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Slide1: 

Hyperspectral DataAssimilation: -Status Progress J. Le Marshall J.Jung

Overview: 

Overview Background JCSDA Hyperspectral Radiance Assimilation Initial Experiments Recent Advances Summary and Future

Slide4: 

Data Assimilation Impacts in the NCEP GDAS AMSU and “All Conventional” data provide nearly the same amount of improvement to the Northern Hemisphere.

The Joint Center for Satellite Data Assimilation: 

The Joint Center for Satellite Data Assimilation John Le Marshall Director, JCSDA January, 2005 Deputy Directors: Stephen Lord – NWS /NCEP James Yoe - NESDIS Lars Peter Riishogjaard – GSFC, GMAO Pat Phoebus – DoD,NRL

Joint Center for Satellite Data Assimilation: 

Joint Center for Satellite Data Assimilation

JCSDA Mission and Vision: 

JCSDA Mission and Vision Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather and climate analysis and prediction models Near-term Vision: A weather and climate analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations Long-term Vision: An environmental analysis and prediction community empowered to effectively use the integrated observations of the GEOSS

Goals – Short/Medium Term: 

Goals – Short/Medium Term Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction 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 analysis and prediction Reduce the average time for operational implementations of new satellite technology from two years to one

JCSDA Road Map (2002 - 2010): 

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 Science Advance 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

Slide10: 

The Challenge Satellite Systems/Global Measurements Aqua Terra TRMM SORCE SeaWiFS Aura Meteor/ SAGE GRACE ICESat Cloudsat Jason CALIPSO GIFTS TOPEX Landsat NOAA/POES GOES-R WindSAT NPP COSMIC/GPS SSMIS NPOESS

Slide11: 

Draft Sample Only

Slide12: 

NPOESS Satellite CMIS- μwave imager VIIRS- vis/IR imager CrIS- IR sounder ATMS- μwave sounder OMPS- ozone GPSOS- GPS occultation ADCS- data collection SESS- space environment APS- aerosol polarimeter SARSAT - search & rescue TSIS- solar irradiance ERBS- Earth radiation budget ALT- altimeter SS- survivability monitor CMIS VIIRS CrIS ATMS ERBS OMPS

5-Order Magnitude Increase in Satellite Data Over 10 Years: 

5-Order Magnitude Increase in Satellite Data Over 10 Years Count (Millions) Daily Upper Air Observation Count Year Satellite Instruments by Platform Count NPOESS METEOP NOAA Windsat GOES DMSP 1990 2010 Year

Slide14: 

GOES - R ABI – Advanced Baseline Imager HES – Hyperspectral Environmental Suite SEISS – Space Environment In-Situ Suite including the Magnetospheric Particle Sensor (MPS); Energetic Heavy Ion Sensor (EHIS); Solar & Galactic Proton Sensor (SGPS) SIS – Solar Imaging Suite including the Solar X-Ray Imager (SXI); Solar X-Ray Sensor (SXS); Extreme Ultraviolet Sensor (EUVS) GLM – GEO Lightning Mapper

Slide15: 

Advanced Baseline Imager (ABI)

Slide16: 

Advanced Baseline Imager (ABI) Total radiances over 24 hours = 172, 500, 000, 000

Slide17: 

Hyperspectral Environmental Suite (HES) (T) = Threshold, denotes required coverage (G) = Goal, denotes coverage under study during formulation

Slide18: 

Hyperspectral Environmental Suite (HES) Total radiances over 24 hours = 93, 750, 000, 000

Satellite Data used in NWP: 

Satellite Data used in NWP HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES, Meteosat, GMS winds GOES precipitation rate SSM/I precipitation rates TRMM precipitation rates SSM/I ocean surface wind speeds ERS-2 ocean surface wind vectors Quikscat ocean surface wind vectors AVHRR SST AVHRR vegetation fraction AVHRR surface type Multi-satellite snow cover Multi-satellite sea ice SBUV/2 ozone profile and total ozone Altimeter sea level observations (ocean data assimilation) AIRS Current Upgrade adds; MODIS Winds…

Short Term Priorities 04/05: 

Short Term Priorities 04/05 SSMIS: Collaborate with the SSMIS CALVAL Team to jointly help assess SSMIS data. Accelerate assimilation into operational model as appropriate MODIS: MODIS AMV assessment and enhancement. Accelerate assimilation into operational model. AIRS: Improved utilization of AIRS Reduce operational assimilation time penalty (Transmittance Upgrade) 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

Some Major Accomplishments: 

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. Implemented 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.

MODIS Wind Assimilation into the GMAO/NCEP Global Forecast System: 

MODIS Wind Assimilation into the GMAO/NCEP Global Forecast System

Slide23: 

Global Forecast System Background Operational SSI (3DVAR) version used Operational GFS T254L64 with reductions in resolution at 84 (T170L42) and 180 (T126L28) hours. 2.5hr cut off

Slide25: 

AVERAGE HURRICANE TRACK ERRORS (NM) 2004 ATLANTIC BASIN Results compiled by Qing Fu Liu.

Slide26: 

AIRS/AQUA/ Assimilation Studies AQUA Initial Studies Targeted studies Pre-Operational trials First Second ……….

Slide27: 

AIRS/AQUA Initial Studies AQUA

AIRs Targeting Study: 

AIRs Targeting Study Contributors: GMAO: L.P. Riishojgaard, EMC: Zoltan Toth,Lacey Holland Summary of Accomplishments GMAO developed a software for stratifying observational data stream that indicates the area having higher background errors EMC had some dropsonde data released in the areas found sensitive to Ensemble Kalman Filter technique where high impact events occurs. Joint EMC/GMAO have identified 10 winter storm cases in 2003 that have large forecast errors for AIRS studies

Slide29: 

conservative detection of IR cloudy radiances examine sensitivity, Tb, of simulated Tb to presence of cloud and skin temperature those channels for which Tb exceeds an empirical threshold are not assimilated SSI modifications

Slide30: 

more flexible horizontal thinning/weighting account for sensors measuring similar quantities specify sensor groupings (all IR, all AMSU-A, etc) specify relative weighting for sensors within group SSI modifications

Motivation: 

Motivation Initially, computationally expensive to include all AIRS data in the GFS Try to mitigate the effects by including a smaller subset of the data over ‘sensitive’ areas determined during the Winter Storm Reconnaissance (WSR) program Why WSR? Already operational (since 2001) Geared toward improving forecasts of significant winter weather by determining where to place additional observations Most years show improvement in 60-80% of cases targeted

How the impact of AIRS was evaluated: 

How the impact of AIRS was evaluated CASE SELECTION 7 Cases selected from Winter Storm Reconnaissance (WSR) program during 2003 Forecasts with high RMSE for given lead time chosen DATA SELECTION AIRS data assimilated only in locations identified as having the most potential for forecast improvement as determined through WSR (areas containing 90% or more of maximum sens. value) Somewhat larger area covered by the AIRS data compared to WSR dropsonde coverage EVALUATION Impact tested by comparing two forecast/analysis GFS cycles (T126L28), identical except that one contains AIRS data while the other does not Control has all operationally available data (including WSR dropsondes)

Slide33: 

Data Impact of AIRS on 500 hPa Temperature (top left), IR Satellite Image (top right), and estimated sensitivity (left) for 18 Feb 2003 at 00 UTC Light purple shading indicates AIRS data selection Violet squares indicate dropsonde locations Red ellipse shows verification region Impact outside the targeted areas is due to small differences between the first guess forecasts. Sensitive areas show no data impact due to cloud coverage.

Slide34: 

Improved/Neutral/Degraded classification based on RMSE of forecasts verified against raobs over WSR pre-defined verification area

Overall impact of AIRS on WSR forecasts: 

determined by comparing the number of fields (temperature, vector wind, humidity between 1000-250 hPa as well as sfc pressure) that were improved or degraded for each case Overall impact of AIRS on WSR forecasts While the addition of dropsondes shows a slight positive impact, the addition of AIRS data has no overall benefit

Slide46: 

RECENT STUDIES JCSDA

Slide47: 

AIRS Data Assimilation J. Le Marshall, J. Jung, J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W. Wolf and H-S Liu, J. Joiner, and J Woollen…… 1 January 2004 – 31 January 2004 Used operational GFS system as Control Used Operational GFS system Plus Enhanced AIRS Processing as Experimental System

Slide48: 

Table 1: Satellite data used operationally within the NCEP Global Forecast System

Slide49: 

Global Forecast System Background Operational SSI (3DVAR) version used Operational GFS T254L64 with reductions in resolution at 84 (T170L42) and 180 (T126L28) hours. 2.5hr cut off

Slide50: 

The Trials – Assim1 Used `full AIRS data stream used (JPL) NESDIS (ORA) generated BUFR files All FOVs, 324(281) channels 1 Jan – 15 Feb ’04 Similar assimilation methodology to that used for operations Operational data cut-offs used Additional cloud handling added to 3D Var. Data thinning to ensure satisfying operational time constraints

Slide51: 

The Trials – Assim1 Used NCEP Operational verification scheme.

Slide52: 

AIRS data coverage at 06 UTC on 31 January 2004. (Obs-Calc. Brightness Temperatures at 661.8 cm-1are shown)

Slide53: 

Figure 5.Spectral locations for 324 AIRS thinned channel data distributed to NWP centers.

Slide54: 

Table 2: AIRS Data Usage per Six Hourly Analysis Cycle

Slide55: 

Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004- Assim1

Slide56: 

Figure1(a). 500hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004 – Assim1

Slide57: 

Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

Slide58: 

Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern Hemisphere, January 2004

Slide59: 

Figure1(a). 500hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern Hemisphere, January 2004

Slide60: 

AIRS Data Assimilation J. Le Marshall, J. Jung, J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W. Wolf and H-S Liu, J. Joiner, P. van Delst, R. Atlas and J Woollen…… 1 January 2004 – 31 January 2004 Used operational GFS system as Control Used Operational GFS system Plus Enhanced AIRS Processing as Experimental System Clear Positive Impact

Slide61: 

AIRS Data Assimilation J. Le Marshall, J. Jung, J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W. Wolf and H-S Liu, J. Joiner, P. van Delst, R. Atlas and J Woollen…… 1 January 2004 – 27 January 2004 Used operational GFS system as Control Used Operational GFS system Plus Enhanced AIRS Processing as Experimental System

Slide62: 

The Trials – Assim 2 Used `full AIRS data stream used (JPL) NESDIS (ORA) generated BUFR files All FOVs, 324(281) channels 1 Jan – 27 Jan ’04 Similar assimilation methodology to that used for operations Operational data cut-offs used Additional cloud handling added to 3D Var. Data thinning to ensure satisfying operational time constraints

Slide63: 

The Trials – Assim 2 AIRS related weights/noise modified Used NCEP Operational verification scheme.

Slide64: 

Figure1(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

Slide65: 

Figure 1(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, January 2004

Slide66: 

Figure 2. 500hPa Z Anomaly Correlations 5 Day Forecast for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, (1-27) January 2004

Slide67: 

Figure3(a). 1000hPa Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern hemisphere, January 2004

Slide68: 

Figure 3(b). 500hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern hemisphere, January 2004

Slide69: 

AIRS Data Assimilation J. Le Marshall, J. Jung, J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W. Wolf and H-S Liu, J. Joiner, P. van Delst, R. Atlas and J Woollen…… 1 January 2004 – 27 January 2004 Used operational GFS system as Control Used Operational GFS system Plus Enhanced AIRS Processing as Experimental System Clear Positive Impact

Slide70: 

AIRS Data Assimilation GSI Studies: 1-13 January 2003 Used next generation GSI system as Control Used next generation GSI system Plus AIRS as Experimental System

Slide71: 

Figure 1(b). 1000hPa Z Anomaly Correlations for the GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Northern hemisphere, January 2003 Figure 2(a). 1000hPa Z Anomaly Correlations for the GMAO GFS with (Ops.+AIRS) and without (Ops.) AIRS data, Southern hemisphere, 1-13 January 2003

Slide72: 

AIRS Data Assimilation Supporting Studies: 1-13 January 2003 Used next generation GMAO GSI system as Control Used next generation GMAO GSI system Plus AIRS as Experimental System Positive Impact

Slide73: 

AIRS Data Assimilation Impact of Data density... 10 August – 20 September 2004 Used operational GFS system as plus AQUA AMSU plus Conv. Cov. AIRS as Control Used operational GFS system as plus AQUA AMSU Plus Enhanced AIRS Sys. as Experimental System

Slide74: 

Impact of AIRS spatial data density/QC (Snow, SSI/eo/April 2005/nw)

AIRS Data Assimilation -The Next Steps: 

AIRS Data Assimilation -The Next Steps Fast Radiative Transfer Modelling (OSS, Superfast RTM) GFS Assimilation studies using: full spatial resolution AIRS data,MODIS cld info. & Є full spatial resolution AIRS and MODIS data full spatial resolution AIRS data with recon. radiances full spatial res. AIRS with cld. cleared radiances (ć AMSU/MODIS/MFG use) full spatial and spectral res. AIRS data full spatial and spectral res. raw cloudy AIRS (ć MODIS/AMSU) data (full cloudy inversion with cloud parameters etc.)

AIRS Assimilation -The Next Steps (Including AMSU/MODIS…..): 

AIRS Assimilation -The Next Steps (Including AMSU/MODIS…..) * All data plus data selection / thinning studies plus є ** all channels plus channel selection / noise red. studies

Surface Emissivity Techniques : 

Surface Emissivity Techniques Regression (NESDIS) Minimum Variance (CIMSS) Eigenvector (Hampton Univ.)

Slide79: 

IR HYPERSPECTRAL EMISSIVITY - ICE and SNOW Sample Max/Min Mean computed from synthetic radiance sample From Lihang Zhou Emissivity Wavenumber

Slide80: 

IR HYPERSPECTRAL EMISSIVITY - LAND Sample Max/Min Mean computed from synthetic radiance sample From Lihang Zhou Emissivity Wavenumber

Summary/Conclusions: 

Summary/Conclusions Results using AIRS hyperspectral data, within stringent current operational constraints, show significant positive impact. Given the many opportunities for future enhancement of the assimilation system, the results indicate a considerable opportunity to improve current analysis and forecast systems through the application of hyperspectral data. It is anticipated current results will be further enhanced through improved physical modeling, a less constrained operational environment allowing use of higher spectral and spatial resolution and cloudy data.

Summary/Conclusions: 

Summary/Conclusions Effective exploitation of the new IR hyperspectral data about to become available from the Infrared Atmospheric Sounding Interferometer (IASI), Cross-track Infrared Sounder (CrIS), and Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) instruments will further enhance analysis and forecast improvement.

Prologue: 

Prologue JCSDA is well positioned to exploit the AIRS and future Advanced Sounders in terms of Assimilation science Modeling science. Computing power Generally next decade of the meteorological satellite program promises to be every bit as exciting as the first, given the opportunities provided by new instruments such as AIRS, IASI, GIFTS and CrIS, modern data assimilation techniques, improving environmental modeling capacity and burgeoning computer power. The Joint Center will play a key role in enabling the use of these satellite data from both current and future advanced systems for environmental modeling.

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