Science Mission Directorate NASA’s Weather Research Program NWS SRH SOO-NASA/SPoRT Joint Workshop Dr. Tsengdar Lee July 11-13, 2006.

Science MissionDirectorate

NASA’s Weather Research Program

NWS SRH SOO-NASA/SPoRT Joint WorkshopDr. Tsengdar Lee

July 11-13, 2006

Turning Observations into Knowledge Products

System of Systems Framework

NASA’s Weather Research Activities

Under Earth Science Research Division/Research and Analysis Program and Applied Science Program Invest in basic and applied weather research and development Collaborate closely with NOAA colleagues Developed algorithms in satellite data assimilation and retrieval Applied directly to short and medium range weather forecast SPoRT and JCSDA are two of the major investments

SMD

ESD HPD PSD APD

R&A ProgramApplied Science

Program

Wx Research Appl Wx Research

Turning Observations into Knowledge Products

Bay

CIEFMidwest

MSFC

DC CIEF

GSFC

CIEF SouthEast

KSC

DFRC

JPL HQ

LRCARC

GRC

SSC MAF

OC48OC48

Lambda Services

CIEF South

CentralJSC

CIEFMidwest

MSFC

DC CIEF

GSFC

CIEF SouthEast

KSC

DFRC

JPL HQ

LRC

ARC

GRC

SSC MAF

OC48OC48

Core Lambda Services

CIEF South Central

JSC

WSC WSCWSTF

CIEFBay

Mission Support Backbone

2.5 Gbps lambdaSONET OC48 (2.5 Gbps)SONET OC12 (622 Mbps)SONET OC3 (155 Mbps)

Interactive Visual Supercomputing

Compute EnvironmentMulti-tiered Platforms Common Front End

Storage Area Network

GB/s

Ideal Architecture VisionData Centric, Multi-Tiered

Shared High

Speed Disk

Hierarchical Storage

Management

High Speed Research Network

High Speed Access to

Other SitesNext

GenerationPlatforms

Visualization Environment

NASA Mission Support Network

Capability Systems Capacity Systems Capacity Systems

•Project Columbia computing facility•World’s fourth fastest computer with 51.8 Teraflops throughput•10240 processors•Earth Science modeling and data assimilation has been the prime usage of the systems

Collaboration with Science Mission Computing and Modeling and Analysis Research

Establishing a Modeling Environment

Project FastPath

TRL DefinitionsNASA

Technology Readiness Level

EMCNCO

R&D Operations Delivery

Criteria

Transition from Research to Operations

Requirements

EMC

NCEP’s Role in the Model Transition Process

OPS Life cycleSupport

Service Centers

NOAAResearch(GFDL/URI)

Concept of Operations

ServiceCenters(TPC)

Test BedsJHT

JCSDA

User

Bas

ic R

esea

rch

O

bse

rvat

ion

S

yste

m

Launch List – Model Implementation Process

FieldOffices

Effort

EMC and NCO have critical roles in the transition from NOAA R&D to operations

Applied researchOther Agency, Academia

1..Identified for Selection

2. Code/Algorithm Assessment and/or

Development

3. Interface with Operational

Codes

4. Level I:-Preliminary

Testing(Lower

Resolution)

5. Level II:-Preliminary

Testing(DA/Higher Resolution)

6. EMC Pre-Implementation

Testing (Packaging and

Calibration)

7. NCO Pre-Implementat

ionTesting

8. Implementation

Delivery

1 2 3 4 5 6 7 8 OPS Support Svc Centrs User

NWS

SPoRT’s Role in the R&O Process

NOAAResearch

Ob

serv

atio

n

Sys

tem

Effort

Highlights

AIRS Data Impact on NCEP GFS

Data Category Number of AIRS Channels

 Total Data Input to AnalysisData Selected for Possible UseData Used in 3D VAR Analysis (Clear Radiances) 

 ~200x106 radiances (channels) ~2.1x106 radiances (channels)~0.85x106 radiances (channels) 

Current preliminary impact study shows that the use of a small fraction (~0.5%) of AIRS clear only data can provide

significant 3 to 6 –day forecast skill improvement in both northern & southern Hemispheres

S. Hemisphere 500mb AC Z 20S - 80S Waves 1-20

1 Jan - 27 Jan '04

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 1 2 3 4 5 6 7

Forecast [days]

An

om

aly

Co

rrel

atio

n

Ops

Ops+AIRS

S. Hemisphere 1000 mb AC Z 20S - 80S Waves 1-20

1 Jan - 27 Jan '04

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 1 2 3 4 5 6 7

Forecast [days]

An

om

aly

Co

rrel

atio

n

Ops

Ops+AIRS

N. Hemisphere 500 mb AC Z 20N - 80N Waves 1-20

1 Jan - 27 Jan '04

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 1 2 3 4 5 6 7

Forecast [days]

An

om

aly

Co

rrel

atio

n

Ops

Ops+AIRS

S.H.N.H.

JCSDA Road Map (2002 - 2010)

Improved JCSDA data assimilation science

2002 2004

2007 2008 2009 2005

OK

Required

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, WINDSAT, GPS ,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, more products (ozone, water vapor winds)

NPOESS sensors ( CMIS, ATMS…) GIFTS, GOES-R

The CRTM include cloud, precipitation, scattering

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

Resources:

3D VAR -----------------------------------------------------4D VAR

2006

Short Term Priorities

MODIS: MODIS AMV assessment and enhancement. Accelerate assimilation into operational models.

AIRS: Improved utilization of AIRS

• Improve data coverage of assimilated data. Improve spectral content in assimilated data.

• Improve QC using other satellite data (e.g. MODIS, AMSU) • Investigate using cloudy scene radiances and cloud clearing

options• Improve RT Ozone estimates• Reduce operational assimilation time penalty (Transmittance

Upgrade)

SSMIS: Collaborate with the SSMIS CALVAL Team to jointly help assess SSMIS data. Accelerate assimilation into operational model as appropriate

Some Major Accomplishments

Common assimilation infrastructure at NOAA and NASA Common NOAA/NASA land data assimilation system Interfaces between JCSDA models and external researchers Community radiative transfer model-Significant new developments, New release

June Snow/sea ice emissivity model – permits 300% increase in sounding data usage

over high latitudes – improved polar forecasts Advanced satellite data systems such as EOS (MODIS Winds, Aqua AIRS, AMSR-

E) tested for implementation• MODIS winds, polar regions - improved forecasts. Current Implementation• Aqua AIRS - improved forecasts. Current Implementation

Improved physically based SST analysis Advanced satellite data systems such as

• DMSP (SSMIS),• CHAMP GPS

being tested for implementation Impact studies of POES AMSU, Quikscat, GOES and EOS AIRS/MODIS with

JCSDA data assimilation systems completed.

SPoRT Center Structure

MODIS / AMSR-E

MODIS imagery• orbital track map• single visible image (250m)• natural color 3 ch. composite (500m)• long wave infrared - 11m (1000m)• short wave infrared – 3.9m (1000m)• 11m - 3.9m– fog product (1000m) • water vapor - 6.7m (1000m)

MODIS products• cloud top pressure (5km)• precipitable water (5km)• lifted index (5km)• land surface temperature (LST) – 1 km• SST - single time and composite – 1km

AMSR-E products (5km)• rain rates (instantaneous)• convective fraction• SST• precipitable water• ocean surface wind speed

MODIS (on the NASA Terra and Aqua polar orbiting satellites) provides up to 4 passes a day for a given region

Terra: nominal 10:30am (d) / 10:30pm (a) overpass time Aqua: nominal 1:30pm (a) / 1:30am (d) overpass

Terra / Aqua Data Availability

Orbital tracks - recent past and future orbital visualizations available in AWIPS

Latency - most MODIS data and products are available on the Southern Region server within 30 minutes of collection – additional 10-15 minute delay based on ftp scripts

Data provided in D2D

• access like GOES satellite data

• correspond to WFO coverage areas at highest resolution

Examples:

• color composites

• TPW

• SSTs

• rain rates

May 28, 2004

COMPOSITE

& composite SST

Previews available http://weather.msfc.nasa.gov/sport/sport_observations.html

MODIS/AMSR-E DataAccess in AWIPS

Methodology:

2 km resolution with 51 levels

Physics differences from operational WRF:

•No cumulus parameterization•WSM 6-class microphysics scheme

24h simulations run daily for May 2004

Parallel runs for both the RTG SSTs and the MODIS SST composites

3h WRF simulation

24h WRF simulation

00 0021

ADAS ADAS

MODIS SST- RTG SST (K)14 May 2004

Impact of MODIS SSTs onMesoscale Weather

WRF Hurricane ForecastsIn collaboration with Goddard Space Flight Center, run test cases to determine if WRF forecasts are sensitive to SSTs

Domain configured like May 2004 runs

24 – 48 h forecasts

Initialized with 40 km NAM analyses

NAM 3h forecasts used for LBCs

Parallel forecasts with either RTG SSTs or MODIS SST composite

New Orleans, LARadar Reflectivity

42h forecast of3h accumulated precip (in)

Hurricane Katrina06 UTC August 29, 2005

Use of MODIS SST composites is currently ongoing in operational WRF forecasts

May 2004 simulations and hurricane forecasts provide the opportunity to determine the impact of MODIS SSTs on regional forecasts

Preliminary work suggests that the WRF model appears to respond appropriately to high-resolution SST data

Greatest impact of MODIS SSTs is seen in the marine boundary layer

Summary