1

The Future of NWP

Stephen J. LordNCEP Environmental Modeling Center

EMC Senior StaffFred Toepfer John Derber

Hua-Lu Pan Ken MitchellGeoff DiMego Naomi Surgi

D. B. Rao

2

Overview• Why have we been so successful?

• What can we do for an encore?

• Shortfalls (or what do we need to do better)?

Caveat

• Mostly a personal perspective, colored by experience at EMC, NCEP…NOAA

3

Why have we been so successful?

• Improved technology (computing, data assimilation & modeling techniques, obs)

• Societally-relevant products with a demand for– Improved product performance– Increased product areas

• Focused goals with quantitative scores– Systems evaluated every day

• Vs obs by weather & climate experts

• By diverse users with a lot at stake

Save Lives & PropertyWeather-Sensitive Commerce ($2+ T)

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Precipitation Forecast Day 1 1"

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CorrelationsOf HPC with:

Eta: 0.99GFS: 0.74NGM: 0.85

1 day of QPFskill gainedevery 25 years

Impact of NCEP Models on HPC Precipitation Forecasts

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Why have we been so successful? (cont)

• Competition– With ourselves

– Across international, operational and research, weather & climate forecast centers

• Diverse approaches– Highly accurate NWP analysis & forecast systems with

different approaches

– No single solution (normalized for available resources)

– Very few “breakthroughs” (although 4D-VAR, “physics”, “vertical coordinate” are some individual reasons for success)

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What can we do for an encore?

1. Continue to exploit the systems we have built*

2. Increase the rate of development for possible operational implementation*

*To be discussed further

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What can we do for an encore?1. Continue to exploit the systems we

have built. Increase the– Range of Skillful forecasts*– Number of Useful products*– Increase the

• Available information used (data assimilation)• Useful information produced

– Probabilistic information (e.g. ensembles)

• Information used from all products– Product accessibility* – User education & training

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Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty

MinutesMinutes

HoursHours

DaysDays

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2 Week2 Week

MonthsMonths

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YearsYears

Initial Conditions

Boundary Conditions

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Range of skillful forecasts

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Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty

MinutesMinutes

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DaysDays

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2 Week2 Week

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Boundary Conditions

Range of skillful forecasts

FUTURE

Seamless Suite of Forecasts

Increase the Range of Skillful Forecasts

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Increase the Range of Skillful ForecastsS/I Climate

The new NCEP Coupled atmosphere-ocean Forecast System (CFS)

Componentsa) T62/64-layer version of the current NCEP atmospheric GFS (Global Forecast System) model and

b) 40-level GFDL Modular Ocean Model (MOM, version 3)

c) Global Ocean Data Assimilation (GODAS)

Notes:• CFS has direct coupling with no flux correction• GODAS

– Implemented September 2003, runs daily– Salinity analysis, improved use of altimeter data– Real time global ocean data base in WMO standard format– Ready for GODAE

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AC=.86

AC=.80

AC=.43

Peitao Peng CPC

Tropical Precipitation Performance

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Increase the Number of Useful Products

• Real-time Ocean• Air Quality• Fire Weather• Homeland Security• Seasonal• Monthly

• Systems sensitive to environmental parameters

– Ecosystems– Disease vectors– Agriculture– Routine Reanalysis and assessment

NCEP Production SuiteWeather, Ocean & Climate Forecast Systems

Version 3.0 April 9, 2004

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RUCFIREWXWAVESHUR/HRWGFSfcstGFSanalGFSensETAfcstETAanalSREFAir QualityOCEANMonthlySeasonal

US GODAE: Global Ocean Prediction with HYCOM

• Goal: to develop and demonstrate real-time, operational, high resolution ocean prediction systems for the Global Oceans and Basins

• NCEP Partners with

• University of Miami/RSMAS

• NRL Stennis, NRL Monterey, FNMOC

• NOAA PMEL, AOML

• Los Alamos National Laboratory

• Others (international, commercial)

• Hybrid isopycnal-sigma-pressure ocean model (called Hybrid Coordinate Ocean Model – HYCOM)

• Funded FY 2003-2007 by NOPP

Chesapeake Bay

ScheduleNorth Atlantic

World Oceans

North-East Pacific

Hawaii

2005 2006 2007

Global atmosphere-ocean Coupling and Hurricane-Ocean Coupling

Initiate interactions with NOS on bay and estuary model boundary conditions; Initiate wave-current interactions.

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Future Enabling Architectures

• Adding Model Components (to increase useful products)– ESMF

• Complexity vs computational efficiency

• Product accessibility*– NOMADS

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ESMF Architecture

Components Layer:Gridded ComponentsCoupler Components

ESMF Superstructure

ESMF Infrastructure

User Code

BLAS, MPI, NetCDF, …

Low Level Utilities

Fields and Grids Layer

Model Layer

External Libraries

1. ESMF provides an environment for assembling geophysical components into applications.

2. ESMF provides a toolkit that components use to

i. increase interoperability

ii. improve performance portability

iii. abstract common services

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RT-NOMADSDistribution of

Real-Time and Retrospective NCEP Model Data Sets

Jordan C. Alpert jordan.alpert@noaa.gov 4/22/03

NOMADSNOAA Operational Model Archive

and Distribution System

On demand access to (x, y, z, t, product) spacedownloaded in user-defined format

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What can we do for an encore? (cont)

1. Continue to exploit the systems we have built

2. Increase the rate of development for possible operational implementation

– Improvements must occur simultaneously for many more applications (waves, hurricanes, precip, aviation, week-2)* • Each improvement gives rise to increasing expectations

– The problems are getting tougher• As perfection is approached• Forecast system output increasingly resembles the atmosphere• Forecast and delivery deadlines shrinking

– It is more difficult to predict the expected improvement from each proposed change

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2001 GFS Implementation• Improved model climate in tropics

– Prognostic liquid water– Radiation interactive with condensed water & cloudiness– Full simulation of water transport

• PBL Convective clouds Detrainment Cirrus

– Cumulus momentum transport• Reduced spurious spinup of tropical systems• Cyclogenesis mostly confined to growth of systems later observed

• Testing involved– 835 days of retrospective data assimilation & model forecasts

• Summer (tropical cyclones)• Spring (severe weather)• Winter (temperature bias)

– Unable to test GFDL initialization thoroughly

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What can we do for an encore? (cont)

2. Increase the rate of development for possible operational implementation

HOW?

22

What can we do for an encore? (cont)

HOW?Focus efforts on improving operational systems

– Improved project management• Operations needs to have increased influence on scientific direction of

applied research• Mutual discussion and execution of highest priority development projects• More rapid transition of development to operations

– Increase computing resources beyond Moore’s Law (constant $)– Consolidate software & forecast systems

• GSI (global and regional analysis system)

– Continue to exploit Test Bed concept• Enhanced Visiting Scientist exchange program • Increase the (potential) workforce capabilities

– Student education and training

• Support for non-operational users of operational systems

– Examples follow

Strong management support at NOAA level & above

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NASA-NOAA-DOD Joint Center for Satellite Data Assimilation

(JCSDA)– NOAA, NASA, DOD partnership– Mission

• Accelerate and improve the quantitative use of research and operational satellite data in weather and climate prediction models

– Current generation data

– Prepare for next-generation (NPOESS, METOP, research) instruments

– Supports applied research• Partners

• University, Government and Commercial Labs

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JCSDA Prioritized Applied Research Areas

• Advanced radiative transfer

• Clouds and precipitation

• Assess impacts of current instruments

• Improve sea surface temperature data and use of altimeter data

• Enhance land surface data sets (surface emissivity model)

• AIRS data implementation

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Improve Sea Surface Temperature Data [X. Li & Derber]

SST Difference 29-28 October 2003 - Experiment

SST Difference 29-28 October 2003 - Control• New physical retrieval from AVHRR data, cast as variational problem• OPTRAN RTM & Linear Tangent Model• Eventual direct use of AVHRR (and other) radiance data

RMS and Bias Fits to Independent Buoy SST Data

NOAA-16 AVHRR data only

Northern Hemisphere Ex. Tropics

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Improved Surface Emissivity Model for Snow [Yan, Okamoto and Weng)

Annual Mean RMS TB Difference (Obs – Simulated)

SnowEM

Operational

The Path to Operational Implementation

Code or Algorithm Development & RefinementRepeated case studies, proof of concept, eliminate bugs

Interface with Operational Codes & Data StructuresConnect input/output to BUFR/GRIB, develop backup version,Make code robust & efficient to fit time/cpu/memory window

Preliminary TestingLow resolution case studies, static initialization, relevant diagnostics,

warm & cool cases, assess short-term model climate (30 days)

Low Resolution Parallel Testing Connect to fully cycled data assimilation, run for all seasons,

accumulate verification statistics, identify&solve problems:e.g. biases, amplification through cycling, spin-up/down etc.

Pre-Implementation TestingOperational resolution fully cycled real-time parallel, more

comprehensive verification, documentation and user notification, real-time forecaster exposure/evaluation

EMCDTC/OTC

1-12+ 3-12

1-12 + 3-12

3-6 1-3

3-9

3-9

1-3

13-39

1-6

1-6

2-44-182-92-4

EMC total: 13-39 mo DTC-OTC-EMC total: 8-31 moWRF DTC

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Shortfalls (or what do we need to do better)?

• Resources have not kept pace with the rapidly increasing complexity of today’s forecasts– Project management and technical support for

• Maintaining and developing complex operational Data Assimilation & Modeling codes and supporting code infrastructure*

• Interacting with external community (data, ideas, code transition, cultural education)*

• Example Hurricane WRF*

– Basic infrastructure (computing, testing & implementation capability)

– Timeliness of data delivery to operational centers and efficient product dissemination are marginal

– Recent additions to NOAA’s computing will help but…

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Maximum Significant Wave Heights: Model vs. JASON

Direct hits: Altimeter through eye and maximum wavesWNA (green), NAH (red): Good track of build-up, set-down and maximumStorm’s eye (lower panel) well captured by both modelsEarly stages missed by WNA (green): weak GFS winds, small hurricanes

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Genesis Summary NCEP GFS 2003 Atlantic Hurricane Season

Selected Storms (Isabel, Juan, Kate, Nicholas, Odette, Peter)

Total or Percent

Total Genesis Opportunities 92

Early or on time 42%

Late 27%

Failed to generate 31%

Timing Within

+- 6 h 13%

+- 12 h 41%

+- 24 h 67%

> 24 h 33%

Location Within

300 km 47%

500 km 77%

>500 km 23%

TimMarchokQingfu

Liu

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GFDL(WRF)Hurricane Model

NCEPWAVEWATCH III

Flux

Wind &Air Temp.

Atmosphere

Ocean Waves

Currents,

Wave Boundary Model

SST

Wave Spectra

Flux

OceanModel

Wind &Air Temp.

Flux

GFDLHurricane Model

POM

SST &Current

SST

Flux

Atmosphere

Ocean

Elevations, & SST

Operational GFDL ModelOperational GFDL Model Future Coupled Hurricane-Wave-Ocean ModelFuture Coupled Hurricane-Wave-Ocean Model

URI & U. Miami partnerships

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Shortfalls (or what do we need to do better)? (cont)

• A solid scientific strategy and policy are needed to guide an expanded and improved observing system– Evaluation of today’s system (e.g. current JCSDA

assessment)*– Ways to assess potential impact of new instruments

(e.g. OSSEs)*– Examples follow*– Recent activities are encouraging

• Ocean observing system for climate is step in right direction• Support for above projects is helping• Still a long way to go

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Tropics 850 mb Vector Difference 20N - 20S (F-A) RMS 15 Jan - 15 Feb '03

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Tropics 850 mb Vector Difference 20N - 20S (F-A) RMS 15 Jan - 15 Feb '03

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Tropics850 mb Vector (F-A)RMS

The REAL problem is Day 1

Jung and Zapotocny

JCSDAFunded by

NPOESS IPO

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Impact of Removing AMSU and HIRS Data on Hurricane Track Forecasts in East Pacific Basin

-25.0

-20.0

-15.0

-10.0

-5.0

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% I

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NOAMSU

NOHIRS

Impact of Removing AMSU and HIRS Dataon Hurricane Track Forecasts in Atlantic Basin

-20.0

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NOHIRS

Satellite data ~ 10% impact

Jung and Zapotocny

JCSDAFunded by

NPOESS IPO

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RMS Brightness Temperature Differences Between Observed Radiances and NCEP 6 Hour Global Forecast and Analysis

Moisture & Surface Channels

Temperature Channels

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Doppler Wind Lidar (DWL) ImpactConv Only

Conv. + TOVS

Conv + TOVS + DWL(best)

Conv + DWL(non-scan)

Conv + DWL(PBL )

Conv + TOVS + DWL(non-scan)

Conv +DWL(Best)

Conv + DWL(Upper)

V at 200 hPa

V at 850 hPa

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Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa. Anomaly correlation are computed for zonal wave number from 10 to 20 components. Differences from anomaly correlation for the control run (conventional data only) are plotted.

Forecast hour

OSSE Results – Masutani et al

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Opinionated Summary(Any opinion can be debated and “proven” wrong)

• Options for product improvement– More observations (needs more focus on anticipated

product improvement)– Field experiments (need more focus on understanding the

forecast system and correcting its errors)– Computational techniques (high priority)– Scientific development (high priority, more innovative

approaches, e.g. “resolvable scale modeling”*)– Testing, engineering and tuning (always necessary)

* Unfeasible unless huge increases in computing

39

Opinionated Summary (cont)

• Product enhancement– Increased societal impact– Broader spectrum of applications

• Research Strategy– Some course corrections needed

• Focus efforts more on improving operational systems• Involve more scientists without detracting from current rate of

development

• Obviously more infrastructure and computing needed• Strong management support at NOAA level & above

40

Opinionated Summary (cont)

The last word:

• Everyone still has a role to play• Sociology and our past may be greater enemies than

the science yet to be conquered*

*On runway, Birmingham AL airport, 10:15 PM, 12% laptop juice“Thunderstorms in Baltimore area have created a traffic jam”