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Recent Advances at the National Centers for Environmental

Prediction

“Where America’s Climate and Weather Services Begin”

Louis W. UccelliniDirector, NCEP

Millersville UniversityApril 8, 2004

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Overview

• Define NCEP• Status of Models• Recent Advancements

– Hurricane forecasts– Wave Watch III– QPF– Climate Model

• JCSDA• Future Plans for Community Models

– Ensembles– WRF– ESMF

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Define NCEP

4*As of 10/1/04*54 FTE

Total FTE: 429*131 Contractors/24 Visitors

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NCEP Center Locations

Space Environment Center Aviation Weather Center

NCEP Central Operations Climate Prediction Center Environmental Modeling Center Hydrometeorological Prediction Center Ocean Prediction Center

Storm Prediction Center

Tropical Prediction Center

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What Does NCEP Do?

Severe Storm Outlooks Fire Weather Outlooks Weather Forecasts to Day 7 Quantitative Precipitation

Forecasts to 5 days Marine Weather Discussions Model Discussions

Severe Weather Watches Hurricane Watches and

Warnings Aviation Warnings

(Convective, Turbulence, Icing) Climate Forecasts (Weekly to

Seasonal to Interannual) Marine High Seas Forecasts Solar Monitoring –

geomagnetic storm forecasts

Guidance to Support WFO/RFC National Products

Model Development and Applications, including Data AssimilationOcean Models for Climate Prediction; Coastal Ocean Forecast System; Wave ModelsSuper Computer, Workstation and Network Operations

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Employment Situation

• Employment situation– Total of 375 Civil Servants; 131 contractors; 24 visitors– Currently have 6 Student Interns (SCEP/STEP) – During the last 12 months

• 39 CS vacancies; 6 SCEPs; • Hired 28 contractors• 7 new visiting scientists• 14 Summer Hires (ORISE, GoHFAS, NOAA Educational

Partnership Programs)

– Projected growth through ’08: 50 – 60 (contractors/visiting scientists/postdocs)

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NCEP’s Future is Built Upon:

Climate-Weather-Water Linkages; for example Seasonal Hurricane Outlooks & Extratropical Storm patterns Meteorological-Hydrological forecasts Ocean and atmosphere coupled forecasts Atmosphere-Land Processes coupled forecasts

“Seamless Suite” of products through a collaborative approach Extension of predictability of Weather and Climate (from

snowstorms to ENSO); Improve the forecasts of Extreme Events Community Model Approach – Common Model Infrastructure Addressing uncertainty in forecasts – Ensemble modeling

NEW Collaborative Forecasting; Unified Model Infrastructure

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Status of Models

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Computing Capability

Commissioned/Operational IBM Supercomputer in Gaithersburg, MD (June 6, 2003)

$20M/Year $20M/Year InvestmentInvestment

•Receives Over 116 Million Global Observations Daily•Sustained Computational Speed: 450 Billion Calculations/Sec•Generates More Than 5.7 Million Model Fields Each Day•Global Models (Weather, Ocean, Climate)•Regional Models (Aviation, Severe Weather, Fire Weather)•Hazards Models (Hurricane, Volcanic Ash, Dispersion)•Backup for operational side: Fairmont, WV installation Fall’04

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NCEP Operational ModelsEta

12 km, 60 levels, 84 hrs at 0 , 6, 12 and 18Z

Global Forecast System (GFS)

T254 (~55 km) to 3.5 days (84 hrs), 64 levels

T170 (~75 km) to 7.5 days (180 hrs), 42 levels

T126 (~105 km) to 16 days (384 hrs), 28 levels

16 days (384 hrs)/4 times per day

RUC

20 km, 50 levels

12 hrs at 0,3,6,9,12,15,18,21Z

3 hrs at 1,2,4,5,7,8,10,11,13,14, 16,17,19,20,22,23Z

Climate

T62 (~200 km), 28 levels, 7 months (20 members)

Ensembles

global 10 members at 00, 06,12,18Z

T126 (~105 km) to 180 hrs, T62 (210 km) to 384 hrs

28 levels, 16 days (384 hrs)

regional 10 members at 0 and 12Z

48 km, 45 levels, 63 hrs from 9 and 21Z

Wave Model

global - 1.25 x 1.0 deg lat/lon

Alaskan Regional - .5 x .25 deg lat/lon

Western North Atlantic - .25 x .25 deg lat/lon

Eastern North Pacific - .25 x .25 deg lat/lon

1 level, 168 hrs/4 times per day

North Atlantic Hurricane (seasonal)

North Pacific Hurricane (seasonal)

.25 x .25 deg lat/lon

1 level

78 hours/4 times per day

GFDL Hurricane Model

coupled ocean-atmosphere

Two nests (0.5, 1/6 deg lat/lon)

42 levels

126 hrs at 00, 06, 12 and 18Z

Status of Distributed ModelsThe Workstation Eta

A means for providing real-time high-resolution numerical model data at the local level

Domain can be placed anywhere on the globe: size and resolution determined by user

Non-NWS use encouraged. About 140 international requests from countries such as China and Brazil (both with >5 users), Turkey and Thailand.

Over 155 domestic users: WFOs, researchers and students at U.S universities

http://www.emc.ncep.noaa.gov/mmb/wrkstn_eta/

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Tiling for Higher Resolution Applications

• 6 High resolution

(all 8 km except 10 km Alaska) Window nested runs - once per day to 48 hours

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Tiling for Higher Resolution Applications

• Fire weather runs – 8

km NMM runs on demand in one of 26 areas of coverage, each about 900 km square up to 4/day

• Dispersion models run on demand using 4 km NMM for Homeland Security

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Recent Advancements

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Recent Advancements: Hurricanes

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NHC Yearly-averaged Atlantic Track Forecast Errors

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TPC Atlantic 72 hr Track Forecast Errors

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Hurricane MichelleOctober 29 - November 5, 2001

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Hurricane Claudette5-Day Hurricane Forecast

Radar 10:45 AMJuly 15, 2003

Error (nm) 12 h 24 h 36 h 48 h 72 h 96 h 120 h

OFCL 35 57 84 112 128 135 147

GFDL 32 56 88 121 163 233 273

GFS 38 66 93 121 193 218 301

# of cases 25 24 22 19 14 8 8

Hurricane Isabel

Thursday, 9/18/0312 PM EDT5-day forecast

3-day forecast

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NATIONAL HURRICANE CENTER ATLANTIC TRACK FORECAST ERRORS

NATIONAL HURRICANE CENTER ATLANTIC TRACK FORECAST ERRORS

12 24 36 48 72 96 120

Forecast Period (hours)

0

100

200

300

400

500

Err

or

(nau

tica

l mile

s)

1964-1973

1984-1993

1974-1983

1994-2002

Isabel

2003

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Recent Advancements: Wave Watch III

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• New model design with emphasis on transparency, vectorization and parallelization (plug compatible, portable).

• More general governing transport equation, allowing for later full coupling with ocean models.

WAVEWATCH III

new model required

• All models use GFS and ice edge information from NCEP's operational ice analysis. A special GFDL driven version of the Western North Atlantic and Eastern North Pacific wave model are run for hurricane wave prediction (72h forecast).

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Isabel 18/9/2003, 12 UTCnowcast

48h forecast24h forecast

12h forecast

Intensity and location of forecast waves consistent and confirmed by altimeter and buoy observations. At 48h forecast lower wave heights due to earlier landfall.

wave height 50+ ft (45+ ft)

Isabel at Field Research Facility Duck NC

pictures from US Army Corps Of Engineers Field Research Facility webcam

9/18 14:00 EDT 9/29 14:00 EDT

Maximum observed wave height at the end of the pier 26.6ft, which is roughly the maximum sustainable wave height for the local water depth. Wave height 2 miles offshore reported up to 49 ft.

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Recent Advancements: QPF

HPC QPF Verification1-inch Threat Score

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Eta Model Performance MetricRatio of Annual 48h Precipitation Threat Score to 24h Score in 1999

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Day 1 0.5 .597 Dec ‘02

1.0 .483 Dec ‘02

2.0 .332 Nov ‘98

3.0 .374 Feb ‘81

Update 0.5 .557 Dec’02

1.0 .423 Dec ‘02

2.0 .286 Sep ‘79

Day 2 0.5 .507 Dec ‘02

1.0 .421 Jan ‘02

Day 3 0.5 .393 Jan ‘02

1.0 .331 Jan ‘02

All Time HPC QPF Threat Score Records

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Recent Advancements: Climate Model

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Climate Model

• Current operational climate model– 200 km, 28 levels, runs to 7 months each month– Linked to SSTs in Pacific basin only

• Improved operational climate model– Fully coupled ocean-atmosphere system

• NCEP operational Global Forecast System (GFS) atmospheric model

– 200 km resolution, 64 levels, model top 0.2 mb

• MOM3 ocean model (GFDL)– 100 km resolution, 40 levels, 30 km between 10 deg N and 10 deg S– Global; between 65 deg N and 75 deg S– Global Ocean Data Assimilation System (GODAS)

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Coupled Model Simulation ENSO SST cycles

Nino 3.4 SSTAnomalies

Simulated 2002-2040 (top)

Observed 1965-2003(bottom)

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Coupled Model Simulation SST Interannual Variability

Observed

64 Level Atm

28 Level Atm

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Examples of ENSO eventsSimulated El Nino 2015-2016 Simulated La Nina 2017-18

Real El Nino 1982-1983 Real La Nina 1988-1989

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Hindcast Skill Assessment

• Methodology– 5-member ensemble over 22 years from 1981-2002

– January and April initial conditions

– 9 month runs– Initial atmospheric states 0000 GMT 19, 20, 21, 22, and

23 for each month– Initial ocean states NCEP GODAS (Global Ocean Data

Assimilation System) 0000 GMT 21st of each month• Forced by Reanalysis 2 parameters

• Preliminary results– 10-15 member ensembles for full calibration runs

(ongoing)

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Hindcast Skill Assessment (cont)

• Hindcast skill– Estimated after bias correction for each year– Uses model climatology based on the other years– Anomaly correlation skill score for Nino 3.4 region

SST prediction– Skill maps for

• Global SST• U.S. temperature • U.S. precipitation

– Comparisons with • Operational dynamical forecast (CMP14)• Operational statistical forecast (NCEP CPC tools)

– Constructed Analog SST (CASST)

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Ensemble Mean

CMP14CASST

April ICEnsemble Mean – mean of 5 member ensemble

CMP14 – operational dynamical forecast

CASST – Constructed Analog SST (statistical forecast used by CPC)

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January IC

CASST

CMP14

Ensemble Mean

Ensemble Mean – mean of 5 member ensemble

CMP14 – operational dynamical forecast

CASST – Constructed Analog SST (statistical forecast used by CPC)

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JCSDA

Count

(Mill

ions)

Daily Upper Air Observation Count

2002

2003

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Joint Center for Satellite Data Assimilationcreated July 2, 2001

Increase uses of current satellite data in NWP models Develop the hardware/software systems needed to assimilate data from

the advanced satellite sensors Advance the common NWP models and data assimilation infrastructure Develop common fast radiative transfer system Assess the impacts of data from advanced satellite sensors on weather

and climate predictions Reduce the average time for operational implementations of new

satellite technology from two years to one

Accelerate use of research and operational satellite data in operational numerical prediction models

Goals:

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JCSDA Partners

NASA/Goddard

Global Modeling & Assimilation Office

NOAA/NESDIS

Office of Research &

Applications

NOAA/OAR

Office of Weather and Air Quality

NOAA/NCEP

Environmental

Modeling Center

US Navy

Oceanographer of the Navy,Office of Naval Research (NRL)

US Air Force

AF Director of WeatherAF Weather Agency

PARTNERS

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JCSDA Road Map (2002 - 2010)

Improved JCSDA data assimilation science

2002 2004 2007 2008 2009 2005

OK

Deficiency

2003

Advanced JCSDA community-based radiative transfer model,Advanced data thinning techniques

Sci

ence

Ad

van

ce

By 2010, a numerical weather prediction community will be empowered to effectively assimilate increasing amounts of

advanced satellite observations

2010

AMSU, HIRS, SSM/I, Quikscat,

AVHRR, TMI, GOES assimilated

AIRS, ATMS, CrIS, VIIRS, IASI, SSM/IS, AMSR, more products assimilated

Pre-JCSDA data assimilation science

Radiative transfer model, OPTRAN, ocean microwave emissivity, microwave land emissivity model, and GFS data assimilation system were developed

The radiances of satellite sounding channels were assimilated into EMC global model under only clear atmospheric conditions. Some satellite surface products (SST, GVI and snow cover, wind) were used in EMC models

A beta version of JCSDA community-based radiative transfer model (CRTM) transfer model will be developed, including non-raining clouds, snow and sea ice surface conditions

The radiances from advanced sounders will be used. Cloudy radiances will be tested under rain-free atmospheres, and more products (ozone, water vapor winds) are assimilated

NPOESS sensors ( CMIS, ATMS…) GOES-R

The CRTM includes scattering & polarization from cloud, precip and surface

The radiances can be assimilated under all conditions with the state-of-the science NWP models

Resources:

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• JCSDA is funding 18 extramural research projects to develop the state of-the-art satellite data assimilation system (e.g. uses of cloudy radiances from advanced satellite instruments, uses of satellite snow and vegetation products)

• Preparation for uses of advanced satellite data such as METOP (IASI/AMSU/HSB), DMSP (SSM/IS) and EOS (Aqua AIRS, AMSR-E)

• NCEP global data assimilation system implemented into NASA Global Modeling and Assimilation Office (GMAO) forecast system

• JCSDA community-based radiative transfer model

• Snow and sea ice emissivity models for improving uses of satellite microwave sounding data over high latitudes

• Impact studies of POES AMSU, EOS AIRS, DMSP SSMIS on NWP through EMC parallel experiments

JCSDA FY03-04 Major Projects

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Recent Accomplishments Land emissivity model tested in NCEP operational models

Positive impacts with AMSU data over land (May,2002) Operational implementation (October, 2002)

Enabled use of microwave radiances over land

New Data used in NCEP operational models SSM/I, TRMM microwave imager precipitation estimates SSM/I, AMSU cloud liquid water GOES-10 IR radiances QuikSCAT data

Preparation for AIRS Computer installed at NASA to deliver data within 180 minutes of ingest Fast radiative model developed, documented, delivered, undergoing testing Sample data set delivered to NWP Centers

“Foundation” Science Issues and Priorities agreed to: Basis for AO

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Including QUIKSCAT data resulted in 3-8% improvement in 10 m winds vs. mid-latitude deep ocean buoys at 24-96 hr7-17% improvement for MSLP

Based on 40 forecasts from 45 days of GDAS (T170, L42) experiment.

Impact of QuikSCAT Data

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Ongoing Activities (cont)JCSDA Announcement of Opportunity

1. Improve radiative transfer model1. UCLA – Advanced Radiative Transfer

2. UMBC – Including Aerosols in OPTRAN

3. NOAA/ETL – Fast microwave radiance assimilation studies

2. Prepare for advanced instruments1. U. Wisconsin – Polar winds assimilation

2. NASA/GSFC – AIRS and GPS assimilation

3. Advance techniques for assimilating cloud and precipitation information1. U. Wisconsin – Passive microwave assimilation of cloud and precipitation

4. Improve emissivity models and surface products1. Boston U. - Time varying Land & Vegetation

2. U. Arizona – Satellite obs for Snow Data Assimilation

3. Colo. State U. – Surface emissivity error analysis

4. NESDIS/ORA – Retrievals of real-time vegetation properties

5. Improve use of satellite data in ocean data assimilation1. U. Md – Ocean data assimilation bias correction

2. Columbia U. – Use of altimeter data

3. NRL (Monterey) – Aerosol contamination in SST Retrievals

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Future Plans for Community Models

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Future Plans• WRF

– Common model infrastructure for mesoscale (NCAR and NCEP Dynamic Core and Physics)

– Sustained by AF, Navy, NCEP, NCAR– Testing underway of all combinations

of 2dynamic cores and 2 physics packages at DoD Major Shared Resource Center (one month from each season)

– First operational implementation at NCEP by Oct ’04, implementation at AF by Spring, ‘05

•ESMF–Global common model infrastructure–NCAR, GFDL, NASA/GSFC, MIT, NCEP–Basis for next generation global data assimilation and forecast system

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Future Plans• Ensemble models

– SREF (10 members twice/day, 48 km, 45 levels, 63 hrs)– Global (10 members twice/day; 105 km to 84 hrs, 210 km

to 384 hrs; 28 levels)

GOAL: To create a North American Ensemble Forecast System with the Canadian Meteorological Centre

Dominant Precip Type63 hour forecast

Valid 12Z,December 5, 2002

Air Quality Prediction at NCEP

• Initial (1-5 years started FY2003) :

–1-day forecasts of surface ozone (O3) concentration

–Develop and validate in Northeastern US in 2 years

–Deploy Nationwide within 5 years

•Intermediate (5-7 years):–Develop and test capability to forecast particulate matter (PM) concentration

•Longer range (within 10 years):–Extend air quality forecast range to 48-72 hours

–Include broader range of significant pollutants

•Program has purchased additional computer power to perform AQF and promised this increment for perpetuity -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68

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0 to 10 1 0 to 20 2 0 to 30 3 0 to 40 4 0 to 50

Spatial Evaluation vs ObsHourly Ozone (rms srror)

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Summary

• NCEP is positioned to deal with important strategic issues– Climate-weather-water linkage– Expand into “environmental” prediction– Extend predictive capabilities into week 2– Extend consistent predictive capabilities for

extreme events out to Day 7

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Summary (cont)• Based on Partnership with larger research community

– Community model approach (global and regional)– Active participation in field programs

• North American Monsoon Experiment• THORPEX

– Test Beds:• USWRP/Joint Hurricane Test Bed (TPC)• Hazardous Weather Forecast Test Bed (SPC) • Aviation Test Bed (AWC)• USWRP/Hydrometeorological Test Bed (HPC)

– Data Assimilation efforts through JCSDA

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End of Slides

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N.H. 500 mb Height Anomaly Correlation for Forecasts Days 3 (blue), 5 (aqua), and 7 (red)

Monthly Values and Annual Averages

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S.H. 500 mb Height Anomaly Correlation for Forecasts Days 3 (blue), 5 (aqua), and 7 (red)

Monthly Values and Annual Averages

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Organizational Structure NASA NOAA DOD

Joint Oversight Board of Directors:NOAA NCEP: L. Uccellini (Chair)

Goddard ESD : F. EinaudiNOAA ORA: M. ColtonNOAA OAR: J. Gaynor

Navy: S. Chang, R. McCoyUSAF: J. Lanicci, M. Farrar

Joint Center StaffCenter Director: John LeMarshall

Executive Directors: Stephen Lord - NWSFuzhong Weng - NESDIS

L. P. Riishogjaard – NASAPat Phoebus - NRLTechnical Liaisons:

DAO – D. DeeEMC – J. Derber

GMAO – M. RieneckerOAR – A. GasiewskiORA – D. TarpleyNavy – N. Baker

USAF – M. McAteeProgram Support: Ada Armstrong

George Ohring (NESDIS)

AdvisoryPanel

Rotating Chair

ScienceSteering

Committee