, Glenn Rutledge, John Ward YOTC Workshop, July 13-15, 2009, Honolulu 1
2 OUTLINE Real time analysis / forecast products Archived high resolution Analysis Forecast Generation & archival of high resolution tendencies
3 REAL TIME DATA ACCESS Global Forecast System (GFS) T382L64 out to 7.5 days, T190L64 out to 16 days 4 times per day 0.5x0.5 degree lat/lon grid on pressure levels Data access: Global Ensemble Forecast System (GEFS) T126L28 out to 16 days (T190L28 from Oct 2009) 20 members 4 times per day 1x1 degree grid Info, images, etc: Data access: North American Ensemble Forecast System (NAEFS) GEFS plus Canadian ensemble 1x1 degree grid 40 members twice a day
4 Real time data access * Keeps the last month of data online Spatial / temporal subsetting options Allows selecting particular fields
5 NCDC archives 0.5 degree resolution data of GFS (analysis and forecast) Grid 004 for halfdegree resolution Forecast lead time resolution: 3hrs Files are 48Mb per lead time.
6 File content 3D Variables HGT, TMP, RH, VVEL UGRD, VGRD, ABSV, CLWMR Levels (in mb) 10,20,30,50,70,100,150,200,250,300,350,400, 450,500,550,600,650,700,750,800,850,900, 925, 950, 975, D Variables PRES:surface:anl: HGT:surface:anl: TMP:surface:anl: TMP:0-0.1 m below ground:anl: SOILW:0-0.1 m below ground:anl: TMP: m below ground:anl: SOILW: m below ground:anl: TMP:0.4-1 m below ground:anl: SOILW:0.4-1 m below ground:anl: TMP:1-2 m below ground:anl: SOILW:1-2 m below ground:anl: WEASD:surface:anl: TMP:2 m above ground:anl: SPFH:2 m above ground:anl: RH:2 m above ground:anl: UGRD:10 m above ground:anl: VGRD:10 m above ground:anl: WILT:surface:anl: LFTX:surface:anl: CAPE:surface:anl: CIN:surface:anl: PWAT:entire atmosphere (considered as a single layer):anl: CWAT:entire atmosphere (considered as a single layer):anl: RH:entire atmosphere (considered as a single layer):anl: TOZNE:entire atmosphere (considered as a single layer):anl: TCDC:convective cloud layer:anl: PRES:tropopause:anl: HGT:tropopause:anl: TMP:tropopause:anl: UGRD:tropopause:anl: VGRD:tropopause:anl: VWSH:tropopause:anl: PRES:max wind:anl: HGT:max wind:anl: UGRD:max wind:anl: VGRD:max wind:anl: TMP:max wind:anl: TMP:1829 m above mean sea level:anl: UGRD:1829 m above mean sea level:anl: VGRD:1829 m above mean sea level:anl: TMP:2743 m above mean sea level:anl:
7 2D Fields (Continuation) UGRD:2743 m above mean sea level:anl: VGRD:2743 m above mean sea level:anl: TMP:3658 m above mean sea level:anl: UGRD:3658 m above mean sea level:anl: VGRD:3658 m above mean sea level:anl: HGT:0C isotherm:anl: RH:0C isotherm:anl: HGT:highest tropospheric freezing level:anl: RH:highest tropospheric freezing level:anl: TMP:30-0 mb above ground:anl: 2 RH:30-0 mb above ground:anl: SPFH:30-0 mb above ground:anl: UGRD:30-0 mb above ground:anl: VGRD:30-0 mb above ground:anl: 4LFTX:surface:anl: CAPE:180-0 mb above ground:anl: CIN:180-0 mb above ground:anl: HPBL:surface:anl: RH: sigma layer:anl: RH: sigma layer:anl: RH: sigma layer:anl: RH: sigma layer:anl: TMP:0.995 sigma level:anl: POT:0.995 sigma level:anl: RH:0.995 sigma level:anl: UGRD:0.995 sigma level:anl: VGRD:0.995 sigma level:anl: VVEL:0.995 sigma level:anl: LAND:surface:anl: ICEC:surface:anl: UGRD:PV=2e-06 (Km^2/kg/s) surface:anl: VGRD:PV=2e-06 (Km^2/kg/s) surface:anl: PV=2e-06 (Km^2/kg/s) surface:anl: HGT:PV=2e-06 (Km^2/kg/s) surface:anl: PV=2e-06 (Km^2/kg/s) surface:anl: VWSH:PV=2e-06 (Km^2/kg/s) surface:anl: UGRD:PV=-2e-06 (Km^2/kg/s) surface:anl: VGRD:PV=-2e-06 (Km^2/kg/s) surface:anl: TMP:PV=-2e-06 (Km^2/kg/s) surface:anl: HGT:PV=-2e-06 (Km^2/kg/s) surface:anl: PRES:PV=-2e-06 (Km^2/kg/s) surface:anl: VWSH:PV=-2e-06 (Km^2/kg/s) surface:anl: PRMSL:mean sea level:anl: 5WAVH:500 mb:anl: GPA:1000 mb:anl: GPA:500 mb:anl: 5WAVA:500 mb:anl:
8 GFS time tendency and diagnostic variables Variables will be retrospectively generated for YOTC period May 08 Oct 09? The timing of data generation will be subject to arrangements with NCDC regarding disk space to archive the data Given that the dynamical time tendency variables are missing in the model, we need to decide on two options: Generate the variables that are currently in the model (see list of variables below) This could occur hopefully later this year Wait until the dynamical tendency variables are coded & implemented in the model Time tendency from shortwave Time tendency from longwave This may happen in 2010 Time tendency from vertical diffusion Time tendency from deep convection x Time tendency from shallow convection Time tendency from grid scale moistening rate from vertical diffusion moistening rate from deep convection moistening rate from shallow convection moistening rate from grid scale ozone vertical diffusion ozone production Variables currently in the model ozone tendency ozone production from temperature term zonal acceleration from vertical diffusion zonal acceleration from gravity wave drag zonal acceleration from convective momentum mixing meridional acceleration from vertical diffusion meridional acceleration from gravity wave drag meridional acceleration from convective momentum mixing Nonconvective cloud fraction Mass flux Upward and downward drafts Entrainment Variables that need to be coded U, V time tendencies
9 BACKGROUND
10 NCEP Data Overview: The NCEP analysis and forecasts products are provided from the global and global ensemble forecast systems. Real-time global forecast systems The GFS is a global spectral numerical model based on the primitive dynamical equations that includes a suite of parameterizations for atmospheric physics. The model has been under constant development and evaluation The current resolution is T382L64 out to 7 days and T190L64 for leads beyond 7 days out to 16 days. T382 is approximately 34 km in horizontal resolution. Products are issued 4 times daily. GEFS generates 20-member ensemble forecasts out to 16 days every 6hr. The forecast model is a low resolution version (T126L28) of the current GFS. Ensemble initial perturbations are obtained using the Ensemble Transform method. The system upgrades at least once a year to allow the latest developments on both modeling improvements and ensemble methods. A new version is planed to be implemented by September 2009 with higher horizontal resolution (T190L28) and stochastic perturbation. NAEFS combines NCEP and CMC (and eventually FNMOC) ensemble forecasts to produce 40-member ensemble forecasts out to 16 days every 6hrs
NCEP Global Ensemble Forecast System (GEFS) Yuejian Zhu Ensemble Team Leader Environmental Modeling Center NCEP/NWS/NOAA February
NCEP Global Ensemble Forecast System (GEFS) Yuejian Zhu Ensemble Team Leader Environmental Modeling Center NCEP/NWS/NOAA February 20 2014 Current Status (since Feb 2012) Model GFS V9.01 (Spectrum, Euler
Development of Ensemble Based Probabilistic Guidance
Development of Ensemble Based Probabilistic Guidance Bo Cui and Yuejian Zhu Presentation for Ensemble User Workshop May 10 th 2011 1 Statistical Post-Processing Issues GOAL Improve reliability while maintaining
Next Global Ensemble Forecast System
Next Global Ensemble Forecast System Yuejian Zhu, Dingchen Hou, Mozheng Wei, Richard Wobus, Jessie Ma, Bo Cui and Shrinivas Moorthi Acknowledgements: Jiayi Peng, Malaquias Pena, Yucheng Song, Yan Luo and
NAEFS Status and Future Plan
NAEFS Status and Future Plan Yuejian Zhu Ensemble team leader Environmental Modeling Center NCEP/NWS/NOAA Presentation for International S2S conference February 14 2014 NOAA Seamless Suite of Forecast
NCEP GEFS System and Ensemble Based Probabilistic Guidance
NCEP GEFS System and Ensemble Based Probabilistic Guidance Yuejian Zhu Ensemble Team Leader EMC/NCEP/NWS/NOAA May 31 2011 1 Evolution of NCEP GEFS configuration Initial uncertainty TS relocation Model
Ensemble Prediction Systems
Ensemble Prediction Systems Eric Blake National Hurricane Center 7 March 2017 Acknowledgements to Michael Brennan 1 Question 1 What are some current advantages of using single-model ensembles? A. Estimates
Weather Research and Forecasting Model. Melissa Goering Glen Sampson ATMO 595E November 18, 2004
Weather Research and Forecasting Model Melissa Goering Glen Sampson ATMO 595E November 18, 2004 Outline What does WRF model do? WRF Standard Initialization WRF Dynamics Conservation Equations Grid staggering
MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction
MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction Grid point and spectral models are based on the same set of primitive equations. However, each type formulates and solves the equations
11 days (00, 12 UTC) 132 hours (06, 18 UTC) One unperturbed control forecast and 26 perturbed ensemble members. --
APPENDIX 2.2.6. CHARACTERISTICS OF GLOBAL EPS 1. Ensemble system Ensemble (version) Global EPS (GEPS1701) Date of implementation 19 January 2017 2. EPS configuration Model (version) Global Spectral Model
Probabilistic Forecast Verification. Yuejian Zhu EMC/NCEP/NOAA
Probabilistic Forecast Verification Yuejian Zhu EMC/NCEP/NOAA Review NAEFS Products (FY07) (December 4 th 2007) Bias corrected NCEP/GFS forecast 4 times daily, every 6 hours, out to 180 hours Bias corrected
JRA-55 Product Users' Handbook
Climate Prediction Division Global Environment and Marine Department Japan Meteorological Agency September 2013 Change record Version Date Remarks 1.0 30 September 2013 First version 2.0 3 March 2014 Corrected
THORPEX OVERVIEW. Acknowledgements: USTEC Members
THORPEX OVERVIEW Acknowledgements: USTEC Members USTSSC Workshop, 19-20 Sept, 2012 1 OUTLINE Historical perspective Objectives International organization and activities Ongoing work, next steps US organization
GMAO Analyses and Forecasts for YOTC
GMAO Analyses and Forecasts for YOTC Michele Rienecker and Max Suarez Arlindo da Silva Global Modeling and Assimilation Office NASA/Goddard Space Flight Center YOTC Implementation Planning Meeting Hawaii,
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE)
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE) Motivation As an expansion of computing resources for operations at EMC is becoming available
Uncertainty in Operational Atmospheric Analyses. Rolf Langland Naval Research Laboratory Monterey, CA
Uncertainty in Operational Atmospheric Analyses 1 Rolf Langland Naval Research Laboratory Monterey, CA Objectives 2 1. Quantify the uncertainty (differences) in current operational analyses of the atmosphere
Exploring stochastic model uncertainty representations
Exploring stochastic model uncertainty representations with relevance to the greyzone Sarah-Jane Lock, Martin Leutbecher, Peter Bechtold, Richard Forbes Research Department, ECMWF ECMWF November 15, 2017
2. Outline of the MRI-EPS
2. Outline of the MRI-EPS The MRI-EPS includes BGM cycle system running on the MRI supercomputer system, which is developed by using the operational one-month forecasting system by the Climate Prediction
C. Reynolds, E. Satterfield, and C. Bishop, NRL Monterey, CA
Using Initial State and Forecast Temporal Variability to Evaluate Model Behavior C. Reynolds, E. Satterfield, and C. Bishop, NRL Monterey, CA Forecast error attribution useful for system development. Methods
ECMWF global reanalyses: Resources for the wind energy community
ECMWF global reanalyses: Resources for the wind energy community (and a few myth-busters) Paul Poli European Centre for Medium-range Weather Forecasts (ECMWF) Shinfield Park, RG2 9AX, Reading, UK paul.poli
2012 AHW Stream 1.5 Retrospective Results
2012 AHW Stream 1.5 Retrospective Results Ryan D. Torn, Univ. Albany, SUNY Chris Davis, Wei Wang, Jimy Dudhia, Tom Galarneau, Chris Snyder, James Done, NCAR/NESL/MMM Overview Since participation in HFIP
Assignment #5: Cumulus Parameterization Sensitivity Due: 14 November 2017
Assignment #5: Cumulus Parameterization Sensitivity Due: 14 November 2017 Objectives In this assignment, we use the WRF-ARW model to run two nearly-identical simulations in which only the cumulus parameterization
Ensemble TC Track/Genesis Products in
Ensemble TC Track/Genesis Products in Parallel @NCO Resolution Members Daily Frequency Forecast Length NCEP ensemble (AEMN-para) GFS T574L64-33km(12/02/15) 20+1_CTL 00, 06, 12, 18 UTC 16 days (384hrs)
Stochastic methods for representing atmospheric model uncertainties in ECMWF's IFS model
Stochastic methods for representing atmospheric model uncertainties in ECMWF's IFS model Sarah-Jane Lock Model Uncertainty, Research Department, ECMWF With thanks to Martin Leutbecher, Simon Lang, Pirkka
AMPS Update June 2017
AMPS Update June 2017 Kevin W. Manning Jordan G. Powers Mesoscale and Microscale Meteorology Laboratory National Center for Atmospheric Research Boulder, CO 12th Workshop on Antarctic Meteorology and Climate
EMC multi-model ensemble TC track forecast
EMC multi-model ensemble TC track forecast Jiayi Peng*, Yuejian Zhu and Richard Wobus* *IMSG at Environmental Modeling Center Environmental Modeling Center /NCEP/NOAA, Camp Springs, MD 2746 Acknowledgements:
JRA-55 Product Users Handbook
Climate Prediction Division Global Environment and Marine Department Japan Meteorological Agency September 2013 Change record Version Date Remarks 1.0 30 September 2013 First version 2.0 3 March 2014 Corrected
Cumulus parameterization in non-convection-resolving models
Cumulus parameterization in non-convection-resolving models Given a column profile of model variables*, what convective tendencies will* occur? Hard questions: *1 is mean thermo. sounding enough information?»if
CHAPTER 2 DATA AND METHODS. Errors using inadequate data are much less than those using no data at all. Charles Babbage, circa 1850
CHAPTER 2 DATA AND METHODS Errors using inadequate data are much less than those using no data at all. Charles Babbage, circa 185 2.1 Datasets 2.1.1 OLR The primary data used in this study are the outgoing
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model Cécile Hannay, Dave Williamson, Jerry Olson, Rich Neale, Andrew Gettelman,
Impact of different cumulus parameterizations on the numerical simulation of rain over southern China
Impact of different cumulus parameterizations on the numerical simulation of rain over southern China P.W. Chan * Hong Kong Observatory, Hong Kong, China 1. INTRODUCTION Convective rain occurs over southern
A Global Atmospheric Model. Joe Tribbia NCAR Turbulence Summer School July 2008
A Global Atmospheric Model Joe Tribbia NCAR Turbulence Summer School July 2008 Outline Broad overview of what is in a global climate/weather model of the atmosphere Spectral dynamical core Some results-climate
Sensitivity to the PBL and convective schemes in forecasts with CAM along the Pacific Cross-section
Sensitivity to the PBL and convective schemes in forecasts with CAM along the Pacific Cross-section Cécile Hannay, Jeff Kiehl, Dave Williamson, Jerry Olson, Jim Hack, Richard Neale and Chris Bretherton*
GFD II: Balance Dynamics ATM S 542
GFD II: Balance Dynamics ATM S 542 DARGAN M. W. FRIERSON UNIVERSITY OF WASHINGTON, DEPARTMENT OF ATMOSPHERIC SCIENCES WEEK 8 SLIDES Eady Model Growth rates (imaginary part of frequency) Stable for large
Imperial College London
Solar Influence on Stratosphere-Troposphere Dynamical Coupling Isla Simpson, Joanna D. Haigh, Space and Atmospheric Physics, Imperial College London Mike Blackburn, Department of Meteorology, University
PV Generation in the Boundary Layer
1 PV Generation in the Boundary Layer Robert Plant 18th February 2003 (With thanks to S. Belcher) 2 Introduction How does the boundary layer modify the behaviour of weather systems? Often regarded as a
JRA-55 Product Users Handbook
Climate Prediction Division Global Environment and Marine Department Japan Meteorological Agency March 2014 Change record Version Date Remarks 1.0 3 March 2014 First version 2 Contents 1. Introduction...
The Canadian approach to ensemble prediction
The Canadian approach to ensemble prediction ECMWF 2017 Annual seminar: Ensemble prediction : past, present and future. Pieter Houtekamer Montreal, Canada Overview. The Canadian approach. What are the
Ensemble Prediction Systems
Ensemble Prediction Systems Eric S. Blake & Michael J. Brennan National Hurricane Center 8 March 2016 Acknowledgements to Rick Knabb and Jessica Schauer 1 Why Aren t Models Perfect? Atmospheric variables
Mesoscale meteorological models. Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen
Mesoscale meteorological models Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen Outline Mesoscale and synoptic scale meteorology Meteorological models Dynamics Parametrizations and interactions
Land Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004
Dag.Lohmann@noaa.gov, Land Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004 Land Data Assimilation at NCEP: Strategic Lessons Learned from the North American Land Data Assimilation System
M.Sc. in Meteorology. Physical Meteorology Prof Peter Lynch. Mathematical Computation Laboratory Dept. of Maths. Physics, UCD, Belfield.
M.Sc. in Meteorology Physical Meteorology Prof Peter Lynch Mathematical Computation Laboratory Dept. of Maths. Physics, UCD, Belfield. Climate Change???????????????? Tourists run through a swarm of pink
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model Cécile Hannay, Dave Williamson, Jerry Olson, Rich Neale, Andrew Gettelman,
Aerosol-Cloud-Precipitation-Climate (ACPC) Initiative: Deep Convective Cloud Group Roadmap Updated: October 2017
Aerosol-Cloud-Precipitation-Climate (ACPC) Initiative: Deep Convective Cloud Group Roadmap Updated: October 2017 S. C. van den Heever, A. M. Fridlind (Deep Convective Cloud Case Co-Leads) P. J. Marinescu,
NOAA Report. John Gaynor US THORPEX Executive Committee 7 October 2009
NOAA Report John Gaynor US THORPEX Executive Committee 7 October 2009 1 EnKF testing NOAA-THORPEX funding supported development of EnKF algorithms at many locations (ESRL, UMD, CSU, NRL) from 2005-2007.
Experimental Extended Range GEFS
Experimental Extended Range GEFS Malaquías Peña, Dingchen Hou, Dick Wobus, YuejianZhu Acknowledgment: EMC s Ensemble Group, EMC s Global C&W Modeling Branch 10 Jan 2014 WMO InternaTonal Conference on Subseasonal
NCEP Ensemble Forecast System
NCEP Ensemble Forecast System Yuejian Zhu Ensemble team leader Environmental Modeling Center NOAA/NWS/NCEP Acknowledgments: Geoff DieMigo, John Ward, Bill Lapenta and Stephen Lord 1 Index SREF implementation
N C E P. Ensemble Systems within the NOAA Operational Modeling Suite. William. M. Lapenta Acting Director Environmental Modeling Center NOAA/NWS/NCEP
US THORPE 19 Sept 2012 1 N C E P Ensemble Systems within the NOAA Operational Modeling Suite William. M. Lapenta Acting Director Environmental Modeling Center NOAA/NWS/NCEP Data Assimilation, Modeling
Boundary layer equilibrium [2005] over tropical oceans
![Boundary layer equilibrium [2005] over tropical oceans](/thumbs/96/128963638.jpg "Boundary layer equilibrium [2005] over tropical oceans")
Boundary layer equilibrium [2005] over tropical oceans Alan K. Betts [akbetts@aol.com] Based on: Betts, A.K., 1997: Trade Cumulus: Observations and Modeling. Chapter 4 (pp 99-126) in The Physics and Parameterization
Unified Cloud and Mixing Parameterizations of the Marine Boundary Layer: EDMF and PDF-based cloud approaches
DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Unified Cloud and Mixing Parameterizations of the Marine Boundary Layer: EDMF and PDF-based cloud approaches Joao Teixeira
Lecture #1 Tidal Models. Charles McLandress (Banff Summer School 7-13 May 2005)
Lecture #1 Tidal Models Charles McLandress (Banff Summer School 7-13 May 2005) 1 Outline of Lecture 1. Introduction 2. Brief description of tides 3. Observations of tides 4. Simulating tides using a general
Update on the KENDA project
Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany and many colleagues from CH, D, I, ROM, RU Km-scale ENsemble-based Data Assimilation : COSMO priority project Local Ensemble Transform Kalman
ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency
ENSO Outlook by JMA Hiroyuki Sugimoto El Niño Monitoring and Prediction Group Climate Prediction Division Outline 1. ENSO impacts on the climate 2. Current Conditions 3. Prediction by JMA/MRI-CGCM 4. Summary
Upgrade of JMA s Typhoon Ensemble Prediction System
Upgrade of JMA s Typhoon Ensemble Prediction System Masayuki Kyouda Numerical Prediction Division, Japan Meteorological Agency and Masakazu Higaki Office of Marine Prediction, Japan Meteorological Agency
Data Assimilation Development for the FV3GFSv2
Data Assimilation Development for the FV3GFSv2 Catherine Thomas 1, 2, Rahul Mahajan 1, 2, Daryl Kleist 2, Emily Liu 3,2, Yanqiu Zhu 1, 2, John Derber 2, Andrew Collard 1, 2, Russ Treadon 2, Jeff Whitaker
An Introduction to Coupled Models of the Atmosphere Ocean System
An Introduction to Coupled Models of the Atmosphere Ocean System Jonathon S. Wright jswright@tsinghua.edu.cn Atmosphere Ocean Coupling 1. Important to climate on a wide range of time scales Diurnal to
Bells and whistles of convection parameterization
Bells and whistles of convection parameterization Article Accepted Version Yano, J. I., Machulskaya, E., Bechtold, P. and Plant, R. S. (2013) Bells and whistles of convection parameterization. Bulletin
Q.1 The most abundant gas in the atmosphere among inert gases is (A) Helium (B) Argon (C) Neon (D) Krypton
Q. 1 Q. 9 carry one mark each & Q. 10 Q. 22 carry two marks each. Q.1 The most abundant gas in the atmosphere among inert gases is (A) Helium (B) Argon (C) Neon (D) Krypton Q.2 The pair of variables that
J11.5 HYDROLOGIC APPLICATIONS OF SHORT AND MEDIUM RANGE ENSEMBLE FORECASTS IN THE NWS ADVANCED HYDROLOGIC PREDICTION SERVICES (AHPS)
J11.5 HYDROLOGIC APPLICATIONS OF SHORT AND MEDIUM RANGE ENSEMBLE FORECASTS IN THE NWS ADVANCED HYDROLOGIC PREDICTION SERVICES (AHPS) Mary Mullusky*, Julie Demargne, Edwin Welles, Limin Wu and John Schaake
HYSPLIT RESULTS: HYSPLIT:-
HYSPLIT RESULTS: HYSPLIT:- Lagrangian particle models compute trajectories of a large number of so-called particles to describe the transport and diffusion of tracers in the atmosphere. The main advantage
Recent Data Assimilation Activities at Environment Canada
Recent Data Assimilation Activities at Environment Canada Major upgrade to global and regional deterministic prediction systems (now in parallel run) Sea ice data assimilation Mark Buehner Data Assimilation
Latest thoughts on stochastic kinetic energy backscatter - good and bad
Latest thoughts on stochastic kinetic energy backscatter - good and bad by Glenn Shutts DARC Reading University May 15 2013 Acknowledgments ECMWF for supporting this work Martin Leutbecher Martin Steinheimer
Quarterly numerical weather prediction model performance summaries April to June 2010 and July to September 2010
Australian Meteorological and Oceanographic Journal 60 (2010) 301-305 Quarterly numerical weather prediction model performance summaries April to June 2010 and July to September 2010 Xiaoxi Wu and Chris
5. General Circulation Models
5. General Circulation Models I. 3-D Climate Models (General Circulation Models) To include the full three-dimensional aspect of climate, including the calculation of the dynamical transports, requires
Univ. of Maryland-College Park, Dept. of Atmos. & Oceanic Science. NOAA/NCEP/Environmental Modeling Center
The Tangent Linear Normal Mode Constraint in GSI: Applications in the NCEP GFS/GDAS Hybrid EnVar system and Future Developments Daryl Kleist 1 David Parrish 2, Catherine Thomas 1,2 1 Univ. of Maryland-College
Improvements to the NCEP Global and Regional Data Assimilation Systems
Improvements to the NCEP Global and Regional Data Assimilation Systems Stephen J. Lord Director NCEP Environmental Modeling Center EMC Staff NCEP: where America s climate, weather, and ocean services begin
Advancements in Operations and Research on Hurricane Modeling and Ensemble Prediction System at EMC/NOAA
Advancements in Operations and Research on Hurricane Modeling and Ensemble Prediction System at EMC/NOAA Zhan Zhang and Vijay Tallapragada EMC/NCEP/NOAA/DOC Acknowledgements: HWRF Team Members at EMC,
The next-generation supercomputer and NWP system of the JMA
The next-generation supercomputer and NWP system of the JMA Masami NARITA m_narita@naps.kishou.go.jp Numerical Prediction Division (NPD), Japan Meteorological Agency (JMA) Purpose of supercomputer & NWP
Seasonal forecast from System 4
Seasonal forecast from System 4 European Centre for Medium-Range Weather Forecasts Outline Overview of System 4 System 4 forecasts for DJF 2015/2016 Plans for System 5 System 4 - Overview System 4 seasonal
Correspondence between short and long timescale systematic errors in CAM4/CAM5 explored by YOTC data
Correspondence between short and long timescale systematic errors in CAM4/CAM5 explored by YOTC data Hsi-Yen Ma In collaboration with Shaocheng Xie, James Boyle, Stephen Klein, and Yuying Zhang Program
Exploring Gravity Wave Dynamics, Sources, and Predictability in DeepWave
Exploring Gravity Wave Dynamics, Sources, and Predictability in DeepWave James D. Doyle 1, Stephen D. Eckermann 2, Eric Hendricks 1, Qingfang Jiang 1, P. Alex Reinecke 1, Carolyn A. Reynolds 1, David C.
Precipitation Calibration for the NCEP Global Ensemble Forecast System
Precipitation Calibration for the NCEP Global Ensemble Forecast System *Yan Luo and Yuejian Zhu *SAIC at Environmental Modeling Center, NCEP/NWS, Camp Springs, MD Environmental Modeling Center, NCEP/NWS,
High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044119, 2010 High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming Yuhji Kuroda 1 Received 27 May
Will it rain? Predictability, risk assessment and the need for ensemble forecasts
Will it rain? Predictability, risk assessment and the need for ensemble forecasts David Richardson European Centre for Medium-Range Weather Forecasts Shinfield Park, Reading, RG2 9AX, UK Tel. +44 118 949
NOAA s Air Quality Forecasting Activities. Steve Fine NOAA Air Quality Program
NOAA s Air Quality Forecasting Activities Steve Fine NOAA Air Quality Program Introduction Planned Capabilities Initial: 1-day 1 forecast guidance for ozone Develop and validate in Northeastern US September,
Report on EN2 DTC Ensemble Task 2015: Testing of Stochastic Physics for use in NARRE
Report on EN2 DTC Ensemble Task 2015: Testing of Stochastic Physics for use in NARRE Motivation: With growing evidence that initial- condition uncertainties are not sufficient to entirely explain forecast
The PRECIS Regional Climate Model
The PRECIS Regional Climate Model General overview (1) The regional climate model (RCM) within PRECIS is a model of the atmosphere and land surface, of limited area and high resolution and locatable over
Potential Operational Capability for S2S Prediction
Potential Operational Capability for S2S Prediction Yuejian Zhu Environmental Modeling Center Acknowledgements: Brian Gross and Vijay Tallapragada Staffs of EMC and ESRL Present for Metrics, Post-processing,
Spatial variability of summer precipitation related to the dynamics of the Great Plains low-level jet
Spatial variability of summer precipitation related to the dynamics of the Great Plains low-level jet Bing Pu and Robert E. Dickinson Department of Geological Sciences, Jackson School of Geosciences The
Welcome to the 5 th NCEP Ensemble Users Workshop William. M. Lapenta Acting Director Environmental Modeling Center NOAA/NWS/NCEP
N C E P Welcome to the 5 th NCEP Ensemble Users Workshop William. M. Lapenta Acting Director Environmental Modeling Center NOAA/NWS/NCEP The National Centers for Environmental Prediction CPC NHC NCO HPC
NCEP Short Range Ensemble Forecast (SREF) System: what we have and what we need? Jun Du. NOAA/NWS/NCEP Environmental Modeling Center
N C E P NCEP Short Range Ensemble Forecast (SREF) System: what we have and what we need? Jun Du NOAA/NWS/NCEP Environmental Modeling Center (for NSF EarthCube Workshop, NCAR, Dec. 17-18, 2012) 1 An evolving
CHAPTER 8 NUMERICAL SIMULATIONS OF THE ITCZ OVER THE INDIAN OCEAN AND INDONESIA DURING A NORMAL YEAR AND DURING AN ENSO YEAR
CHAPTER 8 NUMERICAL SIMULATIONS OF THE ITCZ OVER THE INDIAN OCEAN AND INDONESIA DURING A NORMAL YEAR AND DURING AN ENSO YEAR In this chapter, comparisons between the model-produced and analyzed streamlines,
Mul$- model ensemble challenge ini$al/model uncertain$es
Mul$- model ensemble challenge ini$al/model uncertain$es Yuejian Zhu Ensemble team leader Environmental Modeling Center NCEP/NWS/NOAA Acknowledgments: EMC ensemble team staffs Presenta$on for WMO/WWRP
Performance of the ocean wave ensemble forecast system at NCEP 1
Performance of the ocean wave ensemble forecast system at NCEP 1 Degui Cao 2,3, Hendrik L. Tolman, Hsuan S.Chen, Arun Chawla 2 and Vera M. Gerald NOAA /National Centers for Environmental Prediction Environmental
SPECIAL PROJECT PROGRESS REPORT
SPECIAL PROJECT PROGRESS REPORT Progress Reports should be 2 to 10 pages in length, depending on importance of the project. All the following mandatory information needs to be provided. Reporting year
GFDL, NCEP, & SODA Upper Ocean Assimilation Systems
GFDL, NCEP, & SODA Upper Ocean Assimilation Systems Jim Carton (UMD) With help from Gennady Chepurin, Ben Giese (TAMU), David Behringer (NCEP), Matt Harrison & Tony Rosati (GFDL) Description Goals Products
Overview of the Unified Radiation Package for NCEP models
Overview of the Unified Radiation Package for NCEP models Yu-Tai Hou Jul 2011 DTC/EMC Workshop Development Objectives:: State of the art technology, Standardized component modules, General plug-in compatibility,
A Reformulation of the Logistic Growth Equation Model (LGEM) for Ensemble and Extended Range Intensity Prediction
A Reformulation of the Logistic Growth Equation Model (LGEM) for Ensemble and Extended Range Intensity Prediction Mark DeMaria NOAA/NESDIS, Fort Collins, CO Andrea Schumacher and Kate Musgrave CIRA/CSU,
High-resolution Global Climate Modeling of Saturn s and Jupiter s tropospheric and stratospheric circulations
High-resolution Global Climate Modeling of Saturn s and Jupiter s tropospheric and stratospheric circulations Aymeric SPIGA, S. Guerlet, E. Millour, Y. Meurdesoif (LSCE/CEA), M. Indurain, S. Cabanes, M.
Near future ( ) projection of the East Asia summer monsoon by an atmospheric global model with 20-km grid
Near future (2015-2039) projection of the East Asia summer monsoon by an atmospheric global model with 20-km grid Shoji KUSUNOKI Meteorological Research Institute (MRI) Ryo MIZUTA, Mio MATSUEDA Advanced
lower atmosphere middle atmosphere Gravity-Wave Drag Parameterization in a High-Altitude Prototype Global Numerical Weather Prediction System NRLDC
Gravity-Wave Drag Parameterization in a High-Altitude Prototype Global Numerical Weather Prediction System NRLDC lower atmosphere Marine Meteorology Division (Code 7500) N. Baker T. Hogan M. Peng C. Reynolds
Unified Cloud and Mixing Parameterizations of the Marine Boundary Layer: EDMF and PDF-Based Cloud Approaches
DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Unified Cloud and Mixing Parameterizations of the Marine Boundary Layer: EDMF and PDF-Based Cloud Approaches Joao Teixeira
Current and future configurations of MOGREPS-UK. Susanna Hagelin EWGLAM/SRNWP, Rome, 4 Oct 2016
Current and future configurations of MOGREPS-UK Susanna Hagelin EWGLAM/SRNWP, Rome, 4 Oct 2016 Contents Current configuration PS38 and package trial results Soil moisture perturbations case study Future
ECMWF Forecasting System Research and Development
ECMWF Forecasting System Research and Development Jean-Noël Thépaut ECMWF October 2012 Slide 1 and many colleagues from the Research Department Slide 1, ECMWF The ECMWF Integrated Forecasting System (IFS)
A data-driven stochastic parameterization of deep convection
parameterization of 1 joint work with: Daan Crommelin 1, Pier Siebesma 2,3, Harm Jonker 3, Frank Selten 2, Christian Jakob 4 1 CWI, Amsterdam, The Netherlands 2 KNMI, De Bilt, The Netherlands 3 Delft University
Fleet Numerical Meteorology and Oceanography Center. Current Sub-seasonal to Seasonal Capabilities
Fleet Numerical Meteorology and Oceanography Center Current Sub-seasonal to Seasonal Capabilities presented at Workshop on Metrics, Post-Processing, and Products for S2S 28 Feb 2018 Chuck Skupniewicz Modeling
Numerical Seasonal Prediction: Approaches and Challenges
Numerical Seasonal Prediction: Approaches and Challenges Malaquías Peña Environmental Modeling Center, NCEP/NWS/NOAA Acknowledgments: Huug van den Dool (CPC), Suru Saha (EMC), Yuejian Zhu (EMC), NMME,
NCEP Global Ensemble Forecast System (GEFS) - Review
NCEP Global Ensemble Forecast System (GEFS) - Review Yuejian Zhu Ensemble Team Leader Environmental Modeling Center NCEP/NWS/NOAA March 2011 Highlights Familiar to EMC ensemble team and collaborators.
Influence of Gravity Waves on the Atmospheric Climate
Influence of Gravity Waves on the Atmospheric Climate François Lott, LMD/CNRS, Ecole Normale Supérieure, Paris flott@lmd.ens.fr 1)Dynamical impact of mountains on atmospheric flows 3)Non-orographic gravity
Working group 3: What are and how do we measure the pros and cons of existing approaches?
Working group 3: What are and how do we measure the pros and cons of existing approaches? Conference or Workshop Item Accepted Version Creative Commons: Attribution Noncommercial No Derivative Works 4.0
Investigating Daily Summertime Circulation and Precipitation Over West Africa with the WRF Model: A Regional Climate Model Adaptation Study
University of Colorado, Boulder CU Scholar Environmental Studies Graduate Theses & Dissertations Environmental Studies Spring 1-1-2014 Investigating Daily Summertime Circulation and Precipitation Over