we1.l10 - use of nasa data in the joint center for satellite data assimilation

IGARSS Conference, Honolulu, Hawaii, July, 2010

USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA

ASSIMILATION (JCSDA)

Lars Peter Riishojgaard, Director, JCSDAand

Sid Ahmed Boukabara, Deputy Director, JCSDA (Presenter)

With contributions from:

M. Rienecker, P. Phoebus, S. Lord, J. Zapotocny, E. Liu, R. Gelaro, V. Kumar, C.D. Peters-Lidard, R. Vogel, F. Weng and many others

Paper #4161 (WE1.L10.3)

2

Overview of Satellite Data Used at JCSDA partners2

Land Data Assimilation Highlights5

JCSDA Structure, Objectives & Science Priorities1

Ocean Data Assimilation Highlights4

Impact Study Experiments6

Atmospheric Data Assimilation Highlights3

Contents

Conclusions/Summary7

NASA/Earth Science Division

US Navy/Oceanographer andNavigator of the Navy and NRL

NOAA/NESDIS NOAA/NWS

NOAA/OAR

US Air Force/Director of Weather

Mission:

…to accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction models.

Vision:

An interagency partnership working to become a world leader in applying satellite data and research to operational goals in environmental analysis and prediction

JCSDA Structure and Objectives


JCSDA Science Priorities

• Radiative Transfer Modeling (CRTM)• Preparation for assimilation of data from new instruments• Clouds and precipitation• Assimilation of land surface observations• Assimilation of ocean surface observations• Atmospheric composition; chemistry and aerosol

Driving the activities of the Joint Center since 2001, approved by the Science Steering Committee

Overarching goal: Help the operational services improve the quality of their prediction products via improved and accelerated use of satellite data and related research



5

JCSDA accomplishments

• Common assimilation infrastructure (between NOAA, NASA, AFWA) • Community radiative transfer model (NOAA, Navy, NASA, AFWA)• Common NOAA/NASA/AFWA land data assimilation system (NOAA,

NASA, AFWA)• Numerous new satellite data assimilated operationally, e.g. MODIS (winds

and AOD), AIRS and IASI hyperspectral IR radiances, GPSRO sensors (COSMIC, GRAS, GRACE), SSMI/S, Windsat, Jason-2,…

• Advanced sensors tested for operational readiness, e.g. ASCAT, MLS, SEVIRI (radiances),…

• Ongoing methodology improvement for sensors already assimilated, e.g. AIRS, GPSRO, SSMI/S,…

• Improved physically based SST analysis • Adjoint sensitivity diagnostics• Emerging OSSE capability in support of COSMIC-2, JPSS, GOES-R,

Decadal Survey and other missions

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Contents



Navy/FNMOC(NOGAPS with NAVDAS-AR

4DVAR)

NOAA/NCEP (GFS with GSI 3DVAR)

NASA/GMAO

(GEOS-5 w/ 3DVAR)

SSM/I Wind/radiances (2) OPS Not used (Instrument problem) OPS

SSM/I TPW/radiances (2) OPS Not used (Instrument problem) OPS

SSMIS Wind/radiances (2-3) OPS Preparing for testing

SSMIS TPW/radiances (2-3) OPS Preparing for testing

QuikScat Marine Surface Winds (0) OPS/sensor failed OPS/sensor failed OPS/sensor failed

ASCAT Marine Surface Winds (1) OPS Testing

WindSat Marine Surface Winds (1) OPS OPS OPS

WindSat TPW (1) OPS

MODIS IR Atmospheric Motion Vector (AMV) Winds (2)

OPS OPS OPS

MODIS WV AMV Winds (2) OPS OPS OPS

AVHRR IR AMV Winds (2) OPS Testing

ERS-2 (1) OPS Not used (Instrument problem)

AMSR-E (1) (parameter??) Testing to begin soon Preparing for testing passive

TRMM TMI Precip No OPS OPS

SSM/I Precip Not used (Instrument problem)

SSMIS Precip Preparing for testing

Totals

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Polar Orbiters: Microwave Imagers / Scatterometers / IR/WV Imagers

NASA Sensors

Navy/FNMOC(NOGAPS with NAVDAS-AR 4DVAR)


NASA/GMAO (GEOS-5 w/ 3DVAR)

AMSU-A (6) OPS OPS as quality permits OPS

AMSU-B/MHS (4) testing OPS as quality permits OPS

HIRS No OPS as quality permits OPS

AIRS (1) OPS OPS OPS

IASI (1) OPS OPS OPS

SSMIS Lower Atmosphere Sounding (LAS) Tb (2-3)

OPS

GPS Precipitable Water (PW) No OPS regional

Monitored global

COSMIC GPS Radio Occultation (RO)

testing OPS (refractivity)

Bending angle monitored

OPS

GRAS RO testing testing

CHAMP/GRACE RO testing GRACE Testing; CHAMP data not received

Totals

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Polar Orbiters: Microwave Sounders/ IR Sounders/ GPS Profilers

NASA Sensors

Navy/FNMOC (NOGAPS with NAVDAS-AR 4DVAR)



NOAA AVHRR SST (GAC/LAC) OPS OPS

METOP AVHRR SST (GAC/LAC) OPS Testing

GOES SST (2) OPS OPS

MSG SST (2) (Meteosat)

OPS

MTSAT SST

AMSR-E SST (Aqua)

OPS (at NAVO….not at FNMOC yet)

Testing

AATSR SST (Envisat) OPS

MODIS SST (2) (Terra/Aqua)

No

SSM/I Sea Ice Concentration ( 4) (F-11, F-13, F-14, F-15)

OPS OPS (F-15 only)

SSMIS Sea Ice Concentration (4) (F-16, F-17, F-18)

OPS Testing

SMOS Sea-Surface Salinity (SSS)

MODIS Surface chlorophyll (2) (Terra/Aqua)

Testing R&D system

Coastal Zone Color Scanner (CZCS) Surface chlorophyll

Testing R&D system

SeaWiFS Surface chlorophyll Testing R&D system

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Ocean Sensors: Sea Surface data for NWP, Climate, and/or Ocean Prediction

NASA Sensors

Navy/FNMOC(GNCOM, NCOM w/ NCODA)

NOAA/NCEP

NASA/GMAO (ODAS-2 and ODAS-3 with MOM4)

(2) Jason Altimeter Significant Wave Height (SWH)

OPS --2 OPS -J1

Testing - J2

Envisat –Altimeter SWH OPS Testing

(2) Jason Altimeter Sea Surface Height (SSH)

OPS --2 OPS -J1

Testing - J2

OPS (data from AVISO)

Envisat –Altimeter SSH OPS OPS OPS

TOTAL

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Ocean Sensors: Altimeters

NASA Sensors

Navy/FNMOC(NAAPS, COAMPS w/ NAVDAS-AOD)

NOAA/NCEP


AF/AFWA

MODIS Aerosol Optical Depth (2) (Terra/Aqua)

OPS Preparation for Testing

SBUV O3 testing OPS as quality permits

OPS

EnviSat O3 No

MLS O3 Available in real time

R&D System

OMI O3 OPS R&D System

GOME Testing

TOTAL

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO):Aerosol/Trace Gas Assimilation Sensors

NASA-related Sensors

Navy/FNMOC(No LS DA for our LSM at this time*)

NOAA/NCEP GSI, no LIS at

this time

NASA/GMAO (EnKF with Catchment

LSM)

Soil Moisture (AMSR-E) No Testing R&D system

Soil Moisture (SMOS) No To be Tested

Soil Moisture (SMAP) No Not used

Snow Cover (IMS, multiple sensors) No OPS R&D system

Snow Depth (AFWA, multiple sensors) No OPS R&D system

Land Surface/Skin Temperature No No R&D system

AVHRR Green Vegetation Fraction (GVF, 5-year monthly climatologies

No Testing

AVHRR GVF (weekly, near real time) No Testing

MODIS –IGBP land use (vegetation) class (static field)

No Testing

MODIS maximum deep-snow albedo (static field

No To be tested

MODIS snow-free albedo (sub-monthly static fields)

No To be tested

TOTAL

Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO):Land Surface Assimilation Sensors

NASA Sensorsimbedded

Summary of NASA-related Sensors Used Operationally (at one or several JCSDA partners)

Implemented Operationally MODIS (IR sensor: multiple products) QuickSCAT (MW scatterometer: sensor failed) TRMM/TMI (MW Imager) AIRS AMSR-E* Jason (NASA/CNES sensor) OMI*

Under Testing/To be tested: SeaWIFS SMAP MLS

*Non-NASA sensors onboard NASA platforms

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Contents








Atmospheric Data Assimilation Highlight:GEOS-5/GSI estimate of cloud top height from AIRS compared with CloudSat

and CALIPSO

• Due to large differences in footprint size between AIRS and CPR/CALIOP, the CTH validation is done only in regions A and C where the clouds are more uniform.

• In general, GSI retrieved CTHs from AIRS are underestimated for optically thick clouds.

• Difficulties in retrieving CTH in multi-layer cloud region.• Next: Include MODIS cloud products for further validation.

CloudSat/CALIPSO track

GSI retrieved cloud top height (CTH) from AIRS

A

B

CA

B

C

AB

C

Slide courtesy of Emily Liu15

Ozone Assimilation in GEOS/GSI System (Steven Steven Pawson)Pawson)

Activities in GEOS-5/GSI include:

• Assimilation of SBUV, OMI and MLS ozone observations

• Improvements to system: observation operator for OMI

(+TOMS/GOME/etc.) kernels Background error covariance models

(beginning)

• Investigations of ozone structure in the UTLS and the troposphere

• Impacts of assimilating MLS profiles on AIRS radiances

• OSSEs for NPP-OMPS: MLS+OMI system is baselineGeneration, retrieval and assimilation

of limb profiler observations16

Present system omits the decrease in sensitivity to low tropospheric ozone in OMI – this is being built into H operator, with expected reduction in impact of OMI ozone in middle troposphere. Results for Jan 2006.

Impact (% change) of O3 from OMI data

Expected impact (% change) with kernels

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Contents








18

Ocean Data Assimilation Highlight:Differences between RTOFS SSH analysis and Ssalto/Duacs (independent) SSH analysis

Left panel: with JASON-1/JASON-2/ENVISAT, Right panel : without JASON-2

The right panel shows presence of larger differences in the Gulf Stream region which may lead to formation of spurious mesoscale features.

Increasedvariability

W/OJASON-2

WithJASON-2

Slide courtsey of S. LordRTOFS: Real-Time Ocean Forecast System

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Contents








Land Data Assimilation Highlight:Assimilation of multi-sensor snow observations into a land

surface model

•A blended, multi-sensor snow dataset (ANSA) was generated by utilizing the MODIS and AMSR-E retrieved snow datasets.

•These multi-sensor snow observations are employed in the NASA/NOAA/AFWA common Land Information System (LIS).

•The evaluation of assimilation runs against in-situ observations of snow depth and SWE demonstrate improvements as a result of data assimilation

AN

SA

sn

ow m

ap 1

5 J

anu

ary

2007

Courtesy of S. Kumar et al.

Another Land Data Application Highlight:New land surface emissivity for infrared assimilation:

CRTM land bias improvement & positive forecast impact

• Univ. Wisconsin (Seemann & Borbas) spectral infrared emissivity is derived from MODIS-channel emissivity retrievals (monthly composite, 416 wavenumbers)

• Comparison of CRTM simulation to observation shows reduced Tb bias for desert regions when using this emissivity dataset.

Tb difference (K) CRTM sim minus MODIS obs (3.96 µm)

CRTM run with current emis CRTM run with U.Wisc. emis

Less bias with U.Wisc emis

Slide courtesy of R. Vogel, Y. Chen, Q. Liu, Y. Han, F. WengJCSDA & NESDIS/STAR

SaharaDesert

MODIS Used to validate the JCSDA Community Radiative Transfer Model (CRTM) over land surfaces

Forecast impact with GSI shows improved forecast in Southern HemisphereCRTM current IR emis = black line U.Wisc. new IR emis = red line

22

Contents








Fcst Error Reduction (J/kg)

NASA GEOS-5 Navy NOGAPS

Global domain: 00+06 UTC assimilations Jan 2007

Comparison of Data Impacts in Navy and NASA Forecast Systems using Adjoint Tools:

Daily average 24-h observation impacts

AMSU-A, Raob, Satwind and Aircraft have largest impact in all systems

All obs types, except SSMI speeds in GEOS-5, are beneficial

Fcst Error Reduction (J/kg)

23

NASA Sensorsimbedded

Impacts of Various Observing Systems in GEOS-5.5.124-hr Forecasts from 00z Analyses on 28 Jan – 02 March 2010

Adjoint-Based Global Forecast Error Measure

Total Impact Impact Per Observation

Observation Count

Fraction of Beneficial

Observations

Forecast Error Reduction (J/kg) Forecast Error Reduction (1e-6 J/kg)

Improves Forecast Degrades Forecast

Ron Gelaro, GMAO

AMSU-A, Raob, Satwind and Aircraft have largest impact in all systems

NASA Sensors

Data Impact Studies Using NOGAPS/NAVDAS-AR at FNMOC

Per OB Impacts

NASA Sensors

Summary


NASA Satellite Data are used in many ways in the JCSDA: Operational Implementation in NWP assimilation models Testing-mode implementation in Operational models Used to validate/improve of some components of operational NWP

models (such as CRTM)

NASA Satellite Data used for multitude of data assimilation activities: Atmospheric data assimilation (sounding, cloud, ozone, air quality, etc) Ocean data assimilation Land data assimilation

NASA Sensors Used include: TMI, MODIS, AIRS, AMSR-E, Jason, OMI

Effort is on-going to assimilate more NASA sensors and others (both existing and future sensors): NPP (CrIS, ATMS, VIIRS), SEVIRI, GPM, SMAP, ADM

we1.l10 - use of nasa data in the joint center for satellite data assimilation

Documents

sensor snow observations

satellite data

static field

environmental analysis

gsi 3dvar

j1 testing

multiple sensors

3dvar